Thirty-five years ago, systematic investing was a niche investment style, mainly focused on trend-following systems. The initial algorithms operationalized a century-old investing approach called technical analysis. Although technical analysis has many flavors, the identification and extrapolation of trends is its cornerstone. One drawback is the inevitable turning point. At some moment in time, the trend will reverse. Algorithms evolved so that after an extended trend (or a very strong trend signal), risk was reduced. This capability effectively allowed for reversals and reduced the losses suffered at turning points.
The next wave was quantitative stock-selection models. These models used an algorithmic approach to identify stocks the strategy should buy or sell. For long-only portfolios, these models determined over- and underweighting of securities. These models typically went beyond price data and included fundamental information like valuation, growth, profitability and quality metrics.
The next significant innovation in systematic investment was the emergence of so-called smart beta strategies. These low cost products might focus on a particular factor or strategy, such as value. The name – typically applied to a wide array of formulaic or algorithmic strategies, often with impressive backtest results – plants the impression that the strategies are smart. However, there are plenty of strategies that are not smart offered under this rubric. The smart beta strategies create an index using an algorithmic approach. Investors can access the strategy in many forms, such as exchange-traded funds or mutual funds. Smart beta strategies also have multifactor versions.
Simultaneously as more capital entered the market, many managers realized the easiest way to increase alpha was to reduce costs. One way to reduce costs was through improved execution. Hence, the third wave was the emergence of systematic high-frequency trading. Such trading can produce stand-alone profitability to funds such as Renaissance Technologies—or it can be part of the execution strategies of both systematic and discretionary funds.
Currently, we are at the beginning of the era of using artificial intelligence (AI) tools in both systematic and discretionary strategies. For example, large language models hold the possibility of helping researchers analyze a vast amount of financial information and isolate risk factors.