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In recent years, machine learning (ML) has brought effective approaches andnovel techniques to economic decision, investment forecasting, and riskmanagement, etc., coping the variable and intricate nature of economic andfinancial environments. For the investment in stock market, this researchintroduces a pioneering quantitative fusion model combining stock timing andpicking strategy by leveraging the Multivariate Gaussian-Hidden Markov Model(MGHMM) and Back Propagation Neural Network optimized by Particle Swarm(PSO-BPNN). After the information coefficients (IC) between fifty-two factorsthat have been winsorized, neutralized and standardized and the return of CSI300 index are calculated, a given amount of factors that rank ahead are chooseto be candidate factors heading for the input of PSO-BPNN after dimensionreduction by Principal Component Analysis (PCA), followed by a certain amountof constituent stocks outputted. Subsequently, we conduct the prediction andtrading on the basis of the screening stocks and stock market state outputtedby MGHMM trained using inputting CSI 300 index data after Box-Coxtransformation, bespeaking eximious performance during the period of past fouryears. Ultimately, some conventional forecast and trading methods are comparedwith our strategy in Chinese stock market. Our fusion strategy incorporatingstock picking and timing presented in this article provide a innovativetechnique for financial analysis.

A quantitative fusion strategy of stock picking and timing based on  Particle Swarm Optimized-Back Propagation Neural Network and Multivariate  Gaussian-Hidden Markov Model