Integrated machine learning framework for dynamic portfolio management: clustering, forecasting, and optimization
Researchers have developed a new machine learning framework for portfolio management that combines fuzzy clustering, LSTM (Long Short-Term Memory) forecasting, and advanced optimization. Tested on Nasdaq data from 2017 to 2024, the system outperformed traditional methods in key metrics. The novel clustering algorithm handles outliers and asset links, improving asset selection. Each module—clustering, forecasting, optimization—boosts results independently. While the approach is data-driven and practical, more details on computational costs and broader market tests are needed.
What it examines
This paper introduces a dynamic portfolio management framework that combines fuzzy clustering for asset selection, LSTM deep learning for return forecasting, and advanced optimization for capital allocation. Using Nasdaq data from 2017--2024, the study aims to improve investment decisions by integrating machine learning techniques for better performance and robustness.
What it concludes
The proposed framework outperforms traditional methods, with each module—clustering, forecasting, and optimization—contributing to stronger results. Applications include smarter investment strategies and automated portfolio management. Limitations involve data scope and market changes; future research could expand to other markets or refine the models for even greater accuracy.
Evidence objects
A new machine learning framework for portfolio management, blending fuzzy clustering, LSTM forecasting, and dynamic optimization, dramatically outperforms traditional methods on Nasdaq data from 2017 to 2024 across key metrics.
key_findings bullet 1 · key_findings · validation V0
The standout innovation is a novel fuzzy clustering algorithm that uniquely manages outliers and asset interdependencies, enabling smarter asset selection and boosting performance when combined with LSTM-based return predictions and adaptive optimization.
key_findings bullet 2 · key_findings · validation V0
Each moduleclustering, forecasting, and optimizationindependently enhances results, showcasing the systems flexibility. While robust and data-driven, the study notes future work should address computational costs, scalability, and broader market applications.
key_findings bullet 3 · key_findings · validation V0
This paper uniquely integrates fuzzy clustering, LSTM-based forecasting, and dynamic portfolio optimization into a unified framework, empirically validated on Nasdaq data. Its novel fuzzy clustering addresses outliers and interdependencies, while decomposition analysis clarifies each modules impact. The approachs originality and demonstrated performance gains make it compelling for market prediction research.
key_findings bullet 4 · key_findings · validation V0
Raw abstract and provenance
- … , data-driven portfolio management framework using … accuracy; and a portfolio optimization module that dynamically … approaches across portfolio performance metrics. …
Source row: 1107 · abstract type: snippet