In recent years, with the rise of machine learning and big data technology, factor investing strategies have developed rapidly. However, many studies have found that a large number of factors proposed in academic literature lack economic causality explanations and are mostly discovered through data mining. Studying the causality of factors has important practical significance for enhancing the robustness of factor investing strategies. This article mainly introduces the importance of studying factor causality in factor investing, common research methods, and future development prospects.
The lack of causality explanations for many factors leads to poor robustness
A large number of factors proposed in academic literature are found through data mining methods without clear economic causality explanations. These factors often have problems such as lack of persistence and robustness, and the transaction costs are too high to be implemented in actual investment. Studying factor causality can help filter out pseudo-factors caused by data mining biases, enhance factor persistence and robustness, and develop more explanatory factor investing strategies.
Using machine learning methods to study factor causality
With the rise of machine learning technology, many researchers have begun to use machine learning methods to study the causality of factors. For example, ridge regression, lasso regression, decision tree, random forest and other methods can be used for causality analysis. Incorporating causal analysis into the machine learning factor investing model can not only enhance accuracy, but also enable investors to better understand the driving forces behind model predictions.
Developing rigorous econometric methods to identify causality
In addition to machine learning methods, researchers are also developing more rigorous econometric methods to study the causality between factors and expected returns. Methods such as instrumental variables, difference-in-difference, and regression discontinuity design can help identify causal relationships more accurately. Using these methods for causal analysis is crucial for developing robust factor investing strategies.
Causal factor investing has broad application prospects
Causal factor investing that incorporates causal analysis has broad application prospects. It can not only be used for scientific stock selection and asset allocation in traditional long-only investing, but also provide inspiration for other investment methods such as long-short equity, market neutral, and 130/30 strategies. As technology progresses and more quality data becomes available, the research on causal factor investing will become more in-depth, which will greatly promote the progress of factor investing strategies.
In summary, studying the causality between factors and expected returns is crucial for developing robust and persistent factor investing strategies. Using machine learning, econometric methods and other technical means to analyze factor causality, filter out pseudo-factors caused by data mining biases, and develop explanatory factor investing strategies with clear economic causality has important research significance and broad application prospects.