Research Overview:
This empirical project will be conducted to compare the effects of the domestic market and American market on Chinese American Depository Receipts (ADR) listed on the New York Stock Exchange (NYSE). The results can then be analysed based on underlying theoretical frameworks and used as an extension of previous literature.
Theoretical Framework and Past Research:
Fama (1970) tested the efficient market hypothesis (EMH) by comparing the performances of mutual funds from Standard and Poor 500 (S&P 500) against a norm calculated with the capital asset pricing model (CAPM). Fama implied that efficient markets exist; all information that is available is incorporated into stock prices so that one investor cannot gain more profit over another investor.
If this is true, the location of a company should not have an impact on the performance of the stock price on any stock exchange because the EMH states that information can traverse freely. Rosenthal and Young’s (1990) study on a dual-listed company demonstrates that the stock prices listed on different stock exchanges were not the same. According to EMH, two corporations in a dual-listed company should have the same stock price because they function as one business, having the same business operations. It is important to note that when trading rules were applied, no arbitrage opportunity was found, implying that the parents’ securities held the same value across markets.
Froot and Dabora (1999) expands on the research done by Rosenthal and Young by studying a pair of dual-listed companies and the results were that stock price is influenced more by whichever market the company is trading more intensely in. Investor heterogeneity, among others, were offered as an explanation to this which is related to behavioural finance.
Behavioural finance is the theory in which investors and companies do not always make rational investment decisions (Thaler 1993). This theory is used as a criticism of EMH as it can explain why there are inefficiencies in the market (Bovoras 2012). Furthermore, Feng and Seahole’s (2004) research found that regions of investors in China tend to behave the same, that is, investors in the same region in China tend to buy and sell the same stocks at the same time. If the Chinese market does have an impact on Chinese ADRs, could herding be a reason why?
Previous research has found that the domestic market in which the company resides in has a bigger impact on its stock price than the foreign market it is listed on (Agarwal, Liu and Rhee 2007; Chen, Li and Wu 2010; Xu and Fung 2002). Volatility is where the latter two studies differ on though. Xu and Fung (2002) say that whilst the domestic market determines the initial price of the stock, volatility comes from the other market. Chen et al.’s (2010) research reversed the previous finding and found that domestic volatility directly influenced the stock price in the foreign market.
Other studies have shown that trading location is not important in relation to stock price (Bin, Morris and Chen 2003; Phylaktis and Manalis 2005). Wang et al. (2013)’s study contrasts previous findings because they found that the US market indices had a bigger effect on Chinese ADRs than the Chinese market indices. The time lag between the two countries was considered by analysing daily, day and night returns of Chinese ADRs. They also interpreted their results as country-specific investor sentiment having an influence on stock prices, relating back to the behavioural finance theory.
Some research questions can be theorised from the above readings:
- Does the Chinese market have an impact on Chinese ADRs that are listed on the NYSE?
- Which market has the bigger effect on Chinese ADRs, China or America?
Testing the reasons why there are stock price differences for ADRs in different markets are beyond the scope of this empirical project, but they may be useful in explaining the results that may be obtained in this project, providing an important link to the EMH and behavioural finance theories. This would also provide a possible area for further research. I intend to read more literature on these reasons, which may change or add to the research questions above and thereby may require a change in the title of this project.
Audit of Data Sources and Methodology:
Most of the past researches used a multiple regression model to test their hypotheses (Bin et al. 2003; Fama 1970; Froot and Dabora 1999; Phylaktis and Manalis 2007; Wang, Yao and Fang 2013; Xu and Fung 2002). As Wang et al.’s (2013) research was also conducted on Chinese ADRs on the NYSE, I will follow their method of choosing portfolios to represent the Chinese ADRs, the US market and the Chinese market as closely as possible. In addition to this, a scatter graph with an ordinary least squares (OLS) line will be included for visual representation of the relationship between Chinese ADRs and the US and Chinese market.
52 Chinese ADRs trade on the NYSE that have been active for at least the last 2 years (JPMorgan Chase & Co. 2018). These stocks will form the equally-weighted Chinese ADR portfolio via a simple average of the individual ADR returns. The daily closing prices for these stocks will be taken from Yahoo Finance for the period between 1st October 2016 to 1st October 2018. The Chinese market will be represented by iShares China Large-Cap ETF (tracker FXI). An ETF is an exchange-traded fund, which in this case, tracks 56 of the largest Chinese stocks listed on the Hong Kong Stock Exchange (BlackRock Inc. 2018a). The American market is characterised by the iShares Core S&P 500 ETF (tracker IVV) which contains America’s largest stocks (BlackRock Inc. 2018b). Assan and Thomas’ (2012) found that trading volume of a stock was a determinant of stock price, leading to ETFs being chosen to represent the US and Chinese market because they trade at relatively similar volumes compared to the S&P 500 index and the Hong Kong Stock Exchange index themselves. As the 52 Chinese ADRs are some of China’s biggest firms, a bigger concentration in smaller firms on the either market indices will create a skew in the outcome, so that is why ETFs that consist of the biggest stocks in their respective markets were chosen specifically.
To examine the relationship between the Chinese ADR portfolio (dependent variable), the US market and the Chinese market (independent variables), we run the following (OLS) regression:
ŷ = β0 + β1IVV + β2FXI + ε
where ŷ is the dependent variable Chinese ADR portfolio, IVV represents the daily returns of the US market and the Chinese market daily returns are denoted by FXI. The error term is any variable that does not represent a true view of the relationship between the US market, Chinese market and the Chinese ADR portfolio i.e. if there is a difference between the true value and predicted value of the Chinese ADR portfolio. The betas are important for analysis as they show the extent of the effect the independent variables of the US or Chinese market has on the Chinese ADR portfolio.
A correlation matrix can also be set up to show the Chinese ADR-US market correlation coefficient and the Chinese ADR-China market correlation coefficient. A correlation coefficient is a measure of the relationship between 2 variables. The 2 coefficients will be compared to draw further conclusion on which independent variable affects Chinese ADRs on the NYSE more.
Computing Support:
All data will be collected and comprised in Excel from Yahoo Finance website. Excel can run all the necessary functions for this empirical research, including running the multiple regression model, creating a scatter graph, producing descriptive statistics and creating a correlation matrix.
Schedule of Work:
From 2nd November 2018, there will be 70 days to complete this project. The schedule of work is as follows:
- 2-3 days to collect all data (02.11.18-05.11.18)
- 1-2 days for data transformation (05.11.18-07.11.18)
- 2-3 days to run necessary Excel functions (07.11.18-10.11.18)
- 2-3 days for a quick summary of results and plan of report (10.11.18-13.11.18)
- 10 days for more literature reading (13.11.18-23.11.18)
- 40 days to write and complete empirical project (23.11.18-02.01.19)
9 days are left in case of any delays. This is a flexible schedule-I expect some elements to take longer than expected and some to not take as long so the schedule can be adjusted accordingly.
Bibliography
Agarwal, S., Liu, C., and Rhee, G., (2007), ‘Where does price discovery occur for stocks traded in multiple markets? Evidence from Hong Kong and London’ Journal of International Money and Finance, 26/1: 46–63.
Assan, A., and Thomas, S. (2012), ‘Stock Returns and Trading Volume: Does the Size Matter?’, Investment Management and Financial Innovations, 10/3: 77-88.
Bin, F. S., Morris, G. B., and Chen, D. H., (2003) ‘Effects of exchange-rate and interest-rate risk on ADR pricing behavior.’, North American Journal of Economics and Finance, 14/2: 241–262.
BlackRock Inc., (2018)a, iShares China Large-Cap ETF [online]. Available at: https://www.ishares.com/us/products/239536/ishares-china-largecap-etf [accessed 30th October 2018]
BlackRock Inc., (2018)b, iShares Core S&P 500 ETF [online]. Available at: https://www.ishares.com/us/products/239726/ishares-core-sp-500-etf [accessed 30th October 2018]
Borovas, G., Katarachia, A., Konstantinidis, A., Voutsa, M. E., (2012), ‘FROM EFFICIENT MARKET HYPOTHESIS TO BEHAVIOURAL FINANCE: CAN BEHAVIOURAL FINANCE BE THE NEW DOMINANT MODEL FOR INVESTING?’ Buletin ştiinţific: Universitatea din Piteşti. Seria Ştiinţe Economice, 11/2: 16–26.
Chen, K. C., Li, G., and Wu, L., (2010), ‘Price Discovery for Segmented US‐Listed Chinese Stocks: Location or Market Quality?’, Journal of Business Finance & Accounting, 37/1‐2: 242–269.
Fama, E. F., (1970), ‘Efficient Capital Markets: A Review of Theory and Empirical Work’, The Journal of Finance, 25/2: 383-417.
Feng, L. and Seasholes, M.S., (2004), ‘Correlated Trading and Location.’ Journal of Finance, 59/5: 2117–2144.
Froot, K. A. and Dabora, E. A., (1999), ‘How are stock prices affected by the location of trade?’, Journal of Financial Economics, 53/2: 189–216.
JPMorgan Chase & Co., (2018), Markets [online]. Available at: https://www.adr.com/Investors/Markets [accessed 29th October 2018]
Phylaktis, K. and Manalis, G., (2005), ‘Price transmission dynamics between informationally linked securities.’, Applied Financial Economics, 15/3: 187–201.
Rosenthal, L. and Young, C., (1990), ‘The seemingly anomalous price behavior of Royal Dutch/Shell and Unilever N.V./PLC’ Journal of Financial Economics, 26/1: 123–141.
Thaler, R. H., (1993), Advances in behavioral finance. New York: Russel Sage Foundation.
Wang, X., Yao L. J. and Fang, V., (2013), ‘Stock prices and the location of trade: Evidence from China-backed ADRs.’, North American Journal of Economics and Finance, 26: 677–688.
Xu, X. E. and Fung, H. G., (2002), ‘Information Flows across Markets: Evidence from China–Backed Stocks Dual–Listed in Hong Kong and New York’, Financial Review, 37/4: 563–588.
