Causal discovery and trading

 

Causal discovery techniques can help any quantitative hedge fund, but may be especially helpful for enhancements to trend-following through finding causal links with other markets. The basic structure for a trend-following model is to use past values of a variable to extrapolate ot the future. Look for the trend, yet it would add significant value if you could learn whether other markets may have some causal impact on another variable. 

The standard approach to time series causality is to use Granger causality tests, which simply determine whether some time series Y causes or has an impact on the prediction of X. However, a growing number of alternative techniques are available to aid in causal discovery, thereby improving trading, such as time series data causal inference, vector autoregressive linear non-Gaussian acyclic models, and time-varying interactions models for nonlinear observations. The code for these algorithms is already written, so it is relatively easy to implement for a set of assets.

We are not planning to explore all of these techniques, but there are ways to support better causal discovery that can be used to improve the inputs in investment strategy. See "Trading with Time Series Causal Discovery: An Empirical Study" for a simple application of causal discovery for long-short equity portfolios. Now, these algorithms are not easy to implement due to the time required for computation; however, this seems to be a fruitful area for further research, especially given the growing interest in causal reasoning in finance.