Title: A maximum likelihood estimator for information precision in the financial market
Author: George Li
Address: Business School, Finance Department, San Francisco State University, San Francisco, CA 94130, USA
Abstract: We present a continuous-time model of corporate earnings to study how to estimate the precision of information that investors receive from analyst earnings forecasts about firms' expected earnings growth rates in the real financial world. Based on the model, we develop a maximum likelihood estimator, which is then applied to estimate information precision about the expected earnings growth rate for the S&P 500 index.
Keywords: information precision; maximum likelihood estimation; MLE; financial markets; continuous time modelling; corporate earnings; earnings forecasting; expected earnings; earnings growth rates; S&P 500.
Int. J. of Monetary Economics and Finance, 2015 Vol.8, No.3, pp.318 - 329
Submission date: 30 Nov 2014
Date of acceptance: 31 May 2015
Available online: 09 Oct 2015