Enhancing financial portfolio management with predictive analytics and scalable data modeling techniques
1 Wells Fargo, Charlotte, North Carolina, USA.
2 The Vanguard Group, Charlotte, North Carolina, USA.
3 Independent Researcher, Sheffield, UK.
4 Independent Researcher, United Kingdom.
Review
International Journal of Scholarly Research and Reviews, 2024, 05(02), 089–102.
Article DOI: 10.56781/ijsrr.2024.5.2.0050
Publication history:
Received on 08 October 2024; revised on 14 November 2024; accepted on 17 November 2024
Abstract:
The financial industry faces an increasingly complex landscape for portfolio management, where data-driven insights are crucial for optimizing asset allocation and managing risk. This paper explores the integration of predictive analytics and scalable data modeling techniques in enhancing financial portfolio management. By leveraging machine learning algorithms, big data architectures, and real-time data processing, predictive analytics can forecast asset trends, detect market anomalies, and assess portfolio risk with high precision. We evaluate several predictive models, including time-series forecasting, neural networks, and ensemble methods, for their efficacy in financial prediction. The study also discusses the role of scalable data modeling frameworks, such as Apache Spark and cloud-based data lakes, in handling vast volumes of unstructured data across different markets and asset classes. Findings indicate that predictive analytics, when paired with robust data models, can deliver real-time, actionable insights, enhancing decision-making for fund managers and institutional investors. Furthermore, this paper highlights best practices for implementing scalable models in financial institutions, addressing challenges like data latency, model interpretability, and system scalability. By adopting these advanced analytics frameworks, portfolio managers can achieve improved risk-adjusted returns, better asset diversification, and enhanced adaptability to market volatility.
Keywords:
Predictive Analytics; Financial portfolio; Scalable Data; Machine Learning; Time-Series forecasting
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0