In recent years, machine learning (ML) has brought effective approaches and novel techniques to economic decision, investment forecasting, and risk management, etc., coping the variable and intricate nature of economic and financial environments. For the investment in stock market, this research introduces a pioneering quantitative fusion model combining stock timing and picking 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 factors that have been winsorized, neutralized and standardized and the return of CSI 300 index are calculated, a given amount of factors that rank ahead are choose to be candidate factors heading for the input of PSO-BPNN after dimension reduction by Principal Component Analysis (PCA), followed by a certain amount of constituent stocks outputted. Subsequently, we conduct the prediction and trading on the basis of the screening stocks and stock market state outputted by MGHMM trained using inputting CSI 300 index data after Box-Cox transformation, bespeaking eximious performance during the period of past four years. Ultimately, some conventional forecast and trading methods are compared with our strategy in Chinese stock market. Our fusion strategy incorporating stock picking and timing presented in this article provide a innovative technique for financial analysis.

翻译：近年来，机器学习为经济决策、投资预测和风险管理等领域提供了有效方法及创新技术，以应对经济金融环境的复杂多变性。针对股票市场投资，本研究提出一种开创性的量化融合模型，通过结合多元高斯-隐马尔可夫模型（MGHMM）与粒子群优化的反向传播神经网络（PSO-BPNN），实现选股与择时策略的整合。在计算经缩尾处理、中性化及标准化后的五十二个因子与沪深300指数收益的信息系数（IC）后，选取排名靠前的若干因子作为候选因子，经主成分分析（PCA）降维后输入PSO-BPNN，进而输出一定数量的成分股。随后，基于筛选所得股票及经Box-Cox变换后的沪深300指数数据训练MGHMM输出的市场状态，进行预测与交易，结果显示该策略在过去四年中表现优异。最终，我们将该策略与中国股市中若干传统预测与交易方法进行对比。本文提出的融合选股与择时的策略为金融分析提供了一种创新技术。