Paper Submission: 27 January 2020
Author Notification: 7 to 10 days
Journal Publication: January 2020
Bhanu Teja Reddy , Usha J.C
The primary objective of investors and stockbrokers is to make profits by being able to predict the financial markets. However, forecasting is a complex task since the financial markets have a complicated pattern. This study addresses the direction of the stock price index for Japanese Nikkei 225. The research compares two prediction models, i.e., the Stochastic Neural Networks (SNN) and fusion of Long -Short Term Memory and Stochastic Neural Networks (LSTM - SNN) for predicting the index. The input layer includes computation of fifteen technical indicators using stock market parameters (open, high, low, close prices, and volume). Accuracy of each of the prediction models was evaluated using price and trend performance metrics. The evaluation was carried out for historical data from 23rd January 2007 to 30th December 2013 of the Tokyo Stock Exchange (TSE). The experimental outcomes recommend that for the SNN, the model gave an accuracy of 85.37% and hybrid of LSTM – SNN gave accuracy of 86.28%. The increase in the accuracy of LSTM – SNN was due to the introduction of LSTM layer. Experimental outcomes also illustrate that the performance of both the prediction models progress when these technical indicators are added to the input layer of the proposed models.
 B. G. Malkiel, “The efficient market hypothesis and its,” J. Econ. Perspect., vol. 17, no. 1, pp. 59–82, 2003.
 A. G. Ţiţan, “The Efficient Market Hypothesis: Review of Specialized Literature and Empirical Research,” Procedia Econ. Financ., vol. 32, no. 15, pp. 442–449, 2015.
 Y. S. Abu-Mostafa and A. F. Atiya, “Introduction to financial forecasting,” Appl. Intell., vol. 6, no. 3, pp. 205–213, 1996.
 Y. Kara, M. Acar Boyacioglu, and Ö. K. Baykan, “Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange,” Expert Syst. Appl., vol. 38, no. 5, pp. 5311–5319, 2011.
 E. Hadavandi, H. Shavandi, and A. Ghanbari, “Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting,” Knowledge-Based Syst., vol. 23, no. 8, pp. 800–808, 2010.
 M. Qiu, Y. Song, and F. Akagi, “Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock market,” Chaos, Solitons and Fractals, vol. 85, pp. 1–7, 2016.
 W. Long, Z. Lu, and L. Cui, “Deep learning-based feature engineering for stock price movement prediction,” Knowledge-Based Syst., vol. 164, pp. 163–173, 2019.
 R. Singh and S. Srivastava, “Stock prediction using deep learning,” Multimed. Tools Appl., vol. 76, no. 18, pp. 18569–18584, 2017.
 E. Chong, C. Han, and F. C. Park, “Deep learningnetworks for stock market analysis and prediction: Methodology, data representations, and case studies,” Expert Syst. Appl., vol. 83, pp. 187–205, 2017. G. De Tre, A. Hallez, and A. Bronselaer, “Performance optimization of object comparison,” Int. J. Intell. Syst., vol. 29, no. 2, pp. 495–524, 2014.
L. Nambiar, D. G. Menon, and A. V. Vidyapeetham, “Predicting Market Prices Using Deep Learning Techniques,” vol. 118, no. 20, pp. 217–223, 2018.
M. Hagenau, M. Liebmann, and D. Neumann, “Automated news reading: Stock price prediction based on financial news using context-capturing features,” Decis. Support Syst., vol. 55, no. 3, pp. 685–697, 2013.
C. Evans, K. Pappas, and F. Xhafa, “Utilizing artificial neural networks and genetic algorithms to build an algo-trading model for intra-day foreign exchange speculation,” Math. Comput. Model., vol. 58, no. 5–6, pp. 1249–1266, 2013.
X. Li et al., “Empirical analysis: stock market prediction via extreme learning machine,” Neural Comput. Appl., vol. 27, no. 1, pp. 67–78, 2016.
J. Patel, S. Shah, P. Thakkar, and K. Kotecha, “Predicting stock market index using fusion of machine learning techniques,” Expert Syst. Appl., vol. 42, no. 4, pp. 2162–2172, 2015.
J. Patel, S. Shah, P. Thakkar, and K. Kotecha, “Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques,” Expert Syst. Appl., vol. 42, no. 1, pp. 259–268, 2015.
A. M. Rather, A. Agarwal, and V. N. Sastry, “Recurrent neural network and a hybrid model for prediction of stock returns,” Expert Syst. Appl., vol. 42, no. 6, pp. 3234–3241, 2015.
J. Wang and J. Wang, “Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks,” Neurocomputing, vol. 156, pp. 68–78, 2015.
A. H. Moghaddam, M. H. Moghaddam, and M. Esfandyari, “Stock market index prediction using artificial neural network,” J. Econ. Financ. Adm. Sci., vol. 21, no. 41, pp. 89–93, 2016.
C. L. Cocianu and H. Grigoryan, “Machine learning techniques for stock market prediction. Acase study of OMV Petrom,” Econ. Comput. Econ. Cybern. Stud. Res., vol. 50, no. 3, pp. 63–82, 2016.
L. Di Persio and O. Honchar, “Artificial neural networks architectures for stock price prediction: Comparisons and applications,” Int. J. Circuits, Syst. Signal Process., vol. 10, pp. 403–413, 2016.
X. Zhong and D. Enke, “Forecasting daily stock market return using dimensionality reduction,” Expert Syst. Appl., vol. 67, pp. 126–139, 2017.
C. Krauss, X. A. Do, and N. Huck, “Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500,” Eur. J. Oper. Res., vol. 259, no. 2, pp. 689–702, 2017.
U. M. Mohapatra, B. Majhi, and S. C. Satapathy, “Financial time series prediction using distributed machine learning techniques,” Neural Comput. Appl., pp. 1–16, 2017.
T. Fischer and C. Krauss, “Deep learning with long short-term memory networks for financial market predictions,” Eur. J. Oper. Res., vol. 270, no. 2, pp. 654–669, 2018.
Y.-G. Song, Y.-L. Zhou, and R.-J. Han, “Neural networks for stock price prediction,” vol. 00, no. 00, pp. 1–13, 2018.
M. Zhang, X. Jiang, Z. Fang, Y. Zeng, and K. Xu, “High-order Hidden Markov Model for trend prediction in financial time series,” Phys. A Stat. Mech. its Appl., vol. 517, pp. 1–12, 2019.
E. L. de Faria, M. P. Albuquerque, J. L. Gonzalez, J. T. P. Cavalcante, and M. P. Albuquerque, “Predicting the Brazilian stock market through neural networks and adaptive exponential smoothing methods,” Expert Syst. Appl., vol. 36, no. 10, pp. 12506–12509, 2009.
L. Yu, S. Wang, and K. K. Lai, “A neural-network-based nonlinear metamodeling approach to financial time series forecasting,” Appl. Soft Comput. J., vol. 9, no. 2, pp. 563–574, 2009.
W. Dai, J. Y. Wu, and C. J. Lu, “Combining nonlinear independent component analysis and neural network for the prediction of Asian stock market indexes,” Expert Syst. Appl., vol. 39, no. 4, pp. 4444–4452, 2012.
M. Qiu and Y. Song, “Predicting the direction of stock market index movement using an optimized artificial neural network model,” PLoS One, vol. 11, no. 5, pp. 1–11, 2016.
Financial Management, M.S Ramaiah University of Applied Sciences, Bengaluru, India, Mobile 9738630786, (e-mail: email@example.com)
No. of Downloads: 6 | No. of Views: 114