Estimating stock closing indices using a GA-weighted condensed polynomial neural network
Abstract Accurate forecasting of changes in stock market indices can provide financial managers and individual investors with strategically valuable information. However, predicting the closing prices of stock indices remains a challenging task because stock price movements are characterized by high...
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2018-09-01
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| Series: | Financial Innovation |
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| Online Access: | http://link.springer.com/article/10.1186/s40854-018-0104-2 |
| _version_ | 1818010671364702208 |
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| author | Sarat Chandra Nayak Bijan Bihari Misra |
| author_facet | Sarat Chandra Nayak Bijan Bihari Misra |
| author_sort | Sarat Chandra Nayak |
| collection | DOAJ |
| description | Abstract Accurate forecasting of changes in stock market indices can provide financial managers and individual investors with strategically valuable information. However, predicting the closing prices of stock indices remains a challenging task because stock price movements are characterized by high volatility and nonlinearity. This paper proposes a novel condensed polynomial neural network (CPNN) for the task of forecasting stock closing price indices. We developed a model that uses partial descriptions (PDs) and is limited to only two layers for the PNN architecture. The outputs of these PDs along with the original features are fed to a single output neuron, and the synaptic weight values and biases of the CPNN are optimized by a genetic algorithm. The proposed model was evaluated by predicting the next day’s closing price of five fast-growing stock indices: the BSE, DJIA, NASDAQ, FTSE, and TAIEX. In comparative testing, the proposed model proved its ability to provide closing price predictions with superior accuracy. Further, the Deibold-Mariano test justified the statistical significance of the model, establishing that this approach can be adopted as a competent financial forecasting tool. |
| first_indexed | 2024-04-14T05:58:23Z |
| format | Article |
| id | doaj.art-dfdc66237df04395a65d8baec4ddac8c |
| institution | Directory Open Access Journal |
| issn | 2199-4730 |
| language | English |
| last_indexed | 2024-04-14T05:58:23Z |
| publishDate | 2018-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Financial Innovation |
| spelling | doaj.art-dfdc66237df04395a65d8baec4ddac8c2022-12-22T02:08:52ZengSpringerOpenFinancial Innovation2199-47302018-09-014112210.1186/s40854-018-0104-2Estimating stock closing indices using a GA-weighted condensed polynomial neural networkSarat Chandra Nayak0Bijan Bihari Misra1Department of Computer Science and Engineering, CMR College of Engineering &Technology (Autonomous)Department of Information Technology, Silicon Institute of TechnologyAbstract Accurate forecasting of changes in stock market indices can provide financial managers and individual investors with strategically valuable information. However, predicting the closing prices of stock indices remains a challenging task because stock price movements are characterized by high volatility and nonlinearity. This paper proposes a novel condensed polynomial neural network (CPNN) for the task of forecasting stock closing price indices. We developed a model that uses partial descriptions (PDs) and is limited to only two layers for the PNN architecture. The outputs of these PDs along with the original features are fed to a single output neuron, and the synaptic weight values and biases of the CPNN are optimized by a genetic algorithm. The proposed model was evaluated by predicting the next day’s closing price of five fast-growing stock indices: the BSE, DJIA, NASDAQ, FTSE, and TAIEX. In comparative testing, the proposed model proved its ability to provide closing price predictions with superior accuracy. Further, the Deibold-Mariano test justified the statistical significance of the model, establishing that this approach can be adopted as a competent financial forecasting tool.http://link.springer.com/article/10.1186/s40854-018-0104-2Stock market forecastingPolynomial neural networkPartial descriptionGenetic algorithmMultilayer perceptron |
| spellingShingle | Sarat Chandra Nayak Bijan Bihari Misra Estimating stock closing indices using a GA-weighted condensed polynomial neural network Financial Innovation Stock market forecasting Polynomial neural network Partial description Genetic algorithm Multilayer perceptron |
| title | Estimating stock closing indices using a GA-weighted condensed polynomial neural network |
| title_full | Estimating stock closing indices using a GA-weighted condensed polynomial neural network |
| title_fullStr | Estimating stock closing indices using a GA-weighted condensed polynomial neural network |
| title_full_unstemmed | Estimating stock closing indices using a GA-weighted condensed polynomial neural network |
| title_short | Estimating stock closing indices using a GA-weighted condensed polynomial neural network |
| title_sort | estimating stock closing indices using a ga weighted condensed polynomial neural network |
| topic | Stock market forecasting Polynomial neural network Partial description Genetic algorithm Multilayer perceptron |
| url | http://link.springer.com/article/10.1186/s40854-018-0104-2 |
| work_keys_str_mv | AT saratchandranayak estimatingstockclosingindicesusingagaweightedcondensedpolynomialneuralnetwork AT bijanbiharimisra estimatingstockclosingindicesusingagaweightedcondensedpolynomialneuralnetwork |