Logit-based stock prediction network

Abstract

Stock investors have been making accurate predictions about the stock market in search of maximum profits. However, the stock market has a high degree of uncertainty, which makes it difficult to predict the development trend of the stock market. Existing stock prediction models generally improve the accuracy by changing the network structure and lack in-depth research on abnormal stock data. To solve this problem, we propose a logit-based stock prediction network LogNet, which uses the correctly predicted logits to measure the reliability of stock data, then calculate the confidence interval of the stock data, and use the credible data to make stock predictions. In addition, the model uses the theory of the Extremely Randomized Trees (ExtraTrees) theory to select the historical price data features of stocks. Experimental results show that LogNet has state-of-the-art performance on Twitter data and historical price datasets.