Sentiment Aware Stock Price Forecasting using an SA-RNN-LBL Learning Model
Stock market historical information is often utilized in technical analyses for identifying and evaluating patterns that could be utilized to achieve profits in trading. Although technical analysis utilizing various measures has been proven to be helpful for forecasting and predicting price trends, its utilization in formulating trading orders and rules in an automated system is complex due to the indeterminate nature of the rules. Moreover, it is hard to define a specific combination of technical measures that identify better trading rules and points, since stocks might be affected by different external factors. Thus, it is important to incorporate investors’ sentiments in forecasting operations, considering dynamically the varying stock behavior. This paper presents a sentiment aware stock forecasting model using a Log BiLinear (LBL) model for learning short term stock market sentiment patterns, and a Recurrent Neural Network (RNN) for learning long-term stock market sentiment patterns. The Sentiment Aware Stock Price Forecasting (SASPF) model achieves a much superior performance compared to standard deep learning based stock price forecasting models.
Q. Huang, J. Yang, X. Feng, A. W.-C. Liew, and X. Li, “Automated Trading Point Forecasting Based on Bicluster Mining and Fuzzy Inference,” IEEE Transactions on Fuzzy Systems, vol. 28, no. 2, pp. 259–272, Feb. 2020.
J. Lee, R. Kim, Y. Koh, and J. Kang, “Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network,” IEEE Access, vol. 7, pp. 167260–167277, 2019.
J. H. Kim, A. Shamsuddin, and K.-P. Lim, “Stock return predictability and the adaptive markets hypothesis: Evidence from century-long U.S. data,” Journal of Empirical Finance, vol. 18, no. 5, pp. 868–879, Dec. 2011.
G. W. Schwert, “Chapter 15 Anomalies and market efficiency,” in Handbook of the Economics of Finance, vol. 1, Elsevier, 2003, pp. 939–974.
H. R. Stoll and R. E. Whaley, “Transaction costs and the small firm effect,” Journal of Financial Economics, vol. 12, no. 1, pp. 57–79, Jun. 1983.
C. H. Park and S. H. Irwin, “What Do We Know About the Profitability of Technical Analysis?,” Journal of Economic Surveys, vol. 21, no. 4, pp. 786–826, 2007.
J. Bollen, H. Mao, and X. Zeng, “Twitter mood predicts the stock market,” Journal of Computational Science, vol. 2, no. 1, pp. 1–8, Mar. 2011.
D. Huang, F. Jiang, J. Tu, and G. Zhou, “Investor Sentiment Aligned: A Powerful Predictor of Stock Returns,” The Review of Financial Studies, vol. 28, no. 3, pp. 791–837, Mar. 2015
R. P. Schumaker and H. Chen, “Textual analysis of stock market prediction using breaking financial news: The AZFin text system,” ACM Transactions on Information Systems, vol. 27, no. 2, p. 12:1–12:19, Mar. 2009.
T. Preis, H. S. Moat, and H. E. Stanley, “Quantifying Trading Behavior in Financial Markets Using Google Trends,” Scientific Reports, vol. 3, no. 1, Art. no. 1684, Apr. 2013.
“WHO | Novel coronavirus (COVID-19),” WHO. http://www.who.int/bulletin/online_first/COVID-19/en/ (accessed Sep. 30, 2020).
World Health Organisation, “Virtual press conference on COVID-19”, Mar. 11 2020. [Online]. Available: https://www.who.int/docs/default-source/coronaviruse/transcripts/who-audio-emergencies-coronavirus-press-conference-full-and-final-11mar2020.pdf?sfvrsn=cb432bb3_2
H. Duan, S. Wang, and C. Yang, “Coronavirus: limit short-term economic damage,” Nature, vol. 578, no. 7796, pp. 515–515, Feb. 2020.
J. Han, X.-P. Zhang, and F. Wang, “Gaussian Process Regression Stochastic Volatility Model for Financial Time Series,” IEEE Journal of Selected Topics in Signal Processing, vol. 10, no. 6, pp. 1015–1028, Sep. 2016.
X. Zhou, Z. Pan, G. Hu, S. Tang, and C. Zhao, “Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets,” Mathematical Problems in Engineering, Art. no. 4907423, Apr. 2018.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015.
U. Gurav and N. Sidnal, “Predict Stock Market Behavior: Role of Machine Learning Algorithms,” in Intelligent Computing and Information and Communication, Singapore, 2018, pp. 383–394.
P. Chakraborty and C. Tharini, “Pneumonia and Eye Disease Detection using Convolutional Neural Networks,” Engineering, Technology & Applied Science Research, vol. 10, no. 3, pp. 5769–5774, Jun. 2020.
U. Gurav and S. Kotrappa, “Lbl - Lstm : Log Bilinear And Long Short Term Memory Based Efficient Stock Forecasting Model Considering External Fluctuating Factor,” International Journal of Engineering and Advanced Technology, vol. 9, no. 4, pp. 2057–2063, Apr. 2020.
Uma Gurav, Dr. Nandini Sidnal,"Opinion mining for reputation evaluation on unstructured big data", International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Volume 4 Issue 4, April 2015.
U. Gurav and D. S. Kotrappa, “Impact of COVID-19 on Stock Market Performance Using Efficient and Predictive LBL-LSTM Based Mathematical Model,” International Journal on Emerging Technologies, vol. 11, no. 4, pp. 108–115, Aug. 2020.
S. L. Sanga, D. Machuve, and K. Jomanga, “Mobile-based Deep Learning Models for Banana Disease Detection,” Engineering, Technology & Applied Science Research, vol. 10, no. 3, pp. 5674–5677, Jun. 2020.
Y. Deng, F. Bao, Y. Kong, Z. Ren, and Q. Dai, “Deep Direct Reinforcement Learning for Financial Signal Representation and Trading,” IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 3, pp. 653–664, Mar. 2017.
G. Ding and L. Qin, “Study on the prediction of stock price based on the associated network model of LSTM,” International Journal of Machine Learning and Cybernetics, vol. 11, no. 6, pp. 1307–1317, Jun. 2020.
H. Li, J. Hua, J. Li, and G. Li, “Stock Forecasting Model FS-LSTM Based on the 5G Internet of Things,” Wireless Communications and Mobile Computing, Art. no. 7681209, Jun. 2020.
X. Teng, T. Wang, X. Zhang, L. Lan, and Z. Luo, “Enhancing Stock Price Trend Prediction via a Time-Sensitive Data Augmentation Method,” Complexity, Art. no. 6737951, Feb. 2020.
U. Gurav and S. Kotrappa, “RNNLBL: A Recurrent Neural Network and Log Bilinearbased Efficient StockForecastingModel,” International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 9, no. 4, pp. 1676–1682, Feb. 2020.
U. Gurav and N. Sidnal, “Adaptive Stock Forecasting Model using Modified Backpropagation Neural Network (MBNN),” in 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS), Dec. 2018, pp. 380–385.
Coronavirus (COVID-19) Tweets Dataset, IEEE DataPort. [Online]. Available: https://ieee-dataport.org/open-access/coronavirus-covid-19-tweets-dataset. Accessed: Jul. 30, 2020.
“Yahoo Finance.” https://in.finance.yahoo.com/ (accessed Jul. 30, 2020).
“Yahoo Finance.” https://in.finance.yahoo.com/topic/latestnews/ (accessed Jul. 30, 2020).
“Wind Financial Terminal – Chinese Stock Dataset.” https://www.wind.com.cn (accessed Jul. 30, 2020).
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