Forecasting the total market value of a shares traded in the Shenzhen stock exchange via the neural network

Stock total market value forecasting is a significant issue for policy makers and investors. This study explores usefulness of the nonlinear autoregressive neural network for this forecasting problem in a dataset of the daily total market value of A shares traded in the Shenzhen Stock Exchange during January 4, 2016 - August 23, 2021. Through examining various model settings across the algorithm, delay, hidden neuron, and data splitting ratio, the model leading to generally accurate and stable performance is reached. Usefulness of the machine learning technique for the total market value forecasting problem of the A shares is illustrated. Results here might be used on a standalone basis as technical forecasts or combined with fundamental forecasts to form perspectives of total market value trends and perform policy analysis.