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Author: Rishi Singh - Founder

Screening for Increasing Dividends

Screening for Increasing Dividends

A user asked us if we could allow screening for increasing dividends; but, we figured we’d do one better. What if you could screen for the increases and decreases of any metric? Tiingo now allows this in 3 easy steps.

Step 1: Create a custom metric

To start, visit the custom metrics page here: https://www.tiingo.com/screen/custom

Simply enter the metric you want in the “formula” box. In this example we want to see if the dividend increases, so we type:

“Dividend_Cash”

divcash_premetrictype

But we want to see how many times the cash dividend has increased, so we follow it up with a “.” and use our new metric “.countincr(#)”

The # in this case represents the last # of dividends. Since most dividend stocks pay out quarterly, to see how many times the dividends increased in the past year, we would type in “4” for the #.

Our final formula looks like:

divcashformula

Step 2: Create the Screen

Click “Create Metric” and visit the screening page here: https://www.tiingo.com/screen/

Let’s see how many stocks increased their dividends 2 times or more in the S&P 500. To do that we simply drag-and-drop the metrics we care about and are left with:

final metrics

 

Step 3: Run the Screen

Click “Run Screen”

We can see there were only 3 stocks that increased dividends 2 times in the last four payouts.

results

Looking at Macy’s we can confirm this on: https://www.tiingo.com/f/g/m

macys div graph

 

Want to take it further?

Replace “Dividend_Cash” with any metric in our database. Or, replace .countincr with .countdecr and count the number of decreases!

 

Enjoy! If you have any feedback, reach out to us at feedback@tiingo.com

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Presenting Tiingo Comparatives: Changing the Way We Compare Companies

Presenting Tiingo Comparatives: Changing the Way We Compare Companies

When you’re about to purchase a stock, you want to make sure you are getting the best deal you possibly can. Often we’ll compare a company to a couple others, and maybe even try to find out general market conditions. But is that P/E ratio of 18 high or low? Context in markets is everything.

For the first time in history, Tiingo now allows you to compare a company across industries, sectors, and benchmarks by letting you see how they rank each day. We provide context at a level of detail nobody else does.

The Result?

Announcing: Tiingo Comparative Analytics
Get the context behind the numbers.
xom main
Check out the analytics for Exxon here: XOM
We designed this feature because we identified two major issues when looking at market conditions and companies.

1) What are current market conditions?

Before making an investment, investors and traders want to know the economic backdrop. Why buy an energy company like Exxon if you know oil is going to collapse? Or, is that P/E ratio high or low? Answering these questions requires us to have context. Many of us use the P/E ratio of the S&P to get an idea of valuations, but does it make sense to compare energy companies with tech?

So we went to the whiteboard –

Instead of comparing everything to the S&P, what if we could compare industries, sectors, and benchmarks?. The current solutions are to use sector-specific indices or ETFs. But even those only cover the largest companies and don’t provide us line-item data. Also comparing Twitter to Microsoft doesn’t make much sense even though they are both in tech. We need industry-specific data too

 

A quick example:
Let’s say you want to buy an energy company right now because you think oil will go up from here. But, you also want to make sure the company is stable and can weather a storm. So you decide, “let’s look at a big energy company like Exxon Mobil.”

You see the P/E ratio for Exxon is 12.81, which looks reasonable. But you don’t know what the context of that number means, so you take a look!

valuations 36th

 

We can see within the “Oil, Gas, and Consumable Fuels” industry the P/E is in the bottom 36th percentile. But what about within the S&P 500? The bottom 19th percentile.

But looking to the right of Exxon, we see Chevron (CVX).

 

valuations CVX

 

It has a P/E ratio of 11.51, is also a large company, and its P/E ratio is in the bottom 32nd percentile for the same industry. Additionally, it’s P/E ratio is in the bottom 13th percentile in the S&P.

Assuming all else equal, CVX could be a better way to express our play on oil!

Within one screen we could put Exxon’s valuation ratios within the context of the energy sector.

 

2) Comparing 2-3 companies is good, but we want to know if we’re getting the best deal

We just compared XOM and CVX, but we at Tiingo don’t feel that’s good enough. Industries, sectors, and benchmarks are filled with companies, so shouldn’t you be allowed to see them all?

We love the idea of data transparency.

With a simple click, you can now see all of the data in tabular form.

xom tablular_pe

 

In the next few weeks, we will be spending time iterating the current product offerings and making existing features more powerful and accurate. If there is something in particular you would like to see, please E-mail us at feedback@tiingo.com! We love hearing from our users.

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Why 13-F filings are Poor for Replicating Funds

Why 13-F filings are Poor for Replicating Funds

I’ve seen hedge fund and trader replication ETFs and strategies for some time now and I realized a lot of them are based on 13-F filings.I thought I would go into why these are poor for replication. I hope it’s helpful for some readers out there. And in case I miss something, please feel free to add some more points.

I originally made this post on Reddit, but decided to put it here as well for the readers of this blog. A few Redditors responded and that is below the “Edit” portion below.

1) They aggregate the positions of many different people

Typically the funds they replicate often have a Portfolio Manager structure. Just like with mutual funds you have many different types of funds, on the hedge fund side, you have something similar except you have a ton of different individuals. The 13-F filings are an aggregation of the entire fund so you are seeing the aggregated thesis of the entire fund. You may also be looking at the position of a portfolio manager who fundamentally looks at the world entirely different than you and understands the company in a context you may not. Some people may view this as “crowdsourcing” within hedge funds, but then I present a couple other points.

2) They are delayed

The filings are quarterly so you are getting lagging data. It’s not uncommon for a fund to change positions every month. If you are using 13-F filings, make sure the fund has very long holding periods to account for this. Even then, if there is market-moving news, you wont really know their position until the next report.

3) They show you an incomplete picture

A long/short equity fund will often have a short component. Traders often use pairs trades, or short trades to come up with a trade structure. 13-F filings though only represent the long position.

For example the 13-F filings may be long comcast, when the fund could also be short Timewarner against it. Both companies make up the trade thesis. So even if Comcast loses money, they may be making money on the entire trade as Timewarner was the other leg of the trade. It may appear they are “in it for the long haul” when really you can only see one side of the trade. It’s true long/short equity funds tend to make more money on the long side, but some of that is beta exposure.

What I have used 13-F filings for

1) Trade idea generation.

Sometimes smaller hedge funds will find stocks that I haven’t heard of. I will do my own research though and form my own thesis. It’s almost like a screener I suppose. If I know if a hedge fund is a value fund, a long position may be a value position.

2) To get a hf gig

In college I would look up 13-F filings for local small hedge funds, then research the companies, and cold E-mail hedge funds to discuss the idea. This tended to be received well.

Did I miss anything?

**EDIT:**

Here is what Reddit commenters added – please make sure to give them the karma they deserve

**sayitlikeyoumemeit**
>Yes, 13-F following works best for idea generation from funds with very concentrated portfolios and known for mostly long positions.
One metric that isn’t used much that I like to estimate is the % of overall shares of a particular company that the fund holds (not the % it represents of their own portfolio) . This may give you an even better sense of their conviction in the business. When they start owning close to 20% of a company (many don’t go over this limit because of poison pill arrangements and filing requirements), it implies a high level of conviction, even if it’s a relatively smaller portion of their overall portfolio.

**Mephiska**
(Expanding upon delayed releases)
>Not only that, they will often wait the full 45 day time limit after quarter end to file, so when you see that report you’re already looking 45 day old data.

**FloatsFlysOrFucks**
>Nice post
Could be long the CDS or puts and long the stock to tweak the risk. 13f makes look like the like the position.

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Building the World’s Most Powerful Stock Screener

Building the World’s Most Powerful Stock Screener

I know it’s a bold claim to declare this is the most powerful stock screener out there, so I promise to deliver a bold result. This has been one of the most frequently requested features and for months I struggled with ways to tackle this problem. The end result reenvisions the way we approach screeners to one that is beautiful, intuitive, and has features that nobody has ever seen before.

To check out the screener right away, visit: http://tiingo.com/screen/o

Let’s get into the how and why we made the decisions that we did.

A totally new approach to a UI

A screener is a step in an investor’s workflow and we wanted to capture this. Rather than have a ton of text boxes slapped onto a screen, we took a “stack” approach where users can drag and drop the screens they care about:

drag and drop Drag and Drop Screener

 

 

 

 

 

Creation of custom metrics

Because we know we won’t be able to capture every metric people screen for, we allow you to create your own metrics. On our custom metrics page, simply start typing and our entire database of metrics will start to populate.

For example:
Return on Assets (ROA) = Net_Income/Total_Assets

Not only that, you can calculate stats on our fundamental and price data. For example, if we wanted to take an average of the total assets in the past 4 quarters we could do:

For example:
ROA = Net_Income/Total_Assets.mean(4)

A full list of metrics is available on the custom metrics creation screen

Custom Metrics Page

 

Results are as detailed as you want them to be

Many popular screeners out there won’t show you the values of the metrics you screen for and they don’t let you export to Excel without paying an outrageous fee. Tiingo allows you to do both.

Secondly, since screeners show us stocks we’ve never seen before, how do we learn more? On Tiingo’s screener results page, simply click a company and a box will pop-up showing you a description, a price chart, and the latest news about the company. You never have to leave the results page to learn more about a company.

The Results Page Clicking on Results

 

 

 

 

 

 

 

Integration into your portfolio

If you notice in the above picture there is a “Corr to Portfolio” column. Tiingo leverages the portfolio tracking tools to integrate into our screener. This column shows you the stock’s correlation to your current portfolio so you can effectively find stocks that offer you the most diversification benefit while staying true to your screening thesis.

Portfolio Correlation column highlighted

Saving screens and metrics

People frequently check screens looking for new ideas, so you shouldn’t have to recreate the wheel every time. You can save both your screens and custom metrics.

Saved Screens Page Saved Custom Metrics Page

 

 

 

 

 

Metrics and data not available on any other screener

We are committed to innovating so we wanted to bring important metrics no other platform offered. This includes screens like correlation to global macro factors (Stocks, Treasuries, Bonds, Gold, Oil), and screening not only by an Index (S&P 500, Russell 2000), but also seeing the weight of each stock within that index.

Correlation and Index Weight Highlighted

 

We deliver on our claims. To check out the screener visit: http://tiingo.com/screen/o

Please note: on the more complicated screens, the calculation may take a few seconds. This is because we hit our most recent data directly, so the values you see are the latest in our database. Also it may take a few seconds because we are a start-up and need your payment for faster servers 🙂

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That’s Enough Machine Learning – thanks!

That’s Enough Machine Learning – thanks!

Alright – so I’m going to hammer on one specific topic that’s been bothering me in the tech scene and that’s just machine learning being thrown everywhere. “Need a t-shirt? Let’s use machine learning to find our different habits and predict our tastes.” Or, you know, you could go to a store and see what appeals to you. OK that’s an exaggeration and going to stores and checking merchandise doesn’t scale across variety the web offers you. But I like this analogy so I’m going to keep it.

The problem I see with machine learning, and why I think it’s overused in markets inappropriately, is that it cannot explain in the same way human consciousness can. What I mean by that is that traditional science tells us to form a hypothesis before conducting an experiment. The idea being that by forming an explanation before seeing the data, we are forced to take current observations and make a rational expectation. This of course leads to biases which is shown quantitatively by the inability to replicate research as well as the number of papers that seem to support their hypothesis. What “big data” (I throw up a little in my mouth when I use that phrase) presents us though is the ability to get instant iterative feedback and A/B testing lets us test our samples in the real-world and see if our models hold up.

This is how it “should” be done. What happens though is that machine learning instead of being used as an optimization method becomes used as a method of find explanations. Many of us are using it to find relationships and then we are are backfilling a hypothesis and shows to be the case. While the current method of science is far from perfect, this approach seems far far worse. I have seen some who can master this, but they often have very strict processes in place to ensure the models hold up. Some enforce it via risk management while others run statistical tests – usually a combination of the two.

But do we really need to use advanced machine learning to create explanatory relationships instead of being an optimization method? After speaking with many people using it this way and reading papers on it, it seems like many doing it drastically overfits and their live results/trading do not match their out-of-sample. A common response to this idea is that, “machine learning should work if we properly out-of-sample tests.” Well, something taught to me by Josh + Steve @ AlphaParity (on this list), was that many people inappropriately run out-of-sample tests. What people often do is they initially have an in-sample and out-of-sample but when out-of-sample doesn’t match the in-sample performance, they parameterize the in-sample until the out-of-sample matches what they want. This creates just one in-sample and no out-of-sample.

Using machine learning as an explanatory relationship finder often leads to complexity of models, which just further adds the probability of overfitting. A secondary problem with markets is that regime shifts can happen rapidly, making machine learning less effective on larger time periods where there become new macro drivers. While it absolutely can be done, I know only one who has pulled it off and I have no idea how they do it. The question is, that all of this complexity worth it? The largest hedge funds out there like AQR do not use it to find explanatory relationships but use it for what it was meant to be: an optimization algorithm that slightly boosts performance. The simplicity of models like this reduce the chances of overfitting and also allow us to know when a model will break – when there will a regime shift. This knowing-when-it-fails allow us to assign higher odds as to when to size down risk (or weighting in non-market cases), or use portfolio construction to provide correlation/diversification benefit.

So before we go crazy with machine learning trying to be predictive from the start, I think it’s worthwhile to test the relationships and run studies and then consider ML at a “tweaking” stage. When used properly, it can be an effective tool, I just don’t think as effective as the mass-adoption of this phrase implies for the vast majority of cases. I think a good example of those who properly used it were the winners behind the Netflix Prize, where their solution is public. Their initial papers explored biases and preferences people had when initially ranking movies. Their final solution contained different ML and statistical methods to push results over the edge. Reading Team BellKor’s Pragmatic Chaos’s papers in sequential order is good fun: Direct link to final paper. Ignoring the math, their logic and explanations are fantastic displays of the scientific method + optimization methods.

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Podcast: Ep.8 Back, Back, Back it Up (Backtesting)

Podcast: Ep.8 Back, Back, Back it Up (Backtesting)

 

In this Podcast the following material will help you follow along:

Here is a Google Spreadsheet of a backtest where we buy a stock after it falls 5% in 5 days, and then hold the stock for 10 days. The stock is an S&P 500 ETF (SPY). We use this strategy as an example throughout the podcast and here is how you can make one simply using Excel/Gooogle Spreadsheets!

Google Spreadsheet of a Sample Backtest

Are are some other useful resources mentioned in the podcast


Forming a backtest is a skill I’ve spent the past 8 years honing, and after many years of toiling, I share with you some of the secrets I’ve uncovered. Hear the lessons I’ve learned the hard way and the biggest mistakes I see traders and investors make, including experienced ones at banks and hedge funds. This is an easy-to-follow episode that discusses different ways to conduct backtests and the gotchas behind them. I also share a rigorous 10 question check-list I always use when running a new study. This episode is applicable even if you’re a purely discretionary/gut trader as the greatest discretionary traders also rely on historical studies.  And if you’re a data scientist, you’ll especially enjoy this episode.

iTunes Link

Non-Itunes (tiingo.com)

Here is the script that was used in today’s episode.

Note: I don’t follow scripts word-for-word as they can sound unnatural, but the episodes do closely follow them.

Ep.8  Back, back, back, it up (Backtesting)

Listeners! This is possibly going to be one of the most useful episodes for you all whether or not you know what backtesting is. The reason? This is something I spent many many years trying to hone in and understand and was blessed to be mentored by some of the most fantastic people in trading who know this subject well. So this episode is going to be a combination of the past 8 years of my failures, trials, and eventual success in backtesting. Even if you’ve never backtested, or you’re a data scientist and think you know what this is, trust me – this will shape the way you think about the investing and trading.

So briefly? What is backtesting? You’ve actually seen backtesting not only on CNBC, which hopefully you watch sparingly, but also on ESPN! So if you think you it’s too complicated, trust me – you’ve already been exposed to it.

So backtesting is simply taking an investing or trading strategy, forming it into rules, then seeing how those rules performs historically. A simple example you may see on TV is, “when the S&P was down 3 days in a row, it was also down on the 4th day.” Or on ESPN it may be “a 1st round seed has never lost in the first round in NCAA basketball.” Just a heads up: I’m making these numbers up.

So what’s the rule in the first example? You want to see if it’s worthwhile to buy an S&P ETF since it’s been down 3 days in a row and you think it’s time for it to comeback. So you want to see historically if this has worked out in the past. Typically, somebody would make a test that looks back every time the S&P has been down 3 days in a row and then measure if it would go up the fourth day. There are a lot more fun nuances we’ll get into this and how to properly test it.

In the ESPN example, the backtest’s rule is simple: has any #1 seed ever lost in the first round? You go through all the data historically and test to see if that’s ever happened.

Before you shut off the podcast, know that you don’t have to be a programmer anymore to do stuff like this, you can now use things that look very simple. Tiingo actually has tools to do this, and we’re building more, but this is becoming a trend. This podcast episode will discuss some resources where you can backtest things, whether or not you are a programmer or not a programmer. We will also walk through a backtest example that you can do in excel.

I made this episode also because I see backtests in news articles and the media, and often they do it wrong. The tools are becoming much more accessible, even for hardcore programmers, but we still need to learn how to use them. Likewise, having a hammer, nails, and wood wont build a new house. We still gotta learn how to use the tools!

Tiingo Announcements:

And before we deep dive into this, just want to take a quick break to describe some Tiingo announcements. The magazine issue of Modern Trader featuring Tiingo in the cover story is available at moderntrader.com or Barnes and Noble as the July issue. If the issue is out of print by the time you’re listening, ask me and I’ll send you a scan of the Tiingo page so you can listen it J It was a huge honor and we are incredibly thankful for it.

Secondly Tiingo.com is now available in a mobile version, so check it out on your device! It’s pretty surreal to think people now have a high-end financial app in their pockets. I realized I took this for granted, the fact that I can access google, my E-mail, or Facebook right in my pocket…but it really is extraordinary! And now you can access awesome data and a portfolio risk system in your pocket. This wraps up the major UI overhaul and now changes will be more incremental.

Thirdly, Tiingo is now using modern cryptography, so when using Tiingo, your data is encrypted using the latest security measures.

And finally, the fundamental data has received a massive, massive update. We now have structured fundamental data for over 4,300 companies, including companies that no longer trade and very small microcap companies. Not only that, but you can see annual statements in addition to quarterly that goes back over ten years. Annnd to make It even sweeter, you can now see what fundamental data the company reported when they filed, and also see any restatements they made. This is all structured on Tiingo, so it’s pure data, you don’t have to dig through documents anymore.

If you like what Tiingo’s doing, whether it’s the podcast, the website, mission, or so on, we ask that you pay what you can on Tiingo.com/support,  that’s Tiingo.com/support (spell out).

That concludes the announcements so let’s get back into it!

So let’s walk through a tradeable backtest and how we can create one. This will be the foundation for the rest of the podcast. You may notice, I’m going to spend a lot more time discussing how to test a backtest and the problems with backtests, rather than how to create one. This is because there are so many traps you can make as a data scientist in finance and unlearning then re-learning is so much harder than learning it properly the first time.

First, to continue we need to define a backtest study vs a tradeable backtest. Previously we gave examples of two backtests, but if we think back to them, they are not tradeable. If the S&P falls the past 3 days on noticing what happens the next day is an interesting study, but not tradeable. In order for a backtest to be tradeable we need to meet two conditions

  • There has to be a buy condition
  • There has to be a sell condition

Another markets example would be, “what would happen if I bought a stock after it fell 5% in one week?” This is an incomplete back test because it gives us the condition for buying a stock but not selling it. A complete rule would be, “If a stock falls 5% in 5 days, I will buy and hold the stock for 10 days and then sell shares.” Here we have both a buy and sell condition. I’m going to use this example for the rest of the episode.

To test an idea like this, we can simply do this in Excel or Google spreadsheets. In the blog, blog.tiingo.com, I attached a link to this backtested strategy in Google spreadsheets. Before of the feedback from you all, I’ve learned it’s not very effective to walk through a spreadsheet via podcast haha. So we’re going to skip over, but the spreadsheet document on the blog is well-annotated. It also goes through very simply why we use log returns instead of simple returns when doing backtests. We discussed this in a prior episode, so I won’t repeat it here as to what the differences are. The spreadsheet does a much better job than I could do over voice.

Anyway, with the idea of a tradeable backtest established, I want to dig into something else. I want to now dig into the problems I see all the time in both the news media and publications sent out to hedge funds, banks, and so on. And that’s the topic of poor data science in markets.

A quick story before we move on: There is a general rule in financial backtests and that’s “if it’s too good to be true, it probably is.” A few months ago I had a company come to me trying to pitch me their product. Generally when people do this, I always listen because as a guy trying to grind out a new business himself, I totally empathize. In fact, I’ll often give advice back to the owners and spend an insane amount of time crafting the advice. Many of my users and listeners do that for me, so I will do that for others! It’s the golden rule.
Anyway, this company comes to me and pitches me a product with innnnsane performance. I mean the performance of this strategy was mind-blowing. And as soon as I saw it, I asked them a few questions and realized they didn’t understand the mechanics of backtesting. That’s okay, because if you’re new to markets, why would you expect anybody to understand backtesting? Heck, this is kind of embarrassing but I only knew what the Louve was 3 years ago. I never grew up around art or was exposed to it. Sometimes what seems so obvious to us is not so much for others.

But this is kind of an unintuitive concept isn’t it? A strategy performs so well that you know it can’t be real? This company then told us they went to many quant funds and they haven’t won any contracts. And it hit me, it’s because the people who backtest for a living know something is up. My friend who works at a big fund these days saw the company’s business card on my desk and said, “Ah, they spoke to us too. What did you think?” I responded with, “the same thing your company thought.”

My hope is that for all my listeners listening, that by the end of this episode you will know the gotchyas to backtesting. My goal is that if you were the company presenting, you would be able to defend your performance and thesis from people like me. Or if you have a theory on how markets work,  you will be able to test it.

The problem with a poorly formed backtest is that you will lose money. Your backtest will work historically, but fail miserably in the future for reasons we’ll get into. You will trade the strategy with confidence when it only loses you money.

Often, even discretionary traders back-test ideas. If you’re a discretionary trader, a back-test will help you understand how much value baselines give you. For example, you may try to look for stocks that are undervalued, so you may look at a P/E ratio…basically what a stock’s price is to how much money it makes. A low p/e ratio typically means undervalued, but if you backtest it you can see if buying low p/e stocks actually works. Also, if it does work, you can see how often it works. Maybe it works only 55% of the time? That makes it a much lower conviction trade. So this is why even gut traders like backtests, it puts their view and ideas in the context of how they’ve performed in the past.

I make this argument many times, but even if you are a data scientist who doesn’t focus in finance, I believe you will find good value in this episode. The reason is that data science in tech is becoming a hot topic, but finance was forced to innovate and explore this topic long ago. The truth is that in trading, if your backtest or study is even the slightest bit off, you will know pretty soon when you lose money and you will be out of a job. This has made finance approach studies and data science with an intense rigor, and because of the incentives of trading, it’s often beneifical to keep these a secret as you’re competing with others.

So, let me reveal some of those secrets to you all J

The main issues I have found are overfitting and model robustness, the dual in-sample problem, and product knoweldge

So what is overfitting? Well taking the above example that if a stock drops 5% in 5 days, we will buy the stock and hold it for 10 days, it’s very clear why we chose some of those numbers. 5 days are the number of business days in a week. It’s another way of saying 1 week. 10 days is 2 weeks.  5% is also a nice round number.

What if that above strategy returns, on average, a 2% return a year? But we think, “what only 2% a year? That’s nothing, I want more”

So we start tweaking our model parameters. A parameter is something in our model that we can change. In the example backtest, we have 3 parameters:

  • How much a stock drops , the 5%
  • How many days do we measure that drop? In this case we’re measuring the 5% drop in 5 days
  • And how long do we hold the stock for before we sell it? In this case it’s 10 days, or 2 weeks.

After our tinkering we find that we can get the strategy to return an average of 9% a year if we do the following:

If a stock drops 7.62% in 12 days, we buy and hold the stock for 16 days.

But looking at these numbers, what do they all mean? We chose 5% in the original backtest because it was a nice round number and a multiple of 5. But what Is 7.6%? Where does that number come from. And why are we measuring the drop in 12 days? Where does 12 come from? It’s not really 1 week or 2 weeks, it’s 2 weeks and 2 days. And why did we choose 16 days? That’s not 3 weeks, it’s 3 weeks and 1 day.

All of the parameters above were just randomly chosen. And that is the dangerous part.

But you may be wondering, “Rishi, why does that even matter? Who cares, it results in the best performance.” And this is why the problem is so dangerous. With enough tinkering, any model can be made profitable or predictive.

Let’s take a look at example that may make this more obvious. Every week, on a Thursday at 8:30am, the government releases numbers with the number of people filing for unemployment. This is called the initial jobless claims. Many researchers and wall street analysts try to predict this number as it can sometimes move markets. After the 2008 recession, traders watched this release because it helped guide the economic recovery. If the economy was healing faster than people thought, markets would rise. If it was healing slower than people thought, markets would fall – generally speaking.

So Google has a tool called google correlate. What it does is that it allows you to submit Google data, and it tells you what search results were correlated to that timeseries. So I fed Google a timeseries of these unemployment claims. When we do that, we see initial jobless claims correlated to the search word “load modification” with a correlation of 96%. This could make sense, maybe people want to modify their loans because of foreclosure. But we were also going through a housing crises? What would’ve happened in 2001 where it was a tech bubble bursting rather than the housing bubble?

Also, all of the other correlated search results are nonsense. “laguna beach jeans” correlated 95% with unemployment claim data. Does the search result of laguna beach jeans predict initial jobless claims or is that a statistical artifiact?

I’ll let you play with Google’s data for this. It’s fun stuff and Google actually has a paper out that shows how correlate could be a useful tool for predicting economic data. Wow I’ve plugged google like 3 times in this podcast…. Google google google, use google yay. It’s like when I was watching the terminator 2 the other day and I noticed pepsi cans and vending machines.

Just like our correlation example, if we keep digging into data long enough, we find random relationships. This is called overfitting, modifying the data until we get the result that we want. If you’re reading a financial article or speaking to people on wall street, they may refer to overfitting as “data mining.” For anybody in tech or somebody interested in statistics, this is confusing as data mining means something entirely different. In finance though, data mining is almost always used negatively to mean overfitting. That’s just a quick semantical aside.

But even if the relationship makes sense, it may be so specific that it doesn’t work outside of the timeframe. For example, “load modification” may work for a crises related to the mortgage crises, but what about if it was a tech bubble bursting?  Are people googleing for “loan modification” really a good indicator? Also is that data even applicable today? As Google in 2000 was a far different company than today. Will Google search results be an indicator of the future?

So how do we counter overfitting? How do we measure model robustness?

So we just described overfitting and model robustness.

 

As a data scientist you have to question every single one of your inputs and model parameters. Not just the results, but why everything was chosen.

 

With overfitting, we really have to practice self-discipline.  This is the tough answer. We as people can always torture and twist data to get us to tell us what we want it to. You can see this all the time when political issues where two lobbying groups will use data to support their idea even though they are polar opposites.  How can both parties use data to prove something? Because they take a some truth and use the statistics they want to tell their side of the story.

Unfortunately for us, if we do that in markets, the markets will take our money. We have to find the truth and be real with ourselves. If we are dishonest, we will lose our own money. This is harder than you think and there are trading psychology books that go into this. To combat overfitting, we have to hold ourselves accountable.

And to hold ourselves accountable, all  – and yes I say all – successful traders – both discretionary and quantitative, have a journal or a process in place. These are individually crafted rules that hold ourselves accountable. Here are a few processes and rules I have that let me make sure I am being honest with myself. Maybe some will work for you, and some may not. And noticehow I don’t include any statistical tests below. Those are my last-stage tests because like I said, we can use statistics to tell us the picture we want. I first like to make sure my ideas have grounding before getting stats involved as it prevents me from twisting data and overfitting.

If you ask any experienced trader, all – yes all –  will tell you simplicity is favored over complexity. You absolutely should specific statistical tests like t-tests, p values, distributions and so on, but that’s beyond the scope of this episode and there are really nice simple visualizations online of them.

Also, if you read the papers published by AQR, the 2nd largest quantitative hedge fund, you will find much of their research is totally accessible and their math does not really get any more complex than calculus, much of it can be done with algebra.

The truth is, and this is something I see often, that machine learning, advanced statistical analysis, and so on do not make a better trader. In fact, it gives you more creative ways to part with your money. I see it all the time, and you would be surprised with how simple many quantitative trading strategies can be. I’ll add some links to AQRs papers if you don’t believe me in the blog – blog.tiingo.com

And an aside for those of you who hear about machine learning: right now machine learning in markets is sexy and sells, but remember it very rarely makes money by itself. It’s not the holy grail of trading. In fact, every quantitative trader I know who uses machine learning, uses it after many years of getting their models working without using it. The ones that do use it, often use it as a last optimization. And even the traders I know who use it, I can count on one hand. Their profitability did not drastically change once they used machine learning.  The blog will contain papers by big hedge funds just to show you how simple the math can be.
Anyway, here are some of my snippets I use to hold myself accountable and make sure my models are flexible and robust. The accountability and overfitting really go hand in hand.

  • Why would this idea work? What is the current research and conditions out there that support why this would and wouldn’t work
  • What is my hypothesis, or null hypothesis – what am I testing?
  • Are there any relevant research papers out there? Can I replicate them? My trading mentor told me he’s only been able to replicate 20-30% of papers, and I have found about the same to be true. Some of the errors in research papers out there are horrible
  • Should this theory or idea work across markets and/or across stocks? Or does it only work for one stock or one asset class? If it only works for one why? This is a huge warning sign for me. If looking at stocks, it should at the very very least work in the sector.
  • What is the risk adjusted return of this model? Basically what is the average return and volatility of this model?
  • How many times did I run this model and change parameters? How many times did these changes result in better performance? Keeping a tally of how many times you tweaked parameters is a good way to be honest with yourself about how much you tortured the data
  • Does the model trade all stocks equally or is the majority of returns driven by a couple stocks
  • For all the big gains and losses in the strategy, check them manually for data errors
  • When will this strategy fail? This is such an important question. If you don’t know when or why this strategy fails, then you don’t really know the strategy or all or why it makes money.
  • How does the profitability of a strategy change if I slightly tweak a parameter? Is there a relationship between how much I tweak the parameter, how much the profitability changes?

 

This is an incomplete list, but I think it’s a good starting point.

One thing that people do to help prevent overfitting in the in-sample and out-of-sample  backtest. But I’ve found this often results in something I call the dual-in-sample error.

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Podcast: Ch.1 Sifting Through the Noise and Taking Action – A Chat with Garrett Baldwin

Podcast: Ch.1 Sifting Through the Noise and Taking Action – A Chat with Garrett Baldwin

When I started out in finance, and even now, I get bogged down whenever I read certain financial news outlets. Even after years in the industry, it is tough to weed out what’s important and who is credible.
That’s why I asked Garrett Baldwin, an esteemed financial journalist, academic and the managing editor of AlphaPages.comFutures MagazineModern Trader, and FinAlternatives to be a guest on the podcast.

In this episode, we talk about a variety of topics including Garrett’s journalistic process,  holding Wall St. analysts, journalists and bloggers accountable, and tips on building an investment process.

Check out the podcast to learn how financial journalism is changing and how the latest FinTech tools can help us sift through the noise to find meaningful, actionable data.

Garrett also mentions the Tiingo community in the cover story of his newest publication coming out:  Modern Trader (Available June 23rd at Barnes & Noble, E-mail will be sent out).


 

Here are a few resources we discussed in the episode:
Estimize
Modern Trader
OpenFolio
EidoSearch

Garrett is the Managing editor of AlphaPages.com, Futures Magazine, Modern Trader, and FinAlternatives. In this episode, we touch upon a variety of topics including the journalistic process in finance, holding Wall Street analysts and bloggers accountable, and tips on building an investment process. Learn how financial journalism is changing today and how the latest FinTech tools can sift through the noise and find meaningful, actionable data.

iTunes Link

Non-Itunes (tiingo.com)

Given the back-and-forth nature of this Episode, there is no transcript.

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Podcast: Ep.7 Our First Hedge Fund Strategy

Podcast: Ep.7 Our First Hedge Fund Strategy

 

In this episode we cover not only what hedge funds are, but one of the most recently used hedge fund allocation strategies: risk parity. The largest quantitative hedge funds are using this method and it is now presenting some real dangers. We use this example to touch upon how we can skeptically look at performance and also what to beware of with 13F filings. This episode synthesizes everything we’ve learned into a single practical episode.

iTunes Link

Non-Itunes (tiingo.com)

Here is the script that was used in today’s episode.

Note: I don’t follow scripts word-for-word as they can sound unnatural, but the episodes do closely follow them.

Get excited listeners. We’re going to synthesize everything we’ve learned to create our first hedge fund strategy and go over what a hedge fund is. If you haven’t listened to the other episodes, that’s okay because this can be a good test to see if you need to brush up on anything. For the most part though, this will be a very simple explanation so relax and enjoy listening.  Oh! And I even made an entirely new feature and initiative on Tiingo to aid in this episode.  Actually, I had this podcast all scripted out and then I realized, “I should just make this hedge fund tool for everyone.” So… this is going to be a really fun episode.

I consider this an important episode because we’re going to be using some metrics we’ve learned about and touching upon new ideas like risk management and position sizing and what they mean. We’re also going to discuss criticisms of the hedge fund strategy we’re covering, which will give you a look into how we should all view markets and claims made by individuals. One of the most important skills you can develop as an investor and trader is skepticism.

Here is a fun story that upsets me quite often. I used to work at a big bank, and there was a Managing Director there. A managing director is the most senior title you can get at a bank before you get into CEO or CTO.  In other fields it may be called a Principal, Partner, and so on. Point is, it’s a very high title. Well this MD, managing director not medical doctor, was followed across wall street because his research was popular. What the bank didn’t advertise was that this MD originally traded, but because he lost money for 7 years straight, they no longer allowed him to trade with bank money and instead allowed him to publish research because it helps their relationships with clients. Another fun point? Of the people who read his research, half of them mocked him and used him as a joke of everything wrong in market analysis. This MD would literally look at a price graph and then draw arrows. That’s it. He would circle things, and draw arrows where he thought things were going.

I rarely trash talk as you know in this podcast, but I bring up this example to highlight how important skepticism is. Even if you think somebody is a pundit or brilliant, fact checking is incredibly important. Misinformation is so dangerous because it means we can lose our money. It’s one thing if the misinformation is a genuine mistake and a person tried, it’s another if an institution knows a person had bad research yet still promotes him for sales. I will never stand for the latter and will continue to be vocal on this.

So to recap: always be skeptical. Even of me. Verify everything I say. I try my best but I am human so if you think I’m wrong, please check. If you don’t think I’m wrong, then definitely fact check me! Haha, that’s an important lesson!

OK moving on to some quick Tiingo announcements. This week we have revamped the entire fundamental database so it has the data structured in tables as well as graphs. The data is now also more accurate and had extensive coverage for over 3,500 stocks.  Secondly, I have started the Tiingo Labs initiative, which contains a powerful tool you can use with this podcast. And thirdly, I just added a chat reputation system, as well as something called a Tiinglet. I realized some of the best converrsations among friends happen within chats, but we don’t have a way to save them down. I present a Tiinglet, it lets you turn your discussion about markets into something you formalize and give to the public to help others learn. If you open the Tiingo chat, click a username of a message you like, a box will come up and within a few clicks, you will make a site centered around your dialogue.

For example, if you and a friend are talking about Apple and one of you comes up with great analysis you think you could help others, then you can simply click the text and a message box comes up that lets you turn the conversation into page that is accessible to others who may have the same questions as you do.

In addition, if you like the Tiingo project – the mission, podcast, web app, and so on, please consider paying for Tiingo at http://www.tiingo.com/support once again www.tiingo.com/support. I have a pay what you can model so nobody is excluded, but in order to exist, we will need people to pay for the product.

So let’s move on into our first hedge fund strategy!
To begin let’s discuss what a hedge fund actually is and how news can often misinterprets what they do.

A hedge fund’s goal is to make money that’s uncorrelated to other assets like stocks, bonds, and so on. Think of it as if you invested in real estate. If you bought a condo,you probably wouldn’t compare it to stocks. In fact, many times people invest in property to build equity or have other investments besides stocks and bonds.

So it’s not so much hedge funds have to make more money than the stock market like the S&P 500 or NASDAQ index funds, but that they have to have a return stream that differs from those.  They are a tool used by pension funds, wealthy people, banks, other institutions, and so on to diversify away their risk. For example, if you had 10 billion dollars, stocks and bonds may be nice, but you may want to have other investments too like real estate. So think of a hedge fund as a tool used by wealthy investors to diversify away some of their risk.

You may often see headlines that say, “the stock market returns 20% this year, but hedge funds only returned 12%.” But that’s not a bad thing. A hedge fund’s goal isn’t to beat stocks, it’s be uncorrelated for stocks. For example, if stocks were up 20% and a hedge fund was up 20%, and if stocks were down 10% and a hedgefund was down 10%, why would you pay fees to a hedge fund when you could own an index fund?

So to create strategies uncorrelated to the stock market or bond market, a hedge fund will trade in different styles. They are considered active managers. They also have a tool called leverage. This simply means they can borrow money. If they have $10,000, they may trade as if they had $50,000. They can also sell short, a topic we covered in Q&A. This differs significantly from mutual funds and index funds, which tend not to really use leverage in the same way, and also mutual funds and hedge funds don’t sell short. Because of this, hedge funds are often classified as an “alternative investment.”  They are alternatives to traditional assets like stocks and bonds. They manage money in what is considered non-traditional ways.

Some hedge funds may be long a stock while being short another stock. This is called a long/short equity fund. Others may trade commodities or fx, and these are often called global macro funds. Some hedge funds employ quantitative strategies where they build computer programs that decide what to invest in.

One problem you see in the

The fee structure for a hedge fund is often more aggressive than a mutual fund or index fund. It’s typically assumed a fund takes 2/20 (2 and 20) or maybe you will see 1.5/15. Let’s use 2/20 as an example. The first number, 2, is the management fee.  This is similar to a mutual fund. If you invested $1mm, you would pay 2% of what you invested. IN this case it would be 2% of $1mm, or, $20,000. The second number, 20, is the cut they get based on performance. For example, if they make 15% on $1mm, or $150,000, they will get a cut of that $150,000. The second number represents the % cut they get. So if it’s 20%, they would get 20% of $150,000 which is $30,000. So 2/20 (2 and 20), is a 2% management fee on what’s invested, and a 20% performance fee which is shaved off the additional money they make. If the hedge fund doesn’t make money, or losses money, they still get the management fee but do not get the performance bonus. They get the 2% but not the 20%.

So a hedge fund is a pooled investment, like a mutual fund or index fund, but they take investor’s money and then use alternative strategies to make money in different ways. Their goal is to make money regardless of market conditions while also being uncorrelated to other assets. As usual this should be the case, but often time isn’t.

Anyway, this is what a hedge fund is. It often has a mystique to it like hedge fund traders are brilliant. But just like any profession, you have people who are very good, and others who may not be so good. Often I find the media portrays hedge fund managers, especially quants, as these super brilliant mathematicians. Having gone to that side, I can assure you…unless it’s High frequency trading, the Ph.D.s and the chess champions don’t make a difference.  They’re just normal people that are incredibly passionate about markets.

Now that we know what a hedge fund is, we are going to discuss a popular strategy using the knowledge we’ve gained. We need to understand volatility, correlation, and stock indexes and etfs.

So a hedge fund takes a non-traditional approach to investing. Do not try what we’re discussing at home. There are a lot of caveats to a strategy like this, some of which we’ll get into, but making sure this is done right takes a lot of practice.  I don’t want to be responsible for any execution errors or mishaps. This strategy is not guaranteed to make money, and in fact could very well lose you money. Anyway, with this very scary, yet important disclaimer aside, let’s move forward, woo-hoo!

We’re going to discuss a strategy called a risk-parity strategy. Actually, risk-parity is not a strategy but an allocation method. That simply means, it’s a method to determine how much money you should put in each asset you own. What I mean by that is if you own a stock index fund and a bond index fund, how much should you put in each? In episode 3 we discussed two different ways to determine this, one was simply always keeping 60% of your cash in stocks, and 40% of your cash in bonds. We spoke about how this is naïve because it stays the same regardless of other factors. For example, if you are younger, you may be able to take greater risks, which will let you be in more stocks.

In the same way, a risk parity strategy helps you decide how much to put in each stock. We’re going to use the 60/40, 60% stock, 40% bond, portfolio as an example for this strategy.

So a big trend among large hedge funds, like AQR and bridgewater, is to determine how much to put in each asset using a risk-parity strategy.  They may add a few twists to the idea, but at it’s base core, a lot of it is determined by this method.

So what is risk parity? Well it simply means equal-volatility weighting your portfolio. Before you shut off this podcast, I will actually explain what that means. I can’t stand when people define terms using equally difficult terms or phrases so I won’t do that to you.

So you know how in the 60/40 stock/bond portfolio 60% of our cash was in stocks, and 40% was in bonds? Well we generally assume stocks move around a lot more than bonds do. Bonds are assumed to be a bit more stable.  This is a concept we call volatility. We say, on average, stocks are more volatile than bonds.  Typically, many people measure risk as volatility. Something that moves around a lot, could be said to be more risky. So sometimes volatility and risk are sometimes said to be synontmous.  SO breaking down the term, risk parity, we can say volatility-parity. And parity means for something to be equal.  Using these definitions, we can say “risk parity” roughly translates to “volatility equal”, or more naturally, “equal volatility.” Risk parity means equal volatility.

But what does that mean practically? A common example is if you take a 60/40 stock/bond portfolio, and measure the volatility, we see 90% of the volatility comes from stocks, and 10% of the volatility comes from bonds.  Going forward we are going to use the term “cash.” This means exactly that. If we put 60% of our cash in something, it means if we had $1,000, we would take $600 and invest it in stocks. We would then take $400 and put that in bonds. a 60/40 portfolio is 60% cash in stocks, 40% cash in bonds.

If we took 60% of our cash and put it in stocks, and 40% of our cash  and put it in bonds, 90% of the movement would come from stocks. Only 10% of the movement would come from bonds.  Because stocks are said to be higher risk, or higher volatility in this case, they would make up 90% of the risk in your portfolio, even if they were only 60% of the cash.

So what risk parity says is that we should make stocks only take up 50% of the risk, and bonds make up 50% of the risk. If 60% cash results in 90% risk, how much would we have to scale back? Well if we put 33% of our cash in stocks, that would make the portfolio take up 50% of the risk.

What about bonds? Well since 40% cash results in 10% risk, if we multiply our bond position by 5, we can get 50% risk. That means we have to take the 40% cash position/10% risk position, and multiply both by 5. We can see that 200% cash in bonds results in 50% risk.

But how do we put 200% of our cash in something? Well this is a concept called leverage. This is something hedge funds can do as we mentioned earlier, they can essentially borrow money to multiply their returns.  Individuals can do this too through margin and futures, but we’re not going to cover this here quite yet as this is a more advanced topic and has serious risks involved.

So to recap, in order to take a 60/40 stock/bond cash portfolio, and make the portfolio 50/50 in volatility/risk, we have to cut the position of stocks and lever up the position in bonds.

Notice how we are using the volatility of an asset to determine how much to allocate? This is a dynamic method, and no different than if we did 60/40 or another allocation method. So risk parity just tells us how much to put into each asset. The strategy will tell you what assets, and risk parity will tell you how much to put in each asset.

So let’s get down to it, how much would this strategy make vs a 60/40 strategy? And here is where things are gonna get SO fun.

The risk parity strategy returned 45% total over the past 12 years. The 60/40 portfolio returned 61% total. This wasn’t assuming reinvesting dividends for those wondering – if you want to ask my why shoot me an E-mail.

So you may be thinking,”Rishi you said this was profitable…but I would make less? What is wrong with you.” Well here is the key information and why hedge funds can do this better than 60/40. We have to look at how this strategy performed relative to the path it took. In episode 5 we talked about volatility and how the path to the return we got matters. For example, if invested $100,000 and doubled our money to $200,000 that’s awesome. But what if half way through that $100,000 turned into $50,000?

Likewise, what if you invested $100,000 and made $150,000 but the lowest your portfolio ever got was $99,000. Which would you prefer? Even if you’re telling me the down $50,000 scenario, here is why it’s still worse if you’re a hedge fund.

The risk parity strategy had a volatility of about 5.5%. The volatility of the 60/40 was about 11%, almost double. So what a hedge fund will do is that they will apply even more volatility, because investors want a higher return. So to compare apples to apples, a hedge fund may use leverage and double the amount of money into risk parity, so you take that volatility of 5.5% and double it, and now you have 11% volatility. But you also have double the return.
So if we want to compare apples to apples, we should also compare the volatility, or the path it took us to get to the return we have. So if you double the leverage to a strategy, you not only double volatility but the return. So that 45% we made on risk parity becomes 90%. 90% on risk parity vs. 61% on a 60/40 portfolio. There are a bit more nuances to this strategy that actually improve performance of risk parity, but we’ll get to that soon enough in this podcast series.

If you want to play with this risk parity allocation method, I mentioned I created a tool to help you do this. Know this is an informational tool and you should not trade on the results. I have not put in tradeable assumptions, but this is a good informational off-the-cuff proof of concept. And please treat it as such, it’s not a full replication of the strategy nor how much you should invest. So with that diclaimer, check out the tool on Tiingo.com/labs. You’ll see a link that has risk parity. This is a sweet tool that may let you get an idea. You just type in the tickers you want in your portfolio and press enter. Maybe you want to include the S&P500, bonds, but also small cap stocks? But anyway, the possibilities are endless and I hope you find joy and fun is playing around with this!

The next question we have to ask ourselves, is why does this strategy perform so well?

This is where skepticism in markets is so critical. If a strategy performs very well, it’s important to ask ourselves why? What conditions are allowing it to perform so well? Is it the economy, maybe government policy? Certain changes in technology?

In this case, the common explanation of why risk parity does so well is especially from the bond market. In the U.S., for the past 30 years, bonds have done extremely well. They’ve never really gone down for an extended period of time like stocks have. And after the 2008 crises, the Federal Reserve, which sets an interest rate that bonds are affected by have gone down. The Federal Reserve, or Fed, did this to promote credit and boost the economy. We will get into how that works later, but the take away is that fed policy has allowed rates, like loan or mortgage rates, to stay low. Not only that, awhile ago the Fed committed to doing that for awhile.

In Episode 5 we mentioned how uncertainty creates volatility. Well, when a book government agency that influences rates says, “we’re going to do this for a long time” it removes a lot of uncertainty. This in turn removes volatility from bonds.

So what we’ve seen are that bonds are performing very well, the price goes up. If you’re new to bonds, it’s said the price of bonds is inversely proportional to the interest rate. What that means is that if rates, like you see on loans, goes down, the bond is worth more. We will cover this more in depth later, but if rates are up, bond prices are down. If rates are down, bond prices are up.

So since the Fed committed to keeping rates low, you’ve seen bond prices go up. Secondly, you’ve seen a lot of uncertainty removed in the bond market, resulting in low volatility. And since risk parity equal-volatility weights, in order for the volatility to be 50-50 stocks and bonds, hedge funds have bigger positions in bonds.

So the argument against risk parity is that it applies an unfair amount of leverage to bonds. To mitigate this, hedge funds look at the volatility every month, and do what we call “rebalance.” If volatility was higher for an asset the previous month, they will put less money in the asset the next month. Every month they make take the average volatility for the past 3 months and add or reduce their position in each asset.

However, rebalancing happens once a month. What if the price of bonds fell quickly within a month. Let’s explore it for a moment.

In our example, we borrowed double our money to invest in bonds. Let’s say we had $1,000 and borrowed another $1,000. The $1,000 we had is our equity. So if bonds fell 50%, we would lose 50% of the combined value of $2,000. We would lose half, so $1,000, which would completely wipe out our equity.

If we levered 300%, so we had $1,000, but borrowed another $2,000, a 33% fall in bonds would be a loss of $1,000 and wipe out our equity. The equity is what we actually have, so if we lose all of it, we go bankrupt.

The truth is though, with hedge funds, if they are down 20%, investors get scared and often pull their money away. If your mutual fund was down 20%, you would probably rethink the investment.

Right now, a big worry among investors in hedge funds is that the economy has been doing pretty well. So when is the Fed going to allow interest rates to rise? And what if it happens really quickly? If rates rise quickly, the price of bonds will fall quickly. And remember, the top hedge funds are using this strategy and they manage $200bn among the top few alone. If these $200bn is highly levered, imagine how many billions could be wiped out if bonds just fall 10%.

This is similar to the 2008 crises. Many people were hurt because banks were offering low down-payment mortgages, and that just means people levered. a 10% down payment means you are levered 10:1, or 1000% on your equity. If your house dropped 10%, you were wiped out. This is what hurt people.

In the same way, a big worry is that because funds are so levered on bonds, if they fall in price, you could see billions of dollars wiped out. Funds have tried to come out and recognize this problem and are taking steps to address it. I wont comment specifically if I think these steps are appropriate, at least not publicly, so if you want to have that discussion, shoot me an E-mail!

The caveat though is that leverage needs to be used appropriately, and many people think the reason this strategy has done so well is because bonds have done incredibly well over the past 20 or 30 years. Stocks have also done very well in the past 6 years, so this keeps adding to the returns of the strategy.

These are the drawbacks and everytime you see performance numbers, always ask yourself why? Asking why an opportunity exists is not just a powerful tool in business, but also markets. Maybe there is a reason this strategy works that may make you feel uncomfortable.

Either way, I know this is a lot to take in, so if you have to repeat a few parts, I apologize. But this example is truly an expression of how we can combine the things we learned so far into a strategy the largest hedge funds are using.

This has been fun and if you have any feedback please E-mail me at Rishi@tiingo.com

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Podcast QA2: What is Shorting and How Do People Make Money When Stocks Fall?

Podcast QA2: What is Shorting and How Do People Make Money When Stocks Fall?

Short-selling can allow somebody to make money when the price of a stock falls. I’ve been thinking for years on how to best explain this, because I myself struggled with this concept for a very, very long time. Luckily, I found a way! I share with you not only what short-selling is, but how it works, and what to be wary of. s.

With that! Here are the links. If you have iTunes please use that as it helps my rankings within the store. Don’t forget to subscribe to stay updated on future episodes!

iTunes Link

Non-Itunes (tiingo.com)

Here is the script that was used in today’s episode.

Note: I don’t follow scripts word-for-word as they can sound unnatural, but the episodes do closely follow them.

 

Hey listeners, welcome to QA.2 What is shorting?

Today we are going to discuss, what is shorting? And how can you make money when stocks go down? What are the implications of doing this with commodities or other types of financial instruments? And of course, since this is the Tiingo Investing podcast, we aren’t going to just explain what shorting is, that’s way too easy. We’re going to take shorting and then expand upon the concept to take it a few levels deeper. Everything will be easy to understand I promise.

For those of you just listening in, this is a Q&A episode. In this format we answer questions directly from our listeners.  I personally love these episodes because it means I get to interact with you all and learn what’s important to you.  Actually it’s funny, before a user asked me to explain shorting, I would spend the past couple years thinking, “if I were to teach shorting, how would I do it?” The reason I’ve been thinking about this for years is that this was a concept I struggled with for sooooo long. I mean you google it and people are like “you borrow shares and sell shares you don’t own then buy them back cheaper” And that’s the explanation. And I was still left with so many questions like “wait what? how does this all work?”

So it took me an embarrassingly long time to learn shorting and I think part of it is that sites don’t explain it well. Well I’m looking to change that in this episode and luckily I’ve been thinking about this for years. Now, I wanted to put this in the episode series, instead of Q&A, but I couldn’t figure out how. After all, the goal in these beginning episodes is investing and smart longer-term strategies. Eventually I will get into trading, but it would kinda stink to wait until episode, I don’t know, maybe 20 to learn shorting? Especially since it’s so prevalent in the media. And when a user asked, I decided, “This will be a good Q&A episode.”

Anyway, I’m going to provide a better “what is shorting” education because I can empathize with the confusion of when you google it.

Before we begin, I do want to cover a few things regarding the pace of Tiingo’s future episodes. This is going to take a couple minutes but I want to be clear why the pace of episodes is slowing down a bit. Originally, I was aiming for 1 or more episodes a week, but the truth is that the majority of my Episodes take 16-20 hours each to plan and create. Not only that, my mind is constantly thinking about how to explain things at all times.  For those of you who don’t know what Tiingo is, it’s a broader mission to make high end financial tools and education accessible to everyone.  There is a trust vacuum in finance and investing, and I want Tiingo to fill it. It’s first and foremost mission is to Actively do good. The mantra don’t be evil isn’t good enough for Finance, so Tiingo adopts “Actively be good.”  I see Tiingo as a kickstarter except better as you’re constantly building a business that provides a service to the people.  There isn’t the risk of contributing toward a product that doesn’t make its final form or really fully develop. It’s continually improving.

Anyway,  creating Tiingo allows me to develop and manage a web platform, author a podcast, interact with my users and listeners, which I love btw, and now market to get the word out. Because of all these things the past few weeks I’ve been working 8am to 2 or 3am. Actually sometimes 4 am.  I’m currently a one-man shop and thankfully many in the community are volunteering their time to help out. But in order for me to exist as a person, I have decided instead of 1 or more episodes a week, I’m going to aim for 1 or more episodes every two weeks. This is important as now we are going to get into some more complex stuff and I want to spend a lot of time making sure it’s accessible. Also this release schedule lets me sleep at a reasonable hour and have time for friend, family, and my significant other.

So I hope you can understand if the pace of episodes going forward is a bit slower. I’m still giving the same amount of time, if not more, to each epsiode and that requires a bit of a longer release schedule.

Anyway, last quick thing before we get into it – the Tiingo ecosystem, platform, and community has a “pay what you can” page. Because Tiingo costs money to run and I want to empower, I believe right now this is the best model to create a sustainable business and also empower. If you have a few minutes, I would love it if you could go to Tiingo.com/support and contribute. Even if it’s a couple dollars a month, if everybody does it, I can continue to support myself full-time. I’ve forgone over a year of income to create this project so it is helpful.  If every listener contributes $3/month or more, I can get one-step closer to making sure I can continue to do this full-time.  Think of this podcast as a constantly evolving book that interacts with us. Many of us spend $3/month on a cup of coffee, or for a book that doesn’t change throughout time. Well, why not spend another $3/month for Tiingo? Smiley face.  The link is tiingo.com/support

Anyway, that covers the pace of future episodes and a “I need to pay my bills” shpiel. Let’s move forward!

Shorting is a way somebody can make money when a stock falls. It’s a term for borrowing a shares of a stock, selling them, then rebuying the shares. You may be thinking, “uhhhh,” and that’s exactly how I felt when I heard this. So let me explain in easier terms because that explanation kinda stinks ….as usual I will explain with my favorite dessert: cookies

Let’s say your friend owns a cookie shop. You always stroll in his part of town because you buy his cookies. They aren’t any different than other cookies, and in fact your friend buys his cookies from a big company. This company sells their cookies in a ton of different stores.  Your favorite though is a bag of chocolate chip cookies, which sells for $10 a bag.

To make this scenario even more ridiculous, let’s say you’re a photographer. And you have an obsession with taking pictures of cookies. You love your friend’s chocolate chip cookies for $10/bag so much, you ask him, “hey can I borrow that bag for a hot second? I want to take a picture of it.”

Your friend thinks you’re a little creepy, but you’ve been a good customer so he says, “sure, just make sure you return the bag to me after you’re done.

You tell him that’s fine and borrow the cookies from your friend. You take some pictures of the bag, and have a friend over that night. Your friend looks at the cookies and is say, “they look so good. can I have some?” You tell him, “oh I’m just borrowing them for pictures.” Your friend who’s over looks at you funny and says, “Ok how much? I’ll buy them from you.” You realize your cookie shop friend would love it if you sold his cookies for him. So you say, “sure they’re $10.” Your friend pays you $10 and you put it on your wallet so you can give your friend the cash the next day.

The next day you walk to the cookie store to give your friend the $10 you made for him. You’re strolling along when you look to your right and see another cookie shop. You see the same cookies you just sold last night, but WAIT! you see the price….whoa.  This other cookie store is selling it for $5/bag…at your friends store they were charging  $10/bag yesterday.

You enter an existential crises of realizing you’ve been overpaying for cookies.  What is friendship? Would a friend truly make you overpay for cookies? Why would he do this to you? You’ve bought over 100 bags of cookies from him.  Why? You realize he’s not your friend and you never want to talk to him again. You say, “I don’t care about my friend, I’m going to go make a profit.”

You decide to buy the cookies for $5/bag from this other cookie shop. You go to your friends cookie shop and give him the new bag you just bought from the other store. He can’t tell the difference and thanks you. He then asks you if you want to buy cookies from him and he says the supplier dropped the cost and they are now $5 bag.

You smile because you realize he’s still your friend. You tell him no thanks and walk away.

So what just happened in this scenario? Well you borrowed the cookies from your friend, sold them that night for $10, then saw the same cookies selling for $5/bag in another store the next day. Your friend only asked you for the cookies back, but not the money. So you decided to buy a new bag at $5/bag.  You gave that bag to your cookie store friend and he couldn’t tell the difference: they were the same.

You just pocketed $5.

Once again, you borrowed a bag of cookies that normally cost $10/bag, sold them to somebody for $10 that night, and then bought that same bag of cookies for $5 the next day to give back to your friend. You sold the borrowed cookies at a higher price, then bought them back the next day at a lower price. And then you gave your friend who lent you cookies those new cookies to pay him back.

If this doesn’t make sense yet, it’s okay. Let’s continue on and talk through an example involving stocks.

Taking the perspective of stocks instead of cookies: Let’s say you see Microsoft is $40/share. You think for whatever reason the stock is going to drop. Well, you can go to your stock broker say, “hey can I borrow 10 shares real quick.” They say, “yeah sure, why not? Just give me 10 shares back at some point.”  Since pretty much all shares under the ticker MSFT, Microsoft, are the same, they don’t care if the 10 shares you give them are the ones they lent you or not. They just want 10 shares back. Remember, from episode 1,  because shares are identical, they can be traded on an exchange. 10 shares under ticker MSFT are going to have the same rights and benefits as the next 10 shares under MSFT.

So you borrow the 10 shares from your broker, and then you sell them at the market for $40/share. You collect $400 in cash for selling those 10 shares @ $40/share.

So now Microsoft drops to $35/share, and you think, “okay I’m happy here.” So you buy 10 shares @ $35/share, which costs you a total of $350. You give those 10 shares back to your broker.

Remember when you sold those borrowed shares, you made $400. Now you spent $350 to buy those shares back. Sell price minus buy price gives you profit. So you sold at $400 bought at $350. $400-$350 is a $50 profit.

OK now let’s get into some nuances.

When you ask your broker to borrow 10 shares, they actually ask a clearinghouse which has a “inventory” if you will of shares of Microsoft.  Think of them as sort of the “accountant” of your shares and everybody else’s at your brokerage. They help ensure the stock system is less risky.  Anyway, you want to borrow 10 shares and let’s say the clearinghouse has 1,000 shares of Microsoft on hand. They may say, “Ok that’s 1% of what we have that’s fine.”

But what if the clearinghouse only have 30 shares of Microsoft on hand? Well that 10 shares you want to borrow is now 30% of their inventory.

Think of this like a bank. When you go to your bank, you know the bank has enough cash to satisfy your needs if you want to take some cash out of your bank account. Well, what happens if every person who had an account at that bank asked for a cash withdrawal the same day? The bank would quickly run out of cash and not be able to service everybody. But the vast majority of the time, banks have enough cash to help people on a day-to-day basis.

The same principle holds for these clearing houses.  Their inventory of 1,000 shares of Microsoft exist, because all together, their customers have 1,000 shares. They have to make sure they have some inventory of Microsoft so if a ton of people ask to sell their shares, the clearinghouse can serve those people.

So they may feel comfortable letting you borrow 10 shares if they have 1,000 in inventory. But what if you asked for 10 shares of Microsoft when they have 30 on hand? That’s 30% of their inventory!

So to protect themselves they may say, “that’s fine, we can give 10 shares to you…but you’re going to have to pay up. You’re going to have to pay us a 50% interest rate because Microsoft right now is what we call, “hard to borrow.” You have to pay a 50% interest rate which is an annual rate. What this means, is that for every dollar you make from selling short, you have to pay 50% of it, or 50 cents each year, to the clearinghouse. So if you sold 10 shares of microsoft at $40, that’s $400. That’s $200 over the course of the year. Typically interest rates are based on 360 days to a year, so you take that $200 divide it by 360 days in a year, and that’s about $.55 a day to be short.

So if you bought back microsoft at $35/share, but it took you 100 days to do it, you would’ve paid $55 in interest to the clearinghouse.  Yet your profit was only $50. So with shorting it can not only matter if the stock drops, but also how fast it drops as you may have to pay a large interest rate, especially if it’s hard to borrow. With shorting you can very easily be in a race against time.

If you want to sell short you have to call and ask your broker what their rates are and how they handle the borrowing fee. Each broker will be different and have a different rate schedule. So before you sell short, please call them ahead. Some may charge additional fees.  This short selling interest rate process can very drastically from broker to broker. So my goal is for you to be aware that there are borrow fees and potential interest rates involved when selling short. It can get very broker specific.

The next nuance is something called margin. If you want to short, you have to open your brokerage account with margin approval. When you open up a brokerage account for the first time you can select this option .If you already have a brokerage account, you will probably have to fill out a separate form to request a margin account.

So what does margin mean? It can be confusing because it has two meanings. First, margin lets you borrow money to invest. If you have $2,000, you can apply for margin and double that. You can now trade as if you have $4,000. This is a concept called leverage. IN this case, you are levered 2 to 1. Or for every $1, you can trade $2. Remember, that your account is still only actually $2,000. If you didn’t use margin or leverage, your stocks would have to lose 100% to get wiped out and have $0 left over.

If you levered 2:1 and you traded as if you had $4,000, your account would have to lose 50% to be wiped out. So if you lost 50% of 5,000, that would result in $2,000.  So if you lost 50% when levered 2:1, you lose $21,000, which wipes you out. Using leverage can magnify losses and gains. And because of this, margin gets a second meaning. Margin is also the cushion you have. Remember the broker and clearinghouse want to protect themselves. So they may say, “okay, if you have $2,000 in your account, you can invest up to $4,000….but if your account drops below $1,500  you have to put more money in your account.” Each broker will set a different amount of what they feel comfortable with.

Because brokers and clearinghouses want to protect themselves, they will let you trade with leverage, but if you lose too much, they will ask you to transfer more money into your account. This is called a “margin call.”

But for short sellers the Federal Reserve Board requires if you short sell you have at least 150% of the value in your portfolio .For example, if you sold those 10 shares of Microsoft @40 for $400. The FRB requires you have 150% of the $400 in cash in your portfolio. That would be $600. If your account fell below that, your broker would do a margin call.

When you go to a bank for a loan, you may have cash, but it’s a liability. You owe the bank money. Treat short selling the same way. While you do get money when you sell the shares you borrowed, it’s a liability because you gotta pay your broker back. This is why you need a margin account, because you are essentially borrowing something and creating a liability.  Which gets us into the next nuance:

With short selling you can lose more money than what your account is worth. If we owned 10 shares of Facebook at $80 a share, our account would be worth $800. If Facebook went to $0, our account would be worth $0 becayse $0/share times 10 is zero. Our portfolio couldn’t be worth a negative amount, because shares can’t be worth less than $0.

But when you’re shorting, your account can go into the negatives.

Let’s say you short 100 shares of facebook at $8000. You follow the FRB requirement that you have 150% of the margin in your account. So you had $4,000 in cash, sold 100 shares of FB, and now have $12,000 of cash in your portfolio.

Let’s say markets close and FB announces amazing earnings. The stock opens the next day and starts trading immediately at $125/share. Well, if you bought those shares back right now, it would cost you $125 * 100 = $12500. Your broker would immediately do a margin call and you would be closed out immediately.

So you had $12,000 account the previous night, but you had to spend $12,500 to cover your position. You made $12k and lost $12,500k. You are $500 in the hole and owe your broker $500.

This is the thing about selling short. Stocks can go very very high, and if you’re short you’re going to lose that money. Stocks can’t go below zero, so if you buy stocks to hold long-term, you cant lose more than your account value. With shorting, you can. If the stock suddenly gaps up and catches you off guard, you can end up owing your broker money. This is not as rare s you would think so be careful.

To conclude this is shorting!  Shorting is kind of a term exclusive to stocks and ETFs, including commodity ETFs. As we will soon discuss though, when you get into commodity futures contracts – not ETFs-, you don’t really “short commodity futures.” You can sell contracts but you’re not actually borrowing the commodities. Don’t worry, we will get into these specifics in a few episodes from now. Also fun fact, if you’re a bank, you can make money from shorting stocks from the borrow. This is another topic we’ll cover in depth when we discuss what banks do in the financial sector. But just know, this is not all there is too shorting, but this episode should help you get the fundamentals down iso when you hear about it from your broker or in the news, you know what’s up.

This is such an expansive topic! So let me conclude with this: If you are a beginning investor, or even if you’re experienced, I highly highly recommend you do not short. Shorting is not investing, it’s trading ad speculating. A lot of people think trading and speculating can be fun and cool, myself included, but if you’re thinking about a long-term portfolio or retirement portfolio, it really has no place in it.

Well I hope you enjoyed this Q&A episode. If you have any questions or feedback, please shoot me an E-mail at Rishi@tiingo.com. This is a bit of a tough topic, but I’m really glad I got to answer it for you all.

 

 

 

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Podcast: Ep.6 The Different Styles of Top Investors and Traders (Value, Growth, Discretionary, and Quantitative)

Podcast: Ep.6 The Different Styles of Top Investors and Traders (Value, Growth, Discretionary, and Quantitative)

Many of us have heard of the big name investors and traders out there, but what makes them different? What styles do they use? How do they approach markets? What separates the best from the good? This episode is an intro into the different type of investing and trading styles, so you can find one that best suits you. This will help separate some of the confusion when you see “value” and “growth” funds or when reading about discretionary and quantitative traders. We conclude with a discussion about how the top traders approach markets.

With that! Here are the links. If you have iTunes please use that as it helps my rankings within the store. Don’t forget to subscribe to stay updated on future episodes!

iTunes Link

Non-Itunes (tiingo.com)

You can find the script of this podcast at the bottom of the E-mail.

Supplemental Information:

Courtesy of http://independent-stock-investing.com
Courtesy of http://independent-stock-investing.com

The picture above shows an example of what a candlestick is when looking at prices on a stock chart. The chart below uses candlesticks when showing the prices of AAPL

 

AAPL SMA 50 crossover with SMA 200
Courtesy of http://stockcharts.com

The picture above shows the simple moving average technical indicator. It’s a rolling average of the past 50 days and 200 days (SMA 50 and SMA 200 respectively).  The SMA 50 is in blue and is quicker to react to recent price changes. The SMA 200 is in red and is a longer-term indicator so it is slower to react to recent price changes.

 

Here is the script that was used in today’s episode.

Note: I don’t follow scripts word-for-word as they can sound unnatural, but the episodes do closely follow them.

 

How many types of investing and trading are there??

Welcome listeners to episode 6.  Have you ever wondered what people mean when they say, “I’m a value investor.” or “I use technical analysis?” Some might say, “I’m a momentum trader” or “I trade special situations.” For my regular listeners, you know I’m not just going to define things, that’d be too easy, but I’m going to expand upon them, give some of my experiences, and shed some light on different perspectives.   This is going to be important if you have an interest in picking stocks or even mutual funds, index funds, and ETFs. Many different financial products advertise themselves as using particular type of investing or trading style.  And as usual, you can see the script of this podcast online at blog.tiingo.com if you want to follow along.

Before I begin there is one thing I need to discuss. You can skip over the next few minutes if you decide, but given how much time we put into this podcast, both us creating it and you listening, it would mean a ton to me if you could listen to the next few minutes.

I left my job trading professionally a year ago, I didn’t know what I  wanted to do. Strange, right? I left a great job where I was doing well, the first employee of a hedge fund that grew 5 fold in a year, and on a whim I left. Well, I didn’t really leave on a whim. The reason I left is that something inside of me wanted to do more. Over the past few years, I saw many people lose money to misinformation. I mean it’s nobody’s fault. It’s strange isn’t it? Our whole lives we learn things like writing and science, but nobody teaches us finance. Then, suddenly when we get our first job in our 20s or 30s, everybody is telling us to invest it. For those of us fortunate enough to have 401ks or be able to contribute to IRAs, suddenly it’s people telling you “put this in mutual funds! or put this in index funds!” But wait, who taught us any of this???

This would be like never taking a history class from 1st grade through high school, then the day after graduation saying “write a paper outlining the American Revolutionary war.” Wait, what? This is what financial education is today – non-exist.  And this is where Tiingo has stepped in fill the gap.

This podcast promote education and financial literacy. And not just the basics, soon this podcast is going to touch upon very advanced stuff, but I promise you will be able to follow along. Alongside this, I built tools that allow people to use this education to better their portfolios. I continue to build them and I find more and more users are using them are listening to this podcast.

Tiingo’s intention has always been to help people. I always figured monetizing it would figure itself out. But the reality is, I’ve spent the past year building Tiingo and this podcast out of my own savings, and for this to continue I simply ask you pay what you feel. Even if it’s $2 dollars a month, less than the cost of a cup of coffee…or $5 the cost of a latte. Whatever you feel is appropriate or can afford to pay, I ask that you do so I can continue this.  You can do so at Tiingo.com/support. That’s t-i-i-n-g-o.com forward slash support.  The tools are will be free so if you can’t afford or don’t want to pay, still feel free to use the site. I don’t believe in showing partiality between those who can pay and those who can’t.  You will always know your situation better than I. But, Tiingo’s mantra is “Actively be good.” A famous mantra in tech by Google is, “don’t be evil.” But the financial sector needs to be stronger than that. We’ve violated the trust of people, and because of that, the mantra for Tiingo is always going to be more active, so we have the mantra as “Actively be good.”

So like I said, if you want to support the Tiingo mission, community, web app, or podcast, please consider paying what you feel at http://tiingo.com/support.

With my heartfelt message aside, let’s get into some investing and trading fun! Woo Woo!

So you’re now thinking, “Rishi I think I got the basics down and some more…but what else is there.” This is the perfect podcast episode for you. We’re going to discuss the differences between investors and traders and then dig into the different styles. There are many ways to make money in markets, and you often see the news media discussing certain individuals. A lot of these individuals tend to stick to  a particular type of trading or investing. For example, Warren Buffett is the most well-known, and richest value investor –a term we will explore later.

This way, if a professional trader or investor recommends a stock, you will have a better idea where they’re coming from. This will be important as some of you may hear a trading or investing style and be put off by it. It’s interesting how the way you invest or trade reflects personality sometimes.  You have those who are very relaxed long-term, then others who are constantly moving and taking action.

To begin, let’s go ahead and start off with the basics – what is the difference between an investor and trader. Once we do that, we’ll start getting into a lot of the different kinds that exist and taking it to a further level than your traditional book. As usual, we will always start with the basics and build our way into mind-blowing stuff. Just a heads up, I’ve spent a lot of time thinking about this topic. It’s sort of a philosophy I hold, so you may find these definitions different than what other sites say. I will explain why I think the ones presented here may be a more accurate representation.

The traditional thought is that investors are long-term buy and holders. They are in a company because it is fundamentally a solid company. What I mean by that, is that each publicly traded company is required to publish accounting statement. What investors often do, is that they consider the company’s business prospects, and the sector they are in, then they look at the account statements to verify the company has solid business prospects. They consider them stakeholders in the company and typically have time horizons 5 or more years out. They don’t worry so much about the next month or so, unless something drastic happens, but expect the company to outperform in the long run.

What does this all mean? Investors treat companies they get in like a business. Let’s say one of your work colleagues comes to you and says, “Hey I want to start a restaurant business, lemme get some of your money.” I’m not sure about you, but if one of my work colleagues, whos been a programmer his whole life tells me he wants to start a restaurant I’m not gonna give him  money off the bat. I would want to make sure what he brings for lunch looks delicious first of all.

Ha, no but in all seriousness, while you may know the individual, if he asked you to become a partner and own 50% of the restaurant, you would start making sure it was a good investment. You may look at where the restaurant is, what it sells, how good the food is, whether or not it’s in a good location, and so on.  You would want to make sure that it’s a good business. You may even want to look over the balance sheet of his other restaurants to make sure there was no fraud in the past and so on. This is how an investor sees a business.

 

Sometimes it can be tough for us to conceptualize because we’re so used to seeing prices on a screen and trading on them, we don’t really think about the accounting behind the firm or its prospects.  Warren Buffet is the most well-known example of a long-term investor. When he puts money into a company, he doesn’t have an end date.

Now most of us may be thinking, “well of course, when Warren Buffet buys shares he ends up owning a huge percent of the company.” While that’s true, and companies sometimes hook him up with favorable deals because of his reputation, that doesn’t mean we shouldn’t take his same perspective.  When you buy shares, you are owning a business. It’s a fact and it’s an awesome concept.

Now there is one thing that can make owning shares better than investing in your friends restaurant. And that’s the concept of liquidity. Liquidity is a financial term that means being able to convert any asset you own, stocks, a house, even your laptop, into cash.  Your laptop may be less liquid, as well as your house, but stocks can be seen as very liquid most of the time. You can simply go to your stock broker, sell your shares, and viola you no longer own the business (once the transaction clears). Try doing that with your friend’s business! To recap, investors treat buying shares as owning an actual company.

Also just a quick aside: I said stocks are liquid most of the time. Sometimes they are not if there aren’t many shares issued, or if volatility is high. During times of panic, liquidity can fall because people are not sure what to do. Uncertainty can reflect itself in volatility, as mentioned in the last episode, and also result in lower liquidity.

Okay onto traders:

We are now about to enter some blurred lines between investors and traders.  Sometimes there is no clear cut definition and I’m going to try my best to show why. Technically, when an investor buys or sells shares, they are trading stocks, ETFs, mutual funds, or so on. This is why it gets confusing. Technically a trader is somebody who trades an asset. But this is a technical definition and isn’t really helpful to us when we’re trying to learn about different styles.

When you hear trader in the media, it means something different than the technical definition. And this is the type of trader we’re going to talk about going forward.

A trader looks at a stock as something that has a defined end date. When they enter a position, they have a clearly defined rule for when they get out. Some of my listeners may claim that this is a pretty strict definition of a trader. A lot of long-term investors have criteria for when they will get out of a stock or investment. And this is true, but here is where I will argue the difference lies: a trader trades a position with the intention of them knowing they will get out eventually. An investor enters a position without the intention of getting out, but they accept as a business changes it may no longer be a good investment.

I know you may be wondering what the difference is, and this is why the definitions can be so blurry.
Let’s say your friend who wants to open a restaurant checks out. He is an amazing chef. He found a way to put chocolate on a savory pizza and make it the best thing you’ve ever eaten. Wow, I just cringed at that, but I’m sure somebodys done it.

Anyway, when you’re giving money to your friend as an investor, you’re not thinking, “Ok I’ll give him money, make a ton of money, and get out like a year from now.” You may think, “Ok I’ll give him money, his business idea seems legit, and I think he will be successful. If he isn’t successful, or if he goes off the deep end, I’ll re-evaluate and may pull my money out.”

If you were a trader, you might think, “Okay the quirky  pizza market is so hot right now. It’s like the new up and coming food, like what artisan cupcakes once were. I’m gonna give him money, and pull out 2 years from now when I think the quirky pizza market will top. Then I’ll make the most amount of money”

That’s the difference – the intention of you as a shareholder! One sees it has a business opportunity, and the other sees it as an opportunity to express a viewpoint you have

Here’s why the intention matters so much: it’s really the only way to separate the two. Some traders look at companies balance sheets, some hold positions for more than a year. Traders are often typecast as individuals who are in and out of positions very quickly.  But such a huge variety of styles exist, that I’m putting forth an idea that what really separates a trader from an investor is their intention when they place a trade. One sees it as a business, and the other sees it as an opportunity to capture a recent view they have on markets.

With the differences established, let’s move forward:

Let’s discuss the different types of investing. This is gonna be fun. So many of us read about these amazing investors, but what we often don’t hear is the styles of traders.  As I said earlier, there are so many different styles that reflect different personalities. In this episode, we’re going to describe some of the most common because a lot of mutual funds and ETFs were created to try and replicate these styles.

The first thing we will explore is one of the most common: value-investing.

The most famous value investor is Warren Buffet. The premise behind this style is that you think a company is undervalued.  For some reason, the market is undervaluing this company. This is because you looked at the account sheets, otherwise known as doing fundamental analysis. This is often combined with something else, but before we move onto that something else, I will explain a common concept value investors use.

And that is the book value. The book value is what a company is worth if you take the assets and subtract the liabilities. What I mean by that is imagine everything a company owns. The building, the computers, the technology, the patents, how much cash it has, what investments it has (like stocks), and so on. These are the company’s assets. Now thing of all the loans it owns, who it needs to pay money to and so on. Those are the liabilities. So if you take all those assets, and subtract the liabilities, you get how much the company is worth according to its books, or the book value. So if a company has $200mm worth of assets, and a $40mm loan, its book value is $200mm – $40mm which gives $160mm. That number, the $160mm is the book value of the company.  So now that we know what the value of the company is from an accounting standpoint, or the books standpoint, we need to see what the market thinks it’s worth.

So we have to calculate something called the price/book ratio. First, since a company is broken up into many different pieces, or shares, we take the book value and divide it by the number of shares. So our book value was $160mm, and let’s say the company is broken up into 1,000,000 shares. That means each share represents $160 of book value. In other words:  If the company is worth $160mm according to the accounting statements, and there are 1,000,000 pieces, then each piece is worth $160.

Next, we can see how much the market prices each share. For that, all we have to do is look up the stock price. So let’s say the stock price is $80/share. Whoa! If we closed down the company, sold off all its assets, paid its loan, we would have $160 a share! Yet the market is only selling it for $80/share! A value investor might immediately buy the stock because they think, “Once the market realizes how undervalued it is, other investors and traders will buy a ton and the price will get to $160/share.”

A common metric to look at is the price/book ratio. You take the market price of the stock, divide it by the book value per share, and that gives you the price to book ratio. In this example, it would be $80/share divided by $160 book value per share. This creates a price/book ratio of 0.5.

This situation vey rarely happens. Typically you see price/book ratios of 2 or greater. But let’s say you do see a P/B ratio of 0.5. Typically this will happen if there is something else the market thinks will happen. An accounting statement is typically released once a quarter, or once every 3 months. In those 3 months, something could drastically change. For example let’s say we’re an oil company and we make a lot of money from selling oil. What if in the past 3 months, Oil dropped from $100/barrel to $50/barrel? Well our business would probably be worth less, so the market is taking that into consideration. The accounting statements wont take that into consideration until the next time they’re released.  This is often referred to as lagging data.

SO if you are seeing a P/B ratio of less than one, be skeptical. It could be the case that everything is okay, or you think it’s still undervalued, maybe not as much as the P/B tells you, but maybe the market overreacted. And this is the job of the value investor. To not only look for things that are undervalued, but also to figure out why, and whether or not those considerations make it less attractive.

P/B is just one metric value investors look at. We will cover more metrics and what these accounting statements mean in a future episode.  There are text books written on this topic, and given the limitations of this podcast, we can’t properly cover the topic in one episode. But this is the basic idea behind value investing. You buy things that are undervalued, or in other words, you buy things that are undervalued.

A major drawback to value investing is that you assume the stock price will eventually accurately reflect what your think it’s worth. Often times, stocks may not do that or the price comes down even lower. Sometimes the markets, or other investors, have their own train of thought. Also in a crises period, something may look cheap, but because of panic selling, correlation among stocks becomes higher. In other words, the stocks move up and down together regardless of how good or cheap a company is. Especially during those times, buying something that’s a value could mean the stock continues to go lower because of issues outside the company, like a recession. So always be careful if something looks cheap. Ask yourself why? There may be a reason why the rest of the market thinks the company is worth much less than our personal analysis may show.

The next type of common investing is called growth investing:

Growth investing is a type of investing where you expect a company to rapidly grow in earnings or the potential for future earnings. The premise is easy to understand, but implementing it is a very difficult idea. Many different investors have their own type or measure of criteria for detecting what makes a rapid growth stock, but the general consensus is that you expect the stock’s earnings to grow at 12% or higher a year. Often times if a stock is growing too rapidly though, it could mean management may be getting to aggressive or reckless. They could be taking a lot of loans to achieve that earnings growth.

Everything in moderation.

The biggest issue with growth stocks is that we just can’t predict the future. How do we know a company is going to continue growing? What if people stop liking it or it falls short of expectation? When we think something continues growing, we often have to consider a lot of assumptions have to be made to make that prediction.

For example, if our friends pizza place turns out to be growing rapidly, everybody loves chocolate savory pizza, then our friend may continue to grow. We are excited because at this rate we will be so rich! But wait, what if this pizza has tons and tons of gluten and suddenly the entire town goes gluten free? Your friend tries making a gluten free crust, but it just doesn’t taste the same. When we assume the growth will continue, we’re assuming a lot of things. In this case we assumed the pizza audience stay the same and maintain the same tastes, same ideas of health, and same desire to get pizza.

With investing, just like when buying a business – they’re the same thing really, we make a lot of assumptions. Whether we think the price will go up or the conditions leading to that businesses success will stay the same.

The reason I bring up growth and value investing first, is because these are the two most common types of investing.  You often see mutual funds, etfs, and even some index funds that have some sort of value or growth stock picking strategy. I say even index funds, because they are seen as following a basic index. Often times they will follow an index but a bit more capital on stocks they think are high value or high growth. Every mutual fund, index fund, and etf is different on how they do this.

The next disclaimer I want to make is that the line between value and growth is actually not so clear cut. Often pooled investments like the mutual funds, index funds, and etfs mix a blend of growth and value. And while many people consider Warren Buffett a value investor, he doesn’t. He consideres himself a value and growth investor and argues that they’re really not that different. His process uses both and he thinks they are interrelated.

A general theme you will notice among investors and traders is that they often blend different styles to create their own. This also often reflects personality and what appeals to them.  Think of it as sort of your political view. It’s not common to have a view that’s 100% the same as your political affiliation. There may be issues that you agree with different political groups on.

Let’s focus on some different trading styles

A common stereotype is that investors focus primarily on the fundamentals, or accounting, along with other market conditions, and traders focus on something called technical analysis. The truth is that many mix and match their styles. If you want to learn more about this, I strongly recommend you read Market Wizards, all four of them, and in particular the first two. It’s an interview style series of books that talks to the top traders of a few generations now. It’s an incredible way to learn how some of the top minds look at markets and the backgrounds. It’s very entertaining and easy to read. I highly recommend the book series to anybody trying to learn more.

But let’s discuss what technical analysis is.

Technical analysis is the idea of looking at price, volume, and other historical market data to predict the future. Well, that actually might be the technical definition, but I’m going to expand upon this and say while technically technical analysis tries to predict the future, we have to be careful the way we say predict.

Here’s why: when technical analysis do their work, they aim to be right half the time. Some of the best traders will have hit ratios, a ratio of the # number of winning trades to the # of losing trades,  of like 55%. That’s pretty much only half. So when they predict the future, it’s not with 100% certainty, it’s trying to be right more often than they’re wrong. This is actually true of all traders, not just those who use technical analysis.

And here’s another twist: sometimes traders don’t mind being right less than half the time. But how do they make money? Sometimes traders bet that even though they lose more times than they win, when they do win they win big enough to make up for the losses.

So what’s example of technical analysis? One of the most common is the idea of the “golden cross.” It requires doing a simple moving average, or SMA. That is for each day, you take the average of the past x number of days. For example an SMA 50, is a simple moving average of the past 50 days.  This captures the general price trends, it “smooths things out.” I put a screenshot on the blog of what this looks like, look for the podcast episode 6 blog post. So the golden cross is when the SMA 50 crosses above the SMA 200 you go long. In other words, when the short term trend, SMA 50, breaks the longer term trend, the SMA 200. There is a picture of this on the blog too. But the idea is that the shorter term trend crossing the longer term, means things are starting to look up and it’s time to buy. This strategy tends to work when things are trending, but works terribly if stock prices aren’t going up but moving in a range.

There are thousands of different technical analysis tools, but the question you may all be asking is, “Rishi, does technical analysis work.” I’m not going to back down from this question. This is a hot debate among many people and I will say this: yes, but not in a way many people use it.

The best people I’ve met who use technical analsyis, and keep in mind I am considering all historical market data when making this claim, do not use it in the way that many books teach it. They do not look at candlesticks and think, “this has to happen.” The best traders I know who use it, see it as a framework to look at the world. They use it to compare current events to previous. They understand that it doesn’t work all the time. And they understand that in certain market cycles it works, and in other market cycles it doesn’t work. For a ten year period the strategy may not work, but the next 10 years it may work wonderfully. They spend their time trying to understand why it works and when it works. They spend their time making sure it’s not just randomness.

Some technical analysis traders use computer programs to execute trades quickly. Some academic paper show it works, but not extremely well like you may think. If you’re scanning academic papers, look for things like momentum strategies. Often times they use more advanced concepts like portfolio construction to boost returns, this is a topic we will cover later.

What really upsets me is when I read books like “You can make millions by learning these patterns.” No, that’s a sure way to lose money. The best technical analysis people use very few indicators and don’t clutter their screen. They prefer simplicity not complex models. There is a good mathematical reason for this, why simple is better, that we’ll touch upon in a future episode.

If technical analysis was perfect, everybody would be doing it. It takes a lot of hard work, thousands of hours to get right, and even then success isn’t guaranteed.

There are many ways to make money and it’s not an easy problem to solve. Often times a lot of books and research market technical analysis to beginning investors and I can’t stand this. They make it seem like it’s a get rich quick scheme. They don’t include things like statistics, how many strategies don’t really work, and how it’s an extremely complicated problem.  As a new investor or trader, I can’t stress enough that you don’t trade real money with technical analysis. And a lot of marketing material promises riches for following a new technical analysis system. Any time you see a publication telling you this stock trading robot made a ton of money, always wonder why they wouldn’t keep it a secret? Why wuldn’t they just trade it for themselves. In markets, if a strategy that generates a lot money is made public, it stops making that kind of money. The reason?  Once people know what a strategy is, they put the bets on before anybody else.

Think of it this way, if you knew this stock robot made a ton of money and what it was going to buy, wouldn’t you buy it first? And if everybody bought it, the price would go up too much, and so you would have others selling it. It’s a complex topic we’ll touch upon in a coming episode about market theory, but when a money generating algorithm is creating, you keep it a secret. As soon as other people in the market figure it out, they will place the trades ahead of you and if you were planning to buy the stock at $80 and sell at $90, because other people bought it before you, it’s now at $90 and your trade makes no money.  In fact, some people will start purposely selling it at $80 because they know there will be buyers, and as soon as the price doesn’t go up, the buyer realizes the strategy isn’t working, then sells it. Now you have a ton of sellers.  And now the people who were originally selling? They are now buying again because the price has come down a ton. This is how secret strategies break. So if somebody is promising you an amazing technical analysis strategy, know that there’s almost a 100% guarantee it’s a sham.

Before I conclude this section on trading, I want to discuss two types of trading you hear about: discretionary and quantitative, and the stuff in between

One type of trader you hear about is the discretionary trader. At the extreme end of the spectrum, they are traders who trade on gut feeling, but there is much more to that. The successful ones have been watching markets for many many years and constantly pour themselves into research and sometimes technical analysis studies. They read history books understanding what happened at different points in time and are constantly keeping up with the news. While they do follow risk management and don’t take positions that are too big, they don’t automate this process. They believe their mind allows them to quickly adapt and understand when markets change. This is their edge.

Quantitative traders at the extreme end are those who completely automate their process. They research markets, constantly test strategies…some strategies may be technical analysis based, others may be news reading algorithms that take positions before anybody else can, and when they get a signal to buy or sell, an algorithm takes care of it. Quantitative traders can be highly quantitative in that they look at markets in statistics or a programming problem, while others believe they can use market events and economic trends to create a system.

Most traders often are a blend of the two that fall somewhere on the spectrum. Some may have algorithms but sometimes exercise discretion on them. For example if an algorithm is telling them to buy, they may realize the algorithm doesn’t take into account information it hasn’t seen before, so they will decide not to do it.  After speaking with many people, some traders argue that the best traders are those who take into account both quantitative and discretion. My opinion?

It doesn’t matter. The best trading or investing style will be what works for you and what fits your personality. It will take many years and thousands upon thousands of hours worth of work. People often think of wall st or investing and trading as glamorous jobs…but in reality the top traders I’ve worked with constantly pour themselves into their work. They do it because they love their job, not necessarily for the money or lifestyle. In fact, the top traders I know you would walk right by on the street and never know. For them this is what they love and they spend much of their free time doing it.

Actually, you know what – I’m going to conclude this episode now by telling you the secret of the greatest traders and investors I know. I’ll say it multiple times throughout this series, but I need to declare this right now because trading and investing can often be an art and takes work. So here’s the key before we continue: the best traders and investors are those who view their style as a process. A process they are constantly improving and making better. They know eventually the outcome will follow, but at each step they are studying their mistakes and winners. They realize that their outcome is a mixture of skill and luck. They can’t control the luck, but given enough time, the skill will win out. They keep journals of their winners, losers, and what’s going on in markets. They are generally nice people, humbled many times by getting their butt kicked in markets. Yes, you hear of the ones who are jerks, but jerks who are successful aren’t as common as it appears. Many have a philosophy behind doing what they do.

So becoming a good investor and trader is a lot of hard work. You hear of the people who start immediately and get rich, but many of them are lucky and do not last long. It takes a lot of grit and hard work, which is in my opinon why you often see traders who are very humble. Unfortunately, the ones who are not so humble often make themselves shown on TV.

Oh man, this kinda feels a little motivating. Don’t be intimidated by the hard work. In fact, embrace it. Because if you embrace it you have a two options 1) you can decide to put in the effort to constantly get better or 2) you can decide it’s not worth it for you and you rather be more passive or laid back about your investments. Both are good options and it depends upon your personality.

If you decide even on #2, this podcast will continue to be helpful for you because the types of stuff I’llb e discussing are for those who want to trade or invest actively, and those who want to do so passively. Many of the same tactics to improve portfolios work on both types of traders. They’re some really cool concepts we’re going to discuss and I’m excited. Knowing about the different types of investing styles really opened my mind to markets.

A lot of people don’t typically see financial markets as a creative space, but it really is. I mean, you have to not only be creative to come up with cool ideas many people haven’t though of, but also you have to have courage and discipline. A lot of traders and investors find ways to generate ideas. They love it. You could be walking through the streets of a city or a mall and suddenly see a new store or restaurant. You may eat the food and love it, and now you want to know if it’s a good investment. Maybe it’s only the 12th store open but the company trades ona s tock exchange.  Once you start investing and trading, you see the world filled with opportunities and your brain will constantly be coming up with new ideas.

My goal in this series is to help you get your ideas into your portfolio in the best most possible way. Maybe your ideas lead to index funds of stocks and bonds, but even in that space there is so so so much you can do to improve even the most passive of portfolios. I can’t tell you all how excited I am.

Okay all, hope you had as much fun listening to this episode as I had creating it. Please E-mail me with feedback at Rishi@tiingo.com. If you have any questions, E-mail me as well and I’ll do my best to get back to you. I sometimes send ridiculously long E-mail replies back to people, so I may start institution a 15min call to discuss with you an answer instead haha. Anyway, please support Tiingo so that I may continue doing this. The site to send a few bucks, even $2/month, is Tiingo.com/support. Thanks so much and will be back soon! I’m going to be working on an episode with Brett Harris about student loans. It will be good, so get ready.

 

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