Quantitative stock trading strategies

Quantitative stock trading strategies

Posted: AskaniaNova Date: 22.05.2017

We recently put this question and others to Osman Ali, who's a portfolio manager on Goldman Sachs Asset Management's quantitative investment strategies group. 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 analyzes large sets of daily releases like news articles, analyst reports, earnings call transcripts, etc to quantify what the overall sentiment is around a stock.

Leveraging Artificial Intelligence to Build Algorithmic Trading Strategies

How do you avoid this? 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.

Beginner's Guide to Quantitative Trading | QuantStart

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 utilize in our investment models appears crowded. 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. What's driving the market now? 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.

Basics of Algorithmic Trading: Concepts and Examples | Investopedia

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 , however, that reverted. By our measures, it was the best quarter for value investing since 2Q Markets Stocks Indices Commodities Currencies Mutual Funds ETFs Bonds News.

quantitative stock trading strategies

Here's the strategy that has 'worked really well' for Goldman's quant investing group. What quant strategy or style do you favor right now? Registration on or use of this site constitutes acceptance of our Terms of Service and Privacy Policy.

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