Impact of Technical Indicators and Leading Indicators on Stock Trends on the Internet of Things

Abstract

The Internet of things (IoT) has had an enormous impact on the financial industry. With IoT, people can obtain real-time financial information; moreover, investment and financial management have become more flexible and diverse. Because of their high returns and strong liquidity, stocks have become essential commodities through which people invest and manage money. However, high returns are often associated with high risks. Therefore, it is important for investors to forecast the trends of future stock prices. This study uses a new stock trend prediction framework to predict changes in the stock price direction on the next trading day using data from the past 30 trading days. This framework uses two-dimensional convolutional neural networks to classify stock prices into three categories: up, down, and flat. In addition, to analyze the influence of different types of input on the prediction model, historical data, futures, options, technical indicators, and mixed data are taken as the model's input. Experiments on US and Taiwan stocks proved the validity of the prediction model. The method proposed in this study is compared with buy-and-hold and random choice trading strategies. Results show that the model's profitability is better than the two baseline strategies.