Stock price prediction using deep learning LSTM (long short-term memory)

S, Sanjai; Chitradevi, D; Rajan, Sanju

doi:10.1109/icacite53722.2022.9823639

Cited by 23 publications

(3 citation statements)

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“…The authors train a separate binary classifier, with identical architecture, based on a novel document modeling technique. Namely, the classifier is implemented as a specially designed deep convolution neural network, with injection of the document-level Marissa features, extracted directly from the text, into an inner layer (Majumeder et al, 2017;Xue et al, 2014;Tsugawa et al, 2015;Sathish et al, 2020;Rodrigues et al, 2016). The first layers of the network treat each sentence of the text separately; then the sentences are aggregated into the document vector.…”

Section: Deep Learning Based Document Modeling For Personality Detect...mentioning

confidence: 99%

New Modern Approach to Predict Users’ Sentiment Using CNN and BLSTM

Kumar

2022

Int. J. Inform. Inf. Sys. and Comp. Eng.

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In Today’s world social network play a vital role and provides relevant information on user opinion. This paper presents emotional health monitoring system to detect stress and the user mood. Depending on results the system will send happy, calm, relaxing or motivational messages to users with phycological disturbance. It also sends warning messages to authorized persons in case a depression disturbance is detected by monitoring system. This detection of sentence is performed through convolution neural network (CNN) and bi-directional long-term memory (BLSTM). This method reaches accuracy of 0.80 to detect depressed and stress users and also system consumes low memory, process and energy. We can do the future work of this project by also including the sarcastic sentences in the dataset. We can also predict the sarcastic data with the proposed algorithm

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Section: Deep Learning Based Document Modeling For Personality Detect...mentioning

confidence: 99%

New Modern Approach to Predict Users’ Sentiment Using CNN and BLSTM

Kumar

2022

Int. J. Inform. Inf. Sys. and Comp. Eng.

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“…whereas a value of one means "totally keep this information" (Olah, 2015). decreasing exponentially at each layer (Nandakumar, 2018).…”

Section: Overview Of Lstm Bi-lstm and Grumentioning

confidence: 99%

“…The resulting dataset consisting of companies with active shares was transformed into input and output components for supervised learning. In contemporary stock market predictions, the input is provided in form of sequences in order to capture the trend (context-specific information) in the data and hence improve prediction accuracy (Nandakumar, 2018;Nayak, 2020). This is possible through leveraging recurrent neural network architectures such as LSTM, Bi-LSTM and GRU that support handling of sequential input and memorizing of context-specific information for a long time (Lipton et al, 2015).…”

Section: Supervised Learning Setupmentioning

confidence: 99%

Deep learning model for predicting stock prices in Tanzania

Joseph

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e prediction models help to provide investors with tools for making better data driven decisions. Machine learning and deep learning techniques have been successively utilized in various countries to develop these models. However, there is a shortage of literature on the efforts to exploit these techniques to forecast stock prices in Tanzania. Hence, this study was conducted to address this gap. The study selected active companiesfrom the Dar es Salaam Stock Exchange (DSE) and developed Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Unit (GRU) models to forecast the next day closing prices of the companies. Long Short-Term Memory was the overall best model with a Root Mean Square Error (RMSE) of 4.1818 and Mean Absolute Error (MAE) of 2.1695. Findings revealed that it was significant to account for the number of outstanding shares of each company when developing a joint model for forecasting the stock prices of multiple companies. Specifically, LSTM attained an RMSE of 10.4734 before accounting for outstanding shares and 4.7424 after accounting for outstanding shares, showing an improvement of 54.72%. Furthermore, findings showed that investors’ participation attributes helped to improve prediction accuracy. Specifically, LSTM realized an RMSE of 4.1818 when these attributes were appended from that of 4.7424 without them, showing an improvement of 11.8%. The resulting model was deployed in a web-based prototype, whereby, end-user validation results indicated that 76% of respondents rated the system as High in terms of its forecasting ability. In future, the study recommends exploration of more features.

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