Browse > Article
http://dx.doi.org/10.15207/JKCS.2018.9.6.039

Daily Stock Price Forecasting Using Deep Neural Network Model  

Hwang, Heesoo (Department of Electrical and Electronic Engineering, Halla University)
Publication Information
Journal of the Korea Convergence Society / v.9, no.6, 2018 , pp. 39-44 More about this Journal
Abstract
The application of deep neural networks to finance has received a great deal of attention from researchers because no assumption about a suitable mathematical model has to be made prior to forecasting and they are capable of extracting useful information from large sets of data, which is required to describe nonlinear input-output relations of financial time series. The paper presents a new deep neural network model where single layered autoencoder and 4 layered neural network are serially coupled for stock price forecasting. The autoencoder extracts deep features, which are fed into multi-layer neural networks to predict the next day's stock closing prices. The proposed deep neural network is progressively learned layer by layer ahead of the final learning of the total network. The proposed model to predict daily close prices of KOrea composite Stock Price Index (KOSPI) is built, and its performance is demonstrated.
Keywords
Convergence; Stock Price Forecasting; Stock Price Modeling; Deep Neural Network; Deep Learning; Time Series Forecasting;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 T. Kuremoto & et. al. (2014). Time Series Forecasting Using A Deep Belief Network with Restricted Boltzmann Machines, Neurocomputing, 137(15), 47-56.   DOI
2 Y. Bengio, P. Lamblin P, D. Popovici D & H. Larochelle. (2007). Greedy Layer-wise Training of Deep Networks, Advances in neural information processing systems, 19, 153.
3 L. D. Persio & O. Honcha. (2017). Recurrent Neural Networks Approach to The Financial Forecast of Google Assets, Int. J. of Mathetics and Computers in Simulation, 11, 7-13.
4 X. Ding X, Y. Zhang, T. Liu & J. Duan. (2015). Deep Learning for Event-driven Stock Prediction, Proc. of the 24th Int. Joint Conf. on Artificial Intelligence, 2327-2333.
5 K. H. Lee & G. S. Jo. (1999). Expert System for Predicting Stock Market Timing Using A Candlestick Chart, Expert System With Applications, 16, 357-364.   DOI
6 L. C. H. Leon, A. Liu & W. S. Chen. (2006). Pattern Discovery of Fuzzy Time Series for Financial Prediction, IEEE Trans. Knowledge and Data Engineering, 18(5), 613-625.   DOI
7 M. F. Moller. (1993). A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning, Neural Networks, 6, 525-533.   DOI
8 B. A. Olshausen & D. J. Field. (1997). Sparse Coding with An Overcomplete Basis Set: A Strategy Employed by V1, Vision Research, 37, 3311-3325.   DOI
9 H. S. Hwang & J. S Oh. (2009). Time Series Stock Prices Prediction Based on Fuzzy Model, Journal of The Korean Institute of Intelligent Systems, 19(5), 689-694.   DOI
10 S. H. Koh. (2016). A Converging Approach on Investment Strategies, Past Financial Information, and Investors' Behavioral Bias in the Korean Stock Market, Journal of the Korea Convergence Society, 7(6), 205-212.   DOI
11 C. J. Lu, T. S. Lee & C. C. Chiu. (2009). Financial Time Series Forecasting Using Independent Component Analysis and Support Vector Regression, Decision Support Systems, 42(2), 115-125.
12 H. H. Kim, K. D. Sung, J. W. Jeon & G. T. Yeo. (2016). Analysis of the Relationship Between Freight Index and Shipping Company's Stock Price Index, Journal of digital Convergence, 14(6), 157-165.   DOI
13 S. H. Choi & J. I. Choi. (2017). Analysis of Stock Price Increase and Volatility of Logistics Related Companies, Journal of digital Convergence, 15(2), 135-144.   DOI
14 Z. H. G. Wang, Q. Liu & J. A. Yang. (2014). Feature Fusion Based Forecasting Model for Financial Time Series, Plos One, 9(6), 172-200.
15 W. Huang, Y. Nakamori & S. Y. Wang. (2005). Forecasting Stock Market Movement Direction with Support Vector Machine, Computers & Operations Research, 32(10), 2513-2522.   DOI
16 G. E. Hinton & R. R. Salakhutdinov. (2006). Reducing The Dimensionality of Data with Neural Networks, Science, 313(5786), 504-507.   DOI
17 Y. Bengio, A. Courville & P. Vincent. (2013). Representation Learning: A Review and New Perspectives, IEEE Trans. on Pattern Analysis & Machine Intelligence, 35(8), 1798-1828.   DOI
18 R. C. Cavalcante, R. C. Brasileiro, V. L. F. Souza, J. P. Nobrega & A. L. I. Oliveira. (2016). Computational Intelligence and Financial Markets: A Survey and Future Directions," Expert Systems with Applications, 55, 194-211.   DOI
19 F. Shen, J. Chao & J. Zhao. (2015). Forecasting Exchange Rate Using Deep Belief Networks and Conjugate Gradient Method, Neurocomputing, 167, Issue C, 243-253.   DOI