What Kind of Data is Used by Hedge Funds?
It is interesting to see how technology has enabled access to an in-depth range of information about everything from the weather to consumer spending habits. Hedge funds are using 5 main types of data sets today.
1. Consumer Spending and Lifestyle Data
Transactional data can track consumer discretionary spending patterns and retail revenues. Point of Sale (PoS) transactions can provide information about sales volumes, and help managers pre-empt retail business earnings. Some apps can also provide data about retail footfall in shopping malls. Information about how many consumers visited a store on a particular day can give an idea about business revenues in the earnings season.
2. Social Media Sentiment
Today, online comments, interactions, posts, and tweets can not only provide information on a brand’s positioning but also give the overall sentiment of traders towards a particular asset. It can also provide direction about changes in consumer behaviour, and the direction of market trends.
- Web Crawled Data
Information from web pages, such as searches, click-through rates and demographics are coveted by hedge funds. A great deal of knowledge about a business ecosystem can be gained here. For instance, online reviews say a lot about brand perception. Fund managers can use this data to identify new technologies that are investment worthy. During the pandemic, managers combined information about past outbreaks with that of web crawled data to understand how the markets might react. Internet searches, details on traffic congestion, flight schedules, and test-kit availability helped managers predict and navigate through volatility in the initial stages of the pandemic.
- Business Performance Metrics
It might be surprising for some as to why this is considered an alternative data source. However, for alternative data aggregators, this includes creating unique valuation metrics for companies. These are different from the usual ways of determining asset risks and rewards. This can include consensus estimates of Wall Street analysts, including all growth metrics and drivers that can impact a company’s performance.
- Data Sourced from Expert Networks
Bespoke research and analysis reports from niche websites can help managers gain a perspective on asset performance.
Finally, with climate change now becoming the centre of global policy stance, and increased awareness among investors regarding the negative consequences of fossil fuels, ESG allocations will become prominent in the firms’ pursuit of alpha. In this context, weather and satellite imagery fed into analytical tools can be a valuable source of information for fund managers.
It’s essentially a method to evaluate how sustainable a company is. This has a broader appeal for companies and investors, especially companies that want to prove their green bona fides, and investors who have noticed a significant rise in the performance of ESG funds. As per an analysis by S&P Global Market Intelligence, 19 out of 26 ESG ETFs outperformed the S&P 500 index between March 5, 2020, and March 5, 2021. These ESG funds increased between 27.3% and 55% in the same period, while the S&P 500 rose 27.1%.
The Role of Advanced Technologies in Making Alt Data Useful
Alternative data is derived from disparate sources, which makes it unstructured and difficult to analyse. Here the role of technologies like Natural Language Processing (NLP) and Machine Learning (ML) are critical to deriving value from huge datasets. AI-based models and data providers are thus becoming important for the hedge fund industry to find underlying market patterns and actionable insights for decision making.
Sentiment analysis, which uses AI and NLP, can mine data for investor opinions and feelings. This can help decision-makers in hedge funds gain an edge in many fields, from marketing to portfolio allocation. Automated analysis of social media posts and reviews can provide insights about trends, based on semantics, allowing the firms to modify their marketing campaigns in real-time. When used in the cloud, AI can boost cost-effectiveness, enabling managers to decommission unnecessary resources when needed. Also, AI can be highly useful to filter the noise from huge datasets, since every metric is not useful.
The use of AI and alternative data will help hedge funds refine their models to bring in more accuracy. This could well represent a new frontier in the financial industry.