Abstract
In this era of inexpensive computation and vast data, systematic, or algorithmically driven, investment is increasingly popular. Systematic strategies appear in stand-alone products as well in tail-hedging and defensive-overlay strategies. Indeed, given the enormous growth in data, it is becoming infeasible to process these data without the assistance of systematic tools. The key advantage of the systematic approach is the discipline it imposes—for example, machines are not plagued by behavioral issues such as disposition bias, and in a time of crisis, a systematic strategy keeps a “cool head.” Systematic approaches also pose many challenges. Systematic strategies may not quickly adapt to structural changes in the market. They also present the risk of “tech-washing” whereby an investment product claims to use “the latest machine-learning tools,” but the tools are misapplied or play a minimal role. Importantly, when systematic tools are applied by an inexperienced researcher, the backtests are often overfit, leading to disappointing performance in live trading.
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