FNT502

Articles

This article presents a compelling case for using the Random Forest model with high-frequency data to forecast stock prices of major Indian fintech companies like Paytm, PolicyBazaar, and Niyogin Ltd. With the fintech industry rapidly expanding and attracting significant investment, the article addresses a critical need for precise forecasting tools in this sector. By leveraging 1-minute interval data, the study provides a granular and accurate predictive model, achieving over 95% accuracy in determining stock price trends. The results underscore Random Forest's suitability in capturing complex, non-linear financial data patterns, offering a robust alternative to traditional forecasting methods. This research is valuable for investors, analysts, and policymakers, providing insights that support informed decision-making in a highly volatile market. Additionally, the article highlights a unique contribution by focusing on the Indian fintech market—a previously underexplored area in high-frequency forecasting studies—thereby addressing a significant research gap. This makes it a valuable resource for anyone interested in advanced data-driven forecasting in emerging fintech markets.

This article explores the growing use of machine learning (ML) in financial research, emphasizing its advantages over traditional econometric approaches. They categorize ML applications into three main areas: constructing superior and novel measures, reducing prediction errors, and extending econometric methods. By addressing large datasets and capturing complex data relationships, ML techniques offer financial researchers new ways to enhance prediction accuracy and analyze unconventional data types like text and images. The study also discusses the promising potential of ML in finance, outlining future directions for researchers and practitioners seeking to leverage ML's capabilities to solve complex financial problems.

This article reviews deep learning (DL) applications in finance, highlighting its superior performance over traditional machine learning (ML) for complex data analysis in areas like stock forecasting, algorithmic trading, credit risk assessment, portfolio management, and fraud detection. They evaluate DL models such as CNNs and LSTMs, noting their effectiveness in capturing complex patterns across financial subfields. The authors emphasize the potential of DL in enhancing predictive accuracy and identifying future research opportunities, particularly in innovative data representations, to further advance computational intelligence in finance. 

Reference data plays a critical role in financial data analysis, serving as the foundation for informed decision-making in dynamic financial markets. Despite its importance, financial institutions face significant challenges, including data quality issues, integration complexities, and compliance with evolving regulatory standards. The sheer volume, variety, and velocity of financial data further complicates its management and utilization. However, these challenges present substantial opportunities for innovation through advanced technologies like artificial intelligence, machine learning, and blockchain. These tools can enhance data accuracy, efficiency, and transparency, enabling financial institutions to unlock the full potential of reference data. By addressing these challenges and leveraging technological advancements, organizations can drive actionable insights, improve risk management, and maintain a competitive edge in the global financial ecosystem.