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Q1 hedge fund letters, conference, scoops etc, Also read Lear Capital: Financial Products You Should Avoid?
Data mining is a huge risk with factor-based investment strategies. Many factors have proven to not work in practice and even the most popular factors, like value and momentum, may prove less effective going forward. Crowding in factor strategies, changes in the economy and new business models may eliminate any potential excess return from simple screening metrics that form the basis for many factors. Investors can avoid being fooled by backtests by keeping in mind that most attempts to beat markets will fail because trading is a zero-sum activity.
There are cause and effect relationships in the world and in investing that hold true over time. Many are common sense and easily observable – like fire creates smoke – while others are harder to see and understand. With investing, true relationships can be hard to see because of randomness and noise in data, and there’s a risk we convince ourselves certain relationships exist that really do not (e.g. smoke creates fire). In much of quantitative finance, data is mined to show a certain effect, but the logic behind the cause and effect relationship is not robust. Then suddenly, because of evidence in noisy historical data, investors begin to believe that smoke creates fire.
When historical evidence disagrees with our logic, investors should favor applying a fundamental understanding over what a backtest prescribes.
Factor investing is an area I have researched and written about extensively (see here, here and here) . It is an approach to active management that is lower cost and backed by decades of historical data, compared to the standard high-cost, overdiversified stock picking approach that has failed. But it is still active management, and with active management there is a loser for every winner. That’s a fact of markets. Normally the few winners in active management leverage a few key insights that are not recognized by the masses, at least at the time. In contrast, most active management losers tend to copy each other, using the same strategies and managers.
Factor investing is, by its nature, transparent and therefore easily copied. This is why many factor investing strategies are increasingly problematic. Data mining, factor crowding, as well as economic changes are all reasons why such strategies may disappoint. Popular value and momentum strategies are used as examples throughout this thought piece to illustrate. Keep in mind, I am not trying to definitively say that such factor strategies do not work, but instead hoping potential users of these strategies will pause and ask deeper questions about them. In the end, we can never forget the unavoidable fact that trading and beating markets is a zero-sum game.
Data mining is a risk even with value and momentum strategies
Value is the buying of “cheap” assets, at least based on measures such as a low price-to-earnings (P/E) ratio for stocks. This is the opposite of growth or high P/E stocks, which are statistically expensive. Typically, the way a stock becomes relatively cheap is by underperforming in the recent past, and vice versa for growth stocks. Momentum strategies can be compared similarly. Buying past winners (momentum stocks) is about buying stocks that have recently outperformed on the basis that their trend will continue. The opposite is past losers (or low momentum). The table below summarizes these four strategies and how they logically relate to each other.
But the logic explained above is at odds with the academic research backed by decades of market data across time and geography. The below summarizes the difference between what the backtests say versus logic. Both cannot simultaneously be true. Smoke cannot both be created by fire (logical) and produce it (evidence from backtests).
If this logic is correct, then we should see that value is highly correlated to past losers and same for momentum to growth. To test, I simulated portfolios over the last 25 years where value is defined as the one-third of the market with lowest P/E, growth is one-third of the market with the highest P/E, past winners (high momentum) is the one-third of the market with best trailing 200-day returns, and past losers is the one-third of the market with the worst trailing 200-day returns. I used the Bloomberg equity database to simulate but these results are similar to what one would find in the commonly used Ken French Data Library. Some might ask why test over only 25 years when there is more data available. Markets and economies change and adapt to new information. While I have tested these strategies going back to the 1920s, more recent data is more relevant to today’s market conditions, which I also discuss later in this paper.
Read the full article here by Maneesh Shanbhag, Advisor Perspectives
