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- W2898748397 abstract "Stock investors always consider potential future prices before investing in any stock for making a profit. A large number of studies are found on the prediction of stock market indices. However, the focus on individual stock closing price predictions well ahead of time is limited. In this chapter, a comparative study of machine-learning-based models is used for the prediction of the closing price of a particular stock. The proposed models are designed using back propagation neural networks (BPNN), support vector regression (SVR) with SMOReg, and linear regression (LR) for the prediction of the closing price of individual stocks. A total of 37 technical indicators (features) derived from historical closing prices of stocks are considered for predicting the future price of stock in a time window of five days. The experiment is performed on stocks listed on Bombay Stock Exchange (BSS), India. The model is trained and tested using feature values extracted from the past five-year closing price of stocks of different sectors including aviation, pharma, banking, entertainment, and IT." @default.
- W2898748397 created "2018-11-09" @default.
- W2898748397 creator A5002287372 @default.
- W2898748397 creator A5013636671 @default.
- W2898748397 date "2018-01-01" @default.
- W2898748397 modified "2023-10-16" @default.
- W2898748397 title "Machine Learning Models for Forecasting of Individual Stocks Price Patterns" @default.
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- W2898748397 doi "https://doi.org/10.4018/978-1-5225-3870-7.ch008" @default.
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