Resilient back-propagation algorithm, technical analysis and the predictability of time series in the financial industry
In financial industry, the accurate forecasting of the stock market is a major challenge to optimize and update portfolios and also to evaluate several financial derivatives. Artificial neural networks and technical analysis are becoming widely used by industry experts to predict stock market moves....
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| Format: | Article |
| Language: | English |
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Growing Science
2012-07-01
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| Series: | Decision Science Letters |
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| Online Access: | http://www.growingscience.com/dsl/Vol1/dsl_2012_9.pdf |
| _version_ | 1819001362982109184 |
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| author | Salim Lahmiri |
| author_facet | Salim Lahmiri |
| author_sort | Salim Lahmiri |
| collection | DOAJ |
| description | In financial industry, the accurate forecasting of the stock market is a major challenge to optimize and update portfolios and also to evaluate several financial derivatives. Artificial neural networks and technical analysis are becoming widely used by industry experts to predict stock market moves. In this paper, different technical analysis measures and resilient back-propagation neural networks are used to predict the price level of five major developed international stock markets, namely the US S&P500, Japanese Nikkei, UK FTSE100, German DAX, and the French CAC40. Four categories of technical analysis measures are compared. They are indicators, oscillators, stochastics, and indexes. The out-of-sample simulation results show a strong evidence of the effectiveness of the indicators category over the oscillators, stochastics, and indexes. In addition, it is found that combining all these measures lead to an increase of the prediction error. In sum, technical analysis indicators provide valuable information to predict the S&P500, Nikkei, FTSE100, DAX, and the CAC40 price level. |
| first_indexed | 2024-12-20T22:48:01Z |
| format | Article |
| id | doaj.art-f38e293ea5b6401fafed51d1fb716476 |
| institution | Directory Open Access Journal |
| issn | 1929-5804 1929-5812 |
| language | English |
| last_indexed | 2024-12-20T22:48:01Z |
| publishDate | 2012-07-01 |
| publisher | Growing Science |
| record_format | Article |
| series | Decision Science Letters |
| spelling | doaj.art-f38e293ea5b6401fafed51d1fb7164762022-12-21T19:24:20ZengGrowing ScienceDecision Science Letters1929-58041929-58122012-07-0112475210.5267/j.dsl.2012.09.002Resilient back-propagation algorithm, technical analysis and the predictability of time series in the financial industrySalim LahmiriIn financial industry, the accurate forecasting of the stock market is a major challenge to optimize and update portfolios and also to evaluate several financial derivatives. Artificial neural networks and technical analysis are becoming widely used by industry experts to predict stock market moves. In this paper, different technical analysis measures and resilient back-propagation neural networks are used to predict the price level of five major developed international stock markets, namely the US S&P500, Japanese Nikkei, UK FTSE100, German DAX, and the French CAC40. Four categories of technical analysis measures are compared. They are indicators, oscillators, stochastics, and indexes. The out-of-sample simulation results show a strong evidence of the effectiveness of the indicators category over the oscillators, stochastics, and indexes. In addition, it is found that combining all these measures lead to an increase of the prediction error. In sum, technical analysis indicators provide valuable information to predict the S&P500, Nikkei, FTSE100, DAX, and the CAC40 price level.http://www.growingscience.com/dsl/Vol1/dsl_2012_9.pdfArtificial neural networksResilient back-propagation algorithmTechnical analysisInternational stock marketsForecasting |
| spellingShingle | Salim Lahmiri Resilient back-propagation algorithm, technical analysis and the predictability of time series in the financial industry Decision Science Letters Artificial neural networks Resilient back-propagation algorithm Technical analysis International stock markets Forecasting |
| title | Resilient back-propagation algorithm, technical analysis and the predictability of time series in the financial industry |
| title_full | Resilient back-propagation algorithm, technical analysis and the predictability of time series in the financial industry |
| title_fullStr | Resilient back-propagation algorithm, technical analysis and the predictability of time series in the financial industry |
| title_full_unstemmed | Resilient back-propagation algorithm, technical analysis and the predictability of time series in the financial industry |
| title_short | Resilient back-propagation algorithm, technical analysis and the predictability of time series in the financial industry |
| title_sort | resilient back propagation algorithm technical analysis and the predictability of time series in the financial industry |
| topic | Artificial neural networks Resilient back-propagation algorithm Technical analysis International stock markets Forecasting |
| url | http://www.growingscience.com/dsl/Vol1/dsl_2012_9.pdf |
| work_keys_str_mv | AT salimlahmiri resilientbackpropagationalgorithmtechnicalanalysisandthepredictabilityoftimeseriesinthefinancialindustry |