Year: 2025
Type: article
Abstract: This paper investigates the relationship between risk and return using innovative financial modelling techniques applied to secondary data. By leveraging historical datasets, including stock prices, trading volumes, and market trends, the study develops predictive models that analyse market dynamics and optimize investment strategies. The secondary data-based approach is chosen due to the availability of high-quality, extensive datasets from reliable sources, such as stock exchanges and financial reports, which are essential for studying long-term trends and volatility. The research employs advanced statistical techniques, machine learning algorithms, and visualization tools to uncover hidden patterns and test hypotheses with precision. The results provide insights into risk-return trade-offs, optimal portfolio allocations, and the impact of market volatility on returns. This methodology not only ensures the robustness of the findings but also offers generalizability, making the results relevant for investors, financial institutions, and policymakers aiming to improve decision-making in dynamic market environments.
DOI: 10.14419/r3t5ts54
Authors: Veereedhi V. Deepika, Dr. A. V. N. Murty, Gaurav Kumar, S. Ganapathy, Dr. N. S. V. N. Raju, Dr. Rahmuddin Miyan
Subfield: Finance
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4416466150
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