The inflow-effect - an explanation for the mis-valuation in the Chinese stock market
MASTER THESIS
Submitted in Partial Fulfillment of the Requirements for the Degree of
MASTER OF SCIENCE
in Banking and Finance
Matthias STEFAN, PhD
Department of Banking and Finance
The University of Innsbruck School of Management
Submitted by
Philip HETTICH
Innsbruck, April 2020
Abstract I investigate if an inflow of new traders with cash i.e. the inflow-effect is responsible for the formation of a mis-valuation in Chinese equity markets that culminated mid 2015. So far, the inflow-effect as cause for the formation of asset price bubbles has only been detected in experimental asset markets. The unique setup of mainland China and Hong Kong equity markets allows me to measure mis-valuation using the AH-premium. The AH-premium is the spread between A-shares and H-shares prices of dual-listed firms. Dual-listed firms list their equity both on the mainland China stock exchanges as A-shares as well as on the Hong Kong Stock Exchange as H-shares. Capital controls restrict mainland China investors to trade in Hong Kong and foreign investors to trade on mainland China stock exchanges. This prevents arbitrage and allows prices for A and H-shares to diverge resulting in the AH-premium. The inflow of new investors is observable as the number of newly opened A-shares brokerage accounts and the inflow of cash is observable as the outstanding margin lending balances in A-shares brokerage accounts. I employ multiple linear regression analysis on a panel-dataset of dual-listed firms between mid 2013 and mid 2015. The results attest with a p-value of p < 0.001 that the inflow-effect contributes significantly to the explanation of the mis-valuation in Chinese equity markets, measured by the AH-premium.
Table of contents
List of figures ...... II
List of tables ...... III
1 Introduction ...... 1
2 Literature ...... 4
3 Structure of Mainland China and Hong Kong Equity Markets ...... 8
4 Measuring the inflow-effect ...... 12
5 Empirical Analysis ...... 13
5.1 Dataset and Variables ...... 13
5.2 Statistical Analysis of Variables...... 17
5.3 Empirical Methodology and Research Questions ...... 24
5.4 Empirical Results ...... 27
6 Conclusion and Policy Implications ...... 34
7 Bibliography ...... 36
I
List of figures
Figure 1 – Development China Mainland and Hong Kong Stock Indices ...... 1
Figure 2 – Average RDACP in % ...... 21
Figure 3 – Kernel density estimate of regression residuals...... 26
II List of tables
Table 1 – Dual-listed companies ...... 13
Table 2 - Descriptive Statistics ...... 19
Table 3 - RDACP per company ...... 22
Table 4 - Regression results ...... 30
III Introduction
Introduction
From mid 2014 to mid 2015 a sharp rise in prices of Chinese-mainland stock indices could be witnessed before they crashed back down. This development culminated on June 12th 2015 at which point, The Economist (2015) observed, the Shanghai Composite Index1 doubled from its low of 1991 points a year earlier, closing at 5166 points and the tech-heavy Shenzhen
Composite Index2 almost tripled in value from its 2014 low of 1007 points to close at 3140 points (see Figure 1).
Figure 1 Shanghai Composite Index, Shenzhen Composite Index and Hang Seng China Enterprises Index development between 01.01.2014 and 31.12.2015, where 01.01.2014 equals 1. (Source: Bloomberg and own computations)
Commenting these rapid increase of the Chinese mainland stock indices, The Economist noted that the question was merely “…about how inflated Chinese stock prices are” (The Economist, 2015).
1 The Shanghai Stock Exchange Composite Index (SSE Composite Index) is a market capitalization-weighted stock index that contains all companies listed on the Shanghai Stock Exchange (China Securities Index Co. Ltd., 2020) 2 The Shenzhen Stock Exchange Composite Index is a market capitalization-weighted stock index that contains all companies listed on the Shenzhen Stock Exchange (Bloomberg, 2020b) 1 Introduction
Rising price to earnings ratios (P/E ratio) added additional substance to the observation that stock prices were in bubble-territory3 during the mentioned time frame. P/E ratios can serve as yardstick to determine if equity markets are reasonably valued by comparing the share price of a company to its earnings per share (Shen, 2000). For the Shanghai Composite Index and the Shenzhen Composite Index P/E ratios expanded from a low of 9.5 to 24.9 and from 25.3 to a peak of 75.1 respectively during a short time period of less than a year from mid 2014 to mid 2015. These price increases made the Chinese equity market, measured by P/E ratio, one of the most expensive markets in the world. In contrast to that, valuations of firms listed on Hong
Kong’s Hang Seng China Enterprises Index4 did not increase to similar extent and P/E ratios peaked at only 10.7 during the same time frame. Interestingly, so-called dual listed companies that list their stock both as A-shares and H-shares, saw spreads between the two listings widen. A-shares are equities traded on the Chinese mainland stock exchange in Shanghai and Shenzhen, whereas H-shares are traded in Hong-Kong on the Hong Kong Stock Exchange. At the top A- shares commanded a premium of above 50% over their H-shares counterparts5. During the same time as A-shares prices rose relative to H-share prices, large numbers of openings of new A- shares brokerage accounts could be observed. As The Economist (2015) noted 8 million brokerage accounts alone were opened in the first quarter of 2015. Since foreign investors are not eligible to trade in A-shares, whereas they face no such restriction in trade of H-shares, it seems that primarily new domestic Chinese investors entered the equity market and caused A- shares prices to rise. Foreign investors on the other side, appeared to have remained on the sidelines, evident from the more moderate H-shares valuations. Ultimately, this development is reflected in the mentioned expansion of the premium at which dual-listed A-shares traded over their H-share counterparts. Coinciding with the formation of almost all past asset bubbles are large inflows of new market participants with cash as outlined in Galbraith (1990) and Kindleberger (1996). E.g. in one of the earliest documented bubble episodes, the Tulipmania, craftsmen and farmers entered into trading tulip bulbs, sometimes mortgaging their property to be able to buy the high-priced bulbs. The mentioned large numbers of brokerage account openings that enabled financially inexperienced Chinese citizens to trade in mainland A-shares, coupled with rising A-shares
3 There is no single accepted definition of the term “bubble” in literature. For the present paper I fall back on the definition presented in Stöckl, Huber, & Kirchler (2010). They state that most researchers would agree that a bubble describes the phenomenon of securities trading on high volumes at prices that largely deviate from fundamental value. 4 Hang Seng China Enterprises Index is a stock index that contains all companies listed on the Hong Kong Stock Exchange weighted by market capitalization (Bloomberg, 2020a) 5 Measured by the Hang Seng Stock Connect AH Premium Index which tracks the average price difference of A- shares over H-shares for the most liquid companies with both A and H-share listing 2 Introduction prices and valuations, falls in line with this observation. Kirchler et. al. (2015) formalize the observation of inflow of new traders with cash and label it the inflow-effect. While the inflow- effect has been documented in historical writeups of Galbraith (1990), Kindleberger (1996) and observed in experimental asset markets by Kirchler et. al (2015) it has not been empirically confirmed to lead to the formation of asset bubbles. In the present paper I hence investigate if the inflow-effect can also help to explain mis-valuations in a real-world asset market. To do this, I examine whether the inflow of new traders with cash into the A-shares market contributed to what the Economist (2015) described as price bubble in Chinese mainland A-shares measured by the AH-premium. The AH-premium is the premium with which A-shares trade over their corresponding H-shares listing.
By using multiple linear regression analysis I find a significant positive relationship with a p- value of p < 0.001 between the inflow-effect (inflow of new traders with cash) and an expansion of the AH-premium, after controlling for known effects that explain the AH-premium. These effects are differences in liquidity, differences in volatility, exchange-rate expectations as well as firm size. The AH-premium here defined as relative difference in closing prices of firms that are dual-listed on the Chinese mainland stock exchanges in Shanghai or Shenzhen and on the Hongkong Stock Exchange, serves as a measurable indicator for A-share mis-valuation. Disclosures on the number of weekly opened A-shares brokerage accounts and data from on outstanding margin lending balances in A-shares brokerage accounts make the inflow-effect observable in a real-world asset market. The findings contest that indeed the inflow-effect contributes to the formation of a mis-valuations in a real-world asset market. I find these results when analyzing Chinese dual-listed firms within the period of mid 2013 until mid 2015, which was marked by strong increases in Chinese A-shares stock prices.
The remainder of the paper is structured as follows. Section 2 presents related literature. Section 3 then gives an overview on the structure of the mainland China and Hong Kong equity markets, section 4 describes how the inflow-effect is measured. Section 5 then covers the empirical analysis, including the description of the dataset and analysis of its variables as well as the empirical methodology and results. Section 6 concludes and closes by providing policy implications based on the findings.
3 Literature
Literature
Throughout history, various episodes of asset price bubbles took place. Prominent examples are the Tulipmania in the 17th century and the South-Sea bubble in the 18th century. To mention examples that are more recent the dot.com bubble beginning of this century and the housing bubble in 2007/2008, that eventually led to the global financial crisis come to mind. The global financial crisis is not the only example where euphoria turned into crisis with large negative consequences for the real economy. After bursting, bubble episodes are generally followed by rising unemployment, bankruptcy and recession (Kindleberger, 1996). This highlights the importance to direct research efforts to the field to have at hand instruments that can mitigate or help preventing similar events in future. Research on drivers behind the formation of asset price bubbles has made significant progress over the years. I identify four important strands of research in the mentioned field, which I will cover in following order: First economic models, second analyses in the field of history, third observations from laboratory experiments and fourth empirical findings in real-world asset markets. Focus is put on the inflow-effect explanation and the specific market structure of Chinese equity markets that help to explain bubble formation.
Hyman Minsky (1970) in his economic model hypothesizes that asset price bubbles form if more and more households and firms join in to buy assets and hence bid up asset prices, after being triggered by a positive external shock and rapidly expanding credit from lenders. Importantly, he further mentions that asset prices are boosted by the phenomenon of more participants entering the market after they see others getting rich. In an easy model Harrison & Kreps (1978) detect that if investors have heterogenous expectations of fundamental value, prices of the traded asset exceeds intrinsic value, as investors price in capital gains from reselling to other investors. This phenomenon is also described as resale option by Scheinkman & Xiong (2003), who attribute this effect to the formation of speculative asset bubbles as investors seek to profit from sale to more optimistic investors. Miller (1977) confirms these findings but argues that only if there are restrictions to short selling the minority of most optimistic investors dominates supply and demand and thus dictates the price. He argues further that mis-valuations can only be eradicated by rational investors via purchasing undervalued securities. Prices of excessively valued securities though cannot be brought down, when short selling is restricted as the minimal quantity an investor can hold is zero. In Chinese mainland
4 Literature equity markets short selling is likewise restrained making this a possible reason for the mispricing in Chinese mainland equities (Chan, Kot, & Yang, 2012).
In their historic analyses of bubble episodes throughout history Kindleberger (1996) and Galbraith (1990) similarly observe that all bubble episodes share common reasons of formation. Apart from a displacement that triggers bubbles by positively influencing at least part of the economy, they prominently mention high amounts of leverage relative to the underlying assets and an inflow of new inexperienced participants into the market.
Especially experimental asset markets have brought forward research in the field of asset price bubbles over the recent years. The major advantage of experimental asset markets is the fact that trading takes place in a laboratory environment, making key variables of interest exogenously observable and controllable. This is of particular importance for research on asset price bubbles, since it overcomes the common drawback of non-observability of fundamental values in real-world markets and thus makes price bubbles clearly identifiable. In the following I present meaningful findings from experimental asset markets structured into two sections. The first section of findings is especially relevant for the unique characteristics of the Chinese mainland equity markets, where short selling largely restricted and market participants are mainly retail investors, which are hypothesized to be prone to expressing speculative behavior (Liu, 2019). The second section specifically summarizes findings related to the inflow of traders and cash.
Oechssler, Schmidt & Schnedler (2011) conclude that speculation is a reason for generating excessive prices in experimental asset markets, when they question subjects for an estimation of value of the traded asset and receive estimates that correctly reflect fundamental value, while at the same time they exchange the asset at higher prices in the market. Economic models have found short selling restrictions to be a source of deviation of asset price from fundamental value (Miller, 1977). Findings regarding short selling in experimental asset markets are ambiguous with King et. al. (1993) stating that short selling does not mitigate asset market bubbles, whereas Ackert et. al. (2006) find evidence, that allowing for short selling and making market participants to finance asset purchases themselves, moderates asset price bubbles.
Phenomena related to the inflow of new traders and cash are prominently stated as driver for asset price bubbles in experimental asset markets. King (1991) attests that inexperienced traders
5 Literature are cause for the formation of asset bubbles, when they observe that asset prices converge to fundamental value with increased trading experience of market participants. Lahav, Noussair & Haruvy (2007) confirm this and explain that to profit, traders update their expectations with experience from previous trading markets. Hence, with increased trader experience bubbles peak and crash earlier in the trading session until prices are in accordance with fundamental value. Furthermore, according to e.g. Caginalp, Porter & Smith (1998) and Haruvy & Noussair (2006) high amounts of excess liquidity, measured by the value of cash relative to the value of the securities the traders hold, leads to greater mispricing. Deck, Porter & Smith (2014) extend on these points when they find that an inflow of new traders causes bubbles to form, whereas when traders exit it lets them crash. Kirchler et. al. (2015) then dissect the findings of Deck et. al. (2014) by observing that not the inflow of traders alone, but the joint inflow of traders equipped with cash, reliably causes the formation of asset price bubbles in experimental asset markets. They title this joint inflow of traders together with cash inflow-effect.
Xiong & Yu (2011) seek to verify several theories put forward by research in experimental asset markets and economic models by empirically analyzing a real-world asset bubble in deeply out-of-the-money Chinese put warrants. They find support for the resale option theory and short selling restrictions being cause for forming the bubble in Chinese warrants. Unique to their approach is the neat observability of fundamental value of the traded asset. With help of the universally recognized Black-Scholes Model fundamental values are made observable and prove warrants to be in bubble state with their prices significantly exceeding outputs from Black-Scholes. A price bubble is still detected when they merely rely on upper bound values of put warrants (i.e. exercise price of the put warrant minus 0) as easily observable fundamental value. Utilizing this setting they are further able to confirm the finding from experimental asset markets that bubbles arise despite clearly observable fundamental values. They cannot confirm though that asset price bubbles disappear with trader experience when they state that the Chinese warrant bubble took place over a time-span of three years. Hypothesizing why this is the case they come up with the explanation that an inflow of new inexperienced investors keeps the bubble alive. However, they fail to test this statement.
Similar to Xiong & Yu (2011), who use the Black-Scholes Model to determine fundamental value, I am utilizing H-shares prices of dual listed companies as proxy for fundamental value. This enables me to test if an inflow of traders and cash, named the inflow-effect by Kirchler et al. (2015) is responsible for the formation of a mispricing in Chinese A-shares relative to H-
6 Literature shares during the period of mid 2013 to mid 2015. To overcome Xiong & Yu's (2011) issues of testing the impact of an inflow of new traders on the development of the Chinese warrant bubble,
I am harvesting statistics published by the CSDC6 on the number of newly opened A-shares brokerage accounts. With this approach I am able for the first time in a real-world asset market to make the inflow-effect measurable and connect it to the formation of what the Economist (2015) calls a stock market bubble.
6CSDC, China Securities Depository Corporation Limited, is jointly owned by the SHSE and SZSE and conducts all security registration, clearing and settlement business for these exchanges. 7 Structure of Mainland China and Hong Kong Equity Markets
Structure of Mainland China and Hong Kong Equity Markets
The structure of Chinese equity markets is heavily influenced by ownership restrictions and capital controls imposed by the government of the People Republic of China. The following section will provide an overview of the market structure of Chinese equity markets during the period of interest of mid 2013 to mid 2015. Chinese mainland headquartered firms are able to issue three distinct classes of shares. A-shares and B-shares listed on the Shanghai Stock Exchange (SHSE) and Shenzhen Stock Exchange (SZSE), as well as H-shares which trade on the Hong Kong stock exchange (HKSE). Trade in A-shares is dominated by domestic Chinese retail investors. Until today foreign private investors are fully closed out from trading in A- shares. As reaction to China´s accession to the World Trade Organization (WTO) in 2001, restrictions for foreign institutional investors were relaxed in a sign towards more integration of Chinese capital markets. Consequently foreign institutional investors were allowed to directly invest into Chinese A-shares in very limited scope, if they fall under the Qualified Foreign Institutional Investors (QFII) classification. A different regime is in place for Hong Kong institutional investors called Renminbi Qualified Foreign Investors (RQFII) that, if requirements are fulfilled, allows Renminbi (RMB) funds raised in Honk Kong by subsidiaries of the investor in mainland China to be invested into Chinese A-shares. Subsequently, requirements to classify as QFII and RQFII were further relaxed (Yao, 2013). Nevertheless, barriers to invest into A-shares as foreign institutional investor remain high, hence the Chinese A-shares market remains highly segmented. Among the most stringent barriers are quota that cap the amount of foreign currency, that can be exchanged into Chinese RMB (USD 80 billion under QFII) and be used for equity investments, restrictions to the repatriation of invested funds, lock-up periods before repatriations can take place, as well as uncertainty arising with the discretion of the government to change these laws at any time. Foreign investments under QFII and RQFII together only represent a small ownership percentage of around 2% of the total market cap of Chinese equities listed on Shanghai and Shenzhen exchanges, further highlighting the very limited influence that foreign investors have on supply and demand and thus pricing of Chinese A-shares (Mauerer & Tea, 2019).
Unlike A-shares which require Chinese onshore RMB to purchase shares, B-shares are traded in US dollars and Hong Kong dollars for Shanghai and Shenzhen B-shares respectively. The B-shares market was set up in 1992 with intent to combat a major shortage of foreign currency that China faced at this time. Hence, until 2001, B-shares were exclusively available to foreign
8 Structure of Mainland China and Hong Kong Equity Markets investors. Later the B-shares market was opened for all domestic investors that could bring up foreign currency (Yao, 2013).
The third class of shares firms headquartered in China can issue are H-shares, which unlike B- shares, do not trade on Chinese domestic stock exchanges. Foreign institutional as well as private investors do not face any ownership restrictions or other regulatory hurdles, such as capital controls that prevent investments in H-shares. This makes H-shares very attractive for foreign investors seeking equity exposure to mainland China. Chinese domestic investors, however, are closed out from trade in H-shares. There are several ways for Chinese companies to list their equity in Hong Kong. Persistently low liquidity of Shenzhen and Shanghai listed B- shares caused the Chinese government to encourage so-called transfer listings, that allowed domestic companies to exit the B-shares market and have their equity transfer-listed on the liquid HKSE as H-shares. This transfer mainly aimed at creating value for holders of B-shares by lowering the illiquidity discount, while still being accessible for foreign investors. H-shares status can also be achieved by engaging in so-called direct listings in Hong Kong via initial public offerings (IPO) on the HKSE. Thereby equity is sold directly to foreign investors. Indirect listings e.g. by creating an offshore-subsidiary, which then itself lists shares in Hong- Kong are another way to attain access to the H-shares market (Yao, 2013).
Companies that have their shares trading both as H-shares on the Hong Kong Stock Exchange and as A-shares on mainland China exchanges are referred to as dual-listings. Interestingly A- shares have historically traded at a premium above their H-shares counterparts after adjusting for exchange rates. Capital controls mentioned in the previous section make arbitrage trades between the Hong Kong and mainland China markets impossible. While this explains the possibility for domestic investors to attach a higher value to the same company in Hong Kong than on mainland China exchanges, it does not answer the question of why this is the case. Research cites following reasons that help to explain the premium at which A-shares trade over their corresponding H-shares listing: Arquette, Brown, & Burdekin (2008) find that exchange rate expectations explain as much as 40% of the changes in the AH-premium. They argue that if Hong Kong investors expect the Chinese Yuan (CNY) to appreciate relative to the Hong Kong dollar (HKD), they would bid up H-shares prices causing the AH-premium to narrow. Reason therefore is that with an appreciating CNY, earnings denominated in CNY raise in value measured HKD. To measure investors’ exchange rate expectations they are using one year non-deliverable forwards of
9 Structure of Mainland China and Hong Kong Equity Markets
CNY/USD. Additionally, they conclude that market and company specific investor sentiment is responsible for considerable variation in the AH-premium from company to company over the time frame they investigate. Building up on earlier work of Amihud & Mendelson (1986), who find that illiquidity measured as bid-offer spread helps to explain stock returns, Lee (2009) similarly brings up differences in liquidity as explanation for the AH-premium puzzle. In addition to measuring liquidity using spread, they argue that a comprehensive liquidity measure accounts also for depth, since narrower spread combined with lower depth does not necessarily reflect better liquidity. They observe that A-shares trade at a premium, as they feature both narrower bid-offer spreads and larger depth than their H-shares pendants. Wang & Jiang (2004) add the dimension of a differential in risk attitudes of foreign investors in H-shares and domestic investors in A-shares. Meaning all else equal, if A-shares investors get less risk averse relative to H-share investors the spread between A- and H-shares should widen.
Another widely accepted finding for the explanation of price differences in segmented market is information asymmetry. Information asymmetry describes the effect of one investor group receiving information faster, putting the other group at an informational disadvantage. Reason hereof can be e.g. censorship and language barriers. Chakravarty, Sarkar & Wu (1998) find that foreign investors in B-shares are at an informational disadvantage due to language barriers vs. domestic A-shares investors whereas Chui & Kwok (1998) argue in their study that the exact opposite is the case. They say that foreign B-shares investors are informationally advantaged, as A-shares investors receive information only later due to information barriers in China. When Wang & Jiang (2004) investigate dual-listed AH-shares, they find no evidence for information asymmetries as explanation for price differences between the two listings. They argue that this is due to the fact that there are no language barriers between investors in mainland China and Hong Kong investors, as well as strict disclosure requirements for comprehensive financial information on the HKSE.
Interestingly, during the timeframe of investigation, a pilot program called the Shanghai-Hong Kong Stock Connect launched. In a pledge to increase integration and link China mainland and Hong Kong stock markets, the Shanghai-Hong Kong Stock Connect allows for mutual access between the Shanghai and Hong Kong exchanges. This enabled investors for the first time to buy and sell between the Shanghai Stock Exchange and the Hong Kong Stock Exchange using their domestic broker. The Shanghai-Hong Kong Stock Connect significantly facilitates trade between the exchanges, however, several restrictions apply. While northbound trading i.e.
10 Structure of Mainland China and Hong Kong Equity Markets
Hong Kong investors selling or buying on the Shanghai Stock Exchange, is opened for all global investors, southbound trading (from Shanghai into Hong Kong) is restricted to institutional as well as wealthy private investors with account balances exceeding 500.000 RMB. As of December 2014 the Shanghai-Hong Kong Stock Connect included 82% and 90% of the entire market cap listed on the HKSE and SHSE respectively. (Bank of International Settlement, 2014) A similar program between the exchanges of Shenzhen and Hong Kong went into effect in 2017. Hence, the period of investigation provides an interesting setting, featuring a connection between Shanghai and Hongkong exchanges, but no such connection between Shenzhen and Hong Kong.
11 Measuring the inflow-effect
Measuring the inflow-effect
Already Galbraith (1990) and Kindleberger (1996) describe that inflows of new inexperienced traders and leverage are prerequisite to the formation of asset bubbles. Kirchler et al. (2015) then find that the joint inflow of cash together with traders reliably causes the formation of price bubbles in experimental asset markets. They name the joint inflow of cash and traders inflow- effect. So far the inflow-effect has not been tested in a real-world asset market. Xiong & Yu, (2011) hypothesize that an inflow of traders was cause for the formation of the Chinese Warrants bubble. However, they fail to test their claim due to unobservability of the inflow of new traders into these warrants. For the first time, the set-up of this paper makes it possible to measure the inflow-effect in a real-world asset market and enables me to investigate, if the inflow-effect is cause for a mispricing in Chinese equity prices.
I am using two ways to measure the inflow-effect. First, I am using statistics on the number of opened A-shares brokerage accounts in mainland China, published weekly by the CSDC from 05.07.2013 until 29.05.2015. Such A-shares brokerage accounts are required for both retail and institutional investors to trade A-shares on the Shanghai and Shenzhen Stock Exchanges. Unfortunately, unlike Kirchler et al. (2015) I am not able to separate cash and trader inflow as it is not disclosed, if these newly opened accounts are also funded with cash. However, it can be assumed that the opened accounts are subsequently used to trade and hence must be equipped with cash. To further validate that there is indeed an inflow of cash and not only new account openings, I employ a second measure that tracks outstanding margin trading balances. Margining is the borrowing against securities positions in a brokerage account. Outstanding margin transaction net of repayments balances thus presents a good measure for the inflow of cash. A drawback is, that this measure contains all traders and not just the ones that have a newly opened A-shares brokerage account. Nevertheless, to fully account for both dimensions of the inflow-effect i.e. the inflow of new traders and the inflow cash it is prudent to investigate whether or not the findings are consistent across both measures.
12 Empirical Analysis
Empirical Analysis
1.1 Dataset and Variables
This study examines if Kirchler et al.'s (2015) inflow-effect can be detected as reason for a mispricing in dual-listed Chinese A-shares relative to their H-shares listings during the period of beginning of July 2013 until end of May 2015. During the sample period there were 67 Shanghai dual-listed companies and 14 Shenzhen dual-listed companies. All of these companies have corresponding H-shares listings in Hong Kong. Table 1 lists these companies. The first section presents all Shanghai dual-listed companies with their respective A-shares and H-shares ticker number, while the second section presents the Shenzhen dual-listed companies again with A- and H-shares ticker number.
Table 1 – Dual-listed companies
The table presents all companies that have their shares listed both on China mainland as A-shares and in Hong-Kong as H-shares between 08.07.2013 and 29.05.2015. Column one lists the A-shares ticker symbol under which shares are traded on the Shanghai Stock Exchange (SHSE) or on the Shenzhen Stock Exchange (SZSE). The second column shows the H-shares ticker symbol under which shares are traded on the Hong Kong Stock Exchange (HKSE).
A-shares ticker H-shares ticker Company name SHSE 601288 1288 Agricultural Bank of China Ltd 601111 0753 Air China 601600 2600 Aluminium Corporation of China Ltd 600585 0914 Anhui Conch Cement Co Ltd 600012 0995 Anhui Expressway Co Ltd 601992 2009 BBMG Corp 601988 3988 Bank of China Ltd 601328 3328 Bank of Communications Ltd 600860 0187 Beijing Jingcheng Machinery Electric Co Ltd 601588 0588 Beijing North Star Company Ltd 600030 6030 CITIC Securities Co Ltd 601766 1766 CRRC Corp Ltd 600685 0317 CSSC Offshore & Marine Engineering Group Co Ltd 601998 0998 China CITIC Bank Corp Ltd 601919 1919 China COSCO Holdings Company 601898 1898 China Coal Energy Company Ltd 601898 1898 China Communications Construction Co Ltd 601939 0939 China Construction Bank Corp 600115 0670 China Eastern Airlines Corporation Ltd 601628 2628 China Life Insurance Co Ltd 600036 3968 China Merchants Bank Co Ltd 600016 1988 China Minsheng Banking Corp Ltd 603993 3933 China Molybdenum Co Ltd 601808 2883 China Oilfield Services Ltd 601601 2601 China Pacific Insurance Group Co Ltd 600028 0386 China Petroleum & Chemical Corp 601186 1186 China Railway Construction Corp Ltd 601390 0390 China Railway Group 601088 1088 China Shenua Energy Co Ltd 601866 2866 China Shipping Container Lines Co Ltd
13 Empirical Analysis
600026 1138 China Shipping Development Co Ltd 600029 1055 China Southern Airlines Co Ltd 601005 1053 Chonqing Iron & Steel Co Ltd 601880 2880 Dalian Port PDA Co Ltd 601991 0991 Datang International Power Generation Co Ltd 600875 1072 Dongfang Electric Corp Ltd 601038 0038 First Tractor Co Ltd 601633 2333 Great Wall Motor Co Ltd 601333 0525 Guangshen Railway Co Ltd 601238 2238 Guangzhou Automobile Group Co Ltd 600332 0874 Guangzhou Baiyunshan Pharmaceutical Holdings Co Ltd 600837 6837 Haitong Securities Co Ltd 600027 1071 Huadian Power International Corp Ltd 600011 0902 Huaneng Power International Inc 601398 1398 Industrial & Commercial Bank of China Ltd 600377 0177 Jiangsu Expressway Co Ltd 600362 0358 Jiangxi Copper Co Ltd 600876 1108 Luoyang Glass Co Ltd 600808 0323 Maanshan Iron & Steel Co Ltd 601618 1618 Metallurgical Corp of China Ltd 600775 0553 Nanjing Panda Electronics Co Ltd 601336 1336 New China Life Insurance Co Ltd 601857 0857 Petro China Co Ltd 601381 2381 Ping An Insurance Group Co of China Ltd 601727 2727 Shanghai Electric Group Co Ltd 600196 2196 Shanghai Fosun Pharmaceutical Group Co Ltd 601607 2607 Shanghai Pharmaceutical Holding Co Ltd 600806 0300 Shenji Group Kunming Machine Tool Co Ltd 600548 0548 Shenzhen Expressway Co Ltd 601107 0107 Sichuan Expressway Co Ltd 600871 1033 Sinopec Oilfield Service Corp 600688 0338 Sinopec Shanghai Petrochemical Co Ltd 600874 1065 Tianjin Capital Environmental Protection Group Co Ltd 600600 0168 Tsingtao Brewery Co Ltd 600188 1171 Yanzhou Coal Mining Co Ltd 601717 0564 Zhengzhou Coal Mining Co Ltd 601899 2899 Zijin Mining Group Co Ltd SZSE 000898 0347 Angang Steel Company Ltd 002594 1211 BYD Company Ltd 000039 2039 China International Marine Containers Group Co Ltd 002672 0895 Dongjiang Environmental Co Ltd 000921 0921 Hisense Kelon Electrical Holdings Co Ltd 000666 0350 Jingwei Textile Machinery 000585 0042 Northeast Electric Development Co Ltd 000488 1812 Shandong Chenming Paper Holdings Limited 002490 0568 Shandong Molong Petroleum Machinery 000756 0719 Shandong Xinhua Pharmaceutical Co 000338 2338 Weichai Power Co Ltd 002202 2208 Xinjiang Goldwin Science & Technology Co Ltd 000063 0763 ZTE Corp 000157 1157 Zoomlion Heavy Industry Science and Tech. Co Ltd (Source Bloomberg, own presentation)
For each company and listing I collect closing prices, bid and offer prices at close, number of A- and H-shares outstanding as well as trading volume as obtained from Bloomberg. I only consider weekly intervals that are common to both A- and H-share listings in the panel and drop any observations with missing A- and/or H-share information. Reasons for missing
14 Empirical Analysis observations are different holiday schedules between mainland China and Hong Kong and differences in trading suspensions on the mainland China stock exchanges vs the Hong Kong stock exchange. Trading suspensions are enacted in response to e.g. drastic intra-day share price moves, asset restructurings, bankruptcy reorganization, voluntary suspensions or fraud (He et. al., 2019). After dropping observations as described, 7774 weekly trading observation remain in the sample data-set. Hong Kong H-shares are denominated in HKD. To be able compare prices of H-shares to corresponding CNY denominated A-shares listings, I convert closing prices of H-shares from HKD into CNY using end of trading week spot exchanges rates as provided by Bloomberg.
Central variable is the AH-premium i.e. the premium with which A-shares trade over H-share prices as measure for share price mis-valuation. Similar to Lee (2009), I calculate the AH- premium as the percentage difference in closing price between the A-share and H-share listing of the same company, relative to the H-share closing price. Thus the relative difference in closing prices is RDCP = (PA – PH) / PH. PA reflects the A-share price on the SHSE or SZSE measured in CNY. PH is the H-share price on HKSE, converted from HKD into CNY using the spot exchange rate as PH = PHHKD / (HKDCNY). To arrive at the weekly measure relative difference in average closing prices (RDACP), I calculate the average over each week´s daily RDCPs. To measure the inflow-effect into Chinese mainland A-shares, I am using weekly data on the numbers of opened A-shares brokerage accounts as published by the CSDC. Since the data is only available in weekly granularity, the overall dataset is limited to weekly observations. The variable for the inflow of new investors into the A-shares markets measured by opened A- shares brokerage accounts for the week is AINF. AINF measures all newly opened A-shares brokerage accounts that allow trading on either of the A-shares stock exchanges in Shanghai or Shenzhen. I am further able to separately observe the number weekly opened brokerage accounts that allow for trade of Shenzhen A-shares on the SZSE as AINF_SZ and opened brokerage accounts that only allow trading in SHSE A-shares as AINF_SH. As measure for the inflow of cash, I am using week over week changes in net outstanding margin trading balances on A-shares brokerage accounts. Outstanding margin trading balance is defined as new margin balance less repaid margin balance at the end of a trading day. I then take an average over the trading days of the week to arrive at the weekly average outstanding margin trading balance and denominate the variable as AMAR.
15 Empirical Analysis
To control for effects that past research found to explain the AH-premium (RDACP), I introduce control variables. To account for the liquidity explanation for the AH-premium, I draw on Lee's (2009) findings. I similarly use relative quoted spread as measure for differences in liquidity between A and H-share listings. I first calculate daily relative quoted spread for
(푃표푓푓푒푟−푃푏푖푑) both A and H-share listing as 푄푆 = . Where Poffer is the offer price at close and Pbid 푀 is the bid price at close. M is the mid-quote calculated as average of the sum of bid- and offer price. I then aggregate daily RQS for A- and H-shares and calculate average weekly relative effective spreads for both A- and H-shares denominated as ARQSA and ARQSH. As final step for each listing pair, I calculate the relative percentage difference in ARQS between A and 퐴푅푄푆 − 퐴푅푄푆 respective H-shares listings as 푅푄푆 = 퐴 퐻 . Due to inaccessibility of the order books, 퐴푅푄푆퐻 I am not able to construct a measure of liquidity comprising of both spread and depth. I am, however, employing turnover as additional measure to proxy the depth dimension of liquidity. Total turnover is calculated using daily volume in number of shares traded divided by total 푉표푙푢푚푒 shares outstanding i.e. 푇 = , where SOUTA and SOUTH refer to total A and H- 푆푂푈푇퐴+푆푂푈푇퐻 shares outstanding. Weekly average total turnover (ATT) is then calculated by averaging the total turnover over the week’s trading days. The relative difference in average total turnover 퐴푇푇 − 퐴푇푇 (RDATT) follows as 푅퐷퐴푇푇 = 퐴 퐻 . To control for asymmetric distribution of 퐴푇푇퐻 information between A and H-shares, I use stock market capitalization as shown by Chan & Kwok (2005). Their findings show that the asymmetric information issue decreases with increasing market capitalization. I thus compute stock market capitalization as the average of end of the week market capitalization during the sample period, where end of the week stock market capitalization is 퐶퐴푃 = (푆푂푈푇퐴 ∗ 푃퐴) + (푆푂푈푇퐻 ∗ 푃퐻). SOUTA and SOUTH refer to total A and H-shares outstanding and PA and PH refer to end of the week closing prices of A and H-shares respectively. In a next step, I then take an average over aggregated weekly CAP to arrive at the average stock market cap for each company ACAP. Following a similar approach as Lee (2009) to control for differences in risk between A and H share listings, I use readily observable stock price volatility (VOL). Since I am only able to draw on daily price data, unlike Lee (2009) who uses intraday stock price volatility, I calculate VOL as the rolling standard deviation over the daily log returns of the prior 21 trading days (approx. 1 month) for both A and H-shares listings as RVOLA and RVOLH. To obtain weekly rolling volatility, I calculate average RVOLA and RVOLH over the weeks trading days as ARVOLA and ARVOLH. I then progress to construct a weekly measure for the relative difference in rolling volatility
16 Empirical Analysis
퐴푅푉푂퐿 − 퐴푅푉푂퐿 between A- and H-shares prices as 퐷퐴푅푉푂퐿 = 퐴 퐻 . As outlined before, Arquette, 퐴푅푉푂퐿퐻 Brown & Burdekin (2008) find that traders bid up H-share prices when they expect the CNY to strengthen vs. the HKD causing the AH-premium to narrow. To control for this effect, I fall back on Arquette, Brown & Burdekin´s (2008) approach and employ 12 month renminbi non deliverable forward7 contract prices as measure for expected changes in the exchange rate between CNY and HKD. I am able to draw on the liquid US Dollar renminbi forward contract as proxy for expected changes in the CNYHKD exchange rate, due to the fact that the HKD is pegged to the USD under a fixed currency regime. After taking an average of 12 month renminbi non deliverable forward contract prices for the week using daily closing prices, I denote the measure as AFWDEXR.
1.2 Statistical Analysis of Variables
Table 2 presents descriptive statistics of mean, median, standard deviation as well as minima and maxima for the variables specified in the previous section. Section A of table 2 gives summary statistics for the percentage differences between A and H-shares. Section B presents statistics for the 12-month renminbi non-deliverable forward, the firm specific characteristic market capitalization is analyzed in section C and summary statistics of the inflow-effect variables are given in section D. Panel 1 includes the mentioned descriptive statistics for all companies listed in mainland China, which include Shanghai and Shenzhen dual-listed companies. Panel 2 presents the subsample that consists of only Shanghai dual-listed firms, and Panel 3 is the subsample that contains only Shenzhen dual-listed firms.
The percentage difference in closing prices between A and H-shares (RDACP) is 52.4% for the total sample, 47.4% for the Shanghai dual-listed firms and 76.6% for the Shenzhen dual-listed firms. This indicates that on average A-shares trade at a premium over their H-shares counterpart on both SHSE and SZSE. Remarkable is the fact, that for the overall sample RDACP has a minimum value of -38.7% (representing a discount of A-shares vs the H-shares counterpart) and a maximum value of 413% with wide differences both across firms as well as over time. Due to the importance of RDACP for the present paper, I analyze the variable per company in further detail in table 3. The percentage differences in liquidity measured as quoted spread (RDARQS) and total turnover (RDATT) between A and H-shares also present
712 m CNYUSD NDF is a 12 month non-deliverable forward contract that settles in cash instead of delivering the underlying. Delivery is restricted by capital controls. Settlement reflects a payment in USD of the difference of forward and future spot rate at maturity. 17 Empirical Analysis interesting reads. RDARQS for the entire sample shows a mean of -42.0% and a median of - 49.1%, indicating that on average A-shares feature lower quoted spreads and thus lower transaction cost compared to their H-shares counterparts. RDATT conveys a similar message with total turnover for A-shares being on average 587.0% higher than total turnover for H- shares. This is confirmed by a median of 241.2%. Hence, A-shares show both greater depth and lower spreads vs their H-shares pendants and thus confirm overall better liquidity of A-shares relative to the Hong Kong listings. These findings are similar to Lee`s (2009), as he also attests lower quoted spreads as well as higher total turnover for A-shares vs H-shares listings with means of -72% and 50,769% respectively. Additionally I find standard deviations of 35.6% and 1,388.8% for RDARQS and RDATT respectively point towards heterogeneity of liquidity across firms and time, which is also confirmed by Lee (2009), when he analyzes intraday data of dual-listed AH-shares during the year of 2004. The percentage difference between A-shares and H-shares for 21-day rolling volatility (RDARVOL) is on average 3.1% while the median with -4.7% shows a negative value. It is remarkable that H-shares thus show on average a 3% lower standard deviation than A-shares, indicating that within the present sample Hong Kong listed H-shares express lower volatility and thus lower riskiness vs. their A-share pendants. The negative median of -4.7% on the other hand attests lower volatility and thus lower riskiness of A-shares vs H-shares. Despite Lee (2009) using intra-day volatility data, he arrives at a similar pattern for mean and median of 11.8% and -13.1% respectively for the relative differences in volatility between A and H-shares. Interestingly looking at the Shanghai and Shenzhen subsamples, I only detect contrary mean and median among the Shanghai-listed AH-share pairs with mean of 14.1% and median of -15.7%. Shenzhen listed AH-shares express consistently lower volatility for A-shares with a mean of -5.8% and median of -10.5%.
Section B of table 2 presents summary statistics of the forward exchange rate in RMB per USD measured by prices of the 12-month renminbi non-deliverable forward contract (AFWDEXR). Mean forward exchange rate over the sample period is 6,242 RMB per USD, the median forward exchange rate is similar at 6,244 RMB per USD with a standard deviation of 0,074 RMB per USD.
18 Empirical Analysis
Table 2 - Descriptive Statistics
Table 2 gives descriptive statistics of mean, median, standard deviation (Sd), minimum (min) and maximum (max) for all variables in the dataset. Section A presents percentage differences between RMB denominated A-shares in Mainland China and HKD denominated H-shares Hong Kong. Wherever applicable H-share closing prices are translated into RMB using daily closing spot exchange rates as provided by Bloomberg. There are four distinct weekly measures of relative percentage differences between A- share listing and the respective H-share counterpart (weekly values are calculated by taking the average over the daily observations). Closing price (RDACP) reflects the difference in daily closing prices. To measure differences in liquidity the following two measures are used; relative quoted spread (RDARQS) which indicates the difference in daily quoted spread calculated as average of the sum of bid and ask closing prices relative to the mid-quote and total turnover (RDATT), which represents the difference in total turnover calculated as number of shares traded divided by the total of outstanding A and H-shares. Volatility (RDARVOL) is the measure for differential risk calculated as difference in 21 day rolling standard deviation. Section B presents the prices of the 12 month renminbi NDF contract (AFWDEXR) as measure for expected changes in exchange rates of the RMB vs the HKD denominated in RMB per USD. (HKD is pegged to the USD under a fixed rate currency regime) Section C presents firm characteristics. The average stock market capitalization (ACAP) reflects the average stock market capitalization of dual-listed firms over the sample period and is calculated as average of daily stock market capitalization (SOUTA * PA) + (SOUTH * PH), where SOUTA and SOUTH are A and H-shares outstanding and PA and PH are A and H-shares closing prices. (HKD denominated closing prices translated into RMB using daily closing spot exchange rates as provided by Bloomberg) The measure is denominated in billion RMB. Section D presents inflow-effect measures. Newly Opened A-Shares Accounts (AINF) reflects the weekly number of opened A-share brokerage accounts. The net increase/decrease in margin lending balance (AMAR) reflects the net outstanding margin lending balance (new margin lending balances minus repayment of margin lending balances) in A-shares brokerage accounts. The measure is denominated in million RMB.
Panel 1 reflects descriptive statistics comprising of all 81 AH-share pairs listed both on Shanghai and Shenzhen stock exchanges, Panel 2 describes the subsample containing only the 67 Shanghai dual-listed AH-share pairs, whereas Panel 3 provides descriptive statistics for the 14 Shenzhen dual-listed AH-share pairs.
Panel 1 – Shanghai and Shenzhen Mean Median Standard Deviation Minimum Maximum A: Avg. weekly pct. difference between A-shares & H-shares (N=7774) Closing Price (RDACP) 52.445 30.735 70.528 -38,730 412.974 Relative Quoted Spread (RDARQS) -42.022 -49.070 35.635 -96.979 650.808 Total Turnover (RDATT) 587.033 241.214 1,388.827 -99.486 57,288.27 Rolling Volatility (RDARVOL) 3.061 -4.683 44.028 -82.110 339.123
B: Average weekly forward exchange rate (N=89) 12-Month Renminbi NDF (AFWDEXR) in RMB per USD 6.242 6.244 .074 6.101 6.417
C: Firm-specific characteristics of dual-listed AH-shares (N=81) Average Stock Market Capitalization (ACAP) in bn. RMB 151.058 43.073 300.093 2.280 1,570.826
D: Measures of inflow-effect into A-shares markets (N=89) Newly Opened A-Shares Accounts (AINF) in # of accounts 434,293 132,841 822,657 16,011 4,472,964 Net increase/decrease in margin lending balance (AMAR) in mn. RMB 6,611.947 4,025.174 4,858.549 2,188.820 20,712.600
19 Empirical Analysis
Panel 2 – Shanghai Mean Median Standard Deviation Minimum Maximum A: Avg. weekly pct. difference between A-shares & H-shares Closing Price (RDACP) 47.378 27.393 65.152 -38.730 388.001 Relative Quoted Spread (RDARQS) -38.215 -44.121 35.563 -96.624 650.808 Total Turnover (RDATT) 510.843 194.011 1,346.785 -99.486 57,288.270 Rolling Volatility (RDARVOL) 4.913 -3.526 44.975 -82.110 339.123
B: Average weekly forward exchange rate (N=89) 12-Month Renminbi NDF (AFWDEXR) in RMB per USD 6.242 6.244 .074 6.101 6.417
C: Firm characteristics of dual-listed companies (N=67) Average Stock Market Capitalization (ACAP) in bn. RMB 177.008 49.835 323.921 2.280 1,570.826
D: Measures of inflow-effect into A-shares markets (N=89) Newly Opened A-Shares Accounts (AINF_SH) in # of accounts 234,018 69,851 465,264 8,074 2,454,041
Panel 3 – Shenzhen Mean Median Standard Deviation Minimum Maximum A: Avg. weekly pct. difference between A-shares & H-shares Closing Price (RDACP) 76.601 51.241 88.038 -29.114 412.974 Relative Quoted Spread (RDARQS) -60.172 -68.795 29.942 -96.979 110.659 Total Turnover (RDATT) 950.232 570.355 1,522.878 -88.710 25,079.180 Rolling Volatility (RDARVOL) -5.769 -10.454 37.995 -81.240 177.000
B: Average weekly forward exchange rate (N=89) 12-Month Renminbi NDF (FWDEXR) in RMB per USD 6.244 6.245 .073 6.101 6.412
C: Firm characteristics of dual-listed companies (N=14) Average Stock Market Capitalization (ACAP) in bn. RMB 27.349 27.942 25.928 2.452 98.021
D: Measures of inflow-effect into A-shares markets (N=89) Newly Opened A-Shares Accounts (AINF_SZ) in # accounts 200,276 66,423 354,520 7,937 1,973,923
20 Empirical Analysis
Section C presents the firm specific characteristic average market capitalization (ACAP). For the overall sample of both Shanghai and Shenzhen listed companies the average stock market capitalization is RMB 151.1 bn. with a standard deviation of RMB 300.1 bn. indicating a large variation of firm size. The smallest firm by stock market cap is Luoyang Glass Co. Ltd. with an average market value of equity of RMB 2.3 bn., while the largest capitalization firm is Petro China Co. Ltd. with RMB 1,570.1 bn. in mean stock market capitalization. Shenzhen dual- listed companies on average are smaller than Shanghai dual-listed companies with mean stock market capitalizations of RMB 27.3 bn. and RMB 177 bn. respectively. Section D lists summary statistics for the measures of the inflow-effect. The average number of newly opened brokerage accounts per week (AINF) is 234,018 for Shanghai and 200,276 for accounts that allow trading in Shenzhen or 434,293 for the entire sample. Median number of newly opened brokerage accounts per week are 69,851 in Shanghai, 66,423 in Shenzhen and 132,841 for the entire sample. It is interesting, that the minimum number of newly opened brokerage accounts per week in the entire sample is only 16,011 whereas in the maximum 4,427,964 accounts were opened per week. The standard deviation of the entire sample is at 828,657 newly opened accounts.
Figure 2 shows the mean relative difference in average closing prices of A-shares listings vs their H-share counterparts across all dual-listed companies. The average RDACP reflects the average premium over which A-
21 Empirical Analysis shares trade over H-shares for all dual listed companies in the sample from beginning of July 2013 until end of May 2015. Figure 2 charts the development of the mean RDACP for the sample period. The minimum average RDACP is 18.1% occurring in week 28 of 2014, the maximum RDACP is 102% in week 12 of 2015 at which point A-shares traded at more than twice the price relative to their H-shares counterparts. This depicts the high time variation during the investigation period. However, the size of RDACP does not only vary over time, but there is additionally high cross- sectional variation between the individual dual-listed companies as table 3 outlines.
Table 3 - RDACP per company
Table 3 presents descriptive statistics of mean, median, standard deviation (sd) as well as minima (min) and maxima (max) of the percentage differences in closing prices between A and H-share listings (RDACP) for each dual-listed firm. The table is sorted by mean RDACP. The first part of the table presents dual-listed firms that are trading on the Shanghai Stock Exchange, while the second part presents all firms that are trading on the Shenzhen Stock Exchange.
Company name mean median sd min max SHSE listed Anhui Conch Cement Co Ltd -19.443 -22.662 10.106 -38.730 -1.727 Jiangsu Expressway Co Ltd -15.143 -18.281 9.347 -27.388 5.416 Ping An Insurance Group Co of China Ltd -12.858 -16.371 10.925 -28.042 14.743 China Pacific Insurance Group Co Ltd -12.495 -17.676 11.695 -28.048 14.401 Tsingtao Brewery Co Ltd -7.620 -10.596 8.649 -19.223 25.145 Huaneng Power International Inc -6.814 -10.620 11.943 -20.405 32.889 Industrial & Commercial Bank of China Ltd -6.365 -8.136 6.214 -15.680 9.885 China Merchants Bank Co Ltd -5.292 -6.180 7.087 -18.069 12.883 China Shenua Energy Co Ltd -4.967 -13.723 17.235 -21.884 45.124 Agricultural Bank of China Ltd -4.540 -9.087 11.001 -17.362 21.357 China Life Insurance Co Ltd -2.040 -13.778 22.744 -22.199 54.409 China Construction Bank Corp -1.867 -7.041 11.068 -13.090 30.806 CITIC Securities Co Ltd -0.264 -9.070 21.384 -24.115 48.800 Bank of Communications Ltd 0.428 -4.187 9.881 -11.391 27.065 Bank of China Ltd 2.027 -0.979 9.913 -9.847 35.646 China Petroleum & Chemical Corp 2.867 -3.667 16.361 -15.797 41.202 Shanghai Fosun Pharmaceutical Group Co Ltd 2.876 -0.224 12.913 -17.095 32.701 Shanghai Pharmaceutical Holding Co Ltd 8.698 3.994 15.634 -11.185 46.336 China Railway Construction Corp Ltd 8.811 -16.708 44.988 -25.395 114.376 Huadian Power International Corp Ltd 9.672 13.529 15.809 -18.001 38.043 Air China 9.996 -4.018 25.254 -12.724 71.262 CRRC Corp Ltd 10.558 -6.237 39.521 -23.642 164.134 China Communications Construction Co Ltd 12.355 -11.438 39.662 -20.268 99.460 China Railway Group 14.688 -12.849 48.775 -19.234 150.470 Guangshen Railway Co Ltd 16.095 8.873 24.309 -13.805 78.504 China Minsheng Banking Corp Ltd 20.911 21.849 10.263 2.597 44.266 Haitong Securities Co Ltd 21.127 16.754 19.303 -8.412 62.305 Petro China Co Ltd 23.225 14.644 25.991 -11.498 80.118 Great Wall Motor Co Ltd 23.678 23.849 9.819 6.859 49.053 New China Life Insurance Co Ltd 24.630 19.392 16.759 -0.698 64.415 China CITIC Bank Corp Ltd 28.839 23.307 15.199 6.143 62.916 Anhui Expressway Co Ltd 32.662 24.861 25.052 8.831 168.157 Guangzhou Automobile Group Co Ltd 33.784 28.835 21.735 0.133 102.594 China Southern Airlines Co Ltd 36.948 26.681 23.702 8.499 119.332 Guangzhou Baiyunshan Pharmaceutical Holdings Co Ltd 37.029 32.610 10.968 20.387 64.999 Dongfang Electric Corp Ltd 37.559 27.659 25.610 9.505 106.145 China Oilfield Services Ltd 41.087 22.742 36.820 5.447 131.007 Jiangxi Copper Co Ltd 41.513 32.338 23.088 14.743 99.754
22 Empirical Analysis
Shenzhen Expressway Co Ltd 42.416 32.223 26.956 4.858 102.917 China Shipping Development Co Ltd 42.824 24.507 40.090 1.168 144.755 China Eastern Airlines Corporation Ltd 43.121 29.230 26.078 15.193 117.889 Maanshan Iron & Steel Co Ltd 45.755 19.464 46.543 -0.677 155.505 BBMG Corp 46.497 37.964 20.682 16.758 97.442 China Coal Energy Company Ltd 47.954 37.807 30.689 13.663 129.190 China COSCO Holdings Company 50.090 25.217 49.346 1.447 193.933 Datang International Power Generation Co Ltd 63.847 60.768 29.236 9.614 126.273 Aluminium Corporation of China Ltd 66.163 59.833 28.969 27.652 151.645 Sichuan Expressway Co Ltd 68.881 64.716 26.584 34.603 159.041 Zhengzhou Coal Mining Co Ltd 70.511 64.491 26.271 29.486 145.376 CSSC Offshore & Marine Engineering Group Co Ltd 71.286 79.138 39.439 2.794 162.003 Metallurgical Corp of China Ltd 71.309 55.081 42.352 24.152 189.167 Zijin Mining Group Co Ltd 74.354 72.632 20.399 40.623 142.243 China Shipping Container Lines Co Ltd 80.361 59.312 50.901 22.798 232.728 Yanzhou Coal Mining Co Ltd 88.873 70.177 48.411 28.006 199.280 Shanghai Electric Group Co Ltd 93.968 74.142 51.502 43.821 245.501 Dalian Port PDA Co Ltd 94.937 83.754 30.971 56.511 190.451 Beijing North Star Company Ltd 98.322 104.018 23.862 53.069 165.814 Sinopec Shanghai Petrochemical Co Ltd 102.559 96.605 34.553 58.473 273.773 First Tractor Co Ltd 127.423 111.552 42.289 70.914 234.128 China Molybdenum Co Ltd 144.900 137.526 46.924 72.825 238.004 Sinopec Oilfield Service Corp 146.956 153.443 58.924 19.217 381.723 Nanjing Panda Electronics Co Ltd 153.334 160.717 42.586 77.222 209.413 Chonqing Iron & Steel Co Ltd 154.834 155.548 41.889 72.791 235.335 Shenji Group Kunming Machine Tool Co Ltd 158.362 170.756 49.379 69.523 234.963 Tianjin Capital Environmental Protection Group Co Ltd 161.210 161.220 59.952 74.072 275.798 Beijing Jingcheng Machinery Electric Co Ltd 184.414 186.898 60.544 75.714 290.552 Luoyang Glass Co Ltd 294.170 323.872 79.791 107.626 388.001 Shenzhen listed Weichai Power Co Ltd -10.003 -17.669 18.132 -28.518 40.086 Angang Steel Company Ltd -8.089 -19.448 22.309 -29.114 46.493 ZTE Corp 21.140 14.278 17.772 -0.549 57.330 China International Marine Containers Group Co Ltd 24.885 15.082 22.360 0.653 78.759 Zoomlion Heavy Industry Science & Technology Co Ltd 26.941 17.132 25.478 -6.904 85.071 Xinjiang Goldwin Science & Technology Co Ltd 39.109 39.118 15.900 3.187 73.165 BYD Company Ltd 39.729 33.832 18.332 14.666 81.087 Hisense Kelon Electrical Holdings Co Ltd 52.751 51.475 34.762 4.865 119.267 Dongjiang Environmental Co Ltd 64.121 65.205 24.508 26.534 116.913 Shandong Chenming Paper Holdings Limited 80.471 78.299 21.896 44.141 125.111 Jingwei Textile Machinery 117.818 113.992 30.261 58.981 171.155 Shandong Xinhua Pharmaceutical Co 148.368 159.874 38.444 67.543 199.244 Northeast Electric Development Co Ltd 195.730 211.544 53.722 78.952 277.540 Shandong Molong Petroleum Machinery 282.568 284.641 82.956 120.929 85.071
Interestingly not all companies express positive mean RDCAP. In fact in total there are 15 dual listed firms that have negative mean RDACP i.e. on average their H-shares listing trades at a premium to the A-shares listing. Of these 15 companies 13 are on the SHSE, whereas 2 are Shenzhen listed companies. However, when looking at maximum values within the sample, there is only one firm left whose A-shares did not trade at a premium over their H-shares pendant within the sample period. This company is Anhui Conch Cement Co Ltd. All other remaining 80 companies express positive maximum values. This supports the fact, that while not all dual listed companies on average have their A-shares trading at a premium over their H- shares listing, RDCAP turns positive within the sample period due to large increases in RDACP.
23 Empirical Analysis
As observable from figure 2 on average the highest observations for RDACP occur in week 12 of 2015.
1.3 Empirical Methodology and Research Questions
In the following I develop a panel data model to provide answers to the following research questions:
RQ1: Is the inflow of new traders and cash responsible for the increase in the AH-premium?
RQ2: Is there a difference in the AH-Premium between Shanghai and Shenzhen dual listed AH- shares caused by the launch of the Shanghai-Hong Kong Stock Connect?
In order to provide answers to the above research questions, I investigate if an inflow of new traders with cash can explain the AH-premium with the use of multiple regression techniques. In conformity with findings from Kirchler et al. (2015) that an inflow of traders with cash causes the formation of asset bubbles. I assume that there is a statistically significant positive relationship between the inflow-effect proxy of newly opened SHSE and SZSE A-shares brokerage accounts (AINF) and the AH-premium (RDACP). Since I rely on variables that differ both over time as well as across firms, I make use of panel data estimations based on the dataset described in section 4.1. Benefits of this approach are that the use of panel data features larger degrees of freedom, reduced collinearity of the independent variables, as well as the ability to control for firm-specific effects. Beyond the measure for newly opened A-shares brokerage accounts (AINF), I additionally introduce control variables that past research found to explain the AH-premium and test if AINF is still significant after controlling for these effects. The employed control variables consist of a) the relative difference in quoted spread (RDAQS) between A- and H-shares to control for differences in the spread-dimension of liquidity, b) the relative difference in average total turnover between A- and H-shares to control for differences in the depth-dimension of liquidity (RDATT), c) relative difference in rolling standard deviation between A- and H-shares to control for the differences in riskiness of A- and H-share listing (RDARVOL), d) expected changes in the exchange rate between HKD and CNY measured using 12-month RMB NDF contract prices (AFWDEXR) and e) the two firm specific effects average stock market capitalization (ACAP) and LOC_TRADE. LOC_TRADE is a dummy variable that shows 1 if the company is listed on the Shanghai Stock Exchange and 0 if the company is listed on the Shenzhen stock exchange. I include this variable to determine
24 Empirical Analysis whether or not there are significant differences in RDACP between dual-listed shares that trade in Shanghai and dual-listed shares that trade in Shenzhen. I thus estimate the below model to analyze the AH-Premium (RDACP):
ln(푅퐷퐴퐶푃푖푡 + 1)
= 퐼푁푇 + 푏1 ln(퐴퐼푁퐹푖푡) + 푏2 ln(푅퐷퐴푄푆푖푡 + 1) + 푏3 ln(푅퐷퐴푇푇푖푡 + 1)
+ 푏4 ln(푅퐷퐴푅푉푂퐿푖푡 + 1) + 푏5 ln (퐴퐹푊퐷퐸푋푅푖푡) + 푏6퐴퐶퐴푃푖푡
+ 푏7퐿푂퐶_푇푅퐴퐷퐸푖푡 + 휀푖푡,
where INT is the intercept term, b1 to b7 are the slope coefficients of the described independent variables of the regression equation, subscript i refers to the ith dual listed firm, and subscript t refers to the trading week. ε is the error term. To analyze the AH-premium I employ a random effects linear regression model with heteroscedasticity-consistent White standard errors. In the following I provide evidence that the regression model is correctly specified by analyzing regression residuals and validating the below regression assumptions:
1. Regression residuals are normally distributed The plot in figure 3 shows the kernel density function that estimates the probability density function of the residuals. The chart confirms that the residuals (solid black line) are approximately normally distributed when compared to the normal density function (dotted line). To achieve approximately normally distributed residuals and correctly specify the to be estimated model, I run a log-transformation on both dependent and independent variables by taking the natural log over the variables that express positive skewness in their distributions as suggested by Benoit (2011). Additionally to be able to calculate the natural log of any negative observations, I add +1 to the observed variables if needed. The variables described in section 5.1 are hence transformed as follows:
RDACP: ln (RDACP +1) RDATT: ln (RDATT +1) RDARQS: ln (RDARQS +1) RDARVOL: ln (RDARVOL +1) AFWDEXR: ln (AFWDEXR) AINF: ln (AINF)
25 Empirical Analysis
AINF_SH: ln (AINF_SH) AINF_SZ: ln (AINF_SZ) AMAR: ln (AMAR)
Further, this transformation has the important benefit of achieving the necessary linear relationships between dependent and independent variables to fit a linear regression model.
Kernel density estimate of regression residuals
Figure 3 shows the estimated probability density function of the regression residuals (solid black line) of the estimated model vs. a normal distribution (dotted line).
2. Residuals are homoscedastic (i.e. express constant variance) A likelihood-ratio test that tests whether a regression model that accounts for heteroscedasticity is a better fit than a model that does not, returns a p-value of p < 0.001. Hence, the null-hypothesis of homoscedastic residuals is rejected in favor of significant heteroscedasticity (i.e. non-constant variance) in the residuals. To still obtain unbiased standard-errors and hence p-values I opt to use heteroscedasticity-consistent White standard errors that are robust to heteroscedasticity in the residuals as suggested in Long & Ervin (2000).
26 Empirical Analysis
3. Residuals are not auto correlated As proposed by Drukker (2003) I use the Woolridge-test to correctly test for autocorrelation in a panel data environment . The test returns a p-value of p < 0.001. This significantly rejects the null-hypothesis of no first-order autocorrelation in the residuals. Residuals thus express first-order autocorrelation. White corrected standard errors likewise take care of this issue and ensure unbiased standard errors despite the presence of autocorrelation.
4. No collinearity in model I test all variables included in the model for the presence of multicollinearity to avoid model-misspecification. Model-misspecification occurs when one variable can be expressed as linear combination of another. I employ the standard procedure to detect multicollinearity by analyzing the variation inflation factors (VIF). The VIF for all variables
are below VIF < 2 or below tolerance factor*8 of tolerance factor < 0.5, indicating that multicollinearity is not a problem. In econometrics VIFs below 10 or tolerance factors of more than 0.1 are generally viewed as unproblematic. This is outlined in O’Brien (2007).
5. Fixed- vs. random-effect model To choose between random and fixed effects model I employ a Hausman test albeit on non- robust standard errors. A p-value of p = 0.1877 fails to reject the null hypothesis of a random-effect model to be appropriate. To confirm these findings I am additionally running an overidentification test. Unlike the Hausman test the overidentification test works with robust standard errors that are needed in this case. Likewise, a p-value of this test of p = 0.1254 points towards using a random-effect model, since the null hypothesis of a random- effect model to be appropriate cannot be rejected.
1.4 Empirical Results
To provide answer to the posed research questions, I estimate the multiple linear regression model specified in section 5.3. I estimate four different iterations on both the entire panel data- set as well as on the Shanghai and Shenzhen subsample to be able to investigate the effects of interest. Table 4 presents the results from all four estimated regression models. First, I estimate model (1), which includes the entire sample of dual-listed firms and uses newly opened A- shares brokerage accounts (AINF) as measure for the inflow-effect. I then progress to estimate
8 The tolerance factor is the inverse of variation inflation factor (VIF) i.e. tolerance factor = (1/VIF) (O’Brien, 2007). 27 Empirical Analysis model (2) and model (3) that limit observations to the Shanghai and Shenzhen subsample respectively. The Shanghai subsample comprises of only Shanghai dual-listed firms, whereas the Shenzhen subsample consists of only the Shenzhen dual-listed firms. Likewise in model (2) and model (3) numbers for newly opened A-shares brokerage accounts as measure for the inflow-effect are limited to newly opened brokerage accounts that allow for trading on the SHSE (AINF_SH) and on the SZSE (AINF_SZ) respectively. Due to the limitation of observations to the subsamples, model (2) and (3) do not include the dummy variable LOC_TRADE that measures if there are differences in the AH-premium between Shanghai and Shenzhen listed firms. In order to confirm the findings of model (1), model (4) replaces AINF as measures for the inflow-effect by outstanding margin lending balance in A-shares accounts (AMAR).
Model (1), contains 7,025 weekly observations across a total of 81 dual-listed companies with an average of 87.7 weekly observations per company. Model (2), the Shanghai subsample, includes 5,809 weekly observations across 67 dual-listed AH-share companies with an average of 86.7 weekly observations per company. Model (3), the Shenzhen subsample, comprises of 1,216 weekly observations over 14 dual-listed AH-share companies with an average of 86.9 observations per company. Model (4), which uses outstanding margin lending balance as measure for the inflow-effect, contains 7,774 weekly observations across 81 dual-listed AH- share companies with an average of 96.0 weekly observations per company. Table 4 gives regression coefficients for all independent variables over all estimated models. It states their level of significance (* p-value < 0.10, ** p-value < 0.05, *** p-value < 0.01) as well as standard errors in parenthesis.
First, consistent with findings of prior research by e.g. Chan & Kwok (2005) or Lee (2009) and in support of the liquidity-hypothesis, I find a significant negative relationship of RDACP and RDARQS with a p-value of p < 0.001 in model (1). The results remain significant at the 99% confidence level across all estimated regression models, supporting the prediction that the AH- premium is negatively related to an expansion in the bid-ask spread of A-shares relative to the bid-ask spread of the H-shares counterparts. With a regression coefficient of -0.089 for model (1) a narrowing of the relative spread between A-shares and H-shares of 1% would translate into an expansion of RDACP of 0.089%. Turning to the depth-dimension of liquidity, I find additional confirming evidence in favor of the liquidity hypothesis with RDATT showing a significant positive regression coefficient of 0.03 (p < 0.001) in model (1). This indicates that
28 Empirical Analysis a 1% increase of total turnover of A-shares relative to total turnover of the H-shares counterparts leads to an AH-premium expansion of 0.03%. Again this is consistent over all estimated models at the 99% confidence level. Hence, a relative decrease in transaction cost in A-shares listings relative to H-shares listings leads to an increase in the AH-premium. Second, in line with other findings and the differential-risk-hypothesis, I find a significant positive relationship between RDACP and RDARVOL (p < 0.001 for model (1)). Again this holds true at the 99% confidence interval over all estimated regression models. The regression coefficient obtained from model (1) of 0.11 describes a sensitivity of a 1% increase in A-share volatility relative to H-share volatility leads to an increase of 0.11% in the AH-premium. Interestingly, the positive coefficient points towards a willingness of Chinese A-shares investors to pay more for more volatile stocks which indicates risk-seeking instead of normally assumed risk-averse investor behavior. Chan & Kwok (2005) similarly detect a positive relationship between the AH-premium and relative intraday volatility of A-shares vs. the H- share counterpart. They argue that this is due to Chinese mainland investors expressing speculative behavior.
Third, consistent with Arquette, Brown, & Burdekin (2008), I find a significantly positive relationship between AFWDEXR and RDACP in model (1) with a p value of p < 0.001. This is consistent at the 99% confidence level across models (1), (2) and (4). For model (3) the relationship is significant at the 95% confidence level with a p-value of p = 0.026. The obtained regression coefficient of 1.70 for model (1) indicates that for a 1% increase in the 12m renminbi NDF contract price the AH-premium increases by 1.70%. Consequently, CNY/HKD exchange rate expectations influence the AH-premium to a high degree.
Fourth, as predicted by the information asymmetry-hypothesis, the time-invariant variable ACAP has a significantly negative relationship with RDACP (p = 0.003 for model (1)). Thus, the larger the average market cap of the dual-listed firm over the period of investigation, the smaller the AH-premium. The results are significant at the 99% confidence level for models (1), (2) and (4). Model (3), the Shenzhen subsample, shows a coefficient that is significant at the 95% confidence interval (p = 0.045). Significant evidence is thus provided that information asymmetries decline with firm-size. Lee (2009) comes to a similar conclusion in his paper while Chan & Kwok (2005) don´t find significant evidence to support the information asymmetry- hypothesis as predictor of the AH-premium.
29 Empirical Analysis
Table 4 - Regression results
Table 4 presents the regression results of the estimated log-log model. The model regresses the dependent variable relative difference in average closing prices between A-shares and the H-shares counterpart (RDACP) on the independent variables. The independent variables consist of the three measures of relative difference between A-share and H-share listing, relative quoted spread (RDRQS), relative total turnover (RDATT), which both capture differences in liquidity and relative volatility (RDRVOL), which captures the differences in risk between both listings. Additional independent variables are prices of the 12 month renminbi NDF forward contract (AFWDEXR) capturing exchange rate expectations and the firm specific effect average stock market capitalization (ACAP), capturing the effect of asymmetric distribution of information as well as the dummy variable location of trade (EXCH). The independent variables to measure the inflow-effect are total newly opened A-shares accounts (AINF), reflecting the weekly number of opened A-share brokerage accounts, Shanghai newly opened A-shares accounts (AINF_SH), reflecting the weekly number of opened A-shares accounts that allow trading in Shanghai A-shares, Shenzhen newly opened A-shares accounts (AINF_SZ), reflecting the weekly number of opened A-shares accounts that allow trading in Shenzhen A- shares as well as the outstanding margin lending balance in A-shares brokerage accounts (AMAR).
Four different models are estimated: Model (1) to (3) focus on newly opened A-shares brokerage accounts as measure for the inflow-effect while (1) includes the entire sample of dual-listed AH-shares (2) includes only Shanghai dual- listed AH-shares and (3) includes only Shenzhen dual-listed AH-shares. Model (4) again considers the entire sample of dual-listed AH-shares but uses the outstanding margin lending balance in A-shares brokerage accounts (AMAR) as measure for the inflow-effect.
(1) (2) (3) (4)
LN_RDARQS -0.089*** -0.090*** -0.085*** -0.094*** (0.017) (0.020) (0.029) (0.017) LN_RDATT 0.031*** 0.027*** 0.041*** 0.033*** (0.007) (0.008) (0.014) (0.007) LN_RDARVOL 0.114*** 0.116*** 0.100*** 0.108*** (0.012) (0.014) (0.023) (0.012) LN_AFWDEXR 1.699*** 1.688*** 1.996** 1.750*** (0.361) (0.396) (0.898) (0.311) ACAP -0.000*** -0.000*** -0.009** -0.000*** (0.000) (0.000) (0.005) (0.000) LOC_TRADE -0.042 -0.036 (0.113) (0.113) LN_AINF 0.047*** (0.008) LN_AINF_SH 0.052*** (0.008) LN_AINF_SZ 0.024 (0.018) LN_AMAR 0.073*** (0.015) Constant 0.987 0.957 0.972 0.876 (0.741) (0.782) (1.851) (0.638)
Observations 7025 5809 1216 7774
Robust standard errors clustered by company are in parenthesis *** p<0.01, ** p<0.05, * p<0.1
30 Empirical Analysis
After controlling for the above mentioned effects that past research has found to meaningfully contribute to the explanation of the AH-premium, this paper adds for the first time an inflow- effect explanation for the existence of the AH-premium and thus for its large expansion between 2015 and 2015. Specifically, I find a significantly positive relationship in the number of newly opened A-shares brokerage accounts that allow for trading on the SHSE and SZSE (AINF) and the AH-premium (RDACP) with a coefficient of 0.047 and a p-value of p < 0.001 (model (1)). Hence, a 1% increase in the number of weekly opened A-shares brokerage accounts translates into a 0.047% increase in the AH-premium. These findings are supported by model (4), which replaces the number of A-shares brokerage account openings as measure for the inflow-effect of model (1) by outstanding margin lending balance in A-shares accounts (AMAR). I detect a significantly positive relationship between AMAR and RDACP with a coefficient of 0.073 and a p-value of p < 0.001. Here a 1% increase in margin lending balances in A-shares brokerage accounts translates into a 0.073% increase of the AH-premium. Significant results for both AINF and AMAR provides evidence that the inflow of traders with cash i.e. the inflow-effect, contributes meaningfully to the expansion of the AH-premium after controlling for differences in liquidity, volatility, exchange rate expectations and asymmetric distribution of information. This is in line with Kirchler et al. (2015), who detect in their experimental asset market setup that the inflow-effect reliably causes asset-bubbles and for the first time confirms their findings in a real-world asset market.
Analyzing the Shanghai and Shenzhen subsample in model (2) and (3) respectively, I detect contradictory evidence. The Shanghai subsample (model (2)) confirms the results of model (1) with a statistically significant relationship (p < 0.001) between newly opened A-shares brokerage accounts that allow for trading on the SHSE (AINF_SH) and the AH-premium (RDACP). However, in the Shenzhen subsample (model (3)) that investigates the relationship between newly opened A-shares brokerage accounts that allow for trading on the SZSE (AINF_SZ) and the AH-premium (RDACP), the coefficient is not significant with a p-value of p = 0.1890. Thus, newly opened A-shares brokerage accounts provide an explanation for the AH-premium of Shanghai dual-listed firms, but do not do so for ones listed in Shenzhen. This is an interesting finding. Counter to expectations, the Shanghai-Hong Kong Stock Connect which allows Chinese mainland investors to trade in H-shares and Hong Kong investors to trade in A-shares, does not eliminate the inflow-effect as a key effect to explain the AH-premium. In Shenzhen, on the other hand, where in the period of interest, there was no stock connect program in place, no significant relationship between the AH-premium and newly opened
31 Empirical Analysis brokerage accounts can be attested. This is puzzling since unlike for Shanghai dual-listed firms the flow of funds from mainland China into Hong-Kong equities is restricted. Any inflow of cash and new traders has therefore to be absorbed by the A-shares markets which should result in a widening AH-premium.
To support RQ2 with further evidence the dummy variable LOC_TRADE which investigates any differences between Shanghai- and Shenzhen dual-listings, is introduced in model (1) and (4). It shows 1 if the firm’s A-shares are traded on the SHSE and 0 when the firm’s shares are listed on the SZSE. However, LOC_TRADE is neither significant in model (1) nor in model (4), indicating that there are no significant differences in the AH-premium between Shanghai- and Shenzhen dual-listed AH-shares. This is interesting as I expected a lower AH-premium for Shanghai dual-listed firms due to the launch of the Shanghai-Hong Kong Stock Connect end of 2014 by enabling Chinese mainland investors for the first time to buy H-shares and Hong-Kong investors to purchase A-shares. Hence, I predicted a narrowing of the AH-premium for Shanghai dual-listed firms, when mainland investors chose to buy the cheaper H-shares pendant over the more expensive A-share listing. A reason might be that the stringent constraints (discussed in section 3 of the present paper) for participation and limited scope of the Shanghai- Hong Kong Stock Connect program have curtailed its impact to reduce the Shanghai AH- premium vs. the Shenzhen AH-premium. Large numbers of mainland China investors might have therefore been prevented in participating in the Shanghai-Hong Kong Stock Connect program so that they were unable to purchase the relatively cheaper H-shares. The effect of an ill-accepted and/or undersized program might have therefore been overcompensated by the large inflow of new traders with cash into Shanghai A-shares market. This might have been the reason that the AH-premium for Shanghai dual-listed firms is not significantly different from those of Shenzhen dual-listed firms.
Another reason for the lack of a narrowing AH-premium of Shanghai dual-listed firms vs. their Shenzhen listed peers might be limits to short-selling in the A-shares equity markets. Miller (1977) argues that banned short-selling causes the most optimistic market participants to dictate the price and hinders excessively priced securities to go down in price as the maximum amount of shares a rational investor can hold is zero. Similar it could be the case that the limited ability to short-sell caused A-shares to remain elevated as they reflect the views of the most optimistic investors, while rational investors are not able to express their views by selling any A-shares in excess of their inventories. Further, the resale-option theory might explain why the AH-
32 Empirical Analysis premium has not narrowed considerably for Shanghai dual-listed firms. As put forward by Scheinkman & Xiong (2003) a reason for bubble formation might be that investors expect to be able to resell securities at higher prices to other investor, thus ignoring fundamental values. The same might hold true for the A-shares markets and could, coupled with banned short-selling, explain the persistence of elevated equity prices.
In a last step I evaluate the economic significance of each variable and its relative contribution to the explanation of the AH-premium by investigating the regression coefficients. The regression results from table 4 model (1) suggest that price changes in the 12 month forward exchange rate between CNY and HKD (AFWDEXR) have the greatest economic significance and make the highest contribution to the explanation of the AH-premium with a coefficient of 1.699. With significantly lower coefficients AFWDEXR is followed by changes in the relative difference in rolling volatility (RDRAVOL) with a coefficient of 0.114 and changes in the relative difference of relative quoted spread between A and H-shares (RDARQS) with a coefficient of -0.089. With a smaller coefficient of 0.047 the measure for the inflow-effect (AINF) follows. Changes in the relative difference of relative total turnover (RDATT) and average market capitalization (ACAP) are trailing with coefficients of 0.031 and -0.000 respectively. Nevertheless, the importance of the core findings must not be discarded, since despite showing a low coefficient the weekly number of newly opened brokerage accounts (AINF) fluctuates greatly throughout the sample. The lowest weekly observation for newly opened brokerage accounts is 16,011 while in the maximum 4,472,964 accounts were opened, representing an increase of 27,836% from low to high.
33 Conclusion and Policy Implications
Conclusion and Policy Implications
Throughout history various asset bubble episodes have occurred. Each time after an unsustainable rise in the asset’s price they collapsed again with negative consequences on the real economy that materialized in recessions and rising unemployment. It is exactly due to these severe negative consequences that research in the field of asset bubbles is so important. Especially the rise in importance of the field of behavioral economics has helped to contribute to the understanding of what causes asset price bubbles to form. Many of these findings were produced in so-called experimental asset markets that mimic real-world asset markets, while giving the researcher the benefit to observe variables that normally are unobservable in real- world asset markets, such as the fundamental value of the traded asset. In exactly such an experimental asset market setup Kirchler et al. (2015) find that an inflow of new traders with cash, called the inflow-effect, reliably generates asset bubbles. With help of the unique characteristics of the mainland China and Hong Kong equity markets I am able to test their findings under real-world circumstances. Firms that feature a so-called dual-listing i.e. their equity is listed both on the Chinese mainland stock exchanges in Shanghai or Shenzhen as A-shares and in Hong Kong as H-shares provide the optimal opportunity to investigate the inflow-effect. Capital controls largely restrict foreign investors from trading A- shares and Chinese investors from trading H-shares. Hence, the AH-premium i.e. spread between A-shares price and H-shares price, is a suitable proxy for A-share mis-valuation, after controlling for effects that are known to drive the AH-premium. End of 2014 to mid 2015 the AH-premium has shown a large expansion coinciding with a rise of major A-shares indices. Using multiple linear regression analysis, I find a significant positive correlation between the AH-premium and the inflow-effect with a p-value of p < 0.001 and thus evidence that the inflow-effect indeed contributes to the explanation of the mis-valuation of Chinese equities. The result is consistent across two different regression models that use different measures for the inflow-effect. I am measuring the inflow-effect using the weekly number of newly opened A-shares brokerage account as disclosed by the CSDC and outstanding margin lending balance in A-shares brokerage accounts.
I further analyze if there is a difference in the AH-premium between Shanghai and Shenzhen dual-listed firms due to the late 2014 start of the Shanghai-Hong Kong Stock Connect program that for the first time allows Chinese mainland investors to trade H-shares and foreign investors to trade A-shares. I fail to attest any significant difference between the exchanges. I hypothesize
34 Conclusion and Policy Implications that this might be due to limited scope and/or barriers for investors to enter into the Shanghai- Hong Kong Stock Connect program, short-selling restrictions in the Chinese A-shares markets as well as investors speculating that they will be able to resell securities to other market participants at higher prices. However, I am unable to answer these questions with the dataset at hand and leave further investigation of these hypotheses open to future research.
By confirming in a real-world example that the inflow-effect plays an important role in the formation of mis-valuations, I argue that it is prudent to closely track the number of new investors and margin lending balances in brokerage accounts and view drastic increases in these numbers as warning signs for possible emerging asset price bubbles. Hence, tracking inflow numbers and critically questioning spikes in these numbers could thus benefit the policy makers’ abilities to counteract boom and bust cycles with severe effect on the real-economy already at an early stage.
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