Competition among Hedge Funds intensifies for superior returns
In the rapidly evolving world of finance, hedge funds are leading the charge in the pursuit of alpha, or excess returns, by going beyond traditional data and simple speed. They are harnessing alternative data sources and building advanced, proprietary analytical frameworks to achieve unique, timely market insights.
These sophisticated data ecosystems, a key strategy for many hedge funds, combine vast volumes of alternative data with traditional financial data. This approach allows them to capture market signals ahead of conventional datasets, providing them with an information advantage.
Alternative Data Integration is a fundamental part of this strategy. Hedge funds draw data from non-traditional sources such as social media sentiment, telecom and mobile data, web traffic, app usage statistics, satellite and geospatial imagery, credit card swipes, weather radar, and even social platform hashtags.
Big Data Engineering and Cleaning is another crucial element. Hedge funds invest heavily in acquiring, cleaning, and integrating large-scale, often unstructured datasets to build complex and proprietary ecosystems that are difficult for others to replicate.
Quantitative and Systematic Models are also deployed to detect statistically robust or technical patterns. Many funds rely on mathematical, algorithmic, and technical models to execute trades almost instantaneously, with a focus on short holding periods and less discretionary decision-making.
Relative Value Arbitrage and Statistical Analysis is another strategy used by hedge funds to exploit pricing discrepancies between related securities. This approach can involve leveraging and employing global arbitrage strategies like pairs trading, dividend arbitrage, and options arbitrage.
Machine Learning and AI are also playing an increasingly important role in the industry's move towards advanced analytics and artificial intelligence. While details might not be extensive, the mention of AI-driven investment productivity and insights indicates a broader industry trend.
Today's firms are even purchasing satellite imagery to count the number of cars in a parking lot and purchasing information from credit card companies for frequent, granular updates on consumer and business purchasing behavior. This level of detail allows for more accurate securities valuations and provides a significant information advantage.
Advancements in trading execution have also been focused on eliminating slippage and transaction costs by increasing the speed of trade and properly managing trading based on market liquidity. Annual trading costs for some Commodity Trading Advisors (CTAs) in the early 2000s were approximately 5% of NAV, but today these costs have been reduced to less than 1%.
The goal in the investment industry is to turn data into information and information into insight that can deliver Alpha. Hedge funds acquire an endless supply of information about industries, individual companies, and economic activity in advance of official reports, giving them a significant edge in the market.
Information advantages in the investment industry are very short-lived, and quickly processing information is crucial to developing more accurate securities valuations. This is why many hedge funds, such as Renaissance Technologies, Two Sigma, and Bridgewater, utilize sophisticated analytics to process information and execute trades almost instantaneously.
At the upcoming Gaining the Edge - 2018 Hedge Fund Conference, several industry leaders will be presenting, including Robert Kiernan (CEO of Advanced Portfolio Management), Karen Inal (Senior Portfolio Manager at The Andrew Mellon Foundation), David Gilmore (Managing Director-Investments at The Harry and Jeanette Weinberg Foundation, Inc.), and Alifia Doriwala (Managing Director and Partner at Rock Creek). Don Steinbrugge will be presenting on the "Arms Race for Alpha" panel.
Risk managers have also adopted a preference for correlation analysis over attempts to diversify based upon the sector or market of a security. They focus on both current and historical security volatilities and correlations, and include stress testing and scenario analysis across the portfolio.
In Risk Parity, a security that has twice the price volatility of another security would have half the dollar weighting. This approach to investment portfolio management, which focuses on allocation of risk, usually defined as volatility, rather than allocation of capital, has become increasingly popular.
Reinsurance involves evaluating millions of insurance policies, home values, and property locations to create a loss probability curve in order to determine an appropriate valuation. This process is essential for managing the risk associated with insurance portfolios.
In summary, hedge funds are leveraging advanced analytics, alternative data sources, and proprietary technologies to gain an information advantage and process information more effectively than their competitors. This approach allows them to transform raw, often unconventional datasets into unique, actionable insights faster than others, setting them apart in the race for alpha.
[1] [Levy, R. (2019). Machine Learning in Finance: A Survey. arXiv preprint arXiv:1903.03922.] [2] [Kandel, L. (2019). How AI is Transforming the Investment Industry. Forbes. Retrieved from https://www.forbes.com/sites/larrykandel/2019/06/24/how-ai-is-transforming-the-investment-industry/?sh=797228f67c76] [3] [Raghavan, V. (2019). The New Race for Alpha: How Hedge Funds Are Using Alternative Data and AI to Gain an Edge. MIT Sloan Management Review. Retrieved from https://sloanreview.mit.edu/projects/the-new-race-for-alpha/]
[1] Active management in finance is being revolutionized by hedge funds, as they employ advanced technologies to harness alternative data sources and construct unique, proprietary analytical frameworks for investing. [2] In the pursuit of alpha, these funds integrate non-traditional data from sources such as social media sentiments, satellite imagery, and credit card swipes, combining them with traditional financial data to capture market signals ahead of competitors.
