Dynamic forecasting performance and liquidity evaluation of financial market by Econophysics and Bayesian methods

In a complex financial system, what is the forecasting performance of macro and micro evolution models of Econophysics on asset prices? For this problem, from the perspective of machine learning, we study the dynamic forecasting and liquidity assessment of financial markets, based on econophysics and Bayesian methods. We establish eight dynamic prediction methods, based on our proposed likelihood estimation and Bayesian estimation methods of macro and micro evolution models of econophysics. Combined machine learning thinking and real data, we empirically study and simulate the out-of-sample dynamic forecasting analysis of eight proposed methods and compare with the benchmark GARCH model. A variety of loss functions, superior predictive ability test (SPA), Akaike and Bayesian information criterion (AIC and BIC) methods are introduced to further evaluate the forecasting performance of our proposed methods. The research of out of sample prediction shows that (1) the method of the simplified stochastic model with Bayesian method for only sample return has the best forecasting performance; (2) the method of the stochastic model with Bayesian method for only return samples has the worst forecasting performance. For the liquidity assessment problem, there is a strong correlation between the trading probability evaluated by the proposed eight methods and the real turnover rate, and an increase of liquidity is correspond to the increase of asset risk. In other words, it suggests that all proposed methods can well evaluate market liquidity.