Browsing by Author "Li X-M"
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- ItemA revisit of price discovery dynamics across Australia and New Zealand(ePress, Unitec, Institute of Technology, NZ, 2015) Dassanayake W; Li X-M; Buhr KThis study re-investigates the price discovery dynamics of selected stocks cross-listed on the Australian Stock Exchange (ASX) and the New Zealand Stock Exchange (NZX) during a bear trading phase from January 2008 to December 2011. A differing price discovery dynamic in a bear market versus a bull market may occur because of variations in investor sentiments and disparities in the role of the stock prices. Using intraday data, we employ the vector error correction mechanism, Hasbrouck’s (1995) information share and Grammig e t al. ’s (2005) conditional information share methods. Consistent with previous research, we find that price discovery takes place mostly on the home market for the Australian firms and for all but one of the New Zealand firms. However, not seen in existing studies, we show that the NZX has grown in importance for both the Australian and New Zealand firms. This suggests that the NZX is deviating from being a pure satellite market.
- ItemShortability and Asset Pricing Model: Evidence from The Hong Kong Stock Market(Elsevier BV, 2017-12) Bai M; Li X-M; Qin Y; Alexander, CThis study explores how the violation of free short selling assumption affects the performance of CAPM and the Fama-French three-factor model, as existing studies show that short-sales constraints affect asset pricing of the stocks. Using data from the Hong Kong Stock Market which has unique regulations on short selling, we conduct both time-series and cross-sectional regression analyses to evaluate the performance of the two models under the short-sales-constraints and the no-constraints market environment. The two models perform much worse in the former environment than in the latter, indicating a significant impact of the short sales constraints on the explanatory power of the models. We then augment the two models with a shortability-mimicking factor. Our results show that the factor has a significant power in explaining both time-series and cross-sectional variation in the size-B/M portfolio returns. The addition of the factor to the two models considerably increases their overall performance.