Long-short signals for Japanese stocks
In Chapter 9, Time-Series Models for Volatility Forecasts and Statistical Arbitrage, we used cointegration tests to identify pairs of stocks with a long-term equilibrium relationship in the form of a common trend to which their prices revert.
In this chapter, we will use the predictions of a machine learning model to identify assets that are likely to go up or down so we can enter market-neutral long and short positions, accordingly. The approach is similar to our initial trading strategy that used linear regression in Chapter 7, Linear Models – From Risk Factors to Return Forecasts, and Chapter 8, The ML4T Workflow – From Model to Strategy Backtesting.
Instead of the scikit-learn random forest implementation, we will use the LightGBM package, which has been primarily designed for gradient boosting. One of several advantages is LightGBM's ability to efficiently encode categorical variables as numeric features rather...
