Study of Stock Return Predictions Using Recurrent Neural Networks with LSTM

Naik, Nagaraj; Mohan, Biju R.

doi:10.1007/978-3-030-20257-6_39

Cited by 25 publications

(18 citation statements)

References 14 publications

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“…15 shows the details of each paper's RMSE based on the numerical range. In this table, papers [11,12,24] used the CNN-based model; papers [32,[35][36][37][38][39][45][46][47]57,65] used the LSTM-based model; papers [71][72][73] used the DNN-based model; papers [27,29,31] used the RNN-based model; and paper [86] used another deep learning method. [27,35] It was clear that papers [24,57] achieved the best performance using the DNN model, having RMSEs smaller than 0.001.…”

Section: Discussion and Analysis Based On Performance Metricsmentioning

confidence: 99%

“…Paper [37] was in the fourth lowest order of magnitude, and paper [35] had the highest order of magnitude. 6.…”

mentioning

confidence: 99%

“…For MAE, papers [38,40,42,57] had the lowest order of magnitude, with paper [57] having the best performance; papers [32,37] were in the second-lowest order of magnitude, while paper [41] was in the third lowest order of magnitude. Paper [35] had the highest order of magnitude.…”

mentioning

confidence: 99%

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A Survey of Forex and Stock Price Prediction Using Deep Learning

Zhao

Khushi

2021

ASI

198

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Predictions of stock and foreign exchange (Forex) have always been a hot and profitable area of study. Deep learning applications have been proven to yield better accuracy and return in the field of financial prediction and forecasting. In this survey, we selected papers from the Digital Bibliography & Library Project (DBLP) database for comparison and analysis. We classified papers according to different deep learning methods, which included Convolutional neural network (CNN); Long Short-Term Memory (LSTM); Deep neural network (DNN); Recurrent Neural Network (RNN); Reinforcement Learning; and other deep learning methods such as Hybrid Attention Networks (HAN), self-paced learning mechanism (NLP), and Wavenet. Furthermore, this paper reviews the dataset, variable, model, and results of each article. The survey used presents the results through the most used performance metrics: Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Square Error (MSE), accuracy, Sharpe ratio, and return rate. We identified that recent models combining LSTM with other methods, for example, DNN, are widely researched. Reinforcement learning and other deep learning methods yielded great returns and performances. We conclude that, in recent years, the trend of using deep-learning-based methods for financial modeling is rising exponentially.

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Section: Discussion and Analysis Based On Performance Metricsmentioning

confidence: 99%

“…Paper [37] was in the fourth lowest order of magnitude, and paper [35] had the highest order of magnitude. 6.…”

mentioning

confidence: 99%

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A Survey of Forex and Stock Price Prediction Using Deep Learning

Zhao

Khushi

2021

ASI

198

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“…After the proposal of neural network, many scholars use it to predict stock prices or weighted stock index [21,44]. Not only the original neural network, many studies using recurrent neural network or long short-term memory have also been applied in many journals [22,24]. Furthermore, in order to improve the prediction accuracy, many studies have added many hidden layers or mixed multiple algorithms [1,12,28,29].…”

Section: Long Short-term Memory and Related Workmentioning

confidence: 99%

Convert index trading to option strategies via LSTM architecture

Hung

et al. 2020

Neural Comput & Applic

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In the past, most strategies were mainly designed to focus on stocks or futures as the trading target. However, due to the enormous number of companies in the market, it is not easy to select a set of stocks or futures for investment. By investigating each company’s financial situation and the trend of the overall financial market, people can invest precisely in the market and choose to go long or short. Moreover, how to determine the position size of the transaction is also a problematic issue. In the past, many money management theories were based on the Kelly criterion. And they put a certain percentage of their total funds into the market for trading. Nonetheless, three massive problems cannot be overcome. First, futures are leveraged transactions, and extra funds must be deposited as margin. It causes that the position size is hard to be estimated by the Kelly criterion. The second point is that the trading strategy is difficult to determine the winning rate in the financial market and cannot be brought into the Kelly criterion to calculate the optimal fraction. Last, the financial data are always massive. A big data technique should be applied to resolve this issue and enhance the performance of the framework to reveal knowledge in the financial data. Therefore, in this paper, a concept of converting the original futures trading strategy into options trading is proposed. An LSTM (long short-term memory)-based framework is proposed to predict the profit probability of the original futures strategy and convert the corresponding daily take-profit and stop-loss points according to the delta value of the options. Finally, the proposed framework brings the results into the Kelly criterion to get the optimal fraction of options trading. The final research results show that options trading is closer to the optimal fraction calculated by the Kelly criterion than futures trading. If the original futures trading strategy can profit, the benefits after converting to options trading can be further superior.

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“…e research results show that the prediction performance of the LSTM model is the best. is paper [30] uses LSTM's RNN to predict stocks and calculate returns based on closing prices. Experimental results show that the performance of the LSTM model is better than the feedforward artificial neural network.…”

Section: Introductionmentioning

confidence: 99%

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

Sun

Srivastava

et al. 2021

Mobile Information Systems

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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.

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Study of Stock Return Predictions Using Recurrent Neural Networks with LSTM

Cited by 25 publications

References 14 publications

A Survey of Forex and Stock Price Prediction Using Deep Learning

A Survey of Forex and Stock Price Prediction Using Deep Learning

Convert index trading to option strategies via LSTM architecture

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

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