Category Archives: strategies

Quantitative Investing Beyond Equities

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I recently received a reference request for an alumnus of my class who was seeking employment at a Financial Advisory Firm. It was a very pleasant and productive encounter – my former student advised me via email that I was listed as a reference and I might get a call; I received an email from a pretty high level person at my student’s prospective employer to schedule a call; we had a very productive call.

During the call, I told the employer about some of the quantitative investing stuff we do in in my class. The employer said it would be useful – their firm did similar stuff for a fixed income product. This was my second run-in with a firm that does quant stuff with fixed income. It appears quantitative investing is growing in fixed income, but there may also be issues.  (see https://www.barrons.com/articles/is-fixed-income-ready-for-factors-1530897141 )

Blackrock has a delightful webpage on the space ( https://www.ishares.com/us/strategies/fixed-income-factors ) where they highlight the main factors in fixed income (FI) as value, quality, momentum, carry, and low vol. Very similar to Equities. There’s also academic work in this regard ( https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2516322  for example).

On the other hand, high transactions costs, large minimum investment amounts, minute differences between bonds that broad factors may not pick up (but that may end up making a huge difference), and buy-and-hold-to-maturity investors may prove to be headwinds in the space.

More specifically, there may be additional signals, besides the usual corporate finance and market price signals, that may be informative. The employer I spoke to was in the muni bond space, and was using geographic data ( I imagine micro level data from the various municipalities whose bonds they were considering )  to try and predict future credit moves.

I’d imagine with the wealth of data out there, and the variety of financial instruments traded, there may be some very interesting predictive relationships to be uncovered outside the equity markets.

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

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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.

 

Value investing II

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How to measure value?

There are many ways to measure value –

P/E

EV/EBIT or EV/EBITDA

P/B

replacement value multiples

However you measure it, value is designed to capture the idea that you’re “getting a deal.” P/E and EV/EBIT(DA) type measures reflect this by showing you how quickly you can earn your initial investment back …. A P/E of 20 means that each year you get 5% of your initial investment. A P/E of 3 you therefore mean that each year you get back 33% of your investment. Seems like a steal of a deal, right? 3 years and then it’s gravy. Unfortunately, most of the time, there is a reason that a P/E is 3 … specifically, markets anticipate that earnings are going to fall. If you look at companies with a 2-3 P/E – there are 7 of them below – you can see that in some cases (UAL, MTG, BPT), earnings have been falling. In another case, SDLP, there has been an unusually high earnings number in the recent quarter. For CBB, ARC and ESI, earnings are volatile. The one thing we don’t see for these 2-3 P/E companies is a consistently increasing earnings stream ….if so, it would be valued much higher.P/B is similar, but different. P/B and replacement value multiples, or multiples against reserves for commodity firms capture the deal you are getting on value – in this case, a low multiple may capture some hidden impairment of the assets of the firm. Perhaps the value reserves or machines are being marked to on the books is too high.

Ticker Company earnrecent earn1qago earn5qago
UAL United Continental 313,000,003.08 822,999,996.18 507,999,989.66
MTG MGIC Investment 69,191,000.28 102,418,001.99 133,075,997.27
CBB Cincinnati Bell 32,600,000.43 80,300,000.50 -18,299,999.74
SDLP Seadrill Partners 189,600,001.44 35,400,000.55 33,099,999.69
BPT BP Prudhoe Bay 15,043,999.77 31,508,000.06 43,378,000.73
ARC ARC Document Solutions 3,183,999.96 80,335,999.25 -2,327,000.01
ESI ITT Educational Services 10,446,999.86 1,688,000.00 14,917,000.10

 

Skimming through the list of companies with 10-20% Price to book ratios (the company trades at 10-20% of book equity), there are 13: half are oil and gas and hal fare financials … again situations where it’s easily possible for book value to be way too high (given the recent fall in the price of oil, oil and gas equipment may easily be worthless; and financial assets such as bad loans can also easily be worthless).

 

So there are good reasons why P/E and P/B type ratios can be low – on average, they seem unjustifiably so, as these firms, on average, outperform, but if you own these firms, be prepared for decreasing earnings, writedown of assets and other things that accompany low multiples.

Valutility

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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

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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

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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

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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.”

Value investing I

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Value investing : this is the most common of all quantitative strategies. From the famous Magic Formula (https://www.magicformulainvesting.com/ ) to funds like LSV (http://lsvasset.com/ ) numerous practitioners use value investing concepts quantitatively to enhance their returns.

In a nutshell, value investing is buying things that are “cheap.” But cheap relative to what? Usual comparisons are to earnings, cashflows or book values. For example, Here’s a backtest of three strategies:

1. investing in stocks with a PE of 0 to 18. !8 is a rough estimate for overall levels of market PE over time (although currently, the PE is the market is a bit higher)

2. investing in stocks whit a PE more than 18

3. investing in stocks with negative earnings – note that these stocks do not have a defined PE ratio, since negative earnings typically render the ratio meaningless.

value1The green line is the backtest of the 0 to 18 PE, the blue line is the backtest of the 18+ PE strategy and the purple line is the backtest of the negative earnings strategy. Buying low PE stocks is definitely better for your net worth, over the long run. You may notice the initial blip up in the purple line…. that was the dot com bubble.

These backtests are generated by Equities Lab (www.equitieslab.com). You can conduct your own backtests for your own strategies using the software. If you are interested in learning more, you can also look at my book on Quantitative Investing .