Advanced Quantitative Research in Strategy Dispersion, Correlation Dynamics, and Automated Research Pipelines
Year: 2026
Type: article
Abstract: This working paper presents three complementary case studies in quantitative investment research, focusing on strategy performance dispersion, correlation-based risk management, and automated research workflows. The first study, Why Average Strategy Performance Can Mislead Portfolio Research, uses the Quantpedia API to examine annual performance dispersion across a peer group of 21 trend-following strategies. By analyzing median returns, averages, percentiles, and interquartile ranges, the research demonstrates how conventional benchmarks can conceal substantial differences between strategies, particularly during periods of macroeconomic stress. The second study, Correlation Acceleration: Detecting Market Stress Through Rising ETF Correlations, investigates whether increases in short-term correlations across US equity sector ETFs can provide useful signals for temporarily hedging SPY exposure. The analysis evaluates different correlation thresholds and demonstrates how selective defensive overlays can potentially improve portfolio risk-adjusted performance and reduce drawdowns. The third study, Building an Automated Quantitative Research Pipeline with Quantpedia API, introduces VIKI, an experimental quantitative research platform connecting strategy discovery, structured interpretation, historical backtesting, performance evaluation, and portfolio construction. The case study demonstrates how published Quantpedia strategies can be systematically adapted and evaluated within a LONG-ONLY equity research framework while accounting for transaction costs, implementation constraints, and portfolio diversification. Together, these studies emphasize the importance of moving beyond isolated historical returns toward a structured, reproducible research process. The paper highlights how performance distribution analysis, changing market correlations, and automated strategy validation can contribute to more informed investment decisions and robust quantitative portfolio construction.
Author: Mesíček, David
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7225624262
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