Effective short-term forecasts of Saudi stock price trends using technical indicators and large-scale multivariate time series
Forecasting the stock market trend and movement is a challenging task due to multiple factors, including the stock’s natural volatility and nonlinearity. It concerns discovering the market’s hidden patterns with respect to time to enable proactive decision-making and better futuristic insights. Recu...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2023-01-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-1205.pdf |
| _version_ | 1797958322674991104 |
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| author | Ahmad O. Aseeri |
| author_facet | Ahmad O. Aseeri |
| author_sort | Ahmad O. Aseeri |
| collection | DOAJ |
| description | Forecasting the stock market trend and movement is a challenging task due to multiple factors, including the stock’s natural volatility and nonlinearity. It concerns discovering the market’s hidden patterns with respect to time to enable proactive decision-making and better futuristic insights. Recurrent neural network-based methods have been a prime candidate for solving complex and nonlinear sequences, including the task of modeling multivariate time series forecasts. Due to the lack of comprehensive and reference work in short-term forecasts for the Saudi stock price and trends, this article introduces a comprehensive and accurate forecasting methodology tailored to the Saudi stock market. Two steps were configured to render effective short-term forecasts. First, a custom-built feature engineering streamline was constructed to preprocess the raw stock data and enable financial-related technical indicators, followed by a stride-based sliding window to produce multivariate time series data ready for the modeling phase. Second, a well-architected Gated Recurrent Unit (GRU) model was constructed and carefully calibrated to yield accurate multi-step forecasts, which was trained using the recently published historical multivariate time-series data from the primary Saudi stock market index (TASI index), in addition to being benchmarked against a suitable baseline model, namely Vector Autoregression Moving-Average with Exogenous Regressors (VARMAX). The output predictions from the proposed GRU model and the VARMAX model were evaluated using a set of regression-based metrics to assess and interpret the model precision. The empirical results demonstrate that the proposed methodology yields outstanding short-term forecasts of the Saudi stock price trends price compared to existing efforts related to this work. |
| first_indexed | 2024-04-11T00:17:19Z |
| format | Article |
| id | doaj.art-34c22367cd9840fab213acc69f124e9f |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-04-11T00:17:19Z |
| publishDate | 2023-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-34c22367cd9840fab213acc69f124e9f2023-01-08T15:05:18ZengPeerJ Inc.PeerJ Computer Science2376-59922023-01-019e120510.7717/peerj-cs.1205Effective short-term forecasts of Saudi stock price trends using technical indicators and large-scale multivariate time seriesAhmad O. AseeriForecasting the stock market trend and movement is a challenging task due to multiple factors, including the stock’s natural volatility and nonlinearity. It concerns discovering the market’s hidden patterns with respect to time to enable proactive decision-making and better futuristic insights. Recurrent neural network-based methods have been a prime candidate for solving complex and nonlinear sequences, including the task of modeling multivariate time series forecasts. Due to the lack of comprehensive and reference work in short-term forecasts for the Saudi stock price and trends, this article introduces a comprehensive and accurate forecasting methodology tailored to the Saudi stock market. Two steps were configured to render effective short-term forecasts. First, a custom-built feature engineering streamline was constructed to preprocess the raw stock data and enable financial-related technical indicators, followed by a stride-based sliding window to produce multivariate time series data ready for the modeling phase. Second, a well-architected Gated Recurrent Unit (GRU) model was constructed and carefully calibrated to yield accurate multi-step forecasts, which was trained using the recently published historical multivariate time-series data from the primary Saudi stock market index (TASI index), in addition to being benchmarked against a suitable baseline model, namely Vector Autoregression Moving-Average with Exogenous Regressors (VARMAX). The output predictions from the proposed GRU model and the VARMAX model were evaluated using a set of regression-based metrics to assess and interpret the model precision. The empirical results demonstrate that the proposed methodology yields outstanding short-term forecasts of the Saudi stock price trends price compared to existing efforts related to this work.https://peerj.com/articles/cs-1205.pdfMultivariate time seriesTime series forecastGated recurrent unitsStock market forecastMulti step forecast |
| spellingShingle | Ahmad O. Aseeri Effective short-term forecasts of Saudi stock price trends using technical indicators and large-scale multivariate time series PeerJ Computer Science Multivariate time series Time series forecast Gated recurrent units Stock market forecast Multi step forecast |
| title | Effective short-term forecasts of Saudi stock price trends using technical indicators and large-scale multivariate time series |
| title_full | Effective short-term forecasts of Saudi stock price trends using technical indicators and large-scale multivariate time series |
| title_fullStr | Effective short-term forecasts of Saudi stock price trends using technical indicators and large-scale multivariate time series |
| title_full_unstemmed | Effective short-term forecasts of Saudi stock price trends using technical indicators and large-scale multivariate time series |
| title_short | Effective short-term forecasts of Saudi stock price trends using technical indicators and large-scale multivariate time series |
| title_sort | effective short term forecasts of saudi stock price trends using technical indicators and large scale multivariate time series |
| topic | Multivariate time series Time series forecast Gated recurrent units Stock market forecast Multi step forecast |
| url | https://peerj.com/articles/cs-1205.pdf |
| work_keys_str_mv | AT ahmadoaseeri effectiveshorttermforecastsofsaudistockpricetrendsusingtechnicalindicatorsandlargescalemultivariatetimeseries |