Traditionally, finance relied on parsimonious models to gain economic insights into financial markets. However, there has been a recent shift towards data-driven tools like Artificial Intelligence (AI) and Machine Learning (ML). This shift impacts not just financial markets but also broader areas, such as employment, economic growth, stability, and income distribution, affecting various sectors. Moreover, AI now plays a pivotal role for stakeholders including fund managers, CFOs, regulators, traders, investors, and entrepreneurs in making optimal financial decisions. However, the detailed implications of these shifts remain unclear, necessitating further research.
The objective of the finance track is to convene researchers, academicians, doctoral students, and practitioners from national and international institutions to address current financial issues and research findings related to AI and ML in finance. Specifically, our aim is to contribute to the expanding literature on AI and ML in finance and their broader implications for the local and global economy.
We invite both theoretical and empirical papers from all areas of finance dealing with AI and ML. Special topics include but are not limited to:
- AI and predictive analysis in finance
- AI-driven trading strategies
- Algorithmic Trading and Market Efficiency
- Robo Advisor and Financial Decisions
- High-frequency trading driven by big data.
- Machine learning models for asset pricing
- Innovation in asset pricing
- Portfolio allocation through AI and ML
- Natural language based financial forecasting.
- Sentiment and technical analysis through AI
- Fintech and AI applications to Behavioral finance
- AI and Supply chain finance
- Blockchain, AI, and the future of finance
- Cryptocurrency and Central Bank Digital currency
- Decentralized Finance
- Corporate governance in the AI-driven world
- Generative AI and corporate finance
- Financial inclusion and the role of AI
- The role of AI in risk management
- Implications of AI for financial regulation and financial stability
- AI in fintech and the future of banking
- The ethical considerations of using AI in finance.
- Fraud detection based on big financial data analytics.
- Regulatory challenges with AI and governance of big data finance
- Ethical and Sustainability issues in AI
Note: The list above, though not exhaustive, serves as a guideline for potential topics.
