Journal of Advanced Research in Dynamic Financial Risk Assessment and Investment Strategies

Real-Time Financial Risk Assessment in Dynamic Markets: A Review of Computational and Analytical Approaches

Padmini Shukla — 2026-01-19

Financial markets are increasingly characterised by rapid structural changes, high volatility, and the interplay of multiple risk types, including market, credit, liquidity, and operational risks. Traditional risk-assessment frameworks, which rely on historical data and static end-of-day models, often fail to capture the speed and complexity of modern financial systems. This review
surveys recent advances in real-time financial risk assessment, highlighting interdisciplinary approaches that integrate economics, mathematics, machine learning, and behavioural finance.
The paper examines the evolution of risk-modelling paradigms, from classical statistical and stochastic models to adaptive frameworks that leverage streaming data architectures and high-frequency analytics. Quantitative methods, such as scenario-based stress testing and stochastic differential modelling, are discussed alongside machine-learning techniques, including supervised, unsupervised, and reinforcement-learning algorithms, for dynamic prediction and risk mitigation. The role of behavioural and sentiment-driven indicators, derived from news, social media, and investor psychology, is also considered in enhancing predictive accuracy.
Applications in derivatives pricing, hedging, and dynamic portfolio management demonstrate the practical relevance of these approaches, enabling proactive responses to market shocks and tail-risk events. Regulatory and compliance considerations, including model transparency, explainable AI, and adherence to evolving financial regulations, are addressed to ensure responsible deployment.

Advances in Dynamic Risk Assessment and Portfolio Optimization: Integrating Quantitative Models with Behavioral Insights

Pankaj Sinha — 2026-01-19

Dynamic risk assessment and portfolio optimisation have undergone profound transformations in recent years due to the rise of data-driven models, adaptive allocation strategies, and insights from behavioural finance. Traditional approaches, which largely assume rational investor behaviour, static risk preferences, and normally distributed returns, are increasingly insufficient
in capturing the complexity and heterogeneity of real-world financial markets. Modern frameworks leverage machine learning, real-time data analytics, and scenario-based simulations to model time-varying risk, portfolio sensitivity, and non-linear asset interactions. Simultaneously, behavioural finance research has provided tools to measure investor biases, such as overconfidence, loss aversion, and herding, and incorporate these psychological factors into portfolio decisions. Hybrid models that integrate statistical rigour with behavioural realism have emerged, enabling more adaptive and personalised investment strategies. Key contributions include the development of dynamic allocation algorithms, risk forecasting models that account for regime shifts, and explainable AI approaches that improve transparency and trust. Despite these advances, challenges remain, including model interpretability, robustness under extreme market conditions, and ethical considerations in data usage. Future research is likely to focus on creating portfolio systems that are not only adaptive and predictive but also behaviourally informed, interpretable, and capable of providing actionable insights for diverse investor profiles in increasingly complex financial environments.

Machine Learning and AI in Dynamic Financial Risk Management: A Review of Quantitative Models and Applications

Roop Lal Sharma — 2026-01-19

The integration of Machine Learning (ML) and Artificial Intelligence (AI) into financial risk management has revolutionised the methodologies that institutions employ to assess, monitor, and mitigate various forms of risk. Traditional risk models, which often rely on static assumptions and historical data, are increasingly being supplanted by dynamic, data-driven frameworks capable of processing large volumes of high-frequency and unstructured data from diverse sources, including market feeds, transactional data, social media sentiment, and alternative datasets. This review systematically examines the application of ML and AI-based
quantitative models across critical financial risk domains, including credit risk, market risk, liquidity risk, and operational risk, highlighting how these technologies enhance predictive accuracy, risk sensitivity, and early warning capabilities. Key advances in supervised learning, unsupervised learning, deep learning, reinforcement learning, and explainable AI (XAI) are discussed, emphasising their potential to provide interpretable insights while maintaining regulatory compliance. Furthermore, the study explores the architectural frameworks for implementing these models, addressing integration with existing risk management
infrastructures, data governance, model validation, and ethical considerations. The review also identifies current challenges, such as model overfitting, data bias, explainability limitations, and operational complexities, while proposing future research directions aimed at developing adaptive, transparent, and resilient financial risk systems. By bridging the gap between cutting- edge AI methodologies and practical risk management applications, this work provides a comprehensive roadmap for leveraging intelligent technologies to enhance institutional risk oversight, strategic decision-making, and regulatory alignment.

Behavioral Finance in Dynamic Risk Management: Understanding Investor Psychology and Market Implications

Soumya Mukhopadhyay — 2026-01-19

Behavioral finance has fundamentally reshaped the understanding of financial decision-making
by demonstrating that investors and market participants are not always rational actors. Cognitive
biases, heuristics, emotional responses, and social influences often drive decisions, leading to
market anomalies, mispricing, and deviations from traditional risk-return paradigms. Integrating
behavioral insights into financial risk management provides a more nuanced and realistic
framework for understanding and mitigating these effects.
This review examines the role of behavioral factors in dynamic risk management, emphasizing
applications across portfolio optimization, hedging strategies, risk assessment, and regulatory
compliance. It explores how behavioral biases—such as overconfidence, loss aversion, herding,
and framing effects—can impact investment outcomes and risk exposure, and how these insights
can be leveraged to design more effective risk mitigation strategies. Additionally, the paper
highlights emerging computational approaches, including agent-based modeling, sentiment

analysis, and machine learning, which enable the quantification and simulation of behavioral
effects at scale.
Key frameworks, empirical evidence, and practical applications are discussed, demonstrating
how behavioral finance complements traditional financial models by incorporating psychological
and social dimensions of decision-making. The review underscores the importance of integrating
behavioral factors into modern investment strategies to enhance portfolio resilience, improve
risk-adjusted returns, and support evidence-based regulatory and policy interventions. By
combining behavioral insights with advanced analytical tools, financial institutions can better
anticipate market dynamics, adapt to investor behavior, and build more robust, adaptive risk
management systems.

Interdisciplinary Perspectives on Financial Risk: Integrating Economics, Mathematics, and AI for Smarter Investments

Sachin Sridhar — 2026-01-19

Financial risk management has undergone a profound transformation by integrating insights
from economics, mathematics, statistics, and artificial intelligence (AI). While traditional risk
assessment techniques, such as Value-at-Risk (VaR), scenario analysis, and stochastic modeling,
provide foundational tools for quantifying financial risk, they often fall short in capturing the
complex, nonlinear, and dynamic behaviors of modern financial markets. Increasingly, financial
institutions operate in environments characterized by high-frequency trading, large-scale data
flows, and rapidly shifting market conditions, which require more adaptive, real-time, and data-
driven decision-making frameworks.
Interdisciplinary approaches that combine economic theory, quantitative modeling, and AI-based
analytics offer a more holistic understanding of risk, enabling improved forecasting, anomaly

detection, and scenario planning. The integration of machine learning, deep learning, and
reinforcement learning techniques allows for the modeling of intricate relationships among
market variables, investor behavior, and systemic risk factors, while also supporting dynamic
portfolio optimization and automated risk mitigation strategies.
This review systematically examines the theoretical foundations, quantitative methodologies, and
practical applications of AI in financial risk management, emphasizing how these tools enhance
prediction accuracy, portfolio performance, and operational resilience. It also evaluates empirical
evidence and case studies demonstrating the effectiveness of AI-augmented approaches in credit,
market, liquidity, and operational risk domains. Furthermore, the paper discusses implementation
challenges, regulatory considerations, and governance issues, as well as emerging trends and
future research directions aimed at building robust, adaptive, and explainable risk management
systems.