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"The Evolution of Market Efficiency: 103 Years Daily Data of the Dow"

Anthony Yanxiang Gu and Joseph E. Finnerty


First Author :

Anthony Yanxiang Gu
Jones School of Business, State University of New York

Second Author :

Joseph E. Finnerty
University of Illinois at Urbana-Champaign
1206 S. Sixth Street, M/C 706
Champaign, IL 61820

Abstract :
This study examines the evolution of market efficiency of the Dow Jones Industrial Average over the last 103 years. Technological advances in information system, communication, forecasting, and trading, progress in investorsí ability to use relevant information for their trading strategies are hypothesized to raise the level of market efficiency. Also, the relations between current periodís volatility, rate of return, trading volume and autocorrelation, as well as the effects of autocorrelation, volatility and rate of return of the previous period on autocorrelation of the current period are examined. In addition, previous evidence of significant autocorrelation may not offer adequate information about market efficiency if the level of autocorrelation changes over time and the changes are random. If the changes in actual autocorrelation are random, the market would still be weak form efficient in a sense that investors cannot predict the market using the estimated autocorrelation. Various ratio, serial correlation and runs tests are performed to test first-order autocorrelation (AR 1) between daily returns on the Dow Jones Industrial Average Index over the period 1896-1998, variance ratio and runs tests are also used to test the (non)random changes in autocorrelation. Results of the tests indicate significant autocorrelation for about a third of the 103 years and nonrandom changes in the autocorrelation, reveal some pattern of the evolution of market efficiency. Then regression analyses using the estimated autocorrelation as dependent variables are conducted to analyze the evolution of the market efficiency and to estimate the effect of relevant factors on the level of autocorrelation. The regression analyses demonstrate that volatility, rate of return, and trading volume of the previous year have stronger negative relations with the level of autocorrelation than that of the current yearís, and that the previous yearís level of autocorrelation have a significant positive relation with the current yearís level of autocorrelation. These findings imply that investors consider the previous yearís stock return behavior in determining their trading strategies. This study has also found that positive autocorrelation occurs more frequently in the periods of higher autocorrelation while negative autocorrelation occurs more frequently in the periods of lower autocorrelation. Furthermore, negative autocorrelation is more related with higher volatility or market over reaction is more frequently related with higher volatility.
Manuscript Received : 2000
Manuscript Published : 2000
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