Abstract
Rising dissemination of learning algorithms in almost all spheres of life has been witnessed in the last 5 years. In this regard, stock market has provided a huge landscape for data science to introduce computational intelligence in otherwise traditional method of handling the global economy. This study evaluates whether Lead Mesa Sine Wave (LMSW) can be a good marker in stock price prediction. Our results reflect that LMSW cannot be used for stock price prediction. We validate our result using learning algorithms. Moreover we have also observed that the future price prediction using historical closing price data can be used as a dependable marker. Due to the time scale nature of the data, we have used recurrent neural network [specifically Long Short-Term Memory (LSTM)] for our prediction model design. The results from the prediction model exhibit better performance with respect to literary counterparts. We have used three publicly available datasets from Reliance, Infosys, and Grasim for the study. We claim two results in this study—LMSW cannot be used as stock price predictor, and LSTM can be used as a good predictor using historical closing price data.
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Chatterjee, S., Adhikary, S., Chakraborty, D., Sarkar, N., Sengupta, D. (2023). Experimental Validation of Mesa Sine Wave in Stock Price Prediction. In: Reddy, A.B., Nagini, S., Balas, V.E., Raju, K.S. (eds) Proceedings of Third International Conference on Advances in Computer Engineering and Communication Systems. Lecture Notes in Networks and Systems, vol 612. Springer, Singapore. https://doi.org/10.1007/978-981-19-9228-5_13
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DOI: https://doi.org/10.1007/978-981-19-9228-5_13
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