Alternative Data: A Road to Alpha

Alternative Data: A Road to Alpha

Investment management firms are increasingly hard-pressed to hedge away risks, increase alpha, and lower costs: a daunting agenda. One way of going about that is by knowing what is going on in the underlying markets more quickly than the competitors and counterparties do.  To that end: alternative data. Investment firms, hedge funds among them, now use data ranging from satellite images to unstructured texts in search of actionable insight.

A new paper from Tata Consultancy looks at the state of alternative data, and how alternative investors can leverage it. “Alternative” data is defined as such by its sources, which may include social media, weather reports, satellite imagery, and so forth.

For a time, as the new paper says, only “quants and hedge funds were … consumers for this data,” But now alt data is also showing up in the decision-making of not-so-alternative investors, honed by data analytics, engineering, and machine-learning techniques.

Alternative Data is for Everyone Now

Sticking to traditional sorts of data, portfolio construction and rebalancing are time-consuming processes. They must be explained to investors in ways that can be cumbersome due to siloed data, the lack of centralized statistical models, and other deficits, according to the report.

The “consumer protection” aspect of regulation looks to the client’s best interests and profile, which makes these challenges even more pressing.

Related to this, active managers are under pressure to reduce their expense ratios because they have to compete with passive investments.

In this situation, portfolio managers look to adopt artificial intelligence-related tools with, as the paper indicates, “the ability to assess thousands of stocks per day based on insights gleaned from both traditional and alternative data sources.” Tata’s view is that investment firms should work to combine analytics techniques with cognitive models to provide insights regarding stock picking, capital appreciation, and rebalancing decisions.

Parking Lots and Smart Buildings

Tata elucidates this point in the context of real estate and private equity investments. Real estate investment products include real estate investment trusts (REITs), public and private real estate funds, and real estate financing and infrastructure assets.

Real estate-oriented investing institutions make use of a lot of technological advancements, the report observes, including online portals, quantitative models for real estate investments and analytics, and product solutions for underwriting, financing, accounting, etc. Demographics, traffic conditions, the risks of natural disasters, neighborhood amenities are now available in a very granular way as inputs for algorithms and for portfolio decisions about valuation, potential profit, and rental options.

Firms in this space are also looking to use alternative data such as geospatial and satellite imagery on the one hand and smart building solutions on the other as part of a larger ecosystem play. It is almost a cliche that satellite images of the number of cars in a store’s parking lot are now part of artificial intelligence-assisted decisions for potential investors in the store’s corporate parent.

Smart-building solutions aren’t quite such a cliche yet. But Tata’s report reminds us that “smart buildings” run analytics on themselves to monitor energy and water usage and greenhouse gas emissions, waste management, etc. Tata believes that wise real estate and private equity investment firms will have to tap into these analytics as a source of data, using them to measure the ESG metrics for their real estate portfolios. These numbers, in turn, will help them win the confidence of their customers.

Segment-of-One Marketing

Speaking of winning the confidence of investors, the Tata report speaks to the use of alternative data in segment-of-one marketing. The individual customer of an asset management entity—any asset management entity—has personal preferences, spending and investment patterns, and perhaps an idiosyncratic risk appetite, and so forth. Firms have learned to use natural language processing and natural language generation to personalize their investment portfolios, treating the individual as a market segment of one.

Near the end of the report, Tata cautions that the adoption of alternative sources of data by investment advisers “comes with its own set of challenges.” The data is not readily standardized, which raises uncertainties. “Our algorithms don’t treat this photo of the store parking lot the same as their algorithms.” There is regulatory uncertainty. Effective use of such data may involve a huge initial outlay.

In organizational terms, an entity trying to make effective use of non-traditional data will need to get rid of the old silos that might keep one jigsaw piece apart from another.

Tata concludes that the benefits of gearing up to make effective use of such data far outweighs the costs and challenges.

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