Abstract
The evolution of the Internet of Things (IoT) has promoted the prevalence of the financial industry as a variety of stock prediction models have been able to accurately predict various IoT-based financial services. In practice, it is crucial to obtain relatively accurate stock trading signals. Considering various factors, finding profitable stock trading signals is very attractive to investors, but it is also not easy. In the past, researchers have been devoted to the study of trading signals. A genetic algorithm (GA) is often used to find the optimal solution. In this study, a long short-term (LSTM) memory neural network is used to study stock price fluctuations, and then, genetic algorithms are used to obtain appropriate trading signals. A genetic algorithm is a search algorithm that solves optimization. In this paper, the optimal threshold is found to determine the trading signal. In addition to trading signals, a suitable trading strategy is also crucial. In addition, this research uses the Kelly criterion for fund management; that is, the Kelly criterion is used to calculate the optimal investment score. Effective capital management can not only help investors increase their returns but also help investors reduce their losses.
Highlights
In recent years, the development of Internet of Things (IoT) is facilitating the flourishing of various industries, especially in the economic market
The first section mainly talks about the framework used in this research, that is, long short-term memory based on leading indicators (LSTMLI), which uses LSTMLI to classify the rise and fall of stock prices
The ordinate represents the ratio of profit to capital. It can be seen from the figure that no matter which data set is used, the ratio of profit to capital obtained by using the Kelly criterion for fund management in stock trading is much higher than that obtained without the Kelly criterion
Summary
The development of IoT is facilitating the flourishing of various industries, especially in the economic market. The financial market is very complicated, especially the various transactions in the stock market. If you want to get high returns through investment, the stock market is a very good choice. Advances in financial theory and computer technology have made quantitative trading [1, 2] possible. Different from general trading methods, the behavior of quantitative trading is to use computer technology to mine relevant information from the historical data of the stock market to increase the profit of the transaction [3, 4]. To develop a quantitative trading strategy that can increase returns is what researchers hope. For investors in the stock market, the main purpose is to obtain higher returns, that is, to find a stock trading system that can avoid certain risks and obtain maximum profits
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