The Top Analytical Methods Used by Hedge Funds

There are many analytical methods used by hedge funds to aid decision making and as a result, improve the overall performance. These include; adding alpha, quantitative modeling, NLP-powered fundamental analysis, high frequency data analytics, risk modeling and scenario simulation, machine learning for predictive analysis and sentiment analysis. 

Hegde funds rely upon real-time, precise analytics not only to seek a competitive advantage, but also just to stay afloat amongst the highly competitive world of trading. Analytics has become the lifeblood for hedge funds, enabling these firms to make evidence-based, high-frequency trades. 

Contemporary hedge funds utilize a combination of statistical arbitrage and machine learning to manage risk, generate alpha and to maintain compliance. 

Let’s dissect some of these top analytical methods.

Adding alpha 

Hedge funds and the investing community as a whole seek to add alpha. 

This is to say, deliver a positive return relative to the amount of risk taken, as opposed to just returning a positive against the benchmark return. 

Alpha is a core justification for the management and performance fees that hedge funds can charge, as opposed to mutual funds that seek to either track or slightly outperform the broader market. 

Hedge funds typically utilize a ‘two and twenty’ fee structure, referring to 2% of assets under management (AUM) and the standardised performance payment of 20% of profits made by the fund above a predefined benchmark. 

It is the continuous ability to generate alpha that helps hedge funds to stand apart from standardised commodity investment products. Sustained alpha generation helps to build credibility and reduce investor turnover. 
Adding alpha is made possible by extracting insights from high-volume market data in real time. It is the hedge funds who are able to harness this data to improve trade execution, reduce risk and optimize performance that gain the competitive advantage.

Analytical methods

Quantitative modeling

Quantitative modeling is arguably the most important of all hedge fund strategies. 

These models use statistical methods and historical data to identify potential price discrepancies and trading opportunities that may not necessarily be apparent using traditional analysis. 

A particularly dominant subset of quantitative modeling is statistical arbitrage, which refers to the use of mean-reversion strategies to exploit price inefficiencies. 

Having access to the high-frequency data in real time isn’t enough; hedge funds rely upon the precise risk assessments and predictive modeling to make actionable decisions with confidence. 

An example use case of quantitative modeling is a quantitative model that tracks pairs of historically correlated equities. An automated strategy could be implemented that will short the out-performer and long the under-performer, assuming that they will revert to historical norms over time.

NLP-powered Fundamental Analysis

Fundamental analysis has long been a staple of equity investing, but the developments in natural language processing is helping to more accurately identify patterns that potentially indicate performance shifts before traditional data sets catch up. 

Hedge funds are utilizing NLP to scrape earnings calls, analyst reports, SEC filings and news articles to provide real time sentiment analysis that can be used to provide market signals ahead of the curve.

High frequency data analytics

High-frequency trading strategies rely upon advanced time-series analytics and ultra-low-latency data pipelines to model price movements and volume dynamics right down to the millisecond. 
Using methods such as ARIMA models (autoregressive integrated moving averages) and GARCH volatility forecasting (Generalized Autoregressive Conditional Heteroskedasticity), the larger hedge funds are processing terabytes of financial data every day, monitoring the market microstructures to predict minute short-term price movements.

Risk modeling and scenario simulation

Robust risk analytics are at the cornerstone of hedge fund operations, helping managers to anticipate and seek to avoid drawdowns, whilst also maintaining compliance. 

Funds commonly deploy Monte Carlo simulations, which are computational algorithms that use random sampling to obtain numerical results that provide a statistical range of potential results. 

Value-at-risk (VaR) calculations and targeted stress testing are other simulations utilised not only on the trading floor, but also across CFOs and compliance leaders utilising the insights to clearly communicate portfolio vulnerabilities to internal stakeholders and external investors alike. 

Machine learning for predictive analysis

Machine learning models are being deployed by hedge funds to detect nonlinear relationships and refine trade execution strategies. 

A variety of techniques are used, such as random forests, gradient boosting and deep neural networks. 

The learning and training periods for these hedge fund machine learning models often involve a combination of supervised and unsupervised learning time, helping to capture both patterns that are known and other hidden patterns. 

The use of cross-validation and out-of-sample testing helps to reduce overfitting, which is essential to ensure that the model isn’t just memorizing past data but is instead actually learning patterns that can be used to generalize future, unseen market conditions. 

Overfitting is a common pitfall for financial modeling, especially when the machine learning models are deployed on noisy or limited datasets. 

A model that has been overfitted may show impressive reactive ‘backtest’ results, but fail miserably when it comes to proactive live trading. 

To avoid this, analysts can test models using data that the model hasn’t seen before, helping them to accurately assess the predictive capabilities of the model before being deployed on the live portfolios. 

This process helps to protect the investment capital, as well as the hedge fund credibility by ensuring only models that perform well under real-world pressures are deployed.

Sentiment analysis

It was the infamous British economist John Maynard Keynes who first referred to the ‘animal spirits’ of the market; the psychological factors that drive individuals to act in ways that are not rational or predictable by economic models. 

Markets respond to emotion not just to fundamentals, something that hedge funds are acutely aware of. 

Behavioral finance and sentiment analysis are two of the methods being deployed by hedge funds to track the shifts in investor psychology through the analysis of data from platforms such as Reddit, Twitter and retail trading apps. 

The funds aim to capture signals around euphoric buying or selling that historically precede sharp market moves. It is the use of advanced NLP and LLMs that parse this unstructured data in real time, helping to provide a source of competitive advantage in their trade strategies. 

There is no one unified trading strategy of what a fund should do in the event of a specific sentiment shift, for example one fund might pursue a contrarian stance to take the opposite of the trade, whilst another might seek to take the bull (or the bear) by the horns and invest with the sentiment shift. 

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Check out our exclusive hedge fund sentiment analysis page, helping portfolio managers improve their numbers with cutting-edge data insights.

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