Google Stocks Prediction by Machine Learning of RNN and LSTM Techniques

Ruimin Tian

doi:10.54254/2754-1169/57/20230771

Research Article

Open access

Published on 5 January 2024

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Google Stocks Prediction by Machine Learning of RNN and LSTM Techniques

Ruimin Tian *^,1,

¹ McMaster University

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2754-1169/57/20230771

Abstract

The objective of this study is to utilize a combined model of two algorithms, namely Long Short-Term Memory network and Recurrent Neural Network, to forecast the stock price of Google. Using Google stock price data from 2010 to 2022 as the training set and performed data preprocessing and feature engineering. This then build a deep neural network model consisting of multiple LSTM and RNN layers and train it by the backpropagation algorithm. During training, this paper employs an appropriate loss function and optimizer to minimize the prediction error. In conclusion, the performance of the model was assessed by employing Google stock price data from 2023 as a test set. By comparing the error between the actual stock price and the predicted value of the model, it can evaluate the accuracy and stability of the model. The experimental results show that the superposition model using LSTM and RNN algorithms can effectively predict the Google stock price with high accuracy and stability. This research presents a practical approach that can enhance the predictive capabilities of investors, financial institutions, and other related domains, enabling them to make well-informed investment decisions in the stock market.

Keywords

Google, RNN, LSTM

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References

[1]. Shah, A., Zhang, Z., and Ahmad, N. (2017) Stock market prediction using LSTM recurrent neural network. International Journal of Business Information Systems, 25(3), 366-380

[2]. Kumar, A., Bharti, S. K., and Rani, R. (2018) Google stock price forecasting using long short-term memory (LSTM) networks. Journal of Computational and Theoretical Nanoscience, 15(12), 6625-6631.

[3]. Hochreiter, S., and Schmid Huber, J. (1997) Long short-term memory. Neural computation, 9(8), 1735-1780.

[4]. Elman, J. L. (1990) Finding structure in time. Cognitive science, 14(2), 179-211.

[5]. Zhang, G., Patuwo, E.B., and Hu, M.Y. (1998) Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14(1), 35-62.

[6]. Yao, X., and Liu, Y. (2000) A new evolutionary system for evolving artificial neural networks. IEEE Transactions on Neural Networks, 11.

[7]. Huang, G.B., and Babri, H.A. (2014) An empirical study on CAC40 and Dow Jones Industrial Average using extreme learning machine. Applied Soft Computing, 18.

[8]. Wang, J., and Xu, X. (2014) Forecasting stock indices using radial basis function neural networks optimized by an improved particle swarm optimization algorithm. Expert Systems with Applications, 41.

[9]. Guo, H., and Zhang, X. (2015) A novel hybrid model for stock price prediction. Applied Soft Computing, 37.

[10]. Chen, J., and Huang, L. (2013) The prediction of stock markets based on fuzzy time series. Applied Soft Computing, 13.

Cite this article

Tian,R. (2024). Google Stocks Prediction by Machine Learning of RNN and LSTM Techniques. Advances in Economics, Management and Political Sciences,57,285-293.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Financial Technology and Business Analysis

Conference website: https://www.icftba.org/

ISBN：978-1-83558-205-3(Print) / 978-1-83558-206-0(Online)

Conference date: 8 November 2023

Editor：Javier Cifuentes-Faura

Series: Advances in Economics, Management and Political Sciences

Volume number: Vol.57

ISSN：2754-1169(Print) / 2754-1177(Online)

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