A Stock Trend Forecast Algorithm Based on Deep Neural Networks

Yan, Yingying; Yang, Daguang

doi:10.1155/2021/7510641

Cited by 18 publications

(13 citation statements)

References 25 publications

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“…Dropout technology can alleviate the problem of overfitting [12]. Therefore, the dropout layer is added to the stacked LSTM model in this paper to optimize the model structure.…”

Section: Stacked Lstm Model Statisticsmentioning

confidence: 99%

Incidence trend and prediction of hepatitis C based on stacked LSTM

Zhang

Guo²,

Liu³

et al. 2023

Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022)

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Objective To explore the prediction of hepatitis C incidence by stacked LSTM model. Methods Aiming at the incidence trend and the number of cases of hepatitis C in China from 2007 to 2017, the ARIMA, NNAR, SVR and stacked LSTM were used to train them. The model was used to predict the incidence of hepatitis C in the second half and the last quarter of 2017, and compared with the actual values. The prediction effects of the four models were compared and analyzed using the Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE). Results The SVR model performs not well. However, ARIMA model, NNAR model and stacked LSTM model can identify the incidence trend of hepatitis C in China from 2007 to 2017, and the RMSE values of the ARIMA model and the NNAR model are larger, and these two are relatively similar. On the contrary, the RMSE value of the stacked LSTM model is smaller. On the whole, compared with ARIMA model and NNAR model, it decreases by at least 20 percentage. The predicted MAPE value of the stacked LSTM model is less than 1%, meanwhile it is lower than the value of ARIMA or NNAR models. Conclusion The stacked LSTM model has the best predictive effect on the incidence of hepatitis C.

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“…Dropout technology can alleviate the problem of overfitting [12]. Therefore, the dropout layer is added to the stacked LSTM model in this paper to optimize the model structure.…”

Section: Stacked Lstm Model Statisticsmentioning

confidence: 99%

Incidence trend and prediction of hepatitis C based on stacked LSTM

Zhang

Guo²,

Liu³

et al. 2023

Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022)

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“…Other ML approaches to deploying stock market trading bots use Long Short Term Memory (LSTM) neural networks to feed multiple data into the neural network to come up with the next prediction in the time series [28]. This is practical for longer-term swing trading bots, where the neural network has plenty of time to run computations and execute trades at most once per day, but for a real-time intraday system that learns and reacts to the environment, this would be too slow to compute compared to RL.…”

Section: Related Workmentioning

confidence: 99%

Multi-Timeframe Algorithmic Trading Bots Using Thick Data Heuristics with Deep Reinforcement Learning

Roy

Mohammed

2022

Artificial Intelligence Evolution

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This article presents an augmented Artificial Intelligence (AI) algorithmic trading approach that combines Thick Data Heuristic (TDH), with Deep Reinforcement Learning (DRL), to successfully learn trading execution timing policies. Combining the augmented AI human trader's intuition and heuristics with DRL techniques to provide more focused drivers for trading order execution timing is explored in this study. In this research, the goal is to solve the sequential decision-making problem of AI for profitable day and swing trading order timing executions. Enabling trading bots with cognitive intelligence and common-sense heuristics will offer traders including automatic traders an insight to understand the day-to-day swing trading timeframes indicators and arrive at mature trading decision-making. This article examines the performance of bots with Nasdaq and NYSE stocks that have a strong catalyst (info. which increases directional momentum) to find that they outperform benchmark algorithmic trading approaches. The research illustrates to the reader how to combine TDH and Deep Q-networks (DQN) into a TDH-DQN augmented AI trading bot. The bot learns through test data to predict the optimal timing of order executions autonomously on idealized trading time series data.

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“…Lu et al (2020b) have combined feature extracting functions of a CNN model and predicting functions of an LSTM model for predicting prices of stocks by using predictive information from trading volumes and historical prices. Yan and Yang (2021) have utilized the same predictive information set as that of Lu et al (2020b) in predicting prices of stocks through encoder/ decoder LSTM models. Zhao et al (2021) have considered different machine learning models that include a support vector machine, a random forest and an LSTM, and a graph-based method for predicting trading volumes' movement patterns by using predictive information from prices of stocks.…”

Section: Introductionmentioning

confidence: 99%

“…(2020b) have combined feature extracting functions of a CNN model and predicting functions of an LSTM model for predicting prices of stocks by using predictive information from trading volumes and historical prices. Yan and Yang (2021) have utilized the same predictive information set as that of Lu et al . (2020b) in predicting prices of stocks through encoder/decoder LSTM models.…”

Section: Introductionmentioning

confidence: 99%

High-frequency CSI300 futures trading volume predicting through the neural network

Xu,

Zhang

2023

AJEB

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PurposeFor policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.Design/methodology/approachIn order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?FindingsThe results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.Originality/valueThe results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

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A Stock Trend Forecast Algorithm Based on Deep Neural Networks

Cited by 18 publications

References 25 publications

Incidence trend and prediction of hepatitis C based on stacked LSTM

Incidence trend and prediction of hepatitis C based on stacked LSTM

Multi-Timeframe Algorithmic Trading Bots Using Thick Data Heuristics with Deep Reinforcement Learning

High-frequency CSI300 futures trading volume predicting through the neural network

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