OVER THE COUNTER STOCKS DATA ACQUISITION & ANALYSIS WITH TIME SERIES PREDICTION

Authors

  • YUSUFF ADENIYI GIWA School of Computing, Engineering and Digital Technologies, Teesside University, UK

Keywords:

OTC stocks, traditional exchanges, smaller companies, time series prediction, LSTM - CNN, investment decisions

Abstract

Over-the-counter (OTC) stocks are securities that are traded outside of traditional exchanges and are usually issued by smaller companies. These stocks can be risky and volatile compared to exchange-listed stocks, but they also have the potential for higher returns. In this study, we aim to acquire and analyze OTC stock data and use time series prediction techniques to forecast future price movements. To acquire the OTC stock data, we use a data API approach provided by otcmarkets.com to gather information about the company, industry trends, and historical price and volume data. We then apply an LSTM - CNN time series prediction model for technical analysis and a fine-tuned BERT model for fundamental analysis to forecast future price movements. Our results show that time series prediction techniques can be useful tools for analyzing OTC stock data and making more informed investment decisions.   

References

S. Sarode, H. G. Tolani, P. Kak and C. S. Lifna, "Stock Price Prediction Using Machine Learning Techniques," 2019 International Conference on Intelligent Sustainable Systems (ICISS), 2019, pp. 177-181, doi: 10.1109/ISS1.2019.8907958.

Lv, P.; Wu, Q.; Xu, J.; Shu Y. Stock Index Prediction Based on Time Series Decomposition and Hybrid Model. Entropy 2022, 24, 146. https://doi.org/10.3390/e24020146

Investopedia (2020). OTC Stocks. Retrieved from https://www.investopedia.com/terms/o/otc.asp

OTC Markets Group (2020). OTCQB, OTCQX and OTC Pink. Retrieved from https://www.otcmarkets.com/investors/otcqb-otcqx-otc-pink

Cooper, S. K., Groth, J. C., & Avera, W. E. (1985). Liquidity, exchange listing, and common stock performance. Journal of Economics and Business, 37(1), 19-33.

Teweles, R. J., & Bradley, E. S. (1998). The stock market (Vol. 64). John Wiley & Sons.

Khan, W., Ghazanfar, M. A., Azam, M. A., Karami, A., Alyoubi, K. H., & Alfakeeh, A. S. (2020). Stock market prediction using machine learning classifiers and social media, news. Journal of Ambient Intelligence and Humanized Computing, 1-24.

Zhong, X., & Enke, D. (2019). Predicting the daily return direction of the stock market using hybrid machine learning algorithms. Financial Innovation, 5(1), 1-20.

Arman, K. N., Teh, Y. W., & David, N. C. L. (2011). A novel FOREX prediction methodology based on fundamental data. African Journal of Business Management, 5(20), 8322-8330.

Kavussanos, M. G., & Nomikos, N. K. (2003). Price discovery, causality and forecasting in the freight futures market. Review of Derivatives Research, 6(3), 203-230.

Roondiwala, M., Patel, H., & Varma, S. (2017). Predicting stock prices using LSTM. International Journal of Science and Research (IJSR), 6(4), 1754-1756.

Jin, Z., Yang, Y., & Liu, Y. (2020). Stock closing price prediction based on sentiment analysis and LSTM. Neural Computing and Applications, 32(13), 9713-9729.

Qiu, M., Song, Y., & Akagi, F. (2016). Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock market. Chaos, Solitons & Fractals, 85, 1-7.

Fenghua, W. E. N., Jihong, X. I. A. O., Zhifang, H. E., & Xu, G. O. N. G. (2014). Stock price prediction based on SSA and SVM. Procedia Computer Science, 31, 625-631.

Chang, T. S. (2011). A comparative study of artificial neural networks, and decision trees for digital game content stocks price prediction. Expert systems with applications, 38(12), 14846-14851.

Gidofalvi, G., & Elkan, C. (2001). Using news articles to predict stock price movements. Department of computer science and engineering, university of california, san diego, 17.

Coyne, S., Madiraju, P., & Coelho, J. (2017, November). Forecasting stock prices using social media analysis. In 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech) (pp. 1031-1038). IEEE.

Ma, Z., Bang, G., Wang, C., & Liu, X. (2020). Towards Earnings Call and Stock Price Movement. arXiv preprint arXiv:2009.01317.

Kara, Y., Boyacioglu, M. A., & Baykan, Ö. K. (2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Expert systems with Applications, 38(5), 5311-5319.

Rezaei, H., Faaljou, H., & Mansourfar, G. (2021). Stock price prediction using deep learning and frequency decomposition. Expert Systems with Applications, 169, 114332.

Shen, J., & Shafiq, M. O. (2020). Short-term stock market price trend prediction using a comprehensive deep learning system. Journal of big Data, 7(1), 1-33.

Birba, D. E. (2020). A Comparative study of data splitting algorithms for machine learning model selection.

Neely, C. J., & Weller, P. A. (2012). Technical analysis in the foreign exchange market. Handbook of exchange rates, 343-373.

Lim, M. A. (2015). The Handbook of Technical Analysis+ Test Bank: The Practitioner's Comprehensive Guide to Technical Analysis. John Wiley & Sons.

Levy, R. A. (1966). Conceptual foundations of technical analysis. Financial Analysts Journal, 22(4), 83-89.

Hu, Y., Feng, B., Zhang, X., Ngai, E. W. T., & Liu, M. (2015). Stock trading rule discovery with an evolutionary trend following model. Expert Systems with Applications, 42(1), 212-222.

Waisi, M. (2020). Advantages and disadvantages of aI-based trading and investing versus traditional methods.

Harries, M., & Horn, K. (1995, November). Detecting concept drift in financial time series prediction using symbolic machine learning. In AI-CONFERENCE- (pp. 91-98). World Scientific Publishing.

Hagenau, M., Liebmann, M., & Neumann, D. (2013). Automated news reading: Stock price prediction based on financial news using context-capturing features. Decision Support Systems, 55(3), 685-697.

Helbich, Marco, et al. "Data-driven regionalization of housing markets." Annals of the Association of American Geographers 103.4 (2013): 871-889.

Martin, D. (1977). Early warning of bank failure: A logit regression approach. Journal of banking & finance, 1(3), 249-276.

Schoenecker, T., & Swanson, L. (2002). Indicators of firm technological capability: validity and performance implications. IEEE Transactions on Engineering Management, 49(1), 36-44.

Kao, D. L., & Shumaker, R. D. (1999). Equity style timing (corrected). Financial Analysts Journal, 55(1), 37-48.

Adam, A. M., & Tweneboah, G. (2008). Macroeconomic factors and stock market movement: Evidence from Ghana. Available at SSRN 1289842.

Roy, R. P., & Roy, S. S. (2022). Commodity futures prices pass-through and monetary policy in India: Does asymmetry matter?. The Journal of Economic Asymmetries, 25, e00229.

Su, K., Zhang, M., & Liu, C. (2022). Financial derivatives, analyst forecasts, and stock price synchronicity: Evidence from an emerging market. Journal of International Financial Markets, Institutions and Money, 81, 101671.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.

Chava, S., Du, W., Shah, A., & Zeng, L. (2022). Measuring firm-level inflation exposure: A deep learning approach. Available at SSRN 4228332.

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), 1-19.

Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.

Yang, Z., He, X., Gao, J., Deng, L., & Smola, A. (2016). Stacked attention networks for image question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 21-29).

Li, Q. (2022). Attention-Based Encoder-Decoder Models for Speech Processing (Doctoral dissertation, University of Cambridge).

Ma, X., Zhang, P., Zhang, S., Duan, N., Hou, Y., Zhou, M., & Song, D. (2019). A tensorized transformer for language modeling. Advances in neural information processing systems, 32.

Zhang, X., Chen, Y., & He, L. (2022). Information block multi-head subspace based long short-term memory networks for sentiment analysis. Applied Intelligence, 1-19.

Chen, P., Yu, H. F., Dhillon, I., & Hsieh, C. J. (2021). Drone: Data-aware low-rank compression for large nlp models. Advances in neural information processing systems, 34, 29321-29334.

Wilson, C. (2013). Portable Game Based Instruction of American Sign Language.

Luo, C., He, X., Zhan, J., Wang, L., Gao, W., & Dai, J. (2020). Comparison and benchmarking of ai models and frameworks on mobile devices. arXiv preprint arXiv:2005.05085.

Tran, T. T., Richardson, A. J., Chen, V. M., & Lin, K. Y. (2022). Fast and accurate ophthalmic medication bottle identification using deep learning on a smartphone device. Ophthalmology Glaucoma, 5(2), 188-194.

Araci, D. (2019). Finbert: Financial sentiment analysis with pre-trained language models. arXiv preprint arXiv:1908.10063.

Data source https://www.otcmarkets.com/research/stock-screener

Data Source; https://www.kaggle.com/datasets/sbhatti/financial-sentiment-analysis

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Published

2024-03-27

How to Cite

GIWA, Y. A. (2024). OVER THE COUNTER STOCKS DATA ACQUISITION & ANALYSIS WITH TIME SERIES PREDICTION. International Journal of Social Sciences and Scientific Studies, 4(1), 3643 - 3670. Retrieved from https://ijssass.com/index.php/ijssass/article/view/272