Multi-Sensor Temporal Fusion Transformer for Stock Performance Prediction: An Adaptive Sharpe Ratio Approach
Accurate prediction of the Sharpe ratio, a key metric for risk-adjusted returns in financial markets, remains a significant challenge due to the complex and stochastic nature of stock price movements. This paper introduces a novel deep learning model, the Temporal Fusion Transformer with Adaptive Sh...
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MDPI AG
2025-02-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/3/976 |
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| author | Jingyun Yang Pan Li Yiwen Cui Xu Han Mengjie Zhou |
| author_facet | Jingyun Yang Pan Li Yiwen Cui Xu Han Mengjie Zhou |
| author_sort | Jingyun Yang |
| collection | DOAJ |
| description | Accurate prediction of the Sharpe ratio, a key metric for risk-adjusted returns in financial markets, remains a significant challenge due to the complex and stochastic nature of stock price movements. This paper introduces a novel deep learning model, the Temporal Fusion Transformer with Adaptive Sharpe Ratio Optimization (TFT-ASRO), designed to address this challenge. The model incorporates real-time market sensor data and financial indicators as input signals, leveraging multiple data streams including price sensors, volume sensors, and market sentiment sensors to capture the complete market state. Using a comprehensive dataset of US historical stock prices and earnings data, we demonstrate that TFT-ASRO outperforms traditional methods and existing deep learning models in predicting Sharpe ratios across various time horizons. The model’s multi-task learning framework, which simultaneously predicts returns and volatility, provides a more nuanced understanding of risk-adjusted performance. Furthermore, our adaptive optimization approach effectively balances the trade-off between return maximization and risk minimization, leading to more robust predictions. Empirical results show that TFT-ASRO achieves a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>18</mn><mo>%</mo></mrow></semantics></math></inline-formula> improvement in Sharpe ratio prediction accuracy compared to state-of-the-art baselines, with particularly strong performance in volatile market conditions. The model also demonstrates superior uncertainty quantification, providing reliable confidence intervals for its predictions. These findings have significant implications for portfolio management and investment strategy optimization, offering a powerful tool for financial decision-makers in the era of data-driven investing. |
| format | Article |
| id | doaj-art-505e1c2baabb4cf39ab48ca836d44292 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
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| series | Sensors |
| spelling | doaj-art-505e1c2baabb4cf39ab48ca836d442922025-08-20T03:12:35ZengMDPI AGSensors1424-82202025-02-0125397610.3390/s25030976Multi-Sensor Temporal Fusion Transformer for Stock Performance Prediction: An Adaptive Sharpe Ratio ApproachJingyun Yang0Pan Li1Yiwen Cui2Xu Han3Mengjie Zhou4David A. Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA 15213, USABusiness School, The University of Hull, Hull HU6 7RX, UKMcCallum Graduate School of Business, Bentley University, Waltham, MA 02452, USASchool of Business, Renmin University of China, Beijing 100872, ChinaDepartment of Computer Science, The University of Bristol, Bristol BS8 1QU, UKAccurate prediction of the Sharpe ratio, a key metric for risk-adjusted returns in financial markets, remains a significant challenge due to the complex and stochastic nature of stock price movements. This paper introduces a novel deep learning model, the Temporal Fusion Transformer with Adaptive Sharpe Ratio Optimization (TFT-ASRO), designed to address this challenge. The model incorporates real-time market sensor data and financial indicators as input signals, leveraging multiple data streams including price sensors, volume sensors, and market sentiment sensors to capture the complete market state. Using a comprehensive dataset of US historical stock prices and earnings data, we demonstrate that TFT-ASRO outperforms traditional methods and existing deep learning models in predicting Sharpe ratios across various time horizons. The model’s multi-task learning framework, which simultaneously predicts returns and volatility, provides a more nuanced understanding of risk-adjusted performance. Furthermore, our adaptive optimization approach effectively balances the trade-off between return maximization and risk minimization, leading to more robust predictions. Empirical results show that TFT-ASRO achieves a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>18</mn><mo>%</mo></mrow></semantics></math></inline-formula> improvement in Sharpe ratio prediction accuracy compared to state-of-the-art baselines, with particularly strong performance in volatile market conditions. The model also demonstrates superior uncertainty quantification, providing reliable confidence intervals for its predictions. These findings have significant implications for portfolio management and investment strategy optimization, offering a powerful tool for financial decision-makers in the era of data-driven investing.https://www.mdpi.com/1424-8220/25/3/976multi-task learningsensorsharpe ratiostock predictiontemporal fusion transformeruncertainty quantification |
| spellingShingle | Jingyun Yang Pan Li Yiwen Cui Xu Han Mengjie Zhou Multi-Sensor Temporal Fusion Transformer for Stock Performance Prediction: An Adaptive Sharpe Ratio Approach Sensors multi-task learning sensor sharpe ratio stock prediction temporal fusion transformer uncertainty quantification |
| title | Multi-Sensor Temporal Fusion Transformer for Stock Performance Prediction: An Adaptive Sharpe Ratio Approach |
| title_full | Multi-Sensor Temporal Fusion Transformer for Stock Performance Prediction: An Adaptive Sharpe Ratio Approach |
| title_fullStr | Multi-Sensor Temporal Fusion Transformer for Stock Performance Prediction: An Adaptive Sharpe Ratio Approach |
| title_full_unstemmed | Multi-Sensor Temporal Fusion Transformer for Stock Performance Prediction: An Adaptive Sharpe Ratio Approach |
| title_short | Multi-Sensor Temporal Fusion Transformer for Stock Performance Prediction: An Adaptive Sharpe Ratio Approach |
| title_sort | multi sensor temporal fusion transformer for stock performance prediction an adaptive sharpe ratio approach |
| topic | multi-task learning sensor sharpe ratio stock prediction temporal fusion transformer uncertainty quantification |
| url | https://www.mdpi.com/1424-8220/25/3/976 |
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