About this Research Topic
The aftermath of the 2009 financial crisis illustrated the need to improve the transparency and accountability of the financial sector both within national boundaries and jurisdictions. The crisis also called into sharp relief the need for government regulations to be more efficient and adept in responding to technological innovations and building resilient markets to withstand unexpected risk. Financial participants now face a patchwork of regulatory landscapes and technological innovations, which limit institutions and regulating agencies from quickly responding to emerging threats and business opportunities. Artificial Intelligence (AI) tools, such as Natural Language Processing, Machine Learning, Graph Modelling, and Risk Simulation, provide a path to reconcile these seemingly contradictory mandates for stronger regulatory oversight and greater market efficiency.
This Research Topic will bring together outstanding academics, regulators, and partitioners working at the intersection of Artificial Intelligence, capital markets, and public policy for the purpose to improve the performance and robustness of the global financial system. We invite researchers to submit original papers using a range of big data and AI methods applied to financial markets.
Topics include but are not limited to:
• Developing ML models to improve transparency, accountability, and fairness in predicting financial outcomes and assessing their effects
• Enhancing predictability and interpretability of ML models by developing data-driven AI approaches to improve financial market regulation
• Predicting the impact of alternative regulations on financial institutions and systemic risk
• Evaluating alternative regulations and their impact on managing systemic risk and improving market efficiency
• Evaluating ML models under various risk scenarios
• Predicting market, credit, or liquidity risk by using traditional methods
• Predicting market, credit, or liquidity risk by using machine learning methods
• Predicting financial variables by using machine learning methods
• Comparison of the forecasting performances of traditional methods and ML tools
• Evaluating ML models specification
• Advances in predicting financial market performance using big data
Submissions should be original scholarly research presenting innovative approaches or summarizing ongoing novel findings at the intersection of AI and financial markets. We strongly encourage solution-based approaches illustrating how AI tools can enhance finance markets and policy and their limits. We also support contributions that explore how theory-driven financial models can be merged with AI data-driven approaches to improve model prediction and robustness.
This Research Topic will bring together outstanding academics, regulators, and partitioners working at the intersection of Artificial Intelligence, capital markets, and public policy for the purpose to improve the performance and robustness of the global financial system. We invite researchers to submit original papers using a range of big data and AI methods applied to financial markets.
Topics include but are not limited to:
• Developing ML models to improve transparency, accountability, and fairness in predicting financial outcomes and assessing their effects
• Enhancing predictability and interpretability of ML models by developing data-driven AI approaches to improve financial market regulation
• Predicting the impact of alternative regulations on financial institutions and systemic risk
• Evaluating alternative regulations and their impact on managing systemic risk and improving market efficiency
• Evaluating ML models under various risk scenarios
• Predicting market, credit, or liquidity risk by using traditional methods
• Predicting market, credit, or liquidity risk by using machine learning methods
• Predicting financial variables by using machine learning methods
• Comparison of the forecasting performances of traditional methods and ML tools
• Evaluating ML models specification
• Advances in predicting financial market performance using big data
Submissions should be original scholarly research presenting innovative approaches or summarizing ongoing novel findings at the intersection of AI and financial markets. We strongly encourage solution-based approaches illustrating how AI tools can enhance finance markets and policy and their limits. We also support contributions that explore how theory-driven financial models can be merged with AI data-driven approaches to improve model prediction and robustness.
Keywords: financial policy, risk assessment, theory driven data modelling, Financial Regulation, Financial Risk Analysis, Artificial Intelligence, Systemic Risk, Machine Learning, Forecasting, Exploratory Data Analysis, Deep Learning, AI Decision Making, Financial Risk Management, Cognitive Finance
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
