FINANCIAL TIME-SERIES FORECASTING: EVALUATING PERFORMANCE OF DEEP LEARNING MODELS ON SELECTED NIFTY CONSTITUENTS
DOI:
https://doi.org/10.29121/shodhkosh.v5.i3.2024.4385Keywords:
Deep Learning, Bidirectional LSTM, Stacked LSTM, Nifty, Stock MarketAbstract [English]
For solving the problems related to prediction of time series data the Artificial Intelligence models i.e. Machine learning and deep learning are becoming popular. These models have been proven to deliver greater accuracy than traditional regression models. Among these, Recurrent Neural Networks (RNNs) with features (e.g. memory storage), such as Long Short-Term Memory (LSTM) networks, have proven to show a superior edge over models like Autoregressive Integrated Moving Average (ARIMA). LSTM networks are unique because they use special element namely "gates" which help them remember and process longer sequences of time series data.
Based on hyper-parameter tuning, various LSTM model configurations are possible to be developed, each of which are designed to address specific prediction challenges and improve model performance. Thus, the key consideration is whether the elements of gates in LSTM networks alone are sufficient to deliver better predictions or if further training is necessary to enhance accuracy.
The present study explores the performance of BiLSTMs in comparison of Stacked LSTMs, using stock price data from 10 companies listed on the National Stock Exchange of India. It exhibits the effect of bidirectional training in enhancing model precision. The results demonstrate that BiLSTMs, with their advanced training capabilities, provide significantly more accurate stock price forecasts compared to basic structure of LSTMs. However, it was also observed that BiLSTMs take longer to achieve stability compared to their unidirectional LSTM counterparts.
References
S. S. Namini, N. Tavakoli, and A. S. Namin. ”A Comparison of ARIMA and LSTM in Forecasting Time Series.” 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1394-1401. IEEE, 2018. DOI: https://doi.org/10.1109/ICMLA.2018.00227
G. Box, G. Jenkins, Time Series Analysis: Forecasting and Control, San Francisco: Holden-Day, 1970.
M. Khashei, M. Bijari, “A Novel Hybridization of Artificial Neural Networks and ARIMA Models for Time Series forecasting,” in Applied Soft Computing 11(2): 2664-2675, 2011. DOI: https://doi.org/10.1016/j.asoc.2010.10.015
A. M. Alonso, C. Garcia-Martos, “Time Series Analysis – Forecasting with ARIMA models,” Universidad Carlos III de Madrid, Universidad Politecnica de Madrid. 2012.
A. A. Adebiyi, A. O. Adewumi, C. K. Ayo, “Stock Price Prediction Using the ARIMA Model,” in UKSim-AMSS 16th International Conference on Computer Modeling and Simulation., 2014.
A. Earnest, M. I. Chen, D. Ng, L. Y. Sin, “Using Autoregressive Integrated Moving Average (ARIMA) Models to Predict and Monitor the Number of Beds Occupied During a SARS Outbreak in a Tertiary Hospital in Singapore,” in BMC Health Service Research, 5(36), 2005. DOI: https://doi.org/10.1186/1472-6963-5-36
C. Krauss, X. A. Do, N. Huck, “Deep neural networks, gradientboosted trees, random forests: Statistical arbitrage on the S&P 500,” FAU Discussion Papers in Economics 03/2016, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics, 2016.
S. I. Lee, S. J. Seong Joon Yoo, “A Deep Efficient Frontier Method for Optimal Investments,” Department of Computer Engineering, Sejong University, Seoul, 05006, Republic of Korea, 2017.
T. Fischera, C. Kraussb, “Deep Learning with Long Short-term Memory Networks for Financial Market Predictions,” in FAU Discussion Papers in Economics 11, 2017.
J. Kim, N. Moon, ”BiLSTM model based on multivariate time series data in multiple field for forecasting trading area.” Journal of Ambient Intelligence and Humanized Computing, pp. 1-10.
Z. Cui, R. Ke, Y. Wang, “Deep Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction,” arXiv:1801.02143, 2018.
N. Tavakoli, “Modeling Genome Data Using Bidirectional LSTM” IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), vol. 2, pp. 183-188, 2019. DOI: https://doi.org/10.1109/COMPSAC.2019.10204
Y. Pang, X. Xue, A.S. Namin, “Predicting Vulnerable Software Components through N-Gram Analysis and Statistical Feature Selection,” International Conference on Machine Learning and Applications ICMLA, pp. 543-548, 2015. DOI: https://doi.org/10.1109/ICMLA.2015.99
N. Tavakoli, D. Dong, and Y. Chen, ”Client-side straggler-aware I/O scheduler for object-based parallel file systems.” Parallel Computing, pp. 3-18,82, 2019 DOI: https://doi.org/10.1016/j.parco.2018.07.001
A. Dutta, G. Bandopadhyay and S. Sengupta, Prediction of Stock Performance in the Indian Stock Market Using Logistic Regression, International Journal of Business and Information, 2012
S.-H.Chen, Genetic Algorithms and Genetic Programming in Computational Finance, Springer, 2002 DOI: https://doi.org/10.1007/978-1-4615-0835-9
Y. Wang and In-Chan Choi, Market Index and Stock Price Direction Prediction using Machine Learning Techniques: An empirical study on the KOSPI and HSI, Technical report, 2013 DOI: https://doi.org/10.1504/IJBIDM.2014.065091
Wei Huang, Yoshiteru Nakamori, Shou-Yang Wang, Forecasting stock market movement direction with support vector machine, Computers and Operations Research, archive Volume 32, Issue 10, 2005, pp. 2513–2522 DOI: https://doi.org/10.1016/j.cor.2004.03.016
Marcelo S. Lauretto, B. C. Silva and P. M. Andrade, Evaluation of a Supervised Learning Approach for Stock Market Operations, arXiv:1301.4944[stat.ML], 2013
S. Hochreiter, J. Schmidhuber, “Long Short-Term Memory,” Neural Computation 9(8):1735-1780, 1997. DOI: https://doi.org/10.1162/neco.1997.9.8.1735
M. Schuster, K. K. Paliwal, “Bidirectional recurrent neural networks”, IEEE Transactions on Signal Processing, 45 (11), pp. 2673–2681, 1997. DOI: https://doi.org/10.1109/78.650093
P. Baldi, S. Brunak, P. Frasconi, G. Soda, and G. Pollastri, “Exploiting the past and the future in protein secondary structure prediction,” Bioinformatics, 15(11), 1999. DOI: https://doi.org/10.1093/bioinformatics/15.11.937
J. Brownlee, “How to Create an ARIMA Model for Time Series Forecasting with Python,” 2017.
J. Brownlee, “Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras,” 2016.
J. Gao, H. Liu, and E.T. Kool, “Expanded-size bases in naturally sized DNA: Evaluation of steric effects in Watson- Crick pairing,” Journal of the American Chemical Society, 126(38), pp. 11826–11831, 2004. DOI: https://doi.org/10.1021/ja048499a
F. A. Gers, J. Schmidhuber, F. Cummins, “Learning to Forget: Continual Prediction with LSTM,” in Neural Computation 12(10): 2451-2471, 2000. DOI: https://doi.org/10.1162/089976600300015015
N. Huck, “Pairs Selection and Outranking: An Application to the S&P 100 Index,” in European Journal of Operational Research 196(2): 819-825, 2009. DOI: https://doi.org/10.1016/j.ejor.2008.03.025
R. J. Hyndman, G. Athanasopoulos, Forecasting: Principles and Practice. OTexts, 2014.
R. J. Hyndman, “Variations on Rolling Forecasts,” 2014.
M. J. Kane, N. Price, M. Scotch, P. Rabinowitz, “Comparison of ARIMA and Random Forest Time Series Models for Prediction of Avian Influenza H5N1 Outbreaks,” BMC Bioinformatics, 15(1), 2014. DOI: https://doi.org/10.1186/1471-2105-15-276
J. Patterson, Deep Learning: A Practitioner’s Approach, OReilly Media, 2017.
J. Schmidhuber, “Deep learning in neural networks: An overview,” in Neural Networks, 61: 85-117, 2015. DOI: https://doi.org/10.1016/j.neunet.2014.09.003
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