Abstract
We construct a simple measure to quantify the level of market efficiency. We apply this measure to investigate the level of market efficiency and analyze its variation over time. The main contribution of the new measure is that it makes it easy to compare market efficiency across assets, time, regions, and data frequencies. We find that markets are often efficient, but can be significantly inefficient over longer periods. Our empirical results indicates that in many periods of major economic events, financial markets becomes less efficient. This corroborates earlier results on market efficiency, and simplifies interpretation and comparisons.
Highlights
In this paper, we derive a new measure to quantify the level of market efficiency
The Adjusted Market Inefficiency Magnitude (AMIM) is very easy to compute, and is computationally inexpensive. This implies that comparisons over time, assets, asset classes, and geographical regions are carried out with ease. We show that it has several advantages over existing measures of market efficiency, and are able to detect periods of the economy that is known for much uncertainty about prices and values
The usual focus is on answering the question: Are markets always efficient? We enlarge the discussion of market efficiency by addressing the questions: How large is the inefficiency level, and how does it vary over time? We find that market efficiency is varying over time
Summary
We derive a new measure to quantify the level of market efficiency. We use the term Adjusted Market Inefficiency Magnitude (AMIM). From the return auto-correlation coefficients, TIME captures the time-varying degree of market efficiency, and aims to measure the inefficiency level of the market. The main reason for this is because our confidence intervals can be computed from the sample under investigation This is a major contribution, as comparable measures, for example the one applied in Noda (2016), relies on simulations and bootstrapping. Our measure can be tested on samples that consist of many different assets over time by computing a unique set of confidence intervals. This decrease the computational burden, especially when analyzing big data. AMIM provides the main results of Noda (2016) for the Japanese markets
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