摘要(英) |
In this thesis, mainly researched data is the Taiwan capitalization weighted index (TAIEX),
the purpose of the study is to examine the predictive power of the dividend yields and short
rate for forecasting excess return, cash flows, and interest rates over long period and short
period. We use the monthly data of TAIEX in the Taiwan Economic Journal (TEJ) and
refer to Andrew and Geert ( 2007) that proposed prediction method. The data analysis in
this thesis is collected from January, 1987 to January, 2013, total of 313 months. To collect
data for the selected variables is the closing price of the monthly TAIEX and the monthly
dividend. The empirical results shows that, dividend yields predict excess returns only at
short horizon together with the short rate and do not have long-horizon predictive power.
At short horizon, the short rate predicts returns with strongly negative relationship. |
參考文獻 |
[1] Ang, A. and G. Bekaert (2007). “Stock Return Predictability: Is it
There?.” The Review of Financial Studies, 20, 651-707.
[2] Campbell, J.Y., and Yogo, M. (2006). “Efficient tests of stock return
predictability.” Journal of Financial Economics, 81, 27-60.
[3] Cochrane, J.H. (2008). “The dog that did not bark: A defense of return
predictability.” The Review of Financial Studies, 21, 1533-1575.
[4] Fama, E.F. and French, K.R. (1988). “Dividend yields and expected stock
returns.” Journal of Financial Economics, 22, 3-25.
[5] Hodrickl, R.J. (1992). “Dividend Yields and Expected Stock Returns:
Alternative Procedures for Inference and Measurement.” The Review of
Financial Studies, 5, 357-386.
[6] Kohei Aono and Tokuo Iwaisako (2010). “On the Predictability of
Japanese Stock Returns Using Dividend Yield.” Asia-Pacific Finan Mar-
kets, 17, 141-149.
[7] Lewllen, J. (2004). “Predictive returns with financial ratios.” Journal of
Financial Economics, 74, 209-235.
[8] Richardson, M. (1993). “Temporary Components of Stock Prices: A
Skeptic’s View.” Journal of Business and Economic Statistics, 11, 199-
207.
[9] Stambaugh, R.F. (1986). “Bias in trgressions with lagged stochastic regressors.”
Working paper series, University of Chicago, Chicago, , .
[10] Stambaugh, R.F. (1999). “Predictive Regressions.” Journal of Financial
Economics, 54, 375-421.
[11] Torous, W.,Valkanov, R. and Yan, S. (2004). “On predicting stock returns
with nearly integrated explanatory variables.” Journal of Busi-
ness, 77, 937-966. |