Market Efficiency

Informational Efficiency Foundations and Capital Allocation Mechanisms

Informational Efficiency

A market condition where asset prices instantaneously, fully, and unbiasedly reflect all available relevant information, ensuring that expected future price changes are purely unpredictable random innovations.

Allocational Efficiency

The macro-economic state where scarce economic capital resources are channeled to the most productive, highest-social-return investment projects.

Operational (Internal) Efficiency

A market condition characterized by low transaction costs, tight bid-ask spreads, and rapid order execution.

Operational, Allocative, and Informational Market Dimensions

The Three Interlocking Pillars of Market Efficiency (Tobin’s Taxonomy)

1. Informational Efficiency: Prices reflect facts; no investor can consistently beat the market without insider knowledge.
2. Operational Efficiency: Clearing and brokerage mechanisms function cheaply and smoothly, eliminating excessive rent extraction by financial intermediaries.
3. Allocational Efficiency: Societal savings are allocated to their highest present-value productive uses in the real economy.

• Interdependence: A market cannot achieve allocational efficiency if it lacks informational and operational efficiency.

Comparative Market States: Efficient Markets vs Inefficient Speculative Bubbles

Speed of Price Adjustment and Informational Diffusion Analysis

Applied Problem: Random Walk and Serial Correlation Test

A researcher tests weekly price changes of an index over 100 weeks:

• Autocorrelation coefficient of returns at lag 1: $r_1 = +0.03$.
• Standard error of sample autocorrelation: $\text{SE} = \frac{1}{\sqrt{n}} = \frac{1}{\sqrt{100}} = 0.10$.

1. Compute the calculated t-statistic for the lag-1 serial correlation.
2. Evaluate whether the market satisfies the weak-form Random Walk hypothesis at $\alpha = 0.05$ (Critical $Z = 1.96$).

Solution:

1. t-Statistic Computation:

   Swipe to see the full formula↔

   $$t_{\text{cal}} = \frac{r_1 - 0}{\text{SE}} = \frac{0.03}{0.10} = 0.30$$
2. Hypothesis Decision: Since $|t_{\text{cal}}| = 0.30 < 1.96$, we fail to reject the null hypothesis of zero autocorrelation ($H_0: \rho_1 = 0$). Conclusion: Successive weekly price changes exhibit zero statistically significant dependency; price changes behave as a Random Walk, supporting weak-form informational efficiency.

Key analytical nuance: they equate informational efficiency with operational efficiency, forgetting that a low-commission market can still suffer from gross informational mispricings.