Modeling Stock Prices without Knowing How to Induce Stationarity

1996 ◽  
Vol 12 (4) ◽  
pp. 739-740
Author(s):  
David N. Dejong ◽  
Charles H. Whiteman

In “Modeling Stock Prices without Knowing How to Induce Stationarity” (1994, Econometric Theory 10, 701–719), we used posterior-odds calculations to evaluate restrictions imposed by a present-value model of stock prices across the equations of a VAR representation of stock prices and dividends. The results we reported are tainted by the omission of two factors: the Jacobians induced by the mapping of our priors over VAR parameters β into the restricted sample spaces relevant under hypotheses H2-H4 (hence, tainting our calculations of p(Hi|y,X) in (22) for i = 2–4), and an integrating constant needed in calculating the unrestricted probability p(Hi|y,X) in (22). Table 1 reports our revised calculations, which differ substantively from those reported previously.

1994 ◽  
Vol 10 (3-4) ◽  
pp. 701-719 ◽  
Author(s):  
David N. DeJong ◽  
Charles H. Whiteman

Bayesian procedures for evaluating linear restrictions imposed by economic theory on dynamic econometric models are applied to a simple class of presentvalue models of stock prices. The procedures generate inferences that are not conditional on ancillary assumptions regarding the nature of the nonstationarity that characterizes the data. Inferences are influenced by prior views concerning nonstationarity, but these views are formally incorporated into the analysis, and alternative views are easily adopted. Viewed in light of relatively tight prior distributions that have proved useful in forecasting, the present-value model seems at odds with the data. Researchers less certain of the interaction between dividends and prices would find little reason to look beyond the present-value model.


2004 ◽  
Vol 28 (11) ◽  
pp. 2297-2325 ◽  
Author(s):  
Marı́a-José Gutiérrez ◽  
Jesús Vázquez

2011 ◽  
Vol 06 (01) ◽  
pp. 1150001
Author(s):  
MAN FU ◽  
PRASAD V. BIDARKOTA

This paper uses an artificial neural network (ANN) model to forecast broad dividends, and computes fundamental stock prices with a stochastic discount factor (SDF). Broad dividends are used because they measure payouts to shareholders more accurately. Since nonlinearity is found in broad dividends, an ANN process is fit to these. Empirical results show that the consumption-based broad dividends model with ANN forecasting procedure predicts fundamental prices better, compared with models using linear dividends process, narrow dividends, or a constant discount factor. Nonetheless, actual stock prices remain largely detached from fundamental prices. Deviations between actual and fundamental prices, positive or negative, are found to coincide with business cycles, a result not consistent with alternative models considered in the paper.


1999 ◽  
Vol 27 (3) ◽  
pp. 553-561 ◽  
Author(s):  
Gregory C. Chow ◽  
Zhao-zhi Fan ◽  
Jin-yan Hu

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