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What is the influence of economic and political change in
Mainland China on enterprises listed on the Stock Exchange of Hong Kong?
Lu Jing
Student number: s2151448
University of Groningen
Faculty of Economic and Business
Master Thesis-International Business and Management August 2012
Supervisor: Dr. Jochen Mierau
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Abstract
This paper investigates the exposure of Chinese enterprises listed on the Stock Exchange of Hong Kong to three Chinese risk factors. Namely, exchange risk, political risk and changes in producer prices. Gradually adopting a two-factor, three-factor, and multi-factor extension of the capital asset pricing model. It is also studied how risk exposure differs between state owned and private firms and between manufacturing and other firms. The empirical results do not clearly demonstrate that the exchange rate and changes in producer price have significant influence on the stock prices of Mainland enterprises listed on the Stock Exchange of Hong Kong (SEHK) also the political risk is scarcely priced in the stock prices.
Key words: exchange rate risk, political risk, and producer prices
1. Introduction
The economy of Hong Kong is gradually integrating heavily into the economy of the Mainland of China in the past two decades. Although the Hong Kong Monetary Authority is still trying to maintain the financial independence of Hong Kong. Accompanying the economic globalization, Hong Kong financial market is inevitably affected by the economic and political environment of Mainland China in some way. Correspondingly, measuring whether and to what degree currency risk and political risk from Mainland China are priced in the group of stock prices is deserved consideration. This group of stocks are accustomed to be called as H-shares, which are incorporated in mainland China and traded on the Hong Kong Stock Exchange (SEHK). Many companies simultaneously float their shares on the Hong Kong market as well as on one of the two mainland Chinese stock exchanges (Shenzhen or Shanghai Stock Exchange) . The main motivation for Chinese mainland enterprises choosing to list on Hong Kong is to attract international capital and to obtain relatively advanced experience in corporate management expected by international capital markets. Besides, compared with all the Hong Kong incorporate companies, mainland enterprises are treated equally ,no additional securities regulations and disclosure requirement.
The aim of this paper is to examine the exposure of H-shares to three risk factors from
Mainland China. Namely, exchange risk, political risk and changes in the producer price index.
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Previous empirical works indicate that stock prices have possibility to reflect an ex ante premium for the risk of exchange rate. For instance, Dumas and Solnik (1995) find that the exchange rate is priced in stocks and currency markets in the four largest capital markets (the U.S., Japan, Germany, U.K) on the basis of conditional international asset pricing model that allows for the time variation. Chue and Cook (2008) examine the domestic exchange rate exposure on the emerging market firms. The result shows that the depreciation of the domestic exchange rate has a negative impact on emerging market stock returns. Thus, two kinds exchange rates are specifically utilized to compare the effect on the stock returns of Mainland China enterprises, one is the multi-lateral trade-weighted exchange rate and the other is the bilateral HKD/Renminbi exchange rate.
As we know, Mainland China is undergoing a dramatic development and the Chinese government has been stressed that “the economic lifeline is strictly controlled by state-owned economy”, indicating the state-owned enterprises are the main form of business organization of Mainland China. To illustrate this phenomenon by data, in the study sample of 46 individual stocks listed on SEHK for more than 10 years, only 5 companies are not owned by state, and the rest of the companies are largely owned by the state (the holding ratio is over one third of total shares). Therefore, it is possible to infer that enterprises with these over-concentrated state control properties are more likely to subject to political fluctuations.
Recent cases happened in emerging markets are also good presentations to explain how the political risk affect the state-associated firms. For instance, in Thailand, the enterprises which are in a close relationship with the military are adversely influenced by the democratization process.
Moreover, even in the developed countries with high level market capitalization, the political stability still plays an critical role in the economy, for example, the U.S. banking industries are inevitably affected by the reform of deregulation. Therefore, it is reasonable to introduce the political risk of Mainland China to examine how and to what degree the political fluctuations affect the stock portfolios, which are constructed on the basis of different state-owned holding ratio.
According to the annual reports of Mainland China enterprises in the sample, most of the firms are in upstream-manufacturing industry, which has strong link with the changes in producer price.
And it is known that the Producer Price Index reflects the trend and degree of changes in general
producer prices of all industrial products during a given period. Correspondingly, combined with
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local situation of Mainland China, it is good inspiration to consider the changes in producer price of Mainland China as a pricing factor since most of study objects in the sample are classified into manufacturing industry. Additionally, it is intriguing to examine and compare how the changes in producer price affect the manufacturing firms and firms in other industries. In other words, to investigate whether the producer price risk premium prices into the stock prices of portfolio of manufacturing enterprises and non-manufacturing enterprises.
The framework of paper will be constructed as follows. Firstly, a literature review to motivate why two kinds of exchange rate risks, political risk, and changes in producer prices are selected as the influence factors of the stock prices. Secondly, in the first stage, three models will progressively display the correlations of the explanatory variables and explained variable.
Ordinary Least Squares (OLS) method is adopted to run these regressions. In the second stage, two relevant dummies are introduced. Meanwhile, the coefficients of beta of each variable in different models will be collected and applied to examine the determinant of coefficients. OLS method is also employed to investigate the in-depth influence factors. The purpose of second stage is targeted at partially pinpointing the underlying factors of volatility of Mainland China enterprises listed on Hong Kong. The remainder of this paper is displayed as below: In section 3, data is introduced and each factor is explicitly explained to measure the unanticipated movements. Section 4 illustrates the theoretical background and research methodology. Section 5 represents and compares the empirical results of all these three capital asset pricing models.
Section 6 makes conclusions of this paper and offers suggestion for future researches.
2. Literature Review
Arbitrage pricing theory pioneer Ross (1976) indicates that if the economy can be depicted by
a series of general economic factors, meanwhile, these factors are possibly priced in the stock
returns. Thus, the investors is willing to pay a premium to protect themselves from these kinds of
risks. Inspired by the capital asset pricing model, three types of capital asset pricing models are
constructed. Specifically, one-factor, two-factor and multi-factor asset pricing models are
progressively adopted to measure the effects of exchange rate volatilities, political fluctuations,
and producer price changes imposed on stock prices. These capital asset pricing models are
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extended by the traditional framework by specifying and estimating the parameters of conventional economic factors in the return-generating procedures.
Jorion (1990) studies the foreign exchange exposure of U.S. multinationals in the theoretical framework which exposure represents the sensitivity of the value of the firm to exchange rate randomness, and the multilateral trade-weighted exchange rate is adopted to examine this impact.
To be specific, the exchange-rate randomness is measured by regressed coefficients of the changes in the value of firm on the changes in the multilateral trade- weighted exchange rate.
The empirical result proves that the exchange rate exposure is significantly positively correlated with foreign sale firms in spite of the potential problems of measurement error and instability.
However, before long, Jorion (1991) finds that a controversial issue that the currency risk is not priced in the U.S. market and suggests that this risk may be well hedged through a chain of portfolio managements. Switch from the U.S. market to other influential stock markets, Mukherjee and Naka (1995) find out that exchange rate positively correlates with stock prices in Japan and Hamao (1988) suggests the inflation rate has significant influence on the Japanese stock market among the various macroeconomic variables. In the less developed countries, Adam and Tweneboah (2008) measure the influence of macroeconomic factors on the movement of stock prices in Ghana. They examine how the interest rate, inflation rate and exchange rate affect share prices in the short-term and long-term and find out that interest rate and inflation prove considerably significant only in the long-run while the impact from exchange rate is weak.
Refer to the investigation of political fluctuations. Erb, Harvey, and Viskanta (1996) measure the political risk, economic risk and financial risk and find out that the country-risk measures are associated with the future equity returns and these measures are believed to be highly correlated with the equity valuation measures. Bailey and Chung (1995) indicate that the political risk can have the analogous impact on stock prices as the exchange rate. Specifically,they examine the firms in the Mexico market and find that enterprises whose cash flow are particularly sensitive to the macroeconomic environment are likely to be exposed to the political risk, especially for the firms which enjoy the monopoly and privileges.
Additionally, the third risk factor of changes in producer price, previous empirical studies
shows as follows. Chen, Roll, and Ross (1986) find out the changes in industrial activity have
impact on the equity market in the long run and suggest the monthly changes in industrial
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production correlate with the changes in stock prices. In detail, the outcomes show that changes in the term structure, industrial production, and in risk premiums, all these three variables positively correlate with the expected stock returns. Akpan, Inya-agha, and Aya (2006) indicate that key macroeconomic fundamentals such as overall gross domestic product, economic growth, exchange rates, inflation rates, interest rates, government fiscal policy, monetary policy, and taxation policies, public indebtedness, all these variables have strongly affected the stock movements and market capitalization process. Lijuan and Xu (2010) study the pricing factors of Mainland stock prices in Mainland China Exchanges, namely the Shenzhen Stock Exchange (SZSE) and the Shanghai Stock Exchange (SHSE) , and suggest that the stocks are significantly affected by exchange rates, consumer’s confidence index, and the corporate goods price index of Mainland China.
Later in these years, the researchers suggest that currency risk may be highly time-varying and the unconditional model which assumes the constant risk exposure is not capable of detecting this exposure. For example, De Santis and Gerard (1998) insist that the time variation in the risk premium is the reason why the unconditional model cannot disclose the time-varying currency risk. Choi, Hiraki, and Takezawa (1998) adopte both the unconditional and conditional multi-factor arbitrage pricing model, and utilize the bilateral Yen/US exchange rate to examine whether the exchange risk is generally priced in Japanese stock market. The result shows that the exchange risk is priced in the pre-plaza period and post-plaza period based on unconditional model. Chue and Cook (2008) suggest other regression method such as the Instrument Variable method may be better detect the exchange rate exposure compared with the Ordinary Least Method, which may not be well illustrate the variability of stock prices.
3. Data
According to the statistics calculated from the Hong Kong China Enterprises Association, the Mainland China enterprises have already played an important role in the financial market of Hong Kong. By the end of 2011, the number of Mainland China enterprises has taken up 42% of the number of all the shares listed on SEHK, and the market capitalization has accounted for 57%
of the total market capitalization of SEHK. With the purpose to find out the correlation between
the stock prices of Mainland China enterprises and the macroeconomic and political factors. The
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monthly changes in the individual stock return index , monthly changes in market return index , and the contemporaneous change in macroeconomic risk factors such as the exchange rate, producer price are rigorously collected. The data range from December 2001 to December 2011, namely, 31/12/2001-31/12/2011.
As the previous studies suggest, a ten-year time span is appropriate to cover a variety of macroeconomic events. To be specific, during this 10-year period selected, the bilateral HKD/Renminbi exchange rate experienced huge fluctuations. While Hong Kong dollar keeps pegged to U.S. dollar, Renminbi is undergoing a far-reaching exchange rate reform. The reformed floating-system is based on the relationship of market supply and demand, and becomes less dependent on the government control and regulation. As a consequence, the continuous depreciation of bilateral Renminbi/U.S. dollar induces the bilateral HKD/Renminbi exchange rate steping into the appreciation trend since 2005.
As to the local security market of Hong Kong, the time span also covers the fanatical bull market lasting from 2003 to 2007, reaching the highest record of Hang Seng Index on October 30, 2007. In the context of the dynamics of the Hong Kong economy as well as the Financial reform of Mainland China, it is naturally to expect that the following macro-factors, particularly, the two types of exchange rates, changes in the political risk and producer price may price in the stock prices.
3.1 Monthly stock price of China enterprises listed on SEHK
The return index applied are collected from the Datastream of Thomson Reuters, and the database is on the basis of monthly closing stock price for the period 31/12/2001 to 31/12/2011.
Only 46 China enterprises are qualified to meet the selection of relative longer time span of 10- year and the data collected from Datastream has already adjusted for stock splits and other corporate events. In this sample, these 46 Chinese Mainland enterprises locate in Mainland while list on SEHK. For instance, Tsingtao Brewery is the first Mainland enterprise listed on the SEHK in 1993, additionally, most of shares are classified into manufacturing and energy industries, directly and indirectly owned by state institutions, provincial or municipal in Mainland China.
By the end October, 2009, 109 companies from Mainland China are listed on SEHK . and these
enterprises have raised more than $12 trillion HKD since the starting year of 1993 to the end of
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2008.
3.2 The market Index of Hang Seng China enterprises
is the monthly log-change in the Hang Seng Enterprises Index (HSCEI). The HSCEI is also be called Red-chip Index and is computed as the capitalization-weighted average of prices of 44 stocks of the largest market capitalization by the end of 2009. Moreover, the constituents of HSCEI are not constant and adjusted half an year. The HSCEI acts as the guidepost of the performance of Mainland China enterprises listed on SEHK, although the volume of the constituents are not restricted, the requirement of largest market capitalization must be met. The HSCEI hit the new high on September the 20
th, closing at15,552.23 points. The reason why HSCEI is chosen is to benefit from the segmentation of the Hong Kong capital market while enjoy the wider financing platform as well as the stricter regulation and disclosure disciplines compared with Mainland counterpart Exchanges.
3.3 Economic and political risk factors
Four risk factors are selected to represent the currency fluctuation, general economic environment, and political risk. They act as the proxy for economic shocks , which represents the unexpected influence on the contemporaneous stock prices.
(1) Multilateral Trade-weighted Exchange rate of Hong Kong
The multilateral exchange rate is acted as the core factor, one advantage of using it is to avoid the problem of multi-collinearity and denoted as the monthly log-change.
(2) Bilateral Hong Kong Dollar/Renminbi Exchange rate
Since the reform and opening up in China , the proportion of trade volume of Mainland China
against Hong Kong surged from 9.3 percent in 1978 and to 48.5 percent at the end of 2011,
Mainland China continues to be the largest trade partner of Hong Kong since 1985, at the same
time, Hong Kong ranks the third largest trade partner of Mainland China with the percentage of
7.8 of the total trade volume of China. According to the data derived from the Hong Kong Trade
and Industry Department, Mainland China takes account of 45.1 in import business, accounts for
46.8 percentage of the total trade volume in export business, and re-export trade business
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exceeds 50 percentage of correspond total volume. Therefore, the Mainland investors will inevitably be subjected to the exchange risk of bilateral HKD/Renminbi exchange rate. In views of the particularity of China enterprises listed on SEHK, it is meaning to introduce the bilateral HKD/Renminbi exchange rate as an alternative to the multi-lateral trade-weighted exchange rate.
(3) Political risk of Mainland China
The political risk is derived from the ICRG model, comprising 12 components and 15 subcomponents. As one of the two components of the country risk, the political risk is associated with the willingness to pay. The political assessment scores are on the basis of the subjective staff analysis of each relevant risk components, including the specific government stability, socioeconomic conditions, and bureaucracy quality, etc. Additionally, the model also covers the components of corruption, which is considered to be the proliferated social problem in the context of countries of excessive concentration power and resource monopoly. The basis series are obtained from the assessment results from ICRG model, if denoted as the in the month t, similarly, subsequent statistical work will be led by 1 month to make this variable contemporaneous.
(4) Changes in producer price of Mainland China
The variable reflects the trend and degree of changes in general producer prices of all industrial products during a given period in Mainland China. The basic series is the monthly rate obtained from the National Bureau of China, and if the denotes the changes of producer price in month t.
(5) Summary statistics of variables
Table 1 Correlation
Correlation Trade-weighted FX HKD/Renminbi FX Political Risk Producer Price change HSCE Market
1
-0.29705 0.13224
1 0.21598
1
-0.10447 0.12473 -0.02370 1
-0.08817 - 0.23310 -0.05119 0.04063 1
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The factor are defined as:
=Multilateral trade-weighted exchange rate,
=bilateral HKD/Renminbi exchange rate,
=changes in political risk,
=changes in producer price,
=change of rate of HSCE Market Index.
Table 1 display the correlation matrix for the independent variables The correlation matrix are computed from the period from 2001 to 2011,ranging 10 years. The strongest correlation is between the multilateral exchange rate and the bilateral exchange rate. This is obviously expected that these two variables are highly correlated since the bilateral exchange rate is one of the components of the multilateral exchange rate and these two factors are negatively correlated.
The appreciation of HKD against Renminbi indicates the depreciation of the trade-weighted exchange rate of Hong Kong.
The series of political risk are also positively correlated with the series of bilateral exchange rate while negatively correlated with the Multilateral exchange rate. As we know, Hong Kong has been implemented an currency system, which is much more independent from the control and regulation of Central Bank of China. The increased political risk of China suggest the depreciation of Renminbi, which accounts for the largest percentage of a basket of currencies of Hong Kong dollars. In other words, the more fluctuated political risk indicates the appreciation the Hong Kong dollar.
With regard to changes in producer price, which is negatively correlated with multilateral exchange rate while positively correlated with bilateral exchange rate. The growth of changes in producer price means the raised cost of materials and following inflation, inducing the weaker purchase power, namely the appreciation of Hong Kong dollar (depreciation of Renminbi).
Lastly, the variable of HSCE Market, as the table illustrates, is negatively correlated with multilateral, bilateral exchange rate and political risk respectively while positively correlated with changes in producer price of Mainland China, the formation of market index trends are too complicated to explain by unilateral causes, it is wiser to illustrate these relationships case by case.
(6) Dummy variables
Since dummy variables acted as qualitative variables can be used in the time series or cross-
sectional regressions. Two dummies variable are introduced to measure the determinant of
coefficients derived from the stage 1. In order to collect the holding ratio of state of each
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company, the corresponding annual report is cautiously read
1. Then the two dummies are constructed as follows. The dummy 1 is used for the purpose of telling the different risk exposures between the portfolios of manufacturing companies and companies in other industries.
Similarly, the dummy 2 is aimed to differentiate the risk exposure performances between the portfolios of largely state-owned firms and the relatively small proportion state-owned firms.
Namely, Dummy variable1=1 if the H-share are classified into the manufacturing sector, 0 otherwise. Dummy variable 2=1 if the percentage of holding ratio owned by state is over 50%, 0 otherwise.
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The table of holding ratio owed by state is presented in appendix.
4. Models and Methodology
Stage 1: The risk exposure of China Enterprises listed in Hong Kong
The purpose is to find out whether cross-section differences in exposures to exchange rates, political risk and changes in producer price yields significant difference in stock prices and portfolios in detail. Therefore, three steps are progressively adopted to illustrate the correlations.
Step 1: Five One-factor models are constructed to examine the individual impact on the stock prices of Mainland enterprises listed on SEHK, The time-series regression based on Ordinary Least Square Method is adopted to measure the correlation.One-factor model is generally represented as follows:
(1) To be specific, the one-factor model with the independent variable of multilateral exchange rate.
Where is the rate of monthly return Index on the i th Mainland company’s common stock
listed on SEHK, and the only independent variable of exchange rate, denoted by , which is
measured as the Hong Kong dollar price of foreign currencies of all the trade partners. Therefore
a positive value for suggests a depreciation of Hong Kong dollars. is an idiosyncratic
error term. As previously stressed, the main theme of the paper is to explore the pricing factors of
stocks prices of Mainland China enterprises listed on SEHK. The changes in multilateral trade-
weighted value of Hong Kong dollars is utilized since the avoidance of multi-collinearity in
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light of many constituents of trade-weighted exchange rates are pegged to each other.
(2) The one-factor model with the independent variable of bilateral HKD/Renminbi exchange rate.
Where is the rate of return on the i th Mainland company’s common stock and is the changes in the month-end nominal bilateral HKD/Renminbi, correspondingly, a positive value for indicates a depreciation of Hong Kong dollars.
(3) The one-factor model with the independent variable of changes in political risk.
Where is the rate of return on the i th Mainland company’s common stock, changes of political risk of Mainland China is denoted as , a positive value for indicates a potentially intensified political unrest.
(4) The one-factor model with the independent variable of changes in Producer Price of Mainland China
Where is the rate of return on the i th Mainland company’s common stock, and changes of producer price of Mainland China is denoted as , a positive for indicates the increased cost of production.
Step 2: Four two-factor models are developed to measure the risk exposures of the stock prices and these models are the extension of one-factor models, the HSCE market index factor is added to four one-factor models respectively. Similarly, the time-series regression based on Ordinary Least Square Method is continuously adopted to obtain the coefficients of the risk exposures.
Two-factor model is generally written as follows:
ɳ ,4
Where the market risk variable, denoted as is designed based on HSCE value-weighted return index which has already been adjusted for cash dividends and stock split.
Step 3: All the economic and political risk variable are combined in the model to examine the influence on the stock prices. Two types of exchange rates are utilized and compared,
(1) the first multi-factor model specifically include with multilateral trade-weighted exchange
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rate.
(2) the second multi-factor model specifically use bilateral HKD/Renminbi exchange rate.
To sum up, in these three steps and ten models, take the independent variable of exchange rate of HKD/Renminbi for example. Normally, the changes in the exchange rate and stock price are virtually unpredicted, the intercept represents the expected value under the assumption that the expected changes in exchange rate are constant. Thus, the slope coefficient of explanatory variable will estimate the unexpected change in exchange rate toward the volatility of stock prices. Firstly, the cross-section of individual stock return series are examined to find the evidence on risk exposure premiums. Secondly, the progressive procedures from one-factor model to multi-factor model on the basis of the Fama and MacBeth (1973) are implemented to detect the risk exposures, represented as . Thirdly, after the collection of the risk exposures
of each of 46 shares, four portfolios return series are constructed in order to investigate the different performances of Mainland China enterprises listed on SEHK.
To be specific, portfolio 3
2and portfolio 4
3are examined for evidence whether the series are
significantly difference from zero, in other words, these portfolio are investigated for evidence
whether the risk exposures such as exchange rate and political risk yield unconditional risk
premiums. Moreover, the portfolios divided by classifications are compared about how much the
influences on these portfolios under the same risk exposures. One of the challenges of the
appropriateness is that the assumption, interpreted as the constant risk exposure of the Mainland
China enterprises listed on SEHK. However, it is possible to indicate that the risk exposures are
more volatile in developing countries even though these shares are listed on the relatively more
developed stock market. For example, Chue and Cook (2008) estimate the correspond exchange
rate exposure of emerging market firms and detect considerable variability in the exchange rate
exposure.
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2
Portfolio 3 represents the firms which are largely owned by state (the holding ratio is over 50 percent)
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Portfolio 4 is the combination of firms which are relatively less owned by state (the holding ratio is below 50 percent).
Stage 2: The determinant of the four variables exposure
The main objective of this section is to determine whether the four factors each is related to the dummies that selected in advance. The proposed hypothesis is on the background of cross- sectional regression. Where is the dummy variable, representing whether the enterprises are classified into the manufacturing industry, 1 denotes manufacturing industry, and 0 represents Non-manufacturing industry, is the second dummy variable, demonstrating the percentage of issued-and-outstanding shares owned by state. 1 denotes the percentage of the shares owned by state is over 50 percent, indicating the state processes the absolute control over the enterprises.
Meanwhile, 0 represents the percentage of the shares owned by state is under 50 percent. In these two equations, the value of the intercepts will differ from zero if the return index on Mainland enterprises is correlated with the four explanatory variables respectively.
Ordinary Least Square method is applied to detect the determinant of coefficients of risk exposures, which are derived from the four one-factor models.
= i=1,2
Similarly, OLS method is implemented to examine the coefficients collected from the four two-factor models, the models which the variable of HSCE market index is added to four one- factor models respectively.
= i=1,2
The beta derived from the two multi-factor models, under the condition of three other variables hold constant while the exchange rate switches from multilateral trade-weighted exchange rate to bilateral HKD/Renminbi exchange rate.
= i=1,2
5. Empirical results
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Stage 1:The descriptive table of Coefficients of exposure
According to the regression analysis results, the Mainland China enterprises which are significant to the factors of two types of exchange rates, political risk, and changes in producer price, particularly to the multilateral trade-weighted exchange rate and producer price changes, are concentrated in manufacturing, mining, and energy industries. For example, Huaneng Power International Co Ltd, Datang International Power Generation Co Ltd, which of three are from the energy industries, are statistically significant to the factors of the multilateral trade-weighted exchange rate and producer price changes, no matter which models are adopted, be it the one- factor model, the two-factor model or the multi-factor model.
Table 2 reports the descriptive result of beta derived from the four one-factor models based on the sample of 46 Mainland China enterprises. Clearly, despite of the relatively small sample size, the trade-weighted exchange rate of Hong Kong has strong explanatory power to the volatility of the stock prices. It is illustrated that more than half of stock prices are significant at 5% level.
Moreover, it is more evident when the sample is divided into portfolios classified by the different
industries and holding ratios. Specifically, in the portfolio of manufacturing industry, 66.7% of
companies are subjected to the risk exposure of multilateral trade-weighted exchange rate, and in
the portfolio of other industries, 52.6% of firms are affected by this factor. In the other portfolio
classified by the different holding ratio of state, the results are more intense, in the portfolio 3,
77.3% of companies in this portfolio are statistically significant to the changes in multilateral
exchange rate. As a result, it is reasonable to conclude that Mainland China enterprises are
sensitive to the fluctuations on the trade-weighted value of the HKD, representing by a basket of
currencies of trade partners. Moreover, it is not difficult to link the situation with the bilateral
trade relation of Mainland-Hong Kong. According to the Country Report published by the
Chinese Ministry of Commence, Mainland China is the biggest trade partner of Hong Kong, an d
the mechanical and electrical products are the main imports of Mainland China from Hong Kong,
and textiles and raw materials ranks the top two categories of goods of Hong Kong's imports
from the Mainland, this data well explain why the enterprises such as Harbin Electric Co Ltd,
Jingwei Textile Machinery Co Ltd and Shenji Group Kunming Machine Tool Co Ltd are greatly
significant to the factor of multilateral trade-weighted exchange rate.
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The second one-factor model with the factor of bilateral HKD/Renminbi exchange rate.
Clearly, the impact on stock prices are much less influential than the multilateral counterpart., none of companies is significant at 5% level, only two of the 46 stocks are significant at 10%
level, and these two companies both are from non-manufacturing industries and less controlled by the state. Apparently, the bilateral HKD/Renminbi is barely the key pricing factor of China enterprises in the sample, though the Mainland investors’ enthusiasm toward Mainland enterprises listed on SEHK is well considered. Generally, the enterprises listed on SEHK are mainly aimed to attract the international capital and benefit the convenient payment instruments.
The financing channel from Mainland China investors plays a relative weak role in corporate financing, this may partly explain why the impact of bilateral HKD/Renminbi exchange rate turns out to be un-significant to the stock prices of Mainland China enterprises.
With regard to the factor proxy for the political fluctuations, particularly when the factor is the only variable that explain the dependent variable, changes of political risk have very weak explanatory power toward the stock prices of China enterprises. Specifically, in the sample of 46 shares, none of stocks is affected by the political risk even the significant level is set at 10%. It is not difficult to analyze the main reasons. Firstly, because of the insufficient informations from the data from the ICRG on the basis of subjective conclusions, indicating the judgments may not well reflect the political environment of Chinese characteristics. Secondly, the limitation of sample size, since Mainland China enterprises listed on Hong Kong for a relative short period of less than 20 year since 1993, many shares which are policy-oriented list on the SEHK for a very short time are not included in the sample, such as the bank shares such as Industrial and Commercial Bank of China, China Construction Bank, and Agricultural Bank of China, etc. It is a dilemma to guarantee the inclusion of political-oriented shares and to investigate the shares which listed on SEHK for 10 years.
On account to the factor of changes in producer price in the one-factor model, the influence of this variable is generally not very significant as the factor of multilateral exchange rate, eight companies are significantly affected by the change of producer price at 10 % significance level.
14.8% of firms from the manufacturing sector are significantly affected by the changes in production price, and 21% of firms from the other industries are affected by this factor.
Additionally, as shown by data, 16.6% of companies in the higher holding ratio portfolio are
influenced by this factor, and proportion is a little higher in smaller holding ratio portfolio, say it
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18.2%. At the significance level of 5 %, only 3 firms are significant to the changes in producer price and two of these companies are from energy industry, which are weighty on the lifelines of the national economy of China. It is therefore to infer that the changes in producer price may directly have influence on the stock prices from energy industry, especially when the enterprises operate massive business all over the country and monopolize most of the market share.
As the market factor of Hang Seng China Enterprises (HSCE), all the companies are markedly significant to HSCE market factor. The reasons lie in that some of the shares are the constituents of the HSCEI as well as the small scale of the HSCEI, nearly all the shares listed on it are more or less affected by the volatility of the HSCEI.
Table 2 One-factor model
β
itTrade-weighted FX HKD/Renminbi FX Political Risk Producer Price changes HSCE Market One-factor
Min -7,492308 -4,164011 -2,155539 -2,938032 0,238093
Max -0,285801 4,019766 3,113792 1,559845 1,624533
Mean -3,755967 -0,309691 0,248164 -0,917320 0,901664
Median -3,892323 -0,387812 0,237167 -0,774721 0,896390
Standard- 2,004485 1,820727 0,942202 1,063353 0,299026
Deviation
Significant- 24 /46 0 /46 0 /46 3 /46 46 /46
at 5%
level
Significant- 29 /46 2 /46 0 /46 8 /46 46 /46
at 10% level
β
itTrade-weighted FX HKD/Renminbi FX Political Risk Producer Price change HSCE Market One-factor
Significant- 29 /46 2 /46 0 /46 8 /46 46 /46
at 10% level
Portfolio 1 18 /27 0 /27 0 /27 4 /27 27 /27
Portfolio 2 10 /19 2 /19 0 /19 4 /19 19/ 19
Portfolio 3 12 /24 0 /24 0 /24 4 /24 24 /24
Portfolio 4 17 /22 2 /22 0 /22 4 /22 22 /22
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Portfolio 1 stands for the firms that are classified into manufacturing industry. Portfolio 2 is the combination of firms which are classified into other industries except manufacturing industry. Portfolio 3 represents the firms which are largely owned by state (the holding ratio is over 50 percent) and portfolio 4 is the combination of firms which are relatively less owned by state (the holding ratio is below 50 percent).
Significant at the 10-percent level
Table 3 report the descriptive result of beta collected from the two-factor models, variable of HSCEI-Market index is added to each of the correspond one-factor model. Compared with the results of the Table 1, the number of companies which are significantly affected by multilateral trade-weighted exchange rate is distinctly reduced. In the portfolios divided by the property of manufacturing. Only 3.7%of firms in the manufacturing industry portfolio are significant to the changes in this factor while 47.4% of firms in other industries are affected by the multilateral exchange rate, and in the portfolios of different holding ratios, 16.7% of companies which are largely holding by state are affected by this factor while only 4.5% of companies less hold by state are influenced by this factor.
In summary, the impact from the multilateral trade-weighted exchange rate are less significant in the two-factor model. Given this situation, the result may be partly because of the correlation between the specific factor and the market factor, and the orthogonalization can be used to wipe of this correlation as suggested by Jorion (1991).
However, compared with result of correspond one-factor model, the number of firms which are affected by bilateral HKD/Renminbi exchange rate is increased. In the portfolio of manufacturing industry, 3.7% of firms are significant to bilateral exchange rate, and in the portfolio of other industries, the proportion is even higher, 21.1% of companies are significant, double the percentage of the result derived from the correspond one-factor model. At the same time, in the portfolio of large holding ratio, the percentage of number of effected firms is considerably boosted too, from none to 16.7%.
As to the two-factor model with the specific factor of changes in producer price, the effect
from this factor is increased in a small range. To be specific, in the manufacturing industry
portfolio, the percentage of significantly affected firms is increased by 7.4% and in the other
19
industries portfolio, the increase is 5.3%. Moreover, in the portfolio of small holding ratio, the percentage is raised from 18.2% to 31.8%.
Regard to the model with political risk, the stock prices still turn out to be unaffected by the political risk, only one company is statistically significant at 10% level, the outcome is not persuasive partly because the significant level is too high or the political exposure is imprecisely estimated.
Table 3 Two-factor model
β
itTrade-weighted FX HKD/Renminbi FX Political Risk Producer Price changes HSCE Market Two-factor
Min -3.847254 -2.617955 -2.357550 -2.412567 0.219702
Max 3.393514 5.220162 2.900929 2.114137 1.631125
Mean -0.929023 1.241850 -0.122385 -0.509107 0.898553
Median -1.222322 1.376543 -0.081554 -0.365098 0.892214
Standard- 1.797998 1.792662 0.934219 1.001406 0.296166
Deviation
Significant- 5 /46 4 /46 0 /46 6 /46 46 /46
at 5%
level
Significant- 10 /46 6 /46 1 /46 11 /46 46 /46
at 10% level
β
itTrade-weighted FX HKD/Renminbi FX Political Risk Producer Price change HSCE Market Two-factor
Significant- 10 /46 6 /46 1 /46 11/46 46 /46
at 10% level
Portfolio 1 1 /27 1 /27 1 /27 6 /27 27 /27
Portfolio 2 9 /19 4 /19 0 /19 5 /19 19/ 19
Portfolio 3 1 /24 4 /24 0 /24 4 /24 24 /24
Portfolio 4 9 /22 1 /22 1 /22 7 /22 22 /22
Portfolio 1 stands for the firms that are classified into manufacturing industry. Portfolio 2 is the
combination of firms which are classified into other industries except manufacturing industry. Portfolio 3
represents the firms which are largely owned by state (the holding ratio is over 50 percent) and portfolio 4
20
is the combination of firms which are relatively less owned by state (the holding ratio is below 50 percent).
Significant at the 10-percent level
Table 4 report the descriptive result of beta derived from the multi-factor model. In this multi- factor model which focuses on the multilateral trade-weighted exchange rate, the multilateral exchange rate exposure remains significant, and the outcome is similar to the result from two- factor model. Specifically, the multilateral exchange rate can partly explain the volatility of the stock prices of 10.86% of companies at 5% significant level.
At the same time, the exposure of political risk remains un-significant at 5% level, and 4.3%
of companies are significantly correlated with the political risk at 10% level. A unilateral explanation for this result lies in a unconditional multi-factor model could not fully detect the time-varying nature of political risk exposure. Moreover, the small scale of sample is also responsible for the un-significant result.
On account of changes in producer price of Mainland China, as the data shown, the factor is priced into the stock prices of 15.21% of China enterprises, the outcomes not differ much from the result of one-factor model and two-factor model. In the portfolios classified by the property of manufacturing, the difference is smaller than the portfolios classified by the property of holding ratio of state. As to the variable of HSCE market index, this factor keeps the absolutely significant impact on the volatility of stock prices.
Table 4 Multi-factor model ( multilateral trade-weighted exchange rate)
β
itTrade-weighted FX HKD/Renminbi FX Political Risk Producer Price changes HSCE Market Multi-factor
Min -4.613213 NA -2.288477 -2.985229 0.220544
Max 2.907662 NA 3.214348 1.645933 1.589064
Mean -1.178627 NA -0.023890 -0.683306 0.877968
Median -1.585462 NA -0.018262 -0.619373 0.875523
Standard- 1.818897 NA 0.930570 1.004795 0.293174
Deviation
Significant- 5 /46 NA 0 /46 7 /46 46 /46
21
at 5%
level
Significant- 10 /46 NA 2 /46 10 /46 46 /46
at 10% level
β
itTrade-weighted FX HKD/Renminbi FX Political Risk Producer Price change HSCE Market Multi-factor
Significant- 10 /46 NA 1 /46 10/46 46 /46
at 10% level
Portfolio 1 4 /27 NA 1 /27 5 /27 27 /27
Portfolio 2 6 /19 NA 0 /19 5 /19 19/ 19
Portfolio 3 4 /24 NA 0 /24 6 /24 24 /24
Portfolio 4 6 /22 NA 1 /22 4 /22 22 /22
Portfolio 1 stands for the firms that are classified into manufacturing industry. Portfolio 2 is the combination of firms which are classified into other industries except manufacturing industry. Portfolio 3 represents the firms which are largely owned by state (the holding ratio is over 50 percent) and portfolio 4 is the combination of firms which are relatively less owned by state (the holding ratio is below 50 percent).
Significant at the 10-percent level
Table 5 reports the descriptive result of beta collected from the other multi-factor model. The exposure risk of exchange rate is bilateral HKD/Renminbi. Compared with the outcome of table 3, which concentrates on the multilateral trade-weighted exchange rate, the influence exerted on the stock prices from the bilateral exchange rate is less than the counterpart of multilateral exchange rate, only 6.5% of Mainland China enterprises reflect the exchange rate risk exposure on the stock prices underlying 5% significant level, even the significant level is released from 5%
to 10%, the situation is not much improved. Specifically, in the analysis of manufacturing
industry portfolio, the difference of significant affected companies are narrowed. The same
situation happenes to the portfolios of different holding ratios of state. However, in general, the
bilateral exchange rate is less influential on the stock prices. Therefore it is reasonable to indicate
that the bilateral HKD/Renminbi exchange rate is not the key pricing factor of stock prices of
Mainland China enterprises listed on SEHK.
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With regard to the factor of political risk, the outcomes are similar to the results illustrated in table 1, table 2, and table 3. This risk exposure remains weakly influential on the stock prices of Mainland China enterprises. Only two companies are subjected to political risk in the context that the significant level is set at 10%.
As to the factor of changes in producer price, more firms are significantly impacted by this factor than the number in the multi-factor model with specific multilateral trade-weighted exchange rate. Moreover, this conclusion is also appropriate to portfolios, no matter in the portfolios classified by the property of manufacturing or holding ratio, the number of firms which are influenced by changes in producer price is great than the counterpart in the multi- factor with multilateral trade-weighted exchange rate. Additionally, HSCEI Market, the market factor still be distinct to account for the volatility of the stock prices of Mainland China enterprises.
Table 5 Multi-factor model( bilateral HKD/Renminbi exchange rate)
β
itTrade-weighted FX HKD/Renminbi FX Political Risk Producer Price changes HSCE Market Multi-factor
Min NA -2.115182 -2.247316 -2.593239 0.248166
Max NA 5.153597 3.087878 1.838286 1.629193
Mean NA 1.390602 -0.064114 -0.592385 0.904801
Median NA 1.474956 -0.005810 -0.450101 0.891588
Standard- 1.777231 0.935535 0.993435 0.297959
Deviation
Significant- NA 3 /46 0 /46 8 /46 46 /46
at 5%
level
Significant- NA 5 /46 1 /46 12 /46 46 /46
at 10% level
β
itTrade-weighted FX HKD/Renminbi FX Political Risk Producer Price change HSCE Market Multi-factor
Significant- NA 5 /46 1 /46 12/46 46 /46
at 10% level
Portfolio 1 NA 3 /27 1 /27 7 /27 27 /27
23
Portfolio 2 NA 2 /19 0 /19 5 /19 19/ 19
Portfolio 3 NA 3 /24 0 /24 7 /24 24 /24
Portfolio 4 NA 2 /22 1 /22 5 /22 22 /22
Portfolio 1 stands for the firms that are classified into manufacturing industry. Portfolio 2 is the combination of firms which are classified into other industries except manufacturing industry. Portfolio 3 represents the firms which are largely owned by state (the holding ratio is over 50 percent) and portfolio 4 is the combination of firms which are relatively less owned by state (the holding ratio is below 50 percent).
Significant at the 10-percent level
Stage 2 : The estimation table of determinant of Beta
Table 6 represents the outcome of whether these five selected factors are statistically significant to the dummies variables in each of the relevant one-factor model. All the coefficient collected from five one-factor in stage 1 are acted as the dependent variable in the cross-section regression.
With regard to the dummy 1, which qualitatively reflects the property of manufacturing, it is evident that the factor of producer price is sensitive to the dummy 1 at 5% significant level, the result is logical since the manufacturing sector accounts for a large proportion to the Producer Price Index. Besides, the HSCE Market Index is also significant at 5% level.
On account of dummy 2, illustrated by the holding ratio of state and indirectly represents the degree of nationalization. The outcome is consistent with the expectation, the higher degree of nationalization, the more likely to be affected by political instability.
Table 6 One-factor model
β
itTrade-weighted FX HKD/Renminbi FX Political Risk Producer Price change HSCE Market One-factor Dummy 1
Coefficient -0.629248 0.038077 -0.135239 -0.626415 0.215121
(Std.Error) 0.599562 0.551343 0.284598 0.307857 0.084548
p-value 0.2997 0.9453 0,637 0.0479** 0,0145**
Dummy 2
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Coefficient 0.740090 -0.303615 0.459838 -0.058061 0.025277
(Std.Error) 0.587841 0.541553 0.272568 0.317288 0.089177
p-value 0.2147 0.5779 0,0987* 0.8556 0.7782
The factor are defined as:
=Multilateral trade-weighted exchange rate,
=bilateral HKD/Renminbi exchange rate,
=changes in political risk,
=changes in producer price,
=change of rate of HSCE Market Index.
* Significant at the 5-percent level
** Significant at the 10-percent level
Table 7 shows the result of whether the four factors each is significant to the dummies in the multi-factor model. As it turns out to be, the factor of bilateral HKD/Renminbi exchange rate and political risk remain not sensitive to these two dummies. However, The good news is that the factor of changes in producer price keeps significant to the dummy 1, though significant at 10 % level. Additionally, the HSCE Market Index is statistically significant at 5% level.
In summary, in the multi-factor model with specific bilateral exchange rate, none of the variables can be explained by the dummy 2, even the factor of political risk, which is supposed to be significant to dummy
Table 7 Multi-factor model (bilateral HKD/Renminbi exchange rate)
β
itTrade-weighted FX HKD/Renminbi FX Political Risk Producer Price change HSCE Market Multi-factor Dummy 1
Coefficient NA 0.527419 -0.210893 -0.564482 0.215164
(Std.Error) NA 0.532295 0.281519 0.288556 0.084199
p-value NA 0.3272 0.4578 0,0568* 0,0141**
Dummy 2
Coefficient NA -0.169330 0.435164 -0.028366 0.022491
(Std.Error) NA 0.529886 0.271440 0.296508 0.088876
p-value NA 0.7508 0.1161 0.9242 0.8014
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The factor are defined as:
=Multilateral trade-weighted exchange rate,
=bilateral HKD/Renminbi exchange rate,
=changes in political risk,
=changes in producer price,
=change of rate of HSCE Market Index.
* Significant at the 5-percent level
** Significant at the 10-percent level
Table 8 illustrates the outcome of whether these four factor each is significant to the dummies in the multi-factor model with specific multilateral trade-weighted exchange rate. The result is quite similar as the result derived from the Table 7. Only the factor of changes in producer price is significant to the dummy 1, which is constructed based on the industry classification.
Additionally, the political risk remains un-sigficant to dummy 2, which is on the basis of degree of nationalization.
Table 8 Multi-factor model(Multilateral trade-weighted exchange rate)
β
itTrade-weighted FX HKD/Renminbi FX Political Risk Producer Price change HSCE Market Multi-factor Dummy 1
Coefficient -0.121364 NA -0.226207 -0.551679 0.210852
(Std.Error) 0.550514 NA 0.279734 0.292696 0.082896
p-value 0.8265 NA 0.4231 0,0661* 0,0146**
Dummy 2
Coefficient 0.812511 NA 0.367083 0.079908 0.037817
(Std.Error) 0.52894 NA 0.272205 0.299688 0.087326
p-value 0.1317 NA 0.1844 0.791 0.6671
The factor are defined as:
=Multilateral trade-weighted exchange rate,
=bilateral HKD/Renminbi exchange rate,
=changes in political risk,
=changes in producer price,
