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Understanding developments in the alternative data space is important for Asset Management firms investing in their investment research efforts. Traditional data and alternative data sets are not applicable to every industry nor harmonized for easy utilization. However, if properly applied, such data can provide unique insights into economies, sectors, and companies, beyond the earnings and market information.
Traditional data can be broadly defined as company specific or market data aggregated, harmonized and provisioned by data vendors such as Bloomberg or exchanges such as NYSE. Alternative data on the other hand comes from a wide array of sources, including mobile phone activity, transactional data from credit/debit card sales, social media activity, GPS tracking, drone or satellite imagery etc.
The concept of alternative data initially started with companies trying to improve their own products and services. Examples include Facebook configuring content by users’ likes and dislikes, and Mastercard analyzing transactions for fraud. These companies soon realized the commercial potential of the data they were collecting and started selling to interested buyers. This in turn gave rise to third party providers to collect, process, and supply such data from multiple providers to interested buyers. Examples include credit card sale transactions aggregated by Earnest, and Satellite image processing provided by Orbital Insight. These developments haven’t been without controversies. This evolution has raised many data privacy concerns, leading to new regulations restricting the acquisition or distribution of such information without consent.
As more asset management firms continue to explore the use of alternative data, we would like to suggest best practices and recommendations for developing a blueprint plan for implementation:
Relevance: Application of these capabilities should differ by investment strategy (e.g. investment style, sector, region, asset class, etc.). The type of data relevant to a long/ short equity hedge fund manager will be different from a long term active asset manager. Many signals generated by alternative data can have a short time frame to apply, and the signals themselves can have high turnover with changing market dynamics. At UBS Asset Management, we take different approaches for Active Management, Systematic Investing and Hedge Funds. Systematic and quant teams typically like to source alternative data with good history, allowing them to back test the data through their quant and optimization models, whereas active management teams like to get data to support or reject a research hypothesis at a given point of time.
Long term Thinking: Don’t rush the process. Research processes and investment philosophies were not developed within a few months. They result from decades of academic research and industry experience. A recent benchmarking study, surveying twenty diversified asset managers representing 20 percent of the global AuM, found that asset managers who are making measurable gains through alternative data efforts started the journey 4-5 years back. The study also indicates a prudent, transparent, informed, and engaged process is required to develop these capabilities for long term differentiation, with room for iterations and improvements
Organization and Engagement Model: This is one of the most important aspects, which often gets overlooked. Developing all the scientific and engineering methods to utilize alternative data is not going to produce the intended results unless you begin to transform your research process through these insights. It is imperative to create an engagement model which mandates and incentivizes both the producer of such insights and the research teams who take active interest in evaluating the insights as part of their specific research efforts. Forming dedicated teams within the research function, who focus on insights produced through alternative data, NLP and AI capabilities, promotes ideation and application of insights into traditional research methods.
Talent: You may need skill sets not typically found within traditional asset management organizations. Designing IT architectures to support high volumes of data, agile engineering to process unstructured information, machine learning skills to develop mathematical and statistical models to extract unique trends and insights—these are just some examples of skill sets needed. Given the continuous evolution in these capabilities, you would want to invest in a team who has a similar long term focus with the agility to produce near term outcomes. A growing community of mathematicians, statisticians, and data scientists are joining all sectors, from financial services to healthcare, telecommunications, retail and media, not just technology firms.
Data: Before you go on a buying spree for alternative data, give serious consideration to all the internal data you have at your disposal, including thousands of research documents, transactional activity, historical market data etc. We are discovering useful applications of internal data, from generating unique NLP signals from research documents, to predicting operational errors by analyzing operational process metrics. On the external alternative data side, we have experienced success with structured data such as combining credit card sales data with fundamental data such as earnings and revenue data. On the unstructured data side, we are finding numerous applications ranging from sentiment indicators in earnings transcripts and news, to summarization of research content from thousands of research reports.
Platform: Leverage variety of data and modeling capabilities will require a scalable and flexible platform. Such a platform will need to respond to multi-factor back testing along with natural language processing. Firms should start early on the planning for such a platform. Beyond the technical capabilities, it will require data governance, model governance, model monitoring, PII data considerations etc. UBS Asset Management is deploying a hybrid cloud strategy, with the alternative data research platform to be fully cloud enabled.
To summarize, there is wide array of alternative data sets becoming available, but improving research and alpha through these data sets require a thoughtful and long term approach. I hope my colleagues across the Investment Management industry would find these considerations helpful in successfully harnessing emerging data and data science capabilities within their firms.