Alternative Data News and Insight

We summarise a recent paper on the use of alternative data in investment management, published by Gene Ekster and Petter Kolm. Their paper discusses the alternative data ecosystem, use cases, challenges, and methodologies for the valuation of alternative datasets.

We summarise a study that investigated whether a company’s Form 5500, and the metadata reported within the disclosure, provides equity investors with an information advantage.

The Food and Drug Administration (FDA) can take months to identify, investigate and issue product recalls, often after enough people have gotten sick to trigger negative press. US Health Departments are already using data from Twitter, Yelp, and Google to monitor foodborne illnesses – suggesting that many alternative data sources could provide early signals for product recalls. In this report, we review a public health study that uses a trained NLP engine to predict FDA product recalls using Amazon.com reviews.

We summarise a recent academic work that investigated REITs’ ability to provide data on ESG compliance, and how that transparency impacts their overall ESG performance, stock returns, and investors’ willingness to pay a premium.

We summarize the findings of a whitepaper that analyses how the use of alternative data can help determine whether an equity’s cybersecurity investments reduce their vulnerability to future attacks.

With its decentralized and secure nature, blockchain is drawing attention for its potential to improve big data services and applications. Below we summarize key findings from a 2020 survey paper that explores the latest approaches, opportunities, and future trends regarding blockchain and big data integration.

We summarise a recent paper from the BIS, which outlines the usage of big data and machine learning within the central bank community, based on a recent survey of Irving Fischer Committee members.

We discuss an industry report that polled hedge fund managers on their alternative data usage. We highlight the major challenges that respondents reported and provide a brief discussion of our own findings.

We summarise the findings of a recently published paper that applies an algorithm developed by MIT researchers to infer which products a consumer has purchased when given information on only the total transaction amount. The paper also explores further applications of their algorithm such as the detection of new product launches from credit and debit card transactions.

When expectations for headline economic indicators are off-beat, economists sometimes point to the Keynesian notion of ‘animal spirits’ as the cause of error. Animal spirits can be loosely defined as the emotional and psychological drivers of economic decision making, that commonly diverge from models of rational, self-optimizing behaviors. But what if we could quantify and measure the animal spirits in headline indicators? Would it make forecasts for growth and inflation more accurate?