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Stock return predictability in emerging markets

by Natthawudh Konglumpun

Title:

Stock return predictability in emerging markets

Author(s):

Natthawudh Konglumpun

Degree name:

Doctor of Philosophy

Degree level:

Doctoral

Degree discipline:

Economics

Degree department:

School of Development Economics

Degree grantor:

National Institute of Development Administration

Issued date:

2020

Digital Object Identifier (DOI):

10.14457/NIDA.the.2020.45

Publisher:

National Institute of Development Administration

Abstract:

The stock return predictability still be a puzzle in the financial economics society that have not yet solved for several decades. Some of them believe that they are  predictable, and that stakeholders are able to ensure opportunities to allocate their assets in advance  while others disagree and believe in stock market efficiency.  In spite of the fact that a number of researchers that have recognized the predictive model as fact, there are still some doubts in terms of econometric issues. Econometricians generally agree that the predictive variable has a local–to-unity property that has a significant correlation with stock returns in an infinite set of a given sample and the other issue is the near unit root characteristic of stock returns’ stochastic volatility.  Both of the issues end up with an over-reject characteristic of standard hypothesis testing form of predictive regression. Due to the previously mentioned econometrics issues, the CJP approach will be applied to the predictability of stock returns as well as to testing to rectify the issues.  The CJP approach will utilize the change of time method to rectify the nonstationary of stochastic volatility and the nonparametric instrumental variable estimator known as the Cauchy estimator to fix the endogeneity problem of covariates.  To investigate the stock return predictability with the mentioned econometrics issues, this research applies stock return data from Stock Exchange of Thailand (SET) to extract stochastic volatility and testing of local-to-unity property of it.  Consequently, the time change method is applied to generate robustness hypothesis testing for unit root or near unit root stochastic volatility of stock returns. The last step applies the nonparametric Cauchy estimator to make a set of instrument variable of covariate for the stock return prediction Empirical results show that the stock return significantly generates local-to-unity and near local-to-unity of stochastic volatility.  The time change method can be applied to resolve the local-to-unity of stochastic volatility problem by using stochastic stopping time of the process or volatility time to replace the calendar time, which will make stochastic volatility in each new period become stationary across all observations. The Cauchy estimator was applied for random sampling based on volatility time and  shows no support for predictability at any frequency for the selected predictors, such as the dividend–price and earnings–price ratios. This research concludes that it seems clear that stock returns cannot be predicted by dividend–price and earnings–price ratios if the characteristics of the data are properly checked and managed.

Description:

Thesis (Ph.D. (Economics))--National Institute of Development Administration, 2020

Subject(s):

Economics, Finance
Stock exchanges

Keyword(s):

e-Thesis
Model specification and estimation
Predictability modelling

Resource type:

Dissertation

Extent:

84 leaves

Type:

Text

File type:

application/pdf

Language:

eng

Rights:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

URI:

https://repository.nida.ac.th/handle/662723737/5522
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ทรัพยากรสารสนเทศทั้งหมดในคลังปัญญา ใช้เพื่อประโยชน์ทางการเรียนการสอนและการค้นคว้าเท่านั้น และต้องมีการอ้างอิงแหล่งที่มาทุกครั้งที่นำไปใช้ ห้ามดัดแปลงเนื้อหา และทำสำเนาต่อ รวมถึงไม่ให้อนุญาตนำไปใช้ประโยชน์เพื่อการค้า ไม่ว่ากรณีใด ๆ ทั้งสิ้น



This item appears in the following Collection(s)

  • GSDE: Dissertations [58]

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Except where otherwise noted, content on this site is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International license.

Copyright © National Institute of Development Administration | สถาบันบัณฑิตพัฒนบริหารศาสตร์
Library and Information Center | สำนักบรรณสารการพัฒนา
Email: NIDAWR@nida.ac.th    Chat: Facebook Messenger    Facebook: NIDAWisdomRepository
 

 

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