In the second step we'll take for granted the second interpretation . To do so we'll collect consumption and dividend data for each stock traded at NYSE for which there is a target price from one of those market makers to investigate . Then, we'll estimate by generalized method of moments (GMM) the parameters risk aversion and inter-temporal discount of Lucas model of second interpretation, that is, imposing target prices are actual prices for each stock. with and without target price data as instruments for each one of them. That way we will be able to infer for each stock whether risk aversion and discount rates might be a channel through which accounting may impact on stock returns. Besides, we will surpass the lack of statistical confidence metrics in the aforementioned approaches.
The third step will be estimate the basic pricing equation without target prices and compare the results with results of first interpretation in order to evaluate how an economy populated by uninformed agents (Lucas model without target prices) compares with an economy populated by informed agents in terms of risk aversion and and inter-temporal discount in both setups.
All data analysis will be performed with Python programming language and its scientific stack including the RPy2 communication package necessary to call R environment which will be used to estimate GMM specifications. Consequently, the paper will be fully reproducible for anyone and efficient way. All code will be available in GitHub.