A Novel Synergetic LSTM-GA Stock Trading Suggestion System in Internet of Things

Abstract

The Internet of Things (IoT) play an important role in the financial sector in recent decades since several stock prediction models can be performed accurately according to IoT-based services. In real-time applications, the accuracy of the stock price fluctuation forecast is very important to investors, and it helps investors better manage their funds when formulating trading strategies. It has always been a goal and difficult problem for financial researchers to use predictive tools to obtain predicted values closer to actual values from a given financial data set. Leading indicators such as futures and options can reflect changes in many markets, such as the industry’s prosperity. Adding the data set of leading indicators can predict the trend of stock prices well. In this research, a trading strategy for finding stock trading signals is proposed that combines long short-term memory neural networks with genetic algorithms. This new framework is called long short-term memory neural network with leading index, or LSTMLI for short. We thus take the stock markets of the United States and Taiwan as the research objects and use historical data, futures, and options as data sets to predict the stock prices of these two markets. After that, we use genetic algorithms to find trading signals for the designed stock trading system. The experimental results show that the stock trading system proposed in this research can help investors obtain certain returns.

A Multilevel Wavelet Decomposition Network Hybrid Model Utilizing Cyclic Patterns for Stock Price Prediction

“…In 2021, Wu and Ming-Tai proposed the SACLSTM stock price prediction algorithm, which constructs a sequence array of historical data and its leading indicators and uses the array as the input image of the CNN framework, and this algorithm has achieved excellent forecasting results for Taiwan and American stocks [22], which is similar to the work proposed by the authors in reference [23]. An LSTM-GA stock trading suggestion system in IOT was proposed, based on historical data and leading indicators [24]. In 2022, Zhang et al…”

Developing a novel stock index trend predictor model by integrating multiple criteria decision-making with an optimized online sequential extreme learning machine

“…It is reported that Elman with direct input–output connection helps in improving accuracy, while reducing network complexity and computational cost. Recent work by researchers (Wu et al 2021b ) proposed a trading suggestion system based on Synergetic LSTM-GA. Their experimental outcomes over five Taiwan stocks revealed that LSTMLI-GA framework was able to achieve a higher profit margin as compared to other models. Day-ahead stock price prediction framework by authors (Wu et al 2021a ) combined CNN and LSTM to form a hybrid predictor framework.…”

Algorithm Optimizer in GA-LSTM for Stock Price Forecasting

“…One deep learning technique that can be used for time series is the Recurrent Neural Network (RNN), which is designed to work with sequential data [10] [11]. The progress of RNN is growing quite rapidly in various fields, but RNN has a weakness in processing time series because, [12] performance for prediction will have a negative effect if the sequence size is relevantly long and the other is that the RNN gradient will be lost, resulting in longterm memory failure. Unlike the RNN, LSTM can manage memory for each input by using memory cells and gate units [13].…”