Quantitative investing – also known as systematic investing – is the broad term for a wide range of different investment strategies that employ sophisticated computer-based mathematical models to identify and carry out trades.
Common quant strategies include factor investing, which looks at characteristics of an asset such as the profitability of a company. To take a simple example, an investing algorithm might buy stocks that appear cheap on measures such as price/book value and sell stocks that look dear. This is a classic value investing approach that a human manager might follow. But the algorithm will take into account far more metrics than a human manager might, while ignoring other considerations that might sway a human – such as whether they think the firm’s management is good.
Risk parity strategies allocate between different assets depending on how volatile they are and how much volatility the investor wants. Trend-following strategies (also known as managed futures or commodity trading advisers (CTAs)) look for trends in the price of assets and make decisions based on those. Statistical arbitrage is based on relationships that normally exist between the price of different securities. Event-driven strategies identify patterns around events such as earnings announcements or corporate actions. Systematic global macro decides whether to invest in countries, assets or sectors based on quant models that use economic data.
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Proponents argue that quantitative investing helps remove biases and emotion from investment decisions, ensuring that they are based purely on data. While quant models are initially programmed by people, the role of humans in making individual investment decisions is greatly reduced. With some of the newer artificial intelligence-powered approaches, even the designers may not fully understand why a system chooses specific trades.
For more on QE, see Tim Bennett's video tutorial
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