A Smart Optimization Model for Reliable Signal Detection in Financial Markets Using ELM and Blockchain Technology

Deepak Kumar (), Priyanka Pramod Pawar, Santosh Reddy Addula, Mohan Kumar Meesala, Oludotun Oni, Qasim Naveed Cheema and Anwar Ul Haq
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Deepak Kumar: Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769, USA
Priyanka Pramod Pawar: Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769, USA
Santosh Reddy Addula: Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769, USA
Mohan Kumar Meesala: Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769, USA
Oludotun Oni: Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769, USA
Qasim Naveed Cheema: Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769, USA
Anwar Ul Haq: Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769, USA

FinTech, 2025, vol. 4, issue 4, 1-24

Abstract: This study proposes a novel approach to improve the reliability of trading signals for gold market prediction by integrating technical analysis indicators, Moving Averages (MAs), MACD, and Ichimoku Cloud, with a Particle Swarm-Optimized Extreme Learning Machine (PSO-ELM). Traditional time-series models often fail to capture the complex, non-linear dynamics of financial markets, whereas technical indicators combined with machine learning enhance predictive accuracy. Using daily gold prices from January–October 2020, the PSO-ELM model demonstrated superior performance in filtering false signals, achieving high precision, recall, and overall accuracy. The results highlight the effectiveness of combining technical analysis with machine learning for robust signal validation, providing a practical framework for traders and investors. While focused on gold, this methodology can be extended to other financial assets and market conditions. The integration of machine learning and blockchain enhances both predictive reliability and operational trust, offering traders, investors, and institutions a robust framework for decision support in dynamic financial environments.

Keywords: financial market trends prediction; extreme machine learning; particle swarm optimization; secure communication; blockchain; technical analysis indicators (search for similar items in EconPapers)
JEL-codes: C6 F3 G O3 (search for similar items in EconPapers)
Date: 2025
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