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The accuracy of financial analysts and market response



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Financial analysts play an intermediary role in financial markets, resulting in two steps for information to be fully absorbed into the stock price: analysts’ reaction to information, and investors’ reaction to analysts’ recommendations. Thus any observed inefficiency in stock pricing could result from two possibilities: analysts failed to fully incorporate the market information into their stock analysis, or the information released in the analysts’ report is not fully believed by investors. The documented optimism of financial analysts may suggest the possibility of the later case. To test the accuracy of analysts from another perspective, we follow a market microstructure model and use intraday market data to estimate the probability of an information event, the probability of good or bad news, and the rates that different traders arrive at the market. By comparing those estimates based on days with and without recommendation changes, we find inconsistent results with regard to a difference in the probability of an information event. For some stocks, we do observe an increase in the likelihood of news on days when analysts change their recommendations, but this is not the case for most stocks. However, even though they are inaccurate most of the time, uninformed investors usually believe financial analysts. Furthermore, it seems that uninformed investors disbelieve analyst recommendation changes at those instances when analysts are most accurate. Because of this, we hypothesise that market makers might suspect that orders in the opposite direction of an analyst’s recommendation change are more likely to come from informed traders. This is consistent with the intuition that most traders are uninformed and will simply follow the advice of a perceived expert, and therefore those that don’t follow that advice may be more likely to have special information of their own. We check whether there are any differences in the probability of information-based trading (PIN) and for the conditional probability of information-based trading conditioned on sell (PIN|sell) and buy (PIN|buy) between days with and without recommendation changes. We did not find any significant difference, indicating that although we may observe a higher arrival rate of informed traders on recommendation change days, the probabilities of information-based trading do not change substantially. More informed traders seem to come to the market merely because the higher arrival rate of uninformed traders on recommendation days gives them a good opportunity to camouflage their behaviour. And the specialists likely would not have to change their behaviour on those days by increasing or shifting bid-ask spreads since the increased costs from the higher volume of informed trading are balanced by increased profits from the higher volume of uninformed trading. Furthermore, regression of the probabilities of informed trading (conditional or unconditional) on firm size, trading volume, and volatility of daily return shows nothing significant, so we weren’t able to identify influential factors that affect informed trading or explain differences in informed trading between firms.



probability of information-based trading, microstructure, financial analyst



Master of Science (M.Sc.)


Finance and Management Science


Finance and Management Science


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