Why invest quantitatively?

My current academic research currently focuses on personal characteristics of investors (specifically hedge fund managers) and how these characteristics affect their investments. For example, my paper on hedge fund managers’ marriages and divorecs shows that fund performance suffers during both marriages and divorces and argues this is a results of manager distraction from personal events. Another paper on hedge fund managers’ cars shows that fund managers who drive performance cars take more risks, without yielding additional returns.

This general agenda has led me to the firm belief that investors are swayed by behavioral biases in their investments. These biases can be the result of some intrinsic characteristic (such as a desire for sensation seeking, which leads to a preference for fast cars and risk in investments) or some time varying affect (such as a distraction from a major life event). Either way, these biases are hazardous to investment performance.

Quantitative investing gives an easy out to these biases. If we follow a fixed set of rules  when investing (and are faithful in following them), there’s no room for behavioral biases to creep in. A computer, relying on objective data from the markets, tells us what to buy and sell when. Getting married? No problem – just let the computer tell you what to do. Getting divorced? Same deal. Inherently a risk taker? There’s no scope for your gambling ways to affect your investing. Risk averse to the extreme? Again… no way for your timidity to stymie your investing.

This is one of the big advantages of quantitative investing.

Long short strategies at a time when we are at all time highs

Markets are at all time highs – this is not uncommon – markets (since they are supposed to go up) should be at, or near, all time highs much of the time. That doesn’t mean that investors cannot be nervous about this condition. A number of respected investors will encourage caution, citing “all-time highs” as a reason for caution. The worry, of course, is that the market will come crashing down.

spy

(SPY at all time highs right now!)

Long short strategies, which have both long, and short positions are a nice way to hedge against the potential of a large market downturn. When engaging in a long short strategy, the first decision to make is are you going to be completely hedged (that is, your long positions and short positions are roughly equal, in terms of market sensitivity), or are you going to have a long or short bias. This is ultimately a decision that resolves into the question, “can you time the market?” There are many perspectives on this, but my take is that this is hard to do and I am not going to focus on this.

Rather, I will focus on a question that arises once you have decided on a net exposure: what should you be long? and what should you be short? Some solutions include:

1. Going long and short completely separate strategies – for example, profitability for a long strategy and some short screen for a short. This has the advantage in that you are, at least in back tests, gaining on both ends. However, we know nothing about the correlations of the long and short legs and there may be situations in which we lose money on both ends if the long leg goes down and the short leg goes up!

2. Going long and short separate ends of a single spectrum. For example, if we are separating stocks in the universe based on the PE measures, perhaps we go long low PE stocks (value stocks) and short high PE stocks (growth stocks).

vg

 

 

(Value – top line, growth, bottom line – we would make the difference in a long short strategy)

This has some advantages over the first technique, in that the stocks are otherwise likely to be similar in long and short legs, and hence the correlations should be high – thus the hedge from the short leg should work better. Also, this is the standard way in which academics document characteristics of stocks that affect future returns –  so there is a lot of literature and data easily available under this framework.

3. Going long something that’s “good” and being short the market. This is the most common way industry practitioners seem to run long short portfolios. Their long legs are driven by their proprietary research and secret signals but on the short side, they simply use the market. The real advantage to this is that from a practitioners perspective, shorting the market is much, much easier than shorting a collection of stocks. There is infinite liquidity in market indices and you never have to be worried about getting a locate.

These are just three possible ways to implement a long short strategy. Each with its own advantages and disadvantages. They are definitely psychologically useful when investing in markets that are at “all time highs,” but there’s no reason to restrict their use to such situations – they can be used any time you want to a hedge against potential large downturns in the markets.

 

Change the rules

As in most endeavors in life, quantitative investing requires learning from mistakes. An example I recently encountered involved one of my fairly complex short screens. The idea behind the screen was similar discussed here…. Shorting …. and a Happy New Year, but the execution was infinitely more complicated.

Anyway, this screen was spitting out 5 or so stocks each month when I did my rebalance. Last month, it returned only 2 stocks … I was not too concerned: there’s always variation in the number of stocks returned month to month. However, I decided to increase the allocations to each of these names since there were only two names … and then, one of the names (KPTI)  had a +60% month. With an oversized allocation and the large positive return, it certain made its presence felt in my overall portfolio return. Sigh.

No worries – it was an up March 2016, and my longs rallied to yield an overall profit .. BUT …. I was somewhat shaken. I did a bit of analysis on the short screen I was using and found something interesting. like KPTI, a large chunk of stocks returned in my short screen were biotech stocks. This made sense since biotech stocks generally look terrible from an earnings standpoint. In fact, these stocks are designed to basically lose money, until they get far enough in a clinical trials to be acquired by a large pharma company.

Enough biotech companies fail that, even accounting for the few that are sold at a massive gain to big pharma, the backtest on my screen was looking great. However, I was taking large risks on individual bets. So …. I did what any human (seemingly rational) investor would do and just excluded biotech from my short screen.

I had to reduce the other restrictions to get back to a reasonable number of stocks in the portfolio at all times … and it definitely reduced the spikes and cliffs. So I implemented it for my April rebalance – 4 non biotech names.

Seems like a win all around, but somehow I doubt this will be my only post on changing the rules. I also somehow feel my next post on this practice of changing rules won’t be as positive about the practice as this one, but we’ll see.

 

 

 

Valutility

Low Volatility strategies have become recent darlings in the smart beta space. Since the financial crisis of 2008, there has been tremendous interest in low volatility funds (see http://www.pionline.com/article/20151130/PRINT/311309977/low-volatility-strategies-soar-as-investors-reduce-risks for example). At the same time, there is some reluctance: see http://servowealth.com/resources/articles/dont-get-lured-low-volatility-strategies and http://www.etf.com/sections/index-investor-corner/beware-how-low-vol-anomaly-works?nopaging=1 .

In addition to some of the arguments raised in the 2 sites above, one argument I’ve been considering against low volatility is that low volatility basically proxies for value, in many ways. This makes intuitive sense, as low volatility stocks are likely to be unglamorous, plodding along, generating cashflows that don’t change much – very much like value stocks.

To test this, I ran 3 separate backtests… a value screener (bottom 25% of stocks by PE), a low volatility screener (bottom 25% of stocks by last quarter daily return std. deviation) and a value AND volatility (or what I call valuatility!) screen. The results over the last 15 years are interesting… value > valutility (the screener)>>> low vol only >>> S&P.

 

valutility

 

Additionally, while it overlap is small [ bottom 25% of std dev has abotu 900 stocks, bottom 25% of PE has about 700 and intersection has about 100 ], the returns to the factors definitely seem correlated. Contrast this with the momentum factor, for example, and you can see that there are more points of dissimilarity (see below, the momentum strategy is top 25% of stocks ranked by returns from a year ago to a month ago).

with mom

 

 

* Note that the scale has been changed to log scale.

So …. if the upshot is that low vol by itself underperforms value, and it is pretty correlated with value, and adding it into the mix with value *lowers* backtest performance somewhat… I’m  not really sure what to do with it.

Shorting …. and a Happy New Year

2016 has begun with a fizzle. Markets have plummeted over 5% in the first couple of weeks and sentiment is dim. The perfect time to discuss shorting strategies. Shorting is selling stocks you don’t own. It’s a way to bet on something going down. The exact mechanics of a short is as follows. If I want to short shares of Apple, I will first have to find someone willing to lend me those shares. After I borrow the shares, I’ll sell them. When I’m finished with my bet, I will buy those shares back and return them to the person who lent them to me. If the price has gone down, I’d pay less to buy the shares back than when I sold them, making money. This sounds complicated, but in most brokerage accounts (short-selling requires brokerage accounts to be margin enabled), the process is pretty seamless and short-selling is as easy as selling a stock you own. Instead of a positive share count you see for stocks you’ve bought, short positions will show up as a negative share count.

So how do you decide what to short? It’s pretty much exactly the same as deciding what to buy, except you want things that have historically gone down in value. Think of criteria that were associated with future out-performance (low P/E ratios, small cap stocks, winning stocks, etc.) and flip them. For example, high P/E ratio stocks, or even better, stocks which negative earnings (the companies lose money) will generally do badly going forward. Large cap stocks will do badly compared to other stocks. Stocks that went down the past year will continue going down next year.

Here’s a screen that does these 3 things …

Capture

A hundred dollars invested in this screen goes down to about a quarter in last decade, while the market is up 80%. In the first 16 days of 2016, this screen has decline 15% (vs. the market’s 6-ish% decline). That means if you had been short $100 worth of the stocks that passed this screen on Jan 1st (or Jan 4th, the first trading day) you’d be up $15!

Disclaimer: Short selling has significant risks. Unlike a long position, where the most you can lose is your original investment (if the stock you bought ends up worthless), in a short position, your potential loss is theoretically infinite as a stock price could keep increasing forever. People betting against Netflix over the last few years found this out the hard way … Short with caution.

Gross Profitability Anomaly

A recent anomaly (aka a piece of data that predicts future stock returns well without any apparent addition to risk) that has been discussed heavily over the last 5 years or so is the Gross Profitability Anomaly. This anomaly was formally documented by Robert Novy-Marx in 2009 and a version of the paper can be found here: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1598056 

 

Gross profitability is simply (revenues – COGS) / Total Assets. In the implementation below, I divide trailing 12 month (revenues – COGS) by assets as of the last quarterly or annual report. To make sure results are not driven by illiquid/small cap stocks/some other weird stuff, I apply my standard restrictions screen to reduce the universe of investible stocks to those that are actually trade-able in size.

restrictions

Within the set of stocks that pass these restrictions, I sort stocks into quintiles (5 buckets) based on gross profitability and follow these quintiles, rebalancing quarterly. The backtests are below:

profitability quintiles

 

We can see the top 2 quintiles (top 40%) of stocks, ranked by the gross profitability metric quintuple our investment vale over the last 15 years; the market roughly doubles over the last 15 years and the lowest quintile (or bottom 20%) of gross profitability stocks *lose* money.

This is all the more remarkable when you consider that one of my restrictions above was a 0 to 30 PE … so we aren’t event including stocks that lose money in the bottom quintiles!

Sorting by profitability appears to enhance standard value screens and if you use value strategies, consider adding a profitability element to them to maximize your returns.

 

 

 

 

A discussion on replicating the Magic Formula

I have been asked numerous times about Joel Greenblatt’s Magic Formula ( https://www.magicformulainvesting.com/ ) in reference to quantitative investing.

In this blog post, I’ll share the most recent email exchange I had regarding the topic, and an excerpt from my book that covers the Magic Formula (MF). I think the exchange is informative as to how I view replicating other practitioner and scholarly work … to sum it up, exact replication is not only likely impossible, but could be a gigantic waste of time – data sources are often different, minuscule differences in analytical methods  lead to significantly different results.

I think this email exchange is instructive for understanding some of the issues and some of the goals quant investors often start with,. These goals of replication before creation are noble, but beyond a point, perfect replication is not particularly useful.

Initial email:

“I stumbled across your book, Principles of Quantitative Equity Investing, on Google Scholar while looking for more information on the work of Joel Greenblatt. Your book sounds like it focuses on his much acclaimed stock screen. I am looking for research on proving out his back-testing (essentially a “second test of his back-testing”). As you are undoubtedly aware he does not go into the finer details of how he sets up his screen. Is this something you show in detail in the book using Equities Lab?”

My 1st reply:

“Hi <interested investor>,

I do have a section on the MF (although that’s not the “focus” of the book). I do show how to replicate it, although both backtests and holdings don’t always line up exactly with those from the website and book.

You can look at my backtest below and the text preceding it (and actually, if you buy the book, it includes the software to do the backtest yourself).

pastedImage

Hope this helps – let me know if you need anything else.”

Reply to my email:

“Thanks for your response and the exert (sic) from your book, Sugata, much appreciated!

You mention you are able to return up to half of the stocks he does. Have you managed to improve on this since writing the book? I should also ask, do you use any of the quantitative methods you wrote about to manage your own portfolio? Given the apparent strength of the Greenblatt formula, it is strange to not see it publicised more. You show great results with it yourself, were you convinced?

Using S&P Capital IQ (I have an account through my employer) I manage to return about 2/3s of his list. Though I seem to have maxed out increasing this number by tinkering with the screen, hence my search for research done by others. I am also keen to apply it to other countries (in particular, the UK, <redacted>). I can’t do this until I have done the back-test myself. …”

My reply with answers:

“Answers below.

I’m happy to discuss further.

SR”

and then I answered my reader’s questions in-line in the email:

You mention you are able to return up to half of the stocks he does. Have you managed to improve on this since writing the book?

No – but I don’t really care to – why bother?

I should also ask, do you use any of the quantitative methods you wrote about to manage your own portfolio?

Yes.

Given the apparent strength of the Greenblatt formula, it is strange to not see it publicised more. You show great results with it yourself, were you convinced?

There are many other “formulas” out there – I think MF is an ok value based screen and value investing definitely works. I use simple PE ratios for my value screening, which also works very well. But then, value is only 1 of the 6 or 7 things I look for.

Using S&P Capital IQ (I have an account through my employer) I manage to return about 2/3s of his list. Though I seem to have maxed out increasing this number by tinkering with the screen, hence my search for research done by others.

Why try to recreate his list? I don’t see the point. I think a 2/3 match is good enough to know you’ve got the general idea right.

I am also keen to apply it to other countries (in particular, the UK, <redacted>). I can’t do this until I have done the back-test myself. Coming across historic index data is difficult though.

I’ve done little international work – I use Bloomberg – they have a powerful (if somewhat clunky) screening and backtesting product. The commands are EQS and EQBT if you’re interested.”