What is Alternative Data?
Consider a retail corporation which operates chain of hypermarkets and department stores across the country and regions. The investment and valuation firms who cover the progress of this retail corporation, traditionally, will rely and consume the announcements or publications from this corporate to do the valuations and make the investment decisions. For a public corporation, this kind of financial information is available uniformly and hence would hardly give any decision edge. The obvious question to ask would be, how about tracking this retail corporation on an ongoing basis and not wait for its official announcements. Is there any other data set about the corporation which if acquired and analyzed systematically may help to take faster informed decisions and thus generate alpha? Can proxy information such as the number of cars entering the parking lot, number of foot falls, movement of logistic vehicles, cards swiped at the PoS, customer reviews, and government policy announcements on retail sector provide information that can give comfortable insight about this retail corporation? May be Yes! This kind of continuous information combined with the official public announcements can help in taking more informed investment decisions ahead of time.
All the proxy information mentioned above are qualified as the Alternative data.
‘Alternative data sets are information about a particular company that is published by sources outside of the company, which can provide unique and timely insights into investment opportunities.’ - Alternative data (finance) wikipedia
The Alternative data set can be obtained from various sources such as - Credit/Debit Cards, Emails, Consumer Reviews and Feedbacks, Product reviews and Feedbacks, Point of Sale, Sensor logs, Satellite, supply chain and logistics movements, Social media sentiments, Weather forecasts, Web data, Web traffic, Surveys, Geo Locations. These data sets are generally large, complex and unconventional, limiting their handling and processing through the traditional software. This data is aggregated, loaded into system and consumed by the banks to construct and use its proprietary quantitative models for decision making. The price tag attached to the alternative data positively depends on how close this data is to the entity or subject of interest.
Growing relevance of alternative data in Investment decisions
One of the researches done in 2010 showed indications of relationship between twitter mood and DJIA’s movement with greater accuracy. This was an indication of social media to be the new warehouse for data. Hedge Funds have been pioneers in consuming the alternative date for alpha returns generation. They were the first to launch a fund based on tweeter data sentiments.
JP Morgan Asset Management has utilized sentiments in M&A news to yield positive returns.
Total Alternative Data spending for buy-side firms is projected at $1.7 billion for 2020.
Investment managers are always looking for outperformance over the benchmark and simultaneously over its competitors. As per report from one of the leading research company, 80% of investors those includes Hedge Funds and other Asset Managers want greater access to alternative data source; in which private company data, evaluated pricing data, Logistics and supply chain data, and credit score history data tops the list. Due to investment strategy and goals Hedge Funds usually are more interested in evaluated prices, while Asset Managers are more interested in Private company data. Lately, there has been increasing demand from private equity and corporates for the alternative data with an aim to enrich the deals for acquisitions and related processes.
Alternative data challenges and Risk
As we see wider consensus building up in utilizing the alternative data in decision making, we also need to get familiar with some of the associated challenges and risks.
There is substantial amount of processing that needs to be done to realize the hidden value in alternative data. This data is noisy, messy, ambiguous, unstructured and hence a right candidate for using ML. The entire exercise of alternative data processing and applying it to work for decision making via a predictive model is an ongoing activity. Due to the nonstandard nature of the data, it is highly likely that many of the patterns and signals hidden in the alternative data may change or vanish over time thus altering the accuracy of the model. Hence a close watch is needed on the accuracy of the decisions being made by the underlying model. The model may have to be back tested against the historic data set. There is no standard data set that prevails for alternative data hence sticking to the historical test data time period to prove model accuracy might turn disadvantage. It may also be a good practice to work on multiple data ideas simultaneously as one never knows which may turn favorable.
Integration of the alternative data and its decision model into the core existing system like Portfolio Analytics or Trading is not straight forward, thus being one more reason for low acceptance levels. Single platform integration will be a great booster for wider acceptance.
Regulators have already taken notice of the use and possible information advantage around alternative data. They have already started viewing it from the perspective of privacy, proprietary, fairness, and ethics. In order to safely navigate the regulatory minefield, it would be expected that the firms that use or source such data for provision to others should implement best practices and strong governance policies, procedures, and frameworks. Based on the region the firms would need to understand some of the key laws affecting the usage of alternative data like - MAR, MiFID, Advisers Act, GDPR etc.
Reports from Investment firms highlights some of the challenges they face for procuring alternative data, like - Lack of confidence from management in deriving value from these data sets, Firms internal procurement procedures and cost for onboarding the new data source, Trust in the completeness of data sets. Under such situation, the Providers who can demonstrate availability of complete and quality data can establish its reputation in market and expect quick adoption from the Investment firms. There are already data firms in market e.g: Quandl, AlternativeData, UBS Evidence Lab, Prattle, SciDex GTCOM, Cuemacro, who themselves provide the alternative data or act as aggregators to connect the Investment firms with the network of alternative data providers to purchase the desired data. Many of these data firms are niche as they specialize in collecting, processing, and distributing data sets in specific business area and/or the data source.
If we recall, few years back the low latency solutions were offering an edge in investment decision making. Now it’s considered as minimum requirement. The need for alternative data is seen to be increasing and the firms those delay in adapting might get outclassed by their competitors who would have effectively incorporate alternative data into their decision-making systems like – Securities valuation, trading signal processes, smart advertising. There are already good number of data providers and aggregators in the market offering boutique alternative data sets. Soon alternative data will no more be an alternative!
Greenwich Associates “Alternative Data for Alpha” research https://www.ey.com/en_gl/wealth-asset-management/how-will-you-use-innovation-to-illuminate-competitive-advantages
Alternative Data - Latham & Watkins, LLP - https://www.lw.com/thoughtLeadership/lw-alternative-data-regulatory-and-ethical-issues-for-financial-services-firms-to-consider