Return predictability of prospect theory: Evidence from the Thailand stock market

Chen, Xi; Wang, Junbo; Zhong, Xiaoling

doi:10.1016/j.pacfin.2023.102199

Cited by 4 publications

(2 citation statements)

References 57 publications

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“…• Prospect Theory: This hypothesis, which Kahneman and Tversky developed, shows that people often assess possible losses and profits asymmetrically, undermining the notion that people make rational decisions. It presents the idea of "loss aversion" and implies that people frequently base their decisions on how they perceive their reference point [83]. • Game Theory: Game theory examines decision-making in interactive scenarios where the result of one participant's choice depends on the decisions of other participants.…”

Section: Decision-making Theories and Modelsmentioning

confidence: 99%

Leveraging Artificial Intelligence for Enhanced Sustainable Energy Management

Kaur,

Kumar,

Singh

et al. 2024

JSE

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The integration of Artificial Intelligence (AI) into sustainable energy management presents a transformative opportunity to elevate the sustainability, reliability, and efficiency of energy systems. This article conducts an exhaustive analysis of the critical aspects concerning the AI-sustainable energy nexus, encompassing the challenges in technological integration and the facilitation of intelligent decision-making processes pivotal for sustainable energy frameworks. It is demonstrated that AI applications, ranging from optimization algorithms to predictive analytics, possess a revolutionary capacity to bolster intelligent decision-making in sustainable energy. However, this integration is not without its challenges, which span technological complexities and socio-economic impacts. The article underscores the imperative for deploying AI in a manner that is transparent, equitable, and inclusive. Best practices and solutions are proposed to navigate these challenges effectively. Additionally, the discourse extends to recent advancements in AI, including edge computing, quantum computing, and explainable AI, offering insights into the evolving landscape of sustainable energy. Future research directions are delineated, emphasizing the importance of enhancing explainability, mitigating bias, advancing privacy-preserving techniques, examining socio-economic ramifications, exploring models of human-AI collaboration, fortifying security measures, and evaluating the impact of emerging technologies. This comprehensive analysis aims to inform academics, practitioners, and policymakers, guiding the creation of a resilient and sustainable energy future.

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Section: Decision-making Theories and Modelsmentioning

confidence: 99%

Leveraging Artificial Intelligence for Enhanced Sustainable Energy Management

Kaur,

Kumar,

Singh

et al. 2024

JSE

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“…The StockPTV dataset encompasses daily time series data for multiple stocks, featuring both stock prices and trading volumes (Chen et al, 2024). This dataset serves as a fundamental resource for the empirical investigation conducted in this study, providing a detailed understanding of the dynamics of stock prices and trading volumes.…”

Section: Datasetsmentioning

confidence: 99%

Enhancing Financial Investment Decision-Making With Deep Learning Model

Wang,

2024

Journal of Organizational and End User Computing

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This paper introduces the ISSA-BiLSTM-TPA model to improve financial investment decision-making. Traditional deep learning models face limitations in handling the complexity and uncertainty of financial markets. Our approach incorporates attention mechanisms, Bidirectional Long Short-Term Memory (BiLSTM), and Temporal Pattern Attention (TPA) to enhance accuracy in modeling and forecasting financial time series. The attention mechanism focuses on crucial information, BiLSTM captures bidirectional dependencies, and TPA identifies optimal solutions. Experimental results show higher prediction accuracy compared to traditional models, offering more reliable decision support for financial practitioners. Continuous optimization aims to provide innovative decision-making tools for the finance industry, advancing deep learning technology in finance.

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IoT smart farming adoption intention under climate change: The gain and loss perspective

Piancharoenwong,

Badir

2024

Technological Forecasting and Social Change

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Return predictability of prospect theory: Evidence from the Thailand stock market

Cited by 4 publications

References 57 publications

Leveraging Artificial Intelligence for Enhanced Sustainable Energy Management

Leveraging Artificial Intelligence for Enhanced Sustainable Energy Management

Enhancing Financial Investment Decision-Making With Deep Learning Model

IoT smart farming adoption intention under climate change: The gain and loss perspective

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