Thank you Lewis for introducing me to the field of “Quantitative Equity Portfolio Management”. It opens my eyes to the other spectrum of “Quantitative Trading.” Apparently what Lewis considers quantitative trading is very different from what I consider quantitative trading. I call the former an economist approach and the latter a mathematician approach. This blog piece does a very brief comparison and points out some new research directions by taking the advantages of both.
Briefly, the economist approach is a two-step approach. The first step tries to predict the exceptional excess returns alpha by examining its relationships with macroeconomic factors, such as momentum, dividends, growth, fundamentals and etc. The second step is capitals allocation. The focus in the economist approach is on identifying the “right” economic factors. The mathematics employed is relatively simple: linear regression, (constrained) quadratic programming. The trading horizon is month-on-month, quarter-on-quarter, or even years. An example of such is factor model in QEPM.
In contrast, the mathematician approach tries to predict the short-term price movement by building sophisticated mathematical models for the, e.g., price time series. The focus is on finding the right mathematics to better describe the statistical properties of price process, e.g., stochastic calculus, Markov chain. Macroeconomic and fundamental factors are not often used. The trading horizon is intra-day or seconds. An example of such is volatility arbitrage in different intraday time scales.
One way to appreciate the differences is by looking at their trading horizons. When trading high frequency, the company fundamentals certainly have little relevance because, e.g., the quarterly earnings do not change second-by-second. The statistical properties of the price process dominate in these time scales. As we increase the trading horizon to days, months, quarters, and even years, the macroeconomic information become more relevant and important.
Recent research has combined the advantages from both: the utilization of macroeconomic information from the economist approach and the sophistication of mathematical modeling from the mathematician approach. One example of such a hybrid approach is a Markov switching model on dual-beta modeling. The trading horizon is daily. This trading strategy imposes an advanced modeling on the time series property (namely serialization) of beta which itself is computed using macroeconomic information using the economic theory such as CAPM.