Quants and its Limitations

Quants, known as proprietary quantitative strategies, involving rule-based investments may be marketed as different names, including factor investing, risk parity, smart beta and so forth.

Quantitative investing strategies essentially rely on pattern recognition: Models look to correlate past periods of superior returns with specific factors including value, size, volatility, yield, quality and momentum. Once the latter are identified, fund managers construct portfolios with specified return and risk parameters consisting of securities that match those optimal characteristics. Other techniques exploit short-term dislocations between individual prices and comparable securities or the broader market, betting that the relationship will eventually revert to normal.

Such approaches have several fundamental weaknesses:

1. Quant investing is tainted by hindsight bias — the belief that understanding the past allows the future to be predicted. Given enough time, money and computing power, a strategy predicting high returns can be found and validated using back testing to check its historical performance. But, this heightens the risk of overfitting, or adjusting the model to suit a specific set of historical conditions. Those may look like a winning recipe, but could turn out to be an historical fluke.
2. Modelling is also affected by practical matters, such as what data is or isn’t available. London Business School researchers found over 300 factors that could be used to develop potential strategies, heightening the risk of an overfitted model.
3. There is in addition the problem of ergodicity, that is, the lack of a truly representative data sample. Financial eras are characterized by specific policies, market structures, instruments and investors. Unique conditions that shape returns, volatility and correlation may change. While models create an illusion of sophisticated certainty, they can’t capture the full range of events that produced a particular outcome and could perform poorly where a paradigm shift occurs. Modern markets may simply be too complex to be modeled accurately.
4. Quant strategies naturally lack transparency, given that asset managers are reluctant to disclose too many details and lose their competitive edge. This, however, increases the risk of gaming. A low-volatility fund, for instance, might buy illiquid assets whose prices change infrequently, thus giving the illusion of stability. Some strategies, such as selling options, might produce a lot of small gains but be vulnerable to large losses if market conditions change.
5. Increases in funds under management may create competition that reduces a strategy’s expected return. Where the size of funds increases, the strategy could become crowded, making trading difficult and creating unpredictable profits and losses.

Reference

Bloomberg, Satyajit Das, 2019-01-19, “Why the Quants Aren’t Adding Up”