Forecasting Market Changes using Variational Inference
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Though various approaches have been considered, forecasting near-term market changes of equities and similar market data remains quite difficult. In this paper we introduce an approach to forecast near-term market changes for equity indices as well as portfolios using variational inference (VI). VI is a machine learning approach which uses optimization techniques to estimate complex probability densities. In the proposed approach, clusters of explanatory variables are identified and market changes are forecast based on cluster-specific linear regression. Apart from the expected value of changes, the proposed approach can also be used to obtain the distribution of possible outcomes, which can be used to estimate confidence levels of forecasts and risk measures such as VaR (Value at Risk) for the portfolio. Another advantage of the proposed approach is the clear model interpretation, as clusters of explanatory variables (or market regimes) are identified for which the future changes follow similar relationships. Knowledge about such clusters can provide useful insights about portfolio performance and identify the relative importance of variables in different market regimes. Illustrative examples of equity and bond indices are considered to demonstrate forecasts of the proposed approach during Covid-related volatility in early 2020 and subsequent benign market conditions. For the portfolios considered, it is shown that the proposed approach provides useful forecasts in both normal and volatile markets even with only a few explanatory variables. Additionally the predicted estimate and distribution adapt quickly to changing market conditions and thus may also be useful in obtaining better real-time estimates of risk measures such as VaR compared to traditional approaches.
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