The Evolution of Hedge Funds in the Data Era | Sigma Computing (2024)

Such that, all things considered equal and fair, it is essentially impossible to "beat the market."

While, in theory, this approach can be considered globally accurate, the reality of markets is that Alpha is often defined and achieved by the asymmetric nature of the information shared between buyers and sellers.

The fundamental theory of efficient markets rests upon the basic ideal that information, risk, and Alpha can be made equally accessible to buyers and sellers to create market conditions reflecting a "fair market value." Yet many of these conditions have been, or had been, essentially impossible without the invention of numerous data centric technologies like databases, data streams, live market pricing, and the internet that have developed over time to support efficient markets.

The harsh reality of efficient markets is that they are genuinely only "efficient" when participants can accurately assess ALL of the available information and provide pricing forces on the market to drive the supply and demand of any given asset.

Named after the concept of "a hedge," or a strategy that aims to limit or offset the risk of adverse price movement in an asset or investment. The current Hedge Fund ecosystem comprises tens of thousands of global firms with trillions of dollars in held assets, all actively competing in different areas of the market. These firms employ strategies such as short selling, derivatives investing, and leverage to buy and sell other assets and achieve financial growth.

Yet the one element that defines these firms is their active, if not structurally dependent need for data. From the beginning of the first funds to the modern-day AI-driven, algorithmically traded ETFs, every fund depends on data to make decisions and compete in the market.

In this article, we will provide a quick dive into the evolution of hedge funds and their uses of data for various applications while outlining how the future of markets will depend more on data-based decision-making than ever before.

Hedge funds have long relied on traditional data sources for their investment strategies.

Traditional financial data, such as company filings, earnings reports, and economic indicators, have been the backbone of investment decision-making for over 100 years. Since the early 1960s, financial data companies like Compustat have supplied detailed financial information on publicly held companies via tabular data like income, balance sheets, and cash flow statements. However, the limitations of traditional data sources, such as their inability to provide real-time insights and the potential for biases and inaccuracies, led to the search for more innovative, bleeding-edge data sources.

From the 1960s to the late 1990s, the evolution of data sources like modern databases and the advent of computerized trading significantly transformed the financial industry. This period saw the rise of what can be considered modern-day hedge funds and the adoption/development of algorithmic trading, which relied heavily on data sources for their operations.

These hedge funds, which grew spectacularly in the 1980s and boomed in the 1990s, relied on various databases for their operations (TASS, SEC Filings, etc..). However, these databases often had limitations due to the limits of the data available and the overall secretive nature of hedge funds, as they are not required by law to report their returns to any single database or even to regulators. As such, the firms with the most accurate datasets and the most developed decision-making criteria allowed them to succeed where their competitors failed.

And with the creation of the internet in the early 90s and the big boom in digital data sources, hedge funds began leveraging alternative data sources to add to the models and datasets they were using to pick stocks and place bets on the market. Data sources like credit card transactions, geolocation data, geospatial data, and internet search histories started playing a crucial role in informing investment decisions. These alternative data sources gave hedge funds a more granular view of consumer behavior and market trends, often in real-time.

This shift marked a significant departure from the traditional reliance on historical financial data, enabling hedge funds to anticipate market movements more accurately and react to changes swiftly. While simultaneously learning from and leveraging the fundamental style analysis that had proven successful in the prior years.

In essence, the history of hedge funds is a history that is closely tied with the use of data to make efficient market decisions and to drive better performance within the funds that are created. Now more than ever, data is being leveraged across firms to make the best possible bets and to create models of the market that define a systematic and rules-based approach to creating returns for the customers of these funds.

Forecasting the future based on various assumptions is challenging in existing applications and tools available to asset managers. Providing a unique way of forecasting and modeling virtually any underlying dataset with tools like input tables and charting/analysis workbook capabilities allows funds to model a variety of assumptions or scenarios without building customizable code-driven models.

As the hedge fund industry continues to evolve in the data era, the importance of data will only continue to grow. Tools like Sigma have proven invaluable in this context, offering innovative solutions across these critical areas and facilitating more efficient and effective use of data in the dynamic world of hedge funds.