Engineering alpha factors

Alpha factors are transformations of market, fundamental, and alternative data that contain predictive signals. They are designed to capture risks that drive asset returns. One set of factors describes fundamental, economy-wide variables such as growth, inflation, volatility, productivity, and demographic risk. Another set consists of tradeable investment styles such as the market portfolio, value-growth investing, and momentum investing.

There are also factors that explain price movements based on the economics or institutional setting of financial markets, or investor behavior, including known biases of this behavior. The economic theory behind factors can be rational, where the factors have high returns over the long run to compensate for their low returns during bad times, or behavioral, where factor risk premiums result from the possibly biased, or not entirely rational behavior of agents that is not arbitraged away.

There is a constant search for and discovery of new factors that may better capture known or reflect new drivers of returns. Jason Hsu, the co-founder of Research Affiliates which manages close to $200 Bn, identified some 250 factors that had been published with empirical evidence in reputable journals by 2015 and estimated that this number was likely to increase by 40 factors per year. To avoid false discoveries and ensure a factor delivers consistent results, it should have a meaningful economic intuition that makes it plausible that it reflects risks that the market would compensate.

The data transformations include simple arithmetic such as absolute or relative changes of a variable over time, ratios between data series, or aggregations over a time window such as a simple or exponential moving average. They also include calculations that have emerged from the technical analysis of price patterns such as the relative strength index of demand versus supply and numerous metrics familiar from the fundamental analysis of securities.

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