I’ve recently published a frequently asked questions about quantitative investing (with answers). You can find it here for future reference. If you have ideas for future questions, please contact me.
What is quantitative investing?
Simply put, quantitative investing is a systematic way to choose investments based solely on quantitative measures. Unlike qualitative measures (how competent is management? what are the competitors? where is the industry headed?) that are difficult to judge (unless you’re Warren Buffett, work on Wall Street, or spend all day researching stocks), quantitative measures are available to anyone (historical price data, financial disclosures such as 10-Ks and 10-Qs, etc.). By compiling and analyzing these historical data, it is possible to identify quantitative measures (i.e., factors) that have been associated with excess returns.
But isn’t the market efficient?
The efficient-market hypothesis (EMH) argues that it is impossible to consistently beat the stock market because all stocks are always traded at their fair value and stock prices fully reflect all available information. EMH would have you believe the Warren Buffett’s (or any skilled investor’s) track record is merely luck (which he refutes).
If EMH were true, the performance of cheap stocks (low price to earnings (P/E) ratio) should match the performance of expensive stocks (high P/E) (since all available information is already reflected in the price, cheap stocks are cheap because they aren’t good stocks, not because they’re undervalued). Research has proved this wrong:
If EMH were true, the dots would all fall on a horizontal line. Also add odds with EMH are economic bubbles (or individual stock bubbles), which manifest as a result of cognitive biases such as overconfidence, overreaction, representative bias, information bias, and various other predictable human errors in reasoning and information processing, such as irrational exuberance, when underlying value is ignored and prices can dramatically rise and frantically fall.
To a quantitative investor, holes in the EMH theory — inefficiencies — are opportunities for excess returns.
Aren’t you just curve fitting? Past performance is not an indication of future returns.
You’re absolutely right, past performance of a particular strategy is not an indication that the strategy will perform similarly in the future. However, what else do we have to go on other than past performance? Don’t millions of people invest in US equity indexes with the expectation that future performance will match or come close to historical performance of that index?
Is that such a good assumption? What’s to say the US market doesn’t go the direction of Japan?
The Lost Decades – Japan Nikkei Index Monthly Chart
To combat the threat of overfitting, Portfolio123 offers numerous way to test robustness, including interchangeable universes and ranking systems, to ensure a strategy works across a wide range of assumptions. They’ve also published educational materials, including a strategy design class, that teach best practices in avoiding common pitfalls such as overfitting.
In defense of overfitting in general, O’Shaughnessy published several strategies that have returned above 15% annually since 1964 (limits of his data), and those strategies have continued to work since being published (out of sample).
If your strategies work so well, why aren’t hedge funds using them/hiring you?
I get this one a lot. Firstly, a lot of the strategies discussed on this site were not developed by me. Some were developed by James O’Shaughnessy and have been published in his What Works on Wall Street book. Others, like the methodology from Joel Greenblatt’s book, are built on the shoulders of Warren Buffett’s investing philosophy.
The underlying theme for most of the strategies discussed on this site are based on the two factors of value and momentum, which have been proven time and again to be associated with excess returns.
Assuming this is true, why aren’t all hedge funds investing this way/why aren’t you working for a hedge fund/why haven’t you sold this strategy to a hedge fund for millions of dollars?
The short answer is that they already invest this way. In fact, almost all of the largest hedge funds, including AQR, AHL, Bridgewater, and the Medallion Fund (the most profitable hedge fund ever), use quantitative analysis to make trades (flying in the face of the efficient market hypothesis).
The issue with hedge funds is their scale. They are forced to avoid small stocks, even if those stocks look the most attractive in terms of momentum or value, because the market impact of their large buy and sell orders would significantly affect the price of the stock and erode any excess return they had hoped to achieve. As a result, they are forced to focus on only larger stocks and be less concentrated in factors proven to generate excess return. This disadvantage, along with lack of long-term discipline, is a chief reason for consistent underperformance of the benchmark among hedge funds.
Their disadvantage is the individual investor’s advantage. Your small orders have much less market impact and you can buy the best value or momentum stocks no matter their size, providing much more exposure to proven factors and increasing excess returns.
What about indexing?
By now, you probably know how I feel about indexing. But indexing does have its place; if you don’t want to think about your portfolio at all, indexing is for you. But I argue that even a small amount of effort spent intelligently choosing your investments can be worthwhile.
Would you buy a bag of 10 apples if you were guaranteed that 5 of them were rotten? Probably not, but that’s what you’re doing when you index. An index fund buys all the stocks (winners, losers, and everything in between) in order to replicate the return of that index.
If you can eliminate even 5% of the losers by learning to identify and avoid characteristics often associated with losing stocks (poor earnings quality or financial strength, for example), you can tremendously improve the return of your portfolio.
Another characteristic of index funds that is often cited as their main advantage can be a drag the more your portfolio grows. Index funds like Vanguard’s VTSAX have an expense ratio of 0.05%. That’s 0.05% of your portfolio paid to Vanguard every year, regardless of how large your portfolio is.
Investing in a quantitative strategy like the ones published by O’Shaughnessy, which require rebalancing a portfolio of 25 stocks once a year is cheaper than indexing (assuming $10 per trade) once your portfolio eclipses $500,000. And if you take advantage of low or no-cost brokerages (like Robinhood which offers free trades or larger institutions that offer X free trades a month if you have a certain portfolio value), your cost goes to 0%. Meanwhile, Vanguard would still be taking 0.05%.
The Vanguard Group, a private company with over $3.8 trillion in assets under management, has never disclosed its executive compensation. With an average expense ratio of 0.19%, that $3.8 trillion in assets nets a cool $7.22 billion every year in fees. That’s enough for $515,000 for each of its 14,000 employees. Not that I’m trying to bash Vanguard or promote active managers (I’m not), but just pointing out that indexing is not the end-all, be-all, and that investing for yourself is the best course.
So what’s the catch? Why create this site?
The goal of Portfolio Perfection is to empower ordinary investors to think beyond the index. You do not have to be a hedge fund manager to be a successful investor. You do not have a degree in finance or spend all day reading quarterly earnings statements to implement a quantitative investing strategy.
The number one barrier for ordinary investors is the stigma associated with investing in individual equities. But when presented with evidence, logical minds can make rational conclusions: quantitative strategies have been proven to work. You, the ordinary investor, have an advantage over hedge funds because you can concentrate your holdings in the best stocks, small or large (as discussed in another FAQ).
Once you’ve made it past the first barrier, the second barrier is obtaining the data, organizing it in a way that’s useful, and quickly and easily developing robust strategies. That’s where Portfolio123 comes in.
And that’s where the catch also comes. The fact is, good financial market data is expensive. Free sources exist, but they’re mostly just historical prices (which is fine for technical analysis) and not historical earnings statement information (from which most strategies on this site are built). Portfolio123 not only has the data we need, but an extensive platform on which to build and test quantitative strategies.
This whole site may seem like a paid advertisement for Portflio123, but the truth is that its a tool that I discovered and it’s been tremendously helpful in jumpstarting my portfolio and my path to financial independence. I don’t work (nor have a degree) in finance, but I am passionate about it and in my opinion, portfolio return is the oft-ignored leg of the three-legged stool of financial independence (the other two being earnings rate and savings rate).
I do receive a paltry commission if you use my referral link and become a member of Portfolio123, but my main motivation is continuing to learn and write about quantitative investing and spread the word so that others may benefit as I have.
“A rooster crows only when it sees the light. Put him in the dark and he’ll never crow. I have seen the light and I’m crowing.” – Muhammad Ali