Moneyball on the exchange: how new technologies change not only trading, but also the work of hedge funds

In 2003, Michael Lewis bestseller was published under the title “The Man Who Changed Everything” (“Moneyball”) - a biographical sports drama telling the story of the general manager of the Oakland Athletics baseball team, Billie Bean. He managed to achieve impressive success with the help of data analysis in the formation of the composition.

Bean was able to exploit the inefficiencies that existed in the baseball market — he picked up team members not on the basis of the “sixth sense” that most team scouts relied on, but through a statistical approach. This allowed him with a relatively small budget to show a better result than most of the richer teams.
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The same approach can be applied to investing and trading on the stock exchange - the data helps to detect and use inefficiencies on the financial market, write financiers John Gilchrist and Grant Watson on the pages of Business Live.

New era in the work of hedge funds

Such a “quantitative” approach dates back to the thirties of the last century, but the volume of funds under the management of financiers who adhere to it, until recently, was rather slow. However, in the last eight years, quantitative (or quantum - from the English. Quantitative, “quant”) hedge funds have received almost $ 1 trillion in management, which is almost a third of the total assets of all world hedge funds.

This is due to increased efficiency and reduced risk of the tools they use, which allows them to achieve higher returns for investors.

Quantitative funds are constantly looking for unique methodologies, compiling data sets and developing trading strategies. In this they are helped by big data technology, machine learning and artificial intelligence (AI), combined with ultra-fast computers.

In the report Deloitte Investment Management Outlook 2017, big data, machine learning and AI are called the main trends of the current year.

Even traditional managers are beginning to introduce quantitative investment approaches. For example, the Tudor Investment Corporation fund was forced to cut 15% of staff due to poor results and the subsequent withdrawal of funds from investors - this forced the famous manager Paul Tudor-Jones to make organizational changes in order to softer into more technological data-based approaches. “No man can beat a car, and no car can beat a man with a car,” he was rumored to justify changes in the work of the foundation to the remaining employees.

It is important to distinguish between quantitative trading, which includes algorithmic and high-frequency trading on the exchange, and quantitative investment, that is, an approach that involves building highly efficient asset portfolios using risk control practices.

How it works

A quantitative approach allows you to avoid mistakes when making investment decisions that may be based on behavioral bias of people managing assets. When applied, hedge funds use systems that analyze a variety of fundamental factors and identify those that correlate with good or bad returns on investments in specific assets. These fundamental factors often underlie market trends, and their analysis allows you to see these trends at an early stage and dynamically adapt a portfolio of assets, for example, every month. Thus, it is more likely to stay in the black in the light of any developments in the market.

Also, new statistically significant factors are constantly being searched for, and those that cease to be so are removed from the used calculation models. This allows you to adapt to changing market conditions. For example, the Old Mutual Customized Solutions Foundation has added to its decision making model factors describing the situation with the environment, public sentiment and the activity of regulatory bodies.

To track sentiment, special news analysis systems can be used — this helps to understand the likely course of action of the “crowd,” that is, a multitude of private financial market players. Thus, hedge funds play on the inefficiency of the financial market, arising from the behavioral bias of its participants.

Other materials on finance and stock market from ITinvest :

• ITinvest educational resources
• Analytics and market reviews
• How to determine the best time for a transaction in the stock market: Trend following algorithms
• How Big Data is Used to Analyze the Stock Market
• Finding inefficiencies: What you need to know about creating strategies for trading on the exchange
• Experiment: Using Google Trends to predict stock market crashes