Dissertations, Theses, and Capstone Projects

Date of Degree

2-2016

Document Type

Dissertation

Degree Name

Ph.D.

Program

Economics

Advisor

Christos Giannikos

Subject Categories

Finance

Abstract

Chapter 1 adopted the constraint dummy variables regression model in Cao, Harris and Wang (2007) to examine seasonality in the returns, volatility, and turnover of Shanghai ‘A’, Shanghai ‘B’, Shenzhen ‘A’, and Shenzhen ‘B’ composite indexes in the Chinese Stock Market. Daily data of four composite indexes (Shanghai ‘A’, Shanghai ‘B’, Shenzhen ‘A’, and Shenzhen ‘B’) was collected: the opening index value, the closing index value, the maximum index value, the minimum index value and the volume traded. Volatility (a realized volatility that is based on the daily trading range), trading volume, and three return series of the four indexes were regressed on the 1st lag term and 26 dummy variables. The dummy variables include 5 day effect dummies, 12 month effect dummies and 9 holiday effect dummies. For trading volume, both a linear trend and a quadratic trend were included to capture the non-linear secular growth in this variable over time. Chapter 1 analyzed both the full and split samples. Chapter 1 found a weekend effect, an April effect, and a Tuesday effect in the Chinese Stock Market. Similar seasonality patterns existed in Shanghai ‘A’ and Shenzhen ‘A’ markets. However, Shanghai ‘B’ and Shenzhen ‘B’ markets had very different seasonality patterns. In contrast to the previous findings, only minimal and inconsistent Spring Festival effects were found in the full sample Shanghai ‘A’ market and in the second period in the split sample Shenzhen ‘A’ market. Only minimal and inconsistent Labor Day and National effects were found in ‘B’ markets. There were no other v holiday effects in the Chinese Stock Market. Monthly seasonality patterns were more prominent in ‘B’ markets than in ‘A’ markets. Chapter 2 applied a variant of the Fama-French (1993) model in the monthly returns on all component stocks of the CSI300 Index from January 2006 to December 2011 and identified three risk factors in the returns on those 300 stocks. Both value-weighted and equal-weighted monthly returns of nine portfolios formed on firm size and book-to-market equity were regressed on the value-weighted monthly returns of a market portfolio of stocks and on two Fama-French benchmark factors (mimicking portfolio for firm size and mimicking portfolio for book-tomarket equity). Chapter 2 confirmed the relative suitability of the modified Fama-French 3- factor model in CSI300 component stocks. Chapter 2 identified the same three risk factors as Fama-French (1993) did: an overall market factor, a factor linked to firm size and a factor linked to book-to-market equity. The overall market factor captured most of the time-series variations in stock returns. By adding the two factors linked to firm size and book-to-market equity into the time-series regressions, additional variation was captured. The size effect was much stronger and more consistent than the book-to-market equity effect in the stock returns, which is in contradiction to Fama-French (1993), where the book-to-market equity effect was much stronger. Small-size portfolios tended to have higher returns than big-size portfolios. The book-to-market equity had a relatively weaker power than firm size in explaining returns.

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