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Abstract

Financial markets play a critical role in resource allocation. Their performance depends on the decisions of millions of independent investors constantly reacting to one another. Their informational efficiency remains a subject of debate across economic systems. When informational efficiency is present at the weak form, historical price information should not consistently predict future returns. Several empirical tests of this hypothesis often focus on the behavior of aggregate market indices, and use individual efficiency proxies such as autocorrelation, GARCH-type volatility, or entropy-based measures to measure efficiency. This has often yielded mixed results, particularly in emerging markets. Here we show that testing market efficiency can be more comprehensively done by modeling the whole market as a system of its interdependent sub-markets. We proposed and constructed a Multilayer Composite Efficiency Index (ML-CEI) that aggregates multiple efficiency proxies which test different facets of informational efficiency. Our approach integrates seven different efficiency proxies with weights derived using Principal Component Analysis. Applied to the Egyptian stock market from 2019 to 2026, the CEI captures temporal and sectoral variations in efficiency that are overlooked by common single-index and single proxy approaches.  By identifying inefficiency at both sectoral and market-wide levels, the ML-CEI provides a more robust measure of efficiency and provides an empirical basis for targeted reforms in emerging markets, improved risk management strategies, and the design of information systems that promote more efficient price discovery in emerging financial ecosystems.

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