Quantamental investing is a term that is now being used to describe a combination of these methods on different data sets to improve on the accuracy of predicting outcomes – such as stock prices or a company (or country)’s financial performance. In the most rudimentary form, if a traditional manager is using a quantitative approch to filter out some securities. One approch in the quantitive landscape, is the usage of alternative data to perform securities filteration.
The explosive growth in the number of alternative data sets hasn’t proved to be a panacea. But what was once considered the domain of quantitatively oriented hedge funds has become a must-have for Quantamental fund’s.
Understanding and implementing alternative data is a difficult task—but it’s an increasingly important one for quantamental. If properly applied, such data can provide unique insights into economics, sections, and companies beyond mere earnings and market information.
Quantamental funds have begun to understand the utility of alternative data and that it is forever changing the investment landscape. But what exactly is it? Alternative data (Alt Data) is information gathered from non-traditional information sources. By analyzing it, is possible to gain insight beyond what traditional data sources are capable of providing.
Alternative datasets offer a different starting point from which to construct a comprehensive universe. Financial statements, earnings call transcripts, news data, industry reports and analyst notes can be parsed by using text mining techniques to understand what words and terms correlate strongly with certain companies.
Market data aggregation: Market information is freely available on the web but spans across hundreds of websites. Receive a continuous stream of corporate operational data and eliminate the need to comb through multiple websites and online databases.
Price Monitoring: Track prices and inventory levels to monitor supply, demand, and consumption as well as shifts in inflation across the globe. Tracking pricing data, for example, can provide a directional indicator for the sales of consumer products.
Customer sentiment: Financial professionals are leveraging social media to predict how the activity and buzz around a specific stock or product to identify potential market moving activity.
Competitive Analysis: Competitive analysis can apply to numerous industries, including equity research and investments. The idea behind this method is to figure out how companies in your sector stack up against each other, or your own company. Whether you’re looking at how financial performance or how their products and services differ, collecting competitive analysis data can fuel your analysis.
KYC and risk management: Track regulatory development and evaluate the integrity of potential businesses by monitoring websites and social media and extracting changes automatically.
Financial statement extraction: Getting a financial statement into a usable format for analysis can be daunting. Analysts need hundreds of financial statements to compare data for clients.
News aggregation: Investment firms are increasingly basing recommendations on news. By extracting headlines and article copy and using that data for predictive analysis, investment firms gain valuable insights into trends, events, and shifting views that affect the companies and products they are tracking.
The hope that quantamental investors have about these forms of data is that they will provide either a new investment insight or a more timely investment signal than fundamental information which is available at the quarterly or monthly frequency.
To conclude, we believe the alternative data revolution will hold incredible promise to facilitate so many investment decisions for quantamental.
Big data and NLP technologies to collect, structure, and reveal events from news articles, press releases, and financial social media and share it with our clients via dashboards or API.
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