Predicting Google’s Stock Price with LSTM Model
Download PDF
$currentUrl="http://$_SERVER[HTTP_HOST]$_SERVER[REQUEST_URI]"

Keywords

Google
Stock prediction
LSTM model
Stock trend

DOI

10.26689/pbes.v5i5.4361

Submitted : 2022-09-21
Accepted : 2022-10-06
Published : 2022-10-21

Abstract

Stock market has a profound impact on the market economy, Hence, the prediction of future movement of stocks is of great significance to investors. Therefore, an efficient prediction system can solve this problem to a great extent. In this paper, we used the stock price of Google Inc. as a prediction object, selected 3810 adjusted closing prices, and used long short-term memory (LSTM) method to predict the future price trend of the stock. We built a three-layer LSTM model and divided the entire data into a test set and a training set according to the ratio of 8 to 2. The final results show that while the LSTM model can predict the stock trend of Google Inc. very well, it cannot predict the specific price accurately.

References

Li S, Li W, Cook C, et al., 2018, Independently Recurrent Neural Network (IndRNN): Building a Longer and Deeper RNN. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5457-5466

Staudemeyer RC, Morris ER, 2019, Understanding LSTM: A Tutorial into Long Short-term Memory Recurrent Neural Networks.

Mondal P, Shit L, Goswami S, 2014, Study of Effectiveness of Time Series Modeling (ARIMA) in Forecasting Stock Prices. International Journal of Computer Science, Engineering and Applications, 4(2): 13.

Chen S, Lan X, Hu Y, et al., 2014, The Time Series Forecasting: From the Aspect of Network.

Yuan Q, Shi Y, Hu G, et al., (eds) 2020, Computer Applications in Industry and Engineering. Proceedings of 32nd International Conference on Computer Applications in Industry and Engineering.

Ariyo AA, Adewumi AO, Ayo CK, 2016, Stock Price Prediction Using the ARIMA Model. Proceedings of 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, 106–112.

Zhang G, Patuwo BE, Hu MY, Forecasting with Artificial Neural Networks: The State of the Art. International Journal of Forecasting, 14(1): 35–62.

Vui CS, Soon GK, On CK, et al., 2013, A Review of Stock Market Prediction with Artificial Neural Network (ANN). Proceedings of IEEE International Conference on Control System, Computing and Engineering, 477-482, https://www.doi.org/10.1109/ICCSCE.2013.6720012

Liang X, Ge Z, Sun L, et al., 2019, LSTM with Wavelet Transform-Based Data Preprocessing for Stock Price Prediction. Mathematical Problems in Engineering.

Sands TM, Tayal D, Morris ME, et al., 2015, Robust Stock Value Prediction Using Support Vector Machines with Particle Swarm Optimization. Proceedings of IEEE Congress on Evolutionary Computation (CEC). IEEE, 3327-3331.

Hu H, Tang L, Zhang S, et al., 2018, Predicting the Direction of Stock Markets Using Optimized Neural Networks with Google Trends. Neurocomputing, 285: 188-195.

Hochreiter S, Schmidhuber J, 1997, Long Short-term Memory. Neural Comput., 9(8): 1735–1780. http://dx.doi.org/10.1162/neco.1997.9.8.1735

Ma Q, 2020, Comparison of ARIMA, ANN and LSTM for Stock Price Prediction. E3S Web of Conferences, 218: 01026.

Lu W, Li J, Li Y, et al., 2020, A CNN-LSTM-based Model to Forecast Stock Prices. Complexity, 2020: 6622927

Ghosh A, Bose S, Maji G, et al., 2019, Stock Price Prediction Using LSTM on Indian Share Market. Proceedings of 32nd International Conference on Computer Applications in Industry and Engineering, 101–110.