A Hybrid Stock Price Prediction Model Based on PRE and Deep Neural Network

Srivinay, B. C. Manujakshi, Mohan Govindsa Kabadi and Nagaraj Naik
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Srivinay: Department of Computer Science and Engineering, Presidency University, Bangalore 560065, India
B. C. Manujakshi: Department of Computer Science and Engineering, Presidency University, Bangalore 560065, India
Mohan Govindsa Kabadi: Department of Computer Science and Engineering, GITAM School of Technology, GITAM (Deemed to Be University), Bangalore 561203, India
Nagaraj Naik: Nitte Meenakshi Institute of Technology, Bangalore 560064, India

Data, 2022, vol. 7, issue 5, 1-11

Abstract: Stock prices are volatile due to different factors that are involved in the stock market, such as geopolitical tension, company earnings, and commodity prices, affecting stock price. Sometimes stock prices react to domestic uncertainty such as reserve bank policy, government policy, inflation, and global market uncertainty. The volatility estimation of stock is one of the challenging tasks for traders. Accurate prediction of stock price helps investors to reduce the risk in portfolio or investment. Stock prices are nonlinear. To deal with nonlinearity in data, we propose a hybrid stock prediction model using the prediction rule ensembles (PRE) technique and deep neural network (DNN). First, stock technical indicators are considered to identify the uptrend in stock prices. We considered moving average technical indicators: moving average 20 days, moving average 50 days, and moving average 200 days. Second, using the PRE technique-computed different rules for stock prediction, we selected the rules with the lowest root mean square error (RMSE) score. Third, the three-layer DNN is considered for stock prediction. We have fine-tuned the hyperparameters of DNN, such as the number of layers, learning rate, neurons, and number of epochs in the model. Fourth, the average results of the PRE and DNN prediction model are combined. The hybrid stock prediction model results are computed using the mean absolute error (MAE) and RMSE metric. The performance of the hybrid stock prediction model is better than the single prediction model, namely DNN and ANN, with a 5% to 7% improvement in RMSE score. The Indian stock price data are considered for the work.

Keywords: prediction rule ensembles; deep neural network; moving average (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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