“Monkey versus the Expert”: A Concept for Ongoing Competition between Investment Portfolios
Abstract
Can random selection outperform expert judgment in financial markets? This study revisits this classic question by proposing a structured framework for continuous competition between different types of investment portfolios.
Building on concepts such as the Efficient Market Hypothesis and the well-known Wall Street Journal Dartboard Contest, the paper develops an online system for collecting expert forecasts and constructing aggregated portfolios. The methodology incorporates weighted expert opinions, time-adjusted relevance, and performance-based corrections, alongside a randomly generated portfolio used as a benchmark.
The proposed model enables dynamic portfolio evaluation using both return-based measures and risk-adjusted indicators such as the Sharpe ratio. By combining human expertise with algorithmic aggregation, the approach offers a practical tool for testing market efficiency and improving investment decision-making.
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