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Trading Catalysts and Market Efficiency

The perceived influence of trading catalysts on market activity is intimately intertwined with the notion of market efficiency. In an informationally efficient financial market, prices change only with respect to the arrival of new information.19 There are varying degrees of market efficiency that reflect differences in the amount of information available to market participants. This decomposition is due to Eugene F. Fama, who divides market efficiency into weak form, semi-strong form, and strong form.20 Weak form market efficiency refers to a market whose current prices reflect any information contained in the series of past price changes. The principal implication is that certain forms of technical analysis, such as trend following, should not be profitable. Semi-strong form market efficiency refers to a market where prices fully and correctly reflect all publicly available information. In such a market, an individual cannot profit from investing on publicly available information. Strong form market efficiency refers to a market where prices fully and correctly reflect all available information public or private. In such a market, it is not possible to consistently earn a superior return. Market efficiency does not suggest that it is impossible to earn superior returns. Rather, it suggests that it is not possible to consistently earn superior risk-adjusted returns.21

There are several implications for trading catalysts if the market is informationally efficient. First, true trading catalysts would be limited to the arrival of new information in the marketplace. Prices would not respond to noise—non-fundamental factors that influence market prices. Second, the impact of trading catalysts on market prices would be immediate and complete. Prices would jump or fall instantaneously upon the arrival of new information. Third, the market would interpret any information content of trading catalysts correctly. Prices would not react to a trading catalyst and then a few minutes later return to where they were before the announcement unless new information entered the marketplace during the interim.

There is a considerable amount of evidence in the financial economic literature that suggests that changes in financial market prices do not follow a normal or lognormal distribution. Rather, changes in financial market prices seem to be drawn from a distribution that is leptokurtic—a distribution that has more probability mass in the center and in the tails than the normal distribution does. This means that large price changes should occur more frequently than they would under a normal distribution. A stock market crash of the magnitude of either the 1929 or 1987 crashes should almost never occur if changes in stock prices are normally distributed. Even mid-single-digit daily returns are exceedingly rare in a normal distribution.22 The observation of two stock market crashes in U.S. equities during the twentieth century and numerous days with high single-digit percentage price changes serve as a useful reminder of the practical importance of the distribution of changes in stock prices. It is also a potent reminder of the power of trading catalysts.

There is also a considerable amount of evidence in the financial economic literature that suggests that the volatility of changes in financial market prices tends to both cluster and persist. This leads to the question of whether trading catalysts also cluster. The answer to this question depends on one's view of what causes prices to fluctuate. Individuals who subscribe to the efficient markets hypothesis would argue that trading catalysts cluster because the arrival of new information, and hence volatility, clusters. Market participants who do not subscribe to the efficient markets hypothesis would tend to disagree. They might argue that the more frequently trading catalysts occur, the more potential there is for trading catalysts to reinforce one another and increase perceived volatility.

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