A CNN-BiLSTM-AM method for stock price prediction

Lu, Wenjie; Li, Jiazheng; Wang, Jingyang; Qin, Lele

doi:10.1007/s00521-020-05532-z

Cited by 336 publications

(154 citation statements)

References 26 publications

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“…Das et al [16] discourses how Twitter data can help in decision making such as stock market prediction to forecast prices of a company's stock by using RNNs Long Short-Term Memory (LSTM) which aided in extracting online stock data from various websites to predict future stock prices. Lu et al [17] researched using CNN-BiLSTM-AM to predict the closing price of the stock for the next day. It involved neural networks (CNN), bi-directional long short-term Memory (BiLSTM) and attention mechanism (AM), wherein CNN was used to obtained features of the input data, BiLSTM uses the obtained feature data to predict the closing price of the next day.…”

Section: Literature Reviewmentioning

confidence: 99%

Text Mining of Stocktwits Data for Predicting Stock Prices

Jaggi

Mandal

Narang

et al. 2021

ASI

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Stock price prediction can be made more efficient by considering the price fluctuations and understanding people’s sentiments. A limited number of models understand financial jargon or have labelled datasets concerning stock price change. To overcome this challenge, we introduced FinALBERT, an ALBERT based model trained to handle financial domain text classification tasks by labelling Stocktwits text data based on stock price change. We collected Stocktwits data for over ten years for 25 different companies, including the major five FAANG (Facebook, Amazon, Apple, Netflix, Google). These datasets were labelled with three labelling techniques based on stock price changes. Our proposed model FinALBERT is fine-tuned with these labels to achieve optimal results. We experimented with the labelled dataset by training it on traditional machine learning, BERT, and FinBERT models, which helped us understand how these labels behaved with different model architectures. Our labelling method’s competitive advantage is that it can help analyse the historical data effectively, and the mathematical function can be easily customised to predict stock movement.

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Section: Literature Reviewmentioning

confidence: 99%

Text Mining of Stocktwits Data for Predicting Stock Prices

Jaggi

Mandal

Narang

et al. 2021

ASI

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“…The memory cell can control the transfer of information to the next moment. The forget gate is mainly to selectively forget the input data from the previous node [32]. The input gate is a selective memory of the input data at this stage, with more records for the important part and fewer records for the non-important part.…”

Section: Lstmmentioning

confidence: 99%

A Prediction Model of CNN-TLSTM for USD/CNY Exchange Rate Prediction

Wang

et al. 2021

IEEE Access

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The Exchange rate affects the economic development of various countries. To grasp the information of the changeable exchange rate in time, it is necessary to predict the exchange rate price. This paper proposes the CNN-TLSTM model to predict the United States Dollar/Chinese yuan (USD/CNY) exchange rate closing price of the next trading day. The model consists of two parts, namely convolutional neural networks(CNN)and tanh long short-term memory (TLSTM). The function of CNN is to extract feature factors from the input data. TLSTM is used to receive the output data of CNN for prediction, and finally obtain the prediction result. TLSTM is a new model proposed in this paper to improve the internal structure of the long short-term memory (LSTM). Its advantage is to change the range of the output value of the input gate, retain more data features, and prevent the output value of the input gate in LSTM from being overfitting. This paper selects the exchange rate of USD/CNY data and some stock data for each trading day from January 2, 2006, to October 30, 2020, as the experimental data. To prove the effectiveness of the CNN-TLSTM prediction model, the model is compared with multilayer perceptron (MLP), CNN, recurrent neural network (RNN), LSTM, and CNN-LSTM models. mean absolute percentage error (MAPE), mean square error (MSE), and R-squared (R 2 ) are used for comparative analysis. The experimental results show that the CNN-TLSTM model has the best predictive effect on the USD/CNY exchange rate closing price of the next trading day.

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“…where Y t + T is the predicted object in next T hours, f represents the final model learnt by the historical data, X t denotes the datasets at the predicting moments, and X t -T are the datasets in T hours before the predicting moments. [20,21], with smaller error and higher prediction accuracy. At present, the most widely used LSTM network is to use the LSTM unit to replace the neural nodes in the hidden layer of RNN [22].…”

Section: Problem Definitionsmentioning

confidence: 99%

Meteorological Satellite Operation Prediction Using a BiLSTM Deep Learning Model

Peng¹,

Han²,

et al. 2021

Security and Communication Networks

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The current satellite management system mainly relies on manual work. If small faults cannot be found in time, it may cause systematic fault problems and then affect the accuracy of satellite data and the service quality of meteorological satellite. If the operation trend of satellite will be predicted, the fault can be avoided. However, the satellite system is complex, and the telemetry signal is unstable, nonlinear, and time-related. It is difficult to predict through a certain model. Based on these, this paper proposes a bidirectional long short-term memory (BiLSTM) deep leaning model to predict the operation trend of meteorological satellite. In the method, the layer number of the model is designed to be two, and the prediction results, which are forecasted by LSTM network as the future trend data and historical data, are both taken as the input of BiLSTM model. The dataset for the research is generated and transmitted from Advanced Geostationary Radiation Imager (AGRI), which is the load of FY4A meteorological satellite. In order to demonstrate the superiority of the BiLSTM prediction model, it is compared with LSTM based on the same dataset in the experiment. The result shows that the BiLSTM method reports a state-of-the-art performance on satellite telemetry data.

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A CNN-BiLSTM-AM method for stock price prediction

Cited by 336 publications

References 26 publications

Text Mining of Stocktwits Data for Predicting Stock Prices

Text Mining of Stocktwits Data for Predicting Stock Prices

A Prediction Model of CNN-TLSTM for USD/CNY Exchange Rate Prediction

Meteorological Satellite Operation Prediction Using a BiLSTM Deep Learning Model

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