A Hybrid Stock Price Prediction Model Based on PRE and Deep Neural Network
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 g...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-04-01
|
| Series: | Data |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5729/7/5/51 |
| _version_ | 1797500523765563392 |
|---|---|
| author | Srivinay B. C. Manujakshi Mohan Govindsa Kabadi Nagaraj Naik |
| author_facet | Srivinay B. C. Manujakshi Mohan Govindsa Kabadi Nagaraj Naik |
| author_sort | Srivinay |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T03:04:04Z |
| format | Article |
| id | doaj.art-a351f9465e784ce2928b9f44baef9aaa |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-03-10T03:04:04Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-a351f9465e784ce2928b9f44baef9aaa2023-11-23T10:37:37ZengMDPI AGData2306-57292022-04-01755110.3390/data7050051A Hybrid Stock Price Prediction Model Based on PRE and Deep Neural NetworkSrivinay0B. C. Manujakshi1Mohan Govindsa Kabadi2Nagaraj Naik3Department of Computer Science and Engineering, Presidency University, Bangalore 560065, IndiaDepartment of Computer Science and Engineering, Presidency University, Bangalore 560065, IndiaDepartment of Computer Science and Engineering, GITAM School of Technology, GITAM (Deemed to Be University), Bangalore 561203, IndiaNitte Meenakshi Institute of Technology, Bangalore 560064, IndiaStock 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.https://www.mdpi.com/2306-5729/7/5/51prediction rule ensemblesdeep neural networkmoving average |
| spellingShingle | Srivinay B. C. Manujakshi Mohan Govindsa Kabadi Nagaraj Naik A Hybrid Stock Price Prediction Model Based on PRE and Deep Neural Network Data prediction rule ensembles deep neural network moving average |
| title | A Hybrid Stock Price Prediction Model Based on PRE and Deep Neural Network |
| title_full | A Hybrid Stock Price Prediction Model Based on PRE and Deep Neural Network |
| title_fullStr | A Hybrid Stock Price Prediction Model Based on PRE and Deep Neural Network |
| title_full_unstemmed | A Hybrid Stock Price Prediction Model Based on PRE and Deep Neural Network |
| title_short | A Hybrid Stock Price Prediction Model Based on PRE and Deep Neural Network |
| title_sort | hybrid stock price prediction model based on pre and deep neural network |
| topic | prediction rule ensembles deep neural network moving average |
| url | https://www.mdpi.com/2306-5729/7/5/51 |
| work_keys_str_mv | AT srivinay ahybridstockpricepredictionmodelbasedonpreanddeepneuralnetwork AT bcmanujakshi ahybridstockpricepredictionmodelbasedonpreanddeepneuralnetwork AT mohangovindsakabadi ahybridstockpricepredictionmodelbasedonpreanddeepneuralnetwork AT nagarajnaik ahybridstockpricepredictionmodelbasedonpreanddeepneuralnetwork AT srivinay hybridstockpricepredictionmodelbasedonpreanddeepneuralnetwork AT bcmanujakshi hybridstockpricepredictionmodelbasedonpreanddeepneuralnetwork AT mohangovindsakabadi hybridstockpricepredictionmodelbasedonpreanddeepneuralnetwork AT nagarajnaik hybridstockpricepredictionmodelbasedonpreanddeepneuralnetwork |