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


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


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.   


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

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




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