Leveraging Machine Learning Strategies for Hedge Fund Gains

by October 18, 2018 0 comments

The one risk-on strategy was the norm of the decade since the financial crisis bottom-fishing equity indexes. Machine learning can implement varied versions of this strategy. A hedge fund that was started in the late 1980s started absorbed a few years later went to be known as Renaissance Technologies, specializing in systematic trading with quantitative models derived from mathematical and statistical analyses. The fundamental process that machine learning deploys is a combination of computationally intensive statistical analytics subsequently with a neural-network-type branch which is basically classifiers.


Machine Learning Techniques & Hedge Fund Analysis

Over the years’ hedge funds have lost billions of dollars owing to wrong analysis. As hedge funds operate on high risk and high return trades leaving minuscule transparency for investors to understand the risk of one bad call which may lead to a loss of billions of dollars.

From 1949, when the first hedge fund was formed employing two strategies of short-selling and leveraging to the current times when there are upwards of 10,000 hedge funds managing an estimated $3 trillion in assets, hedge funds have come a long way. With changing times, as the market has evolved, hedge funds have developed offering more flexibility in their investment options. Today, funds deploy a variety of strategies such as achieving market neutrality, equity hedging, relative value arbitrage, and convertible arbitrage to gain maximum from the volatility. Despite the high risks involved and the complexities involved, there is, however, a lack of transparency for investors who often are unknown how their money is being managed by the hedge funds they are investing with.

Hedge fund managers analyze the market data in two different ways, fundamental analysis and quantitative analysis. The fundamental analysis of hedge funds aims to deploy market research over the value of different securities to determine which are the undervalued and overvalued assets. On the other hand, the quantitative analysis uses complex mathematical formulas and computer models to develop time-series models to identify ideal different asset’s short and long-term positions.


Artificial Intelligence for Hedge Funds

The static form of quantitative analysis lies in the use of computers to develop models created by traders and mathematicians. As the market is constantly changing and shifting these models, however, are not useful for extended periods of time. To obtain accurate results from the quantitative analysis, models must be formulated which will regularly reflect changing market conditions.

The interest in artificial intelligence (AI) has increased immensely in recent years due to the advancements made in the field of emerging technologies specifically deep learning which is based on the large virtual neural network to point and understand varying data patterns for results and analysis. Initially crafted by humans, these AI systems must be able to adapt to changing circumstances on their own which is still a long way to go. Machine learning algorithms have enabled trading systems to be highly attractive to trading firms looking for new strategies to decrease risk and increase returns.

The amount of information that these systems can infer is mammoth. Machine learning algorithms take in information from posts and news stories from social media sites to identify connections in the data and patterns among data points. By identifying data patterns, machine learning algorithms can make predictions about the direction of the market for multiple firms to make a trade without any human intervention.

As per reports from a well-known provider of financial industry data – Preqin, currently there are about 1,360 hedge funds making a maximum of their trades with the help from machine learning models managing about $197 billion in total.


The Path to a Future

In the coming years, hedge funds are poised to dominate the artificial intelligence sector within the finance industry, providing technology to investors that lower risk and increases potential returns. Emerging technologies will offer a uniquely customizable solution for hedge funds both giants and new players enabling fund managers to choose their preferred parameters. Deploying machine learning to financial markets especially hedge fund analysis could signal the start of a new trading era.

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