Stock market prediction provides the decision-making ability to the different stockholders for their investments. Recently, stock technical indicators (STI) emerged as a vital analysis tool for predicting high-dimensional stock data in various studies. However, the prediction performance and error rate still face limitations due to the lack of correlational analysis between STI and stock movement. This paper proposes a correlational strategy to overcome these challenges by analyzing the correlation of STI with stock movement using neural networks with the feature vector. This strategy adopts the Pearson coefficient to analyze STI and close index of stock data from 8 Chinese companies in the Hong Kong stock market. The results reveal the price prediction of BiLSTM outperformed the GRU and LSTM in various datasets and prior studies.

This paper surveys how and why the share spilt reform enhance the corporate governance using agency cost as proxy from the perspective of stockholders’ conflict and liquidity increase in the process of share spilt reform respectively. We find that share spilt reform brings significant governance improvement. Besides, we use some governance effect and liquidity theory proposed by Edmans et al. (2011) to testify by which means the share split reform enhance the corporate governance. What is more, we find that the corporations with great difficulty, which represented for severe shareholders’ conflict, in carrying forward the reform tend to have severe governance problems while it was this kind of corporation that benefited most from the reform and formed the main driving force of the realization of the goal of reform. It has some implication on China’s current reform; that is, only when toughest problems have been overcome will the goal of reform be achieved.