Abstract
We report complex phenomena arising among financial analysts, who gather information and generate investment advice, and elucidate them with the help of a theoretical model. Understanding how analysts form their forecasts is important in better understanding the financial market. Carrying out big-data analysis of the analyst forecast data from I/B/E/S for nearly thirty years, we find skew distributions as evidence for emergence of complexity, and show how information asymmetry or disparity affects financial analysts’ forming their forecasts. Here regulations, information dissemination throughout a fiscal year, and interactions among financial analysts are regarded as the proxy for a lower level of information disparity. It is found that financial analysts with better access to information display contrasting behaviors: a few analysts become bolder and issue forecasts independent of other forecasts while the majority of analysts issue more accurate forecasts and flock to each other. Main body of our sample of optimistic forecasts fits a log-normal distribution, with the tail displaying a power law. Based on the Yule process, we propose a model for the dynamics of issuing forecasts, incorporating interactions between analysts. Explaining nicely empirical data on analyst forecasts, this provides an appealing instance of understanding social phenomena in the perspective of complex systems.
Highlights
The twentieth century witnessed exponential growth in equity capital markets as more countries opened their securities exchanges and as the barrier to accessing capital markets became lower for the average household
We focus on optimistic forecasts because analyst optimism is a well-documented practice, and analysts are known to walk down their forecasts towards the end of a fiscal year [17]
We first test whether information disparity in the stock market measured by the dispersion in analyst forecasts is statistically associated with economic uncertainty and whether the association has changed significantly since the Regulation Fair Disclosure (FD)
Summary
The twentieth century witnessed exponential growth in equity capital markets as more countries opened their securities exchanges and as the barrier to accessing capital markets became lower for the average household. Constructing an empirical cumulative distribution from the data on forecast errors in the tail, we use OLS regression to estimate the exponent α of the power-law distribution. In year 1993, with 16508 forecast errors in the tail, the estimated power-law exponent is α = 1.00 ± 0.01.
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