Integrating Macroeconomic and Technical Indicators into Forecasting the Stock Market: A Data-Driven Approach
Forecasting stock markets is challenging due to the influence of various internal and external factors compounded by the effects of globalization. This study introduces a data-driven approach to forecast S&P 500 returns by incorporating macroeconomic indicators including gold and oil prices, the...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Economies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7099/13/1/6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588622831812608 |
|---|---|
| author | Saima Latif Faheem Aslam Paulo Ferreira Sohail Iqbal |
| author_facet | Saima Latif Faheem Aslam Paulo Ferreira Sohail Iqbal |
| author_sort | Saima Latif |
| collection | DOAJ |
| description | Forecasting stock markets is challenging due to the influence of various internal and external factors compounded by the effects of globalization. This study introduces a data-driven approach to forecast S&P 500 returns by incorporating macroeconomic indicators including gold and oil prices, the volatility index, economic policy uncertainty, the financial stress index, geopolitical risk, and shadow short rate, with ten technical indicators. We propose three hybrid deep learning models that sequentially combine convolutional and recurrent neural networks for improved feature extraction and predictive accuracy. These models include the deep belief network with gated recurrent units, the LeNet architecture with gated recurrent units, and the LeNet architecture combined with highway networks. The results demonstrate that the proposed hybrid models achieve higher forecasting accuracy than the single deep learning models. This outcome is attributed to the complementary strengths of convolutional networks in feature extraction and recurrent networks in pattern recognition. Additionally, an analysis using the Shapley method identifies the volatility index, financial stress index, and economic policy uncertainty as the most significant predictors, underscoring the effectiveness of our data-driven approach. These findings highlight the substantial impact of contemporary uncertainty factors on stock markets, emphasizing their importance in studies analyzing market behaviour. |
| format | Article |
| id | doaj-art-57cf76dd06014991b992c8f29d866c03 |
| institution | Kabale University |
| issn | 2227-7099 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Economies |
| spelling | doaj-art-57cf76dd06014991b992c8f29d866c032025-01-24T13:29:59ZengMDPI AGEconomies2227-70992024-12-01131610.3390/economies13010006Integrating Macroeconomic and Technical Indicators into Forecasting the Stock Market: A Data-Driven ApproachSaima Latif0Faheem Aslam1Paulo Ferreira2Sohail Iqbal3Department of Management Sciences, COMSATS University, Park Road, Islamabad 45550, PakistanDepartment of Management Sciences, COMSATS University, Park Road, Islamabad 45550, PakistanVALORIZA—Research Center for Endogenous Resource Valorization, 7300-555 Portalegre, PortugalSchool of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, PakistanForecasting stock markets is challenging due to the influence of various internal and external factors compounded by the effects of globalization. This study introduces a data-driven approach to forecast S&P 500 returns by incorporating macroeconomic indicators including gold and oil prices, the volatility index, economic policy uncertainty, the financial stress index, geopolitical risk, and shadow short rate, with ten technical indicators. We propose three hybrid deep learning models that sequentially combine convolutional and recurrent neural networks for improved feature extraction and predictive accuracy. These models include the deep belief network with gated recurrent units, the LeNet architecture with gated recurrent units, and the LeNet architecture combined with highway networks. The results demonstrate that the proposed hybrid models achieve higher forecasting accuracy than the single deep learning models. This outcome is attributed to the complementary strengths of convolutional networks in feature extraction and recurrent networks in pattern recognition. Additionally, an analysis using the Shapley method identifies the volatility index, financial stress index, and economic policy uncertainty as the most significant predictors, underscoring the effectiveness of our data-driven approach. These findings highlight the substantial impact of contemporary uncertainty factors on stock markets, emphasizing their importance in studies analyzing market behaviour.https://www.mdpi.com/2227-7099/13/1/6economic policy uncertaintyfinancial stress indexforecastinggated recurrent unitgeopolitical riskhighway networks |
| spellingShingle | Saima Latif Faheem Aslam Paulo Ferreira Sohail Iqbal Integrating Macroeconomic and Technical Indicators into Forecasting the Stock Market: A Data-Driven Approach Economies economic policy uncertainty financial stress index forecasting gated recurrent unit geopolitical risk highway networks |
| title | Integrating Macroeconomic and Technical Indicators into Forecasting the Stock Market: A Data-Driven Approach |
| title_full | Integrating Macroeconomic and Technical Indicators into Forecasting the Stock Market: A Data-Driven Approach |
| title_fullStr | Integrating Macroeconomic and Technical Indicators into Forecasting the Stock Market: A Data-Driven Approach |
| title_full_unstemmed | Integrating Macroeconomic and Technical Indicators into Forecasting the Stock Market: A Data-Driven Approach |
| title_short | Integrating Macroeconomic and Technical Indicators into Forecasting the Stock Market: A Data-Driven Approach |
| title_sort | integrating macroeconomic and technical indicators into forecasting the stock market a data driven approach |
| topic | economic policy uncertainty financial stress index forecasting gated recurrent unit geopolitical risk highway networks |
| url | https://www.mdpi.com/2227-7099/13/1/6 |
| work_keys_str_mv | AT saimalatif integratingmacroeconomicandtechnicalindicatorsintoforecastingthestockmarketadatadrivenapproach AT faheemaslam integratingmacroeconomicandtechnicalindicatorsintoforecastingthestockmarketadatadrivenapproach AT pauloferreira integratingmacroeconomicandtechnicalindicatorsintoforecastingthestockmarketadatadrivenapproach AT sohailiqbal integratingmacroeconomicandtechnicalindicatorsintoforecastingthestockmarketadatadrivenapproach |