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From Hedge Fund to Quant Hedge Fund – The Data Analytics Revolution

Hedge Fund
Published on Mar 05, 2019

Did you know that almost 90% of today’s data was generated over the last two years? As per an article published in Forbes, data is being generated faster than ever, and by the year 2020, about 1.7 megabytes of new information will be created every second, for every human being on the earth. This expanding data load has been observed across most industries, and the case with the hedge fund industry is no different. The industry deals in complex and large data which can be leveraged for intelligence with data analytics strategies. The process will act as a booster to increase returns, maximize profits, and optimize investment operations. Data analytics helps unite the massive amounts of available public and consumer data with numerical optimization. It provides hedge fund managers with additional avenues for investment analysis by revealing patterns, correlations, and insights which may have been overlooked by businesses.

Data analytics is mostly used in quantitative funds, the most prevailing term in the industry. In standard hedge funds, investment managers and analysts constitute the major part of the team along with traders whereas quant hedge funds call for additional talent in quant and programming. The unit usually includes an army of statisticians and programmers who develop data access and analytical tools to help traders. The primary objective of the analysis is to uncover signals, patterns, and correlations that can help outperform the market with increasing data size, which is impossible to do manually.

A quant hedge fund is a hedge fund where trading decisions are based on algorithmic or systematic techniques and strategies. It emphasizes the same asset classes as a typical hedge fund. However, it employs automatic trading rules instead of the traditional stock-picking approach based on fundamental/technical analysis. Quant funds are buying stored electronic data and applying it in the algorithms above to derive profitable trading strategies based on the acquired data. It also tries to buy as much data as possible from various data vendors such as credit cards, financial transaction details, customer reviews, etc. However, creating actionable insights by utilizing this information is still the biggest challenge.

Predictive Analytics and Machine learning are the most trending data analytics techs in the industry currently. Predictive Analytics helps to understand and predict the future based on past data. The concept applies complex techniques of classical statistics like regression and decision trees through which future fund performance can be predicted.

Predictive Analytics is a step ahead of Descriptive Analytics, which analyzes past (historical) data to understand trends and evaluate metrics over time. It requires minimal to no coding/programming. Cash Flow Analysis, Sales and Revenue Reports, Performance Analysis, etc., are common examples of Descriptive Analytics. Descriptive Analytics helps to gain an insight into what approach to take in the future; it analyzes past data to influence the future.

The advance version of Predictive Analytics is Machine Learning, where patterns/trends are efficiently recognized, and models are evolved automatically. It provides predictions to guide and take real-time decisions with less dependency on human intervention. While predictive analytics still depends on human expertise, machine learning facilitates minimal human interference. As per a survey held by Barclay Hedge in Jul 2018, 56% of respondents were using Machine Learning/Predictive Analytics.

Data analytics provides a tremendous competitive advantage in the trading environment. A renowned market research firm in the finance industry estimated that around 13,060 hedge funds are using artificial intelligence for their trades. It seems that not before long, the application of data analytics techniques will become a necessity for this industry.