Abstract
Stock price prediction is the act of predicting the value of the stock of a particular company in the future to maximize an investor’s profit. In this paper, we propose a machine learning model for stock price prediction. The machine learning model uses LSTM (Long short term memory networks) and Multiple regression algorithms. Along with the machine learning model we look at a couple of important ratios and sentiment analysis which are indicative of whether a stock is overvalued or undervalued. Our model is designed to be particularly helpful for short-term investors for deciding entry and exit points during stock trading.
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Sujatha, R., Abhyankar, V., Gehlot, A., Gupta, P., Subramaniam, S. (2021). Stock Market Trend Prediction Using Regression Model, RNNs, and Sentiment Analysis. In: Komanapalli, V.L.N., Sivakumaran, N., Hampannavar, S. (eds) Advances in Automation, Signal Processing, Instrumentation, and Control. i-CASIC 2020. Lecture Notes in Electrical Engineering, vol 700. Springer, Singapore. https://doi.org/10.1007/978-981-15-8221-9_27
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DOI: https://doi.org/10.1007/978-981-15-8221-9_27
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