Financial Market Prediction using News and Financial Data by Sentimental Analysis
Keywords:
Companies, models, Data ,Feature extraction, Facebook, Recurrent neural networks, Stock markets.Abstract
There was a period when economists and other scientists were deeply captivated by the anticipation of speculative exchange costs. Political factors, economic factors, leadership changes, investor perception, and a host of other attributes all made stock prices extremely volatile and difficult to predict. It has been proven to be inadequate to attempt to predict stock prices based on either historical information or published data. Existed studies on opinion analysis show that good amount of correlation has been found between stock price movements and news story publications. A number of estimation studies have been conducted at various stages using algorithms including support vector machines, naive Bayes regression, and deep learning. The accuracy of deep learning algorithms depends on the amount of training data provided. However, the amount of text data collected and analyzed in previous studies was insufficient, resulting in low accuracy in predictions. In this paper, we demonstrate that stock price prediction accuracy can be improved through the collection and analysis of time series data in conjunction with related news using deep learning models. The assembled datasets include daily stock prices for S&P500 companies for a decade, along with more than 265,000 financial news articles related to these companies. Due to the large size of the datasets, we utilize cloud computing as a primary resource for training forecast model and performing predictions for a particular stock in real time. Keywords: stock market prediction, cloud, big data, machine learning, regression.
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Zhong, X., & Enke, D. (2019). Predicting the daily return direction of the stock market using hybrid machine learning algorithms. Financial Innovation, 5(1), 4
Pierdzioch, C., & Risse, M. (2018). A machine‐learning analysis of the rationality of aggregate stock market forecasts. International Journal of Finance & Economics, Cabitza, F., Locoro, A., & Banfi, G. (2018). Machine learning in orthopedics: a literature review. Frontiers in Bioengineering and Biotechnology, 6, 75.
Chong, E., Han, C., & Park, F. C. (2017). Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications, 83, 187-205.
Li, X., Xie, H., Wang, R., Cai, Y., Cao, J., Wang, F., ... & Deng, X. (2016). Empirical analysis: stock market prediction via extreme learning machine. Neural Computing and Applications, 27(1), 67-78.
Dash, R., & Dash, P. K. (2016). A hybrid stock trading framework integrating technical analysis with machine learning techniques. The Journal of Finance and Data Science, 2(1), 42-57.
Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Systems with Applications, 42(1), 259-268.
Chavan, P. S., & Patil, S. T. (2013). Parameters for stock market prediction. International Journal of Computer Technology and Applications, 4(2), 337.
Dai, W., Wu, J. Y., & Lu, C. J. (2012). Combining nonlinear independent component analysis and neural network for the prediction of Asian stock market indexes. Expert systems with applications, 39(4), 4444-4452.
Chiu, D. Y., & Chen, P. J. (2009). Dynamically exploring internal mechanism of stock market by fuzzy-based support vector machines with high dimension input space and genetic algorithm. Expert Systems with Applications, 36(2), 1240-1248.
Das, S. P., & Padhy, S. (2012). Support vector machines for prediction of futures prices in Indian stock market. International Journal of Computer Applications, 41(3).
Guresen, E., Kayakutlu, G., & Daim, T. U. (2011). Using artificial neural network models in stock market index prediction. Expert Systems with Enke,
D., & Thawornwong, S. (2005). The use of data mining and neural networks for forecasting stock market returns. Expert Systems with applications, 29(4), 927-940.
Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS quarterly, 213-236. Applications, 38(8), 10389-10397.
Holland, J. H. (1992). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence.
MIT press.Jasic, T., & Wood, D. (2004). The profitability of daily stock market indices trades based on neural network predictions: Case study for the S&P 500, the DAX, the TOPIX and the FTSE in the period 1965–1999. Applied Financial Economics, 14(4), 285-297.
Kim, H. J., & Shin, K. S. (2007). A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets. Applied Soft Computing, 7(2), 569-576.
Kim, K. J., & Han, I. (2000). Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert systems with Applications, 19(2), 125-132.
Kim, K. J., & Lee, W. B. (2004). Stock market prediction using artificial neural networks with optimal feature transformation. Neural computing & applications, 13(3), 255-260.
Kim, M. J., Min, S. H., & Han, I. (2006). An evolutionary approach to the combination of multiple classifiers to predict a stock price index. Expert Systems with Applications, 31(2), 241- 247.
Kumar, L., Pandey, A., Srivastava, S., & Darbari, M. (2011). A hybrid machine learning system for stock market forecasting. Journal of International Technology and Information Management, 20(1), 3.
Lee, K. H., & Jo, G. S. (1999). Expert system for predicting stock market timing using a candlestick chart. Expert systems with applications, 16(4), 357-364.
Lee, M. C. (2009). Using support vector machine with a hybrid feature selection method to the stock trend prediction. Expert Systems with Applications, 36(8), 10896-10904.
Liao, Z., & Wang, J. (2010). Forecasting model of global stock index by stochastic time effective neural network. Expert Systems with Applications, 37(1), 834-841.
Malhotra, R. (2015). A systematic review of machine learning techniques for software fault prediction. Applied Soft Computing, 27, 504-518.
Mosavi, A., Ozturk, P., & Chau, K. W. (2018). Flood prediction using machine learning models: Literature review. Water, 10(11), 1536.
O, J., Lee, J., Lee, J. W., & Zhang, B-T. (2006). Adaptive stock trading with dynamic asset allocation using reinforcement learning. Information Sciences, 176(15), 2121-2147.
Ou, P., & Wang, H. (2009). Prediction of stock market index movement by ten data mining techniques. Modern Applied Science, 3(12), 28-42.
Pound, J. (2019, December 24). Global stock markets gained $17 trillion in value in 2019. Retrieved from https://www.cnbc.com/2019/12/24/global-stock-markets-gained-17-trillion-in-value-in- 2019.html.
Qian, B., & Rasheed, K. (2007). Stock market prediction with multiple classifiers. Applied Intelligence, 26(1), 25-33.
Schumaker, R. P., & Chen, H. (2009). Textual analysis of stock market prediction using breaking financial news: The AZFin text system. ACM Transactions on Information Systems (TOIS), 27(2), 12.
Schumaker, R. P., & Chen, H. (2010). A discrete stock price prediction engine based on financial news. Computer, 43(1), 51-56.
Wen, J., Li, S., Lin, Z., Hu, Y., & Huang, C. (2012). Systematic literature review of machine learning based software development effort estimation models. Information and Software Technology, 54(1), 41-59.
Yu, L., Chen, H., Wang, S., & Lai, K. K. (2008). Evolving least squares support vector machines for stock market trend mining. IEEE Transactions on evolutionary computation, 13(1), 87- 102.Innovation, 5(1), 4
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