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Forecasting Stock Price with the Residual Income Model.
Forecasting Stock Price with the Residual Income Model
Abstract
This paper demonstrates a method to forecast stock price using analyst 代写留学生论文earnings forecasts asessential signals of firm valuation. The demonstrated method is based on the Residual Income Model(RIM), with adjustment for autocorrelation. Over the past decade, the RIM is widely accepted as atheoretical framework for equity valuation based on fundamental information from financial reports.This paper shows how to implement the RIM for forecasting, and how to address autocorrelation to
improve forecast accuracy. Overall, this paper provides a method to forecast stock price that blendsfundamental data with mechanical analyses of past time series.Forecasting Stock Price with the Residual Income Model
Introduction
This paper demonstrates a method to forecast stock price using analyst earnings forecasts as
essential signals of firm valuation. The demonstrated method is based on the Residual Income Model
(RIM), a widely used theoretical framework for equity valuation based on accounting data. Despite its
importance and wide acceptance, the RIM yields large errors when applied for forecasting. This paper
discusses a statistical approach to improve stock price forecasts based on the RIM, specifically by
showing that adjusting for serial correlation in the RIM’s model (autocorrelation) yields more
accurate price forecasts. The demonstrated approach complements other valuation techniques, as
employing a basket of valid techniques builds confidence in pricing. Accurate price forecasts help
build a profitable trading strategy, for example by investing in stocks with the largest differencebetween current price and forecast future price. In practice, although fundamentalists rely on trueeconomic strengths of the firm for valuation, there is ample room for mechanical analyses of pricetrends. This paper serves investment professionals by providing a pricing method that blendsfundamental information in analyst earnings forecasts with mechanical analyses of time series.
The RIM is a theoretical model which links stock price to book value, earnings in excess of anormal capital charge (abnormal earnings), and other information ( t v ). Other information t v can beinterpreted as capturing value-relevant information about the firm’s intangibles, which are poorlymeasured by financial reported numbers. This interpretation recognizes that a portion of valuationstems from factors not to be captured in financial statements. Other information t v canalso be
interpreted as capturing different sorts of errors and noises, including model mis-specification,
measurement error, serial correlation, and white noise. Given the possible imperfections of any
valuation model, the content of t v is elusive and it is the purpose of this paper to exploit it to the best
using statistical tools to predict stock price. To the extent that t v contains serial correlation, as
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expected in firm data, modeling its time series properties should improve the forecasting performance
of the RIM.
First, I demonstrate how to implement the RIM using one term of abnormal earnings. I
review the theoretical framework, and model the RIM to parallel forecasters’ task just before time t to
forecast stock price at time t based on expected earnings for the period ending at t. The forecaster’s
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