AI applications in the financial sector are numerous. In this article, we will look at some key AI use cases for hedge funds. We also look at potential obstacles to implementing AI-based solutions and how hedge funds can overcome them.
Cerulli, a consulting and research firm, claims in a 2020 analysis that there is growing evidence for hedge funds to use AI technologies. According to Cerulli’s findings, the cumulative return of [Europe-domiciled] AI-led hedge funds was nearly three times that of the overall hedge fund universe during this [2013 – 2019] period: 33.9 percent versus 12.1 percent.
Machine learning is currently used by AI-driven hedge funds for tasks such as data analysis, stock trading, and payout calculation. Some of these applications are worth delving into in-depth.
AI applications in hedge funds
Here are four ways we believe AI can help hedge funds maximize their trading results:
Algorithmic trading:
Trading entails taking into account a variety of independent variables that influence the value of assets and making investment decisions that result in higher returns. Traditional computational modeling and human traders may not be able to sift through large amounts of data quickly enough to make timely trading decisions.
Trading entails taking into account a variety of independent variables that influence the value of assets and making investment decisions that result in higher returns. Traditional computational modeling and human traders may not be able to sift through large amounts of data quickly enough to make timely trading decisions.
Forecasting volatility:
Because of market uncertainties, accurate volatility forecasting is critical in fund management. Naturally, a better understanding and prediction of volatility results in better trading decisions and higher returns. Traditional approaches and econometric modeling can predict volatility, but they are frequently incapable of mapping complex and nonlinear relationships between factors that contribute to volatility.
Machine learning-based approaches, on the other hand, can make much more precise predictions of volatility. Machine learning models can improve volatility predictions by taking more flexible approaches to understand variance (an underlying measure of volatility).
Signal tracking:
According to studies, alpha on new trades decays in about 12 months on average. Because trading decisions are based on predictive relationships and signals, funds must monitor and retrieve high-quality signals. Overcrowding of signals can be especially concerning, resulting in overlapping trading positions and alpha decay.
However, with machine learning-enabled technologies, hedge funds can identify diverse and previously unknown signals, allowing them to avoid trading on overcrowded signals.
Natural Language Processing (NLP), a branch of AI that focuses on analyzing and deriving insights from large amounts of text data, is especially useful for hedge funds to derive foresight and signals from unstructured textual data from a variety of sources like news, social media, blogs, and transactions.
Generating alpha factors:
Alpha compares a fund’s performance to an appropriate benchmark and is an indicator of the value a fund manager adds or subtracts from a portfolio. Alpha factors are defined as signals that generate more alpha than the benchmark index’s returns.
Alpha factors are used to explain market behavior and to capture market risk. A key component of machine learning, feature engineering, can be used in trading to supplement alpha-factor research.
In AI-based trading, factors that better capture the risks embodied by the return drivers are generated from original data and manipulated to derive more impactful features using feature engineering. These characteristics are transformed into alpha factors, which can then be used to generate more alpha.
Causal inference:
There are numerous factors (features) that can assist in explaining financial phenomena of interest. Unfortunately, as is often the case, we don’t know which factors are directly influencing (causing) each other.
AI-based solutions can help with understanding the interrelationships between features and selecting the most relevant subset of features for a specific machine learning task. Understanding the causal direction will allow hedge fund managers to ask more insightful questions and make more informed trading decisions.
Challenges in adopting AI-based solutions
While the use of AI in hedge funds appears to be appealing conceptually, it is less so in practice.
- Time-sensitive investment decisions must be made quickly before market conditions change, which is especially important for hedge funds. Building a custom AI solution or a machine learning model for a specific investment task, on the other hand, can be extremely time-consuming.
Companies can take anywhere from 8 to 90 days or more to deploy a single machine learning model, according to data from the Algorithmia 2020 State of Enterprise Machine Learning report. Hedge funds do not always have the luxury of devoting so much time to developing, testing and deploying a machine learning model.
- Even the most basic version of a machine learning model can be expensive to develop, including but not limited to model infrastructure, data support, and engineering costs. The model will cost you around $60K over the first five years.
- The model-building process would necessitate subject matter experts, but finding and hiring AI experts is often difficult and expensive.
Getting around the obstacles: ML code optimization
To successfully integrate AI into the investment decision-making process, hedge funds must look into technological solutions that have low financial, time, and computational requirements. These AI systems should also be able to generate models that are both explainable and do not overfit or underfit.
Efficiency is money in trading. A one-millisecond advantage in trading is estimated to be worth $100 million per year. Code optimization reduces inefficiencies at the source code level, lowering latency and improving model performance by more than 50%, resulting in faster and more profitable trading outcomes.
