Abstract
Fintech organizations require integrated analysis of structured and unstructured data to maintain competitiveness. This study develops a Big Data Analytics framework combining technical indicators and machine learning to optimize strategic decision-making across multiple countries. Using open-source ETF data and rigorous walk-forward validation we demonstrate that a systematic analytical pipeline can generate above-random predictions across different markets. The framework provides a reproducible methodology for sector-level investment screening that small and medium enterprises can adopt without expensive proprietary tools. The study contributes to fintech strategy literature by showing how proper validation techniques prevent look-ahead bias in financial prediction research. The findings demonstrate that Big Data Analytics when combined with rigorous validation can inform strategic decision-making across different regulatory and market environments.
| Original language | English |
|---|---|
| Journal | Quality and Quantity |
| Early online date | 20 Aug 2026 |
| DOIs | |
| Publication status | Published - 20 Aug 2026 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 8 Decent Work and Economic Growth
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SDG 9 Industry, Innovation, and Infrastructure
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