Stock price prediction through GRA-WD-BiLSTM model with air quality and weather factors.

Accurately predicting stock prices is crucial for reducing investment-related risks in decision-making. Contemporary challenges to financial behavior, posed by environmental issues such as pollution and climate change, have received limited attention in existing studies on capital market predictability. This paper focuses on the Shanghai Stock Exchange Composite Index (SSEC) and employs air quality and weather data from the Shanghai area as input variables. Subsequently, a hybrid prediction model is constructed by integrating Grey Relational Analysis (GRA), Wavelet Decomposition (WD), and Bidirectional Long Short-Term Memory (BiLSTM) neural networks. The objective is to achieve precise predictions of closing prices. Additionally, this study validates the feasibility of incorporating environmental factors as input variables for stock price prediction, using the Shenzhen Component Index (SZI) and Hang Seng Index (HSI) as case studies, while also assessing the applicability of the GRA-WD-BiLSTM model. The findings demonstrate that the GRA-WD-BiLSTM model exhibits superior applicability and prediction performance in stock price forecasting, with respective prediction accuracies of 95.93%, 93.02%, and 97.07% when accounting for environmental factors. The incorporation of GRA and WD contributes to enhancing single models' performance while integrating air quality and weather factors, which prove valuable in accurately predicting stock prices. The findings also indicate that the impact of regional environmental factors on local stock exchange index prices shows variability. [ABSTRACT FROM AUTHOR]