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This research aims to optimize the management of investment portfolios through the collaborative integration of analytical prediction and blockchain-based technology. The investment sector can establish secure data exchange operations through blockchain technology which enables decentralized features while being fixed and transparent. The project utilizes multiple machine learning models that are specialized for individual asset classes to forecast price variations of stocks, real estate, gold, and cryptocurrency. The analysis uses dynamic moving averages along with LSTM neural networks and ARIMA models to understand price patterns associated with real estate market fluctuations while stock price forecasting depends on moving averages. A friendly user interface offered by Streamlit makes it possible to display projected asset values based on user-supplied criteria and risk preferences. The system gives investors the ability to research market behavior patterns while examining potential risks and helping them specify asset allocations and restructuring their portfolios accordingly. Our research introduces an innovative time-bound evaluation component as a distinct feature that enables users to find investment opportunities during specific time spans. Experimental results validate the effectiveness of our approach, achieving MAPE values of 0.55% for gold, strong predictive accuracy for real estate with ARIMA, and reliable short-term forecasting for stocks and cryptocurrencies. Practical applications of this system span across financial institutions, hedge funds, and individual investors who seek to leverage AI-driven insights for risk mitigation and strategic portfolio management. Financial institutions can utilize this system for algorithmic trading, asset allocation, and regulatory compliance, while hedge funds can improve quantitative investment strategies and optimize capital deployment. Individual investors can benefit from real-time insights that enhance decision-making and reduce financial risks. Additionally, blockchain integration enhances data security, transparency, and auditability. Our study aspires to develop a strategy for investing by offering a secure, accurate, and data-driven platform that facilitates the best portfolio efficacy.

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Blockchain-Integrated Predictive Analytics for Diversified Investment Portfolio Optimization

  • 会议论文

  • 发表日期：

    2026年

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  • Amruta Mankawade,
  • Aditi Kharche,
  • Nikhil karmankar,
  • Vallabh Kathar,
  • Kishor Gatave,
  • Ayush Kawane

摘要

This research aims to optimize the management of investment portfolios through the collaborative integration of analytical prediction and blockchain-based technology. The investment sector can establish secure data exchange operations through blockchain technology which enables decentralized features while being fixed and transparent. The project utilizes multiple machine learning models that are specialized for individual asset classes to forecast price variations of stocks, real estate, gold, and cryptocurrency. The analysis uses dynamic moving averages along with LSTM neural networks and ARIMA models to understand price patterns associated with real estate market fluctuations while stock price forecasting depends on moving averages. A friendly user interface offered by Streamlit makes it possible to display projected asset values based on user-supplied criteria and risk preferences. The system gives investors the ability to research market behavior patterns while examining potential risks and helping them specify asset allocations and restructuring their portfolios accordingly. Our research introduces an innovative time-bound evaluation component as a distinct feature that enables users to find investment opportunities during specific time spans. Experimental results validate the effectiveness of our approach, achieving MAPE values of 0.55% for gold, strong predictive accuracy for real estate with ARIMA, and reliable short-term forecasting for stocks and cryptocurrencies. Practical applications of this system span across financial institutions, hedge funds, and individual investors who seek to leverage AI-driven insights for risk mitigation and strategic portfolio management. Financial institutions can utilize this system for algorithmic trading, asset allocation, and regulatory compliance, while hedge funds can improve quantitative investment strategies and optimize capital deployment. Individual investors can benefit from real-time insights that enhance decision-making and reduce financial risks. Additionally, blockchain integration enhances data security, transparency, and auditability. Our study aspires to develop a strategy for investing by offering a secure, accurate, and data-driven platform that facilitates the best portfolio efficacy.