Smarts at scaleArrowstreet

There are three types of market inefficiency that create opportunities for active managers: informational, where ‘faster’ investors can react to new information more quickly than the market; behavioural, where investors act on the same information but disagree on the interpretation, with some investors subject to biases; and constraints, where some investors are constrained from making optimal decisions leading to exploitable market inefficiencies. The main threat to active managers is competition, from those seeking to similarly exploit these opportunities. Arrowstreet focuses its research on identifying new opportunities, particularly unique signals, while appropriately reducing the weights placed on signals identified as having become crowded.

The increasing popularity of an investment style, such as value, quality, and price momentum, changes and likely increases the risks associated with that investment style. Arrowstreet are cognisant of the risks of getting caught in “crowded trades” and strive to produce alpha not tied to ‘commoditised’ sources of outperformance. Arrowstreet use several methods to avoid large permanent style tilts in their portfolio, while taking tactical style tilts when those tilts are expected to be rewarded.

“Big data” can be defined as difficult data, due to its size, complexity or its level of ‘noise’. But the difficulty of big data typically comes from collecting, storing and making it flow – data engineering – rather than from data analysis. For fundamental managers able to solve these issues, big data analytics can add significant value. Arrowstreet most notably uses big data within its expanded linkages model to identify economically related companies – with 10,000+ stocks in the investable universe, Arrowstreet needs to forecast the relatedness of over 50,000,000 pairs of stocks over time.

Successful active management – or generating a positive excess return after fees, relative to a passive alternative – requires overcoming four key obstacles: market efficiency, or the assertion that all relevant information is already reflected in prices; the zero-sum nature of active investing, with an active ‘loser’ for every active ‘winner’; the difficulty of distinguishing luck from skill; and diminishing returns to scale for managers. Through quantitative tools, Arrowstreet seeks to identify investor behaviour driving prices from efficient levels and that can be exploited by active management, while carefully monitoring performance drivers, transaction costs, and capacity within its strategy.