3-WAY GATED RECURRENT UNIT NETWORK ARCHITECTURE FOR STOCK PRICE PREDICTION

AbstractAs one of the most popular financial market instruments, the stock has formed one of the most massive and complex financial markets in the world. It could handle millions of transactions within a short period of time and highly unpredictable. In this study, we aim to implement a famous Deep Learning method, namely the long short-term memory (LSTM) networks, for the stock price prediction. We limit the stocks to those that are included in the LQ45 financial sectors indices, i.e., BBCA, BBNI, BBRI, BBTN, BMRI, and BTPS. Rather than using too deep network architecture, we propose using a simple three-layer LSTM network architecture to predict the stocks’ closing prices. We found that the prediction results fall in the reasonable forecasting category. Moreover, it is worth noting that two of the considered stocks, namely, BBCA and BMRI, have the lowest MAPE values at 19.1020 and 18.6135, which fall in the good forecasting results. Hence, the proposed LSTM model is most recommended to be used on those two stocks.

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Construction of an Ensemble Scheme for Stock Price Prediction Using Deep Learning Techniques

International Journal of Intelligent Information Technologies ◽

10.4018/ijiit.2021040104 ◽

2021 ◽

Vol 17 (2) ◽

pp. 72-95

Author(s):

Justice Kwame Appati ◽

Ismail Wafaa Denwar ◽

Ebenezer Owusu ◽

Michael Agbo Tettey Soli

Keyword(s):

Deep Learning ◽

Stock Price ◽

Short Term Memory ◽

Absolute Error ◽

Stock Price Prediction ◽

Price Prediction ◽

Learning Techniques ◽

Proposed Model ◽

Bidirectional Lstm ◽

Gated Recurrent Unit

This study proposes a deep learning approach for stock price prediction by bridging the long short-term memory with gated recurrent unit. In its evaluation, the mean absolute error and mean square error were used. The model proposed is an extension of the study of Hossain et al. established in 2018 with an MSE of 0.00098 as its lowest error. The current proposed model is a mix of the bidirectional LSTM and bidirectional GRU resulting in 0.00000008 MSE as the lowest error recorded. The LSTM model recorded 0.00000025 MSE, the GRU model recorded 0.00000077 MSE, and the LSTM + GRU model recorded 0.00000023 MSE. Other combinations of the existing models such as the bi-directional LSTM model recorded 0.00000019 MSE, bi-directional GRU recorded 0.00000011 MSE, bidirectional LSTM + GRU recorded 0.00000027 MSE, LSTM and bi-directional GRU recorded 0.00000020 MSE.

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Predicting LQ45 Financial Sector Indices using RNN-LSTM

10.21203/rs.3.rs-471941/v1 ◽

2021 ◽

Author(s):

Seng Hansun ◽

Julio Christian Young

Keyword(s):

Financial Markets ◽

Network Architecture ◽

Stock Price ◽

Short Term Memory ◽

Short Term ◽

Stock Price Prediction ◽

Price Prediction ◽

Short Period ◽

Long Short Term Memory ◽

Lstm Network

Abstract As one of the most popular financial market instruments, the stock has formed one of the most massive and complex financial markets in the world. It could handle millions of transactions within a short period and highly unpredictable. In this study, we aim to implement a famous Deep Learning method, namely the Long Short-Term Memory (LSTM) networks, for the stock price prediction. We limit the stocks to those that are included in the LQ45 financial sectors indices, i.e., BBCA, BBNI, BBRI, BBTN, BMRI, and BTPS. We propose to use a simple three layers LSTM network architecture in predicting the stocks’ closing prices and found that the prediction results fall in the reasonable forecasting category. It is worthy to note that two of the considered stocks, i.e., BBCA and BMRI, have the lowest MAPE values at 19.1020 and 18.6135 which fall in the good forecasting results. Hence, the proposed LSTM model is recommended to be used on those two stocks.

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A Historical Data based Ensemble System for Efficient Stock Price Prediction

Recent Patents on Computer Science ◽

10.2174/2213275912666190730161807 ◽

2019 ◽

Vol 12 ◽

Author(s):

Vijay Kumar Dwivedi ◽

Manoj Madhava Gore

Keyword(s):

Stock Price ◽

Prediction Models ◽

Historical Data ◽

Stock Exchange ◽

Primary Objective ◽

Lasso Regression ◽

Stock Price Prediction ◽

Price Prediction ◽

Consistent Manner ◽

Selection Operator

Background: Stock price prediction is a challenging task. The social, economic, political, and various other factors cause frequent abrupt changes in the stock price. This article proposes a historical data-based ensemble system to predict the closing stock price with higher accuracy and consistency over the existing stock price prediction systems. Objective: The primary objective of this article is to predict the closing price of a stock for the next trading in more accurate and consistent manner over the existing methods employed for the stock price prediction. Method: The proposed system combines various machine learning-based prediction models employing least absolute shrinkage and selection operator (LASSO) regression regularization technique to enhance the accuracy of stock price prediction system as compared to any one of the base prediction models. Results: The analysis of results for all the eleven stocks (listed under Information Technology sector on the Bombay Stock Exchange, India) reveals that the proposed system performs best (on all defined metrics of the proposed system) for training datasets and test datasets comprising of all the stocks considered in the proposed system. Conclusion: The proposed ensemble model consistently predicts stock price with a high degree of accuracy over the existing methods used for the prediction.

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