How Alternative Data Sets are Reshaping How Investors Think
Information has always been the source of competitive advantage for investors, and the importance of gathering new information and quickly implementing it into investment decisions has been key. As new technologies and skill sets emerge, the potential to obtain even more information related to investments arises. Though, alternative data sets can be incomprehensible in size and at times require more processing than typical software tools can handle. For that reason, it is critical for investors to ask the right questions when evaluating data sets.
A surge in unstructured data from social media, blogs, and corporate filings provides a constant stream of investment related information. Quantitative hedge funds combine this influx of data with the lower costs of computer storage and the creation of the cloud to ultimately rely on an algorithm that assesses this influx of data. To put this growth in perspective, today, quantitative hedge funds manage just over $300 billion, double the amount managed before 2012.
With this shift in assets under management, traditional hedge fund managers are watching closely and even implementing quantitative technology and algorithms into their fundamental processes. This process has evolved into a new strategy, ‘quantamental,’ that is becoming increasingly prominent. In many respects, these methods focus on helping portfolio managers with timing and risk management. Grappling these to critical elements of the investment process is even more relevant in the political context. With the amount of uncertainty around government deregulation, applying quantitative tools to explain and predict government behavior is becoming all the more important for funds worldwide.
As such, demand for alternative data to help answer questions around subjects like political risk, has exploded. According to Greenwich Associates, 80% of US and European institutional investment professionals want greater access to alternative data sources because established firms immediately rummage through traditional data sources. Nowadays, investors need unique perspective. Even though huge growth in popularity is expected, alternative data does not necessarily apply to all firms and problems. In order to add value, one must already have a hypothesis instead of aimlessly looking into the vast alternative data network. Additionally, one must have the proper tools or third party vendor to navigate these data sources. Typical methodologies now include Machine Learning, Genetic Algorithms, and Natural Language Processing, but simple linear regressions and older methods may be all a company needs for analyzing alternative data. However, one thing remains constant: without domain expertise around alternative data, it is all zeros and ones.
VogelHood Group has perceived a technique using alternative data sets to measure and manage political and regulatory risk across every company in the S&P 500. THEIATM, which balances political risk (self imposed in corporate filings) against political response (lobbying and campaign contributions) allows investors and corporate risk officers to quantify risk through a unique lens. For more information on how to use our models, contact us at firstname.lastname@example.org.
Samir N. Kapadia
Managing Director, VogelHood Group
Summer Analyst, VogelHood Group