Wall Street is growing more dependent on complex models and data science to make trading decisions.
This strategy, known as quant trading, is raising concern that some winning trades may get too crowded, and the resulting volatility may hurt returns.
We recently put this question and others to Osman Ali, who heads Goldman Sachs Asset Management’s quantitative investment strategies group.
Akin Oyedele: What quant strategy or style do you favour right now?
Osman Ali: A strategy that has worked really well for us is using sentiment analysis to help identify companies that the market in aggregate is getting more excited about (or ones where the market is getting more cautious about). We have built technology that analyses large sets of daily releases (like news articles, analyst reports, earnings call transcripts, etc) to quantify what the overall sentiment is around a stock. This is a strategy that served us well in markets that have been very sentiment driven and we don’t see that changing going forward.
Oyedele: There’s some concern that winning quant trades may get too crowded and start hurting returns. How do you avoid this?
Ali: Crowding is something that we think a lot about. We define crowding as an investment strategy that, as a result of over-popularity, delivers little persistent and consistent results. We are a team of investors who have been investing in equity markets for over 25 years. Over that time, our team has gained significant experience in identifying investments that are unique and proprietary to our process, and which may be more popular across the market.
Crowding can come from many sources and isn’t always driven by quantitative investors. We spend time looking at various data such as ownership of stocks across managers, market microstructure data (such as individual intra-day stock level trading patterns), and changes to the returns of various investment strategies over time to identify those that are still valid ways of investing and those that are being crowded.
Over the years we have seen some strategies become crowded and lose their efficacy. In some cases, this has prompted us to remove them from our process. In many other cases, we have enhanced our strategy in a way that preserves their economic appeal but relies on unique data sources so that we can act before other investors. At this point, nothing we utilise in our investment models appears crowded.
Oyedele: What can non-quant investors can learn from quants?
Ali: We can learn from each other, of course. But what we find helpful on the quant side is having a very disciplined and unbiased framework for identifying what you like and don’t like. We list out what we’re looking for in a company and have metrics that calculate how a company scores in all the characteristics we look for in a sustainable, growing business.
We have an intuitive and transparent framework to aggregate our scores across each all of our criteria and come up with recommendations that are updated every day. This helps us objectively identify companies that look attractive to buy and also those that no longer look attractive and should be sold.
Oyedele: Earlier this year, you said stocks with the largest bets against them (or short interest) was the biggest influence on returns. What’s driving the market now?
Ali: More recently, we have seen less abnormal activity around stocks that have high short interest. Instead, one of the more powerful themes in the market has been the returns to the valuation strategy.
A value strategy is one that identifies companies that are cheap (i.e. companies with lower price to earnings ratios) versus their industry peers and tilts towards those cheaper stocks.
Over the last few years, this had not been a very profitable way to invest as the more expensive higher growth stocks have out-performed their less expensive value peers. In the 3Q of 2016, however, that reverted. By our measures, it was the best quarter for value investing since 2Q 2009.
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