The Stock Exchange of Thailand is one of emerging stock markets. After the 1992 financial liberalization, capital inflows to the stock market have been rising continuously. The 1997 financial crisis caused the adoption of flexible exchange rate system, which was believed to distort the portfolio investment by foreign investors due to exchange rate uncertainty. The Thai stock market is open to foreign investors. Foreign investors’ investment accounted for approximately 24 percent of the trading. The Thai stock market is developing to more mature market. The data from the Stock Exchange of Thailand reveal that the market capitalization has been increasing. In 1981, the market capitalization was 23, 471.22 million baht. By the end of the decade, it jumped to 659,493.07 million baht and increased to more than 5 billion baht in 2009. Most of previous empirical studies using the daily and weekly data from the Thai stock market tend to confirm the positive-risk return tradeoff, but this tradeoff is insignificant in monthly data. The main objective of the present study is to provide new evidence on the risk-return tradeoff in the Stock Exchange of Thailand using monthly data. The time period is from January 1981 to December 2009. Since the present analysis employs a GARCH-M model to obtain the relationship between excess return and its volatility. The ex-post capital gain excess return is computed as the percentage change in the stock market index minus the risk-free rate. The other excess return is the market dividend yield subtracted by the risk-free rate. This study is closely related to the studies by Chiang and Doong (2001) and Shin (2005). The first study indicates no relationship between risk and return in monthly data while the latter study estimates both parametric and semi-parametric GARCH-in-mean model using Thailand’s weekly stock market data from January 1989 to May 2003, and finds insignificantly positive risk-return tradeoff. The present
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This research uses the stock price as of 31 March of the following year-ending because Thai listed firms need to send their yearly financial statements to the Securities and Exchange Commission (SEC) within three months after the fiscal year ended. Thus, the available accounting information disclosed will be reflected in the stock prices as of 31 March. The sample firms were not listed on the Rehabilitation Sector or the Non-performing Groups (NPG). The firms listed on the Stock Exchange of Thailand (SET) as of 30 May 2013 composed of seven industries and 23 sectors. The total number of firms listed was 401 excluding firms with negative book values of equities. The period of study is the year 2011 and 2012. This research uses accounting data from the year 2011 ending 2012 and the reasons are as follows: (1) TAS 40 (Revised 2009) requires the IP to be separated from the PPE as a new accounting item in the Statement of Financial Position since the year 2011; and (2) the FAP had revised 18 Thai Accounting Standards and Thai Financial Reporting Standards and 15 Thai Standing Interpretations and Thai Financial Reporting Interpretations in 2012 and their draft forms were issued in 2013.
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Studies by Chui & Wei (1998) are suggestive of the fact that equity market size in Thailand increased from $1.72 billion in 1984 to $133.66 billion in 1993, an increase of over 77 times within a decade. Thailand is nota- bly a free market economy with intensified objectives of attracting Foreign Direct Investment (FDI) in all sec- tors of the economy and as such, the Stock Exchange of Thailand is at the spotlight of attracting international investors. Scholarly studies by Lipsey (2000) attribute to the fact that FDI contributes significantly to economic development through job creation, technology transfers and spillovers, and thus the Stock Market of Thailand is a major channel for FDI flows. Therefore, this study plays a significant role in investigating information that is of particular relevance to investors with regard to the Thai equity market. Previous studies have entirely focused on large stocks, ignoring the crucial need to equally analyze small stocks. This paper explores a distinctive ap- proach to include small and tiny stocks in order to widen the academic and market scopes of small-caps and large-caps and value and growth stocks. Further, there is a visible distinct absence of detailed academic research on small-caps and large-caps and value and growth stocks in the Stock Exchange of Thailand especially after the crisis and this paper seeks to bridge the academic gap and also provides new frontiers of knowledge for prospec- tive scholars in different countries.
This study aims to investigate narrative TBL reporting in the annual reports of the top 50 largest companies listed on the Stock Exchange of Thailand (SET), to establish whether there is any relationship between the extent of TBL reporting and a variety of factors used in previous studies conducted in more developed countries. By using a non-probability sampling method, the top 50 listed companies were sampled based on their 2010 annual reports. Statistical analysis (descriptive, multiple regression, independent samples t-tests, and ANOVA), was employed to analyse the extent of reporting found and the relationship between TBL disclosure based on a measured score and ten characteristics influencing disclosure identified in previous studies.The findings show that there are statistically significant differences between the TBL reporting scores of high and low profile companies. There are also significant differences in reporting based on industry groups. Although the results did not indicate any relationship between TBL disclosure scores and the various factors considered in previous studies, there was a correlation between the age, type of business, and liquidity of companies and their economic information reporting score as well as between the size, risk, and profitability of the company and the environmental information disclosure score.
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We consider the compound Ornstein–Uhlenbeck process in order to create dividend yield models to study in a case of Stock Exchange of Thailand through consideration of the earn- ing yield as an additional stochastic factor. Using the least squares technique and simulat- ing results through the Euler–Maruyama technique, we can conduct parameter estimation with respect to three diﬀerent models: SDY, SEY, and MSEY. Python’s simulation results in the model with earning yield slightly reduce the RMS error. The numerical result shows that both SEY and MSEY models reduce the RMS error of estimation. The new MSEY model is especially proﬁcient at reducing error at a rate of 14%. This suggests that our proposed dividend yield models with an extension of earning yield have more accurate data comparing to the original model. To further improve our model for future studies, we should focus on improving our estimation technique and using more ﬁnancial factors in the real world.
Abstract: This research aims to study the effect of the days in week on the return of the stock price index, particularly in the Stock Exchange of Thailand (SET). The daily closing prices of SET50 index from June 2, 2003 to June 2, 2017 are taken into account, i.e., there are totally 3,425 days. The stock returns of the 50 companies are calculated according to the daily historical stock prices of companies. Both descriptive and inferential statistics are employed in data analysis including average, standard deviation, multiple comparisons and multiple regression analysis. Applying ordinary least square method, the linear equation with five dummy variables is formulated for multiple regression analysis. The results show that the means of daily return rate of SET50 index are significantly different. Monday has a negative influence on the return rate of SET50 index whereas Friday has a positive influence at the significance level of 0.05. The return rate of SET50 index on Monday is lowest whilst Friday is highest during the week.
Some previous studies focus on Asian stock markets. Moosa and Al-Loughani (1995) employ monthly data to examine the price volume relationship in Malaysia, Philippines, Singapore and Thailand. They find evidence of causality running from trading volume to absolute price changes and from price changes to trading volume in these stock markets. In addition, nonlinear and linear causality tests seem to produce the similar results. Using daily data during 1990 and 2004, Pisedtasalasai and Gunasekarage (2007) examine the dynamic relationships among stock returns, return volatility and trading volume for Indonesia, Malaysia, Philippines, Singapore and Thailand. They find that stock returns in these economies are important in predicting their future dynamics as well as those of the trading volume, but trading volume has a very limited impact on future dynamics of stock returns. The trading volume in some markets seems to contain information for predicting future dynamics of return volatility. Gebka (2012) employs daily and weekly data from January 1990 to November 2003 to examine the dynamic relationship between returns, trading volume, and volatility in Hong Kong, Indonesia, Japan, South Korea, Malaysia, Singapore, Taiwan and Thailand, and finds weak evidence indicating that trading volume plays dominant role on return and volatility. Using daily data from January 1990 to June 2008, Lin (2013) investigates the dynamic relationship between returns and trading volume in Indonesia, Malaysia, Singapore, South Korea, Taiwan and Thailand and finds evidence showing that trading volume contains information to predict stock returns in most of the markets, except the case of Singapore. Using daily data of the Culcatta Stock Exchange in India, Bose and Rahman (2015) find that the contemporaneous or lagged trading volume as a proxy for latent information arrival to the market do not adequately convey information to induce traders’ view of the desirability to trade.
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government support were successful, which had been adjusted by the production and marketing strategies. According to the economic data of Thailand, the economic system had the ability to continue as one of the world’s largest producer of agricultural products, food and beverages in this decade. Thus, the agro and food industry has an important role in the structural transformation of agriculture in Thailand. Currently, regarding the agro and food industry’s turnover, the agribusiness, food and beverage sectors have the ability to keep profit of their listed companies by increas- ing the volumes of their products (Stock exchange of Thailand 2014). According to the statistic of the Customs Department of Thailand, the increments in the volumes of exports of agricultural products were running in the opposite direction with the values of agricultural products which had been declining since year 2009 (The Customs Department of Thailand 2014). This problem is becoming a major challeng- ing issue in the sub-industry sectors of agro and food industry of Thailand. The problem of this industry concerns both in the term of internal restriction and external challenges.
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The main objectives of this paper are to assess the behavior of stock prices using the overall market index in the stock exchange of Thailand. The variance-ratio approach is employed to test whether stock prices follow a random walk process, while the GARCH process is employed to test the volatility of stock return measured in terms of capital gain or loss. The volatility of stock market return is caused by market expectations and speculations with new information. The generalized autoregressive conditional heteroskedisticity (GARCH) process can capture the volatility of stock return. The first technique is widely used in testing whether stock prices are pure random walks, while the latter technique can tell how volatile are stock prices which can cause volatile stock market returns. The results from this study can indicate whether efficiency exists in the Thai stock market. Section 2 analyzes the data and their properties. Section 3 explains the econometric methods used in the analysis. Section 4 highlights the empirical results. The last section concludes.
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Financial instrument investments have become significant and widely popular across the globe. However, not all investors who invest in the financial market gain positive returns or above-the- market returns from their investments. This is because investors tend to utilize similar investment models, which as a result cause some strategies to fail to win in the market. Due to the problem on how to make investments for optimal remunerations in respect to risks and how to augment financial ratios to screen for the most attractive stocks, the study was conducted to test the investment strategy model of Reinganum, which was previously tested in the New York stock exchange (NYSE) from 1970 to 2006 and to explore the outcomes of excess returns (Alpha) above the market from all forms of investment.
Before looking at the external condition, variable δ shows whether or not domestic shock is asymmetric. The results show that the probability δ is not significant (0.889500) > α. This means negative return shocks do not give a bigger effect than positive. The result from variable α1 which is not significant shows that Indonesian stock exchange is not domestically affected. It can be seen from the variance equation that the GARCH variable (β) is positive and significant (0.0000). This means that news has a huge and consistent impact during the research period. This is consistent with the global economic fluctuations in 2016 which weakned stocks in ASEAN. If we look at variable ɸ1 which is significant, it shows that there is volatility spillover from Singapore to Indonesia. This is consisten with previous researches from Saadah (2013) and Lestano (2010) that conclude that there volatility spilloer from Singapore to Indonesia, which means that if there is a shock in Singapore it will direct affect Indonesia. This means that volatility spillover went from development market to emerging market (Saadah 2013).
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Oke (2013) applied the Capital Asset Pricing Model (CAPM) to the Nigerian stock market using weekly stock returns from 110 companies listed on the Nigerian stock exchange (NSE) from January 2007 to February 2010. The study undermines the CAPM’s predictions that higher risk (beta) is associated with a higher level of return and that the intercept should be equal to zero when estimating SML. The claim by the CAPM that the slope of the Security Market Line (SML) should equal the excess return on the market portfolio is also not supported by this study. This in effect, invalidates the prediction of the CAPM as far as Nigeria is concerned. Similarly, Adedokun and Olakojo (2012) investigated the empirical validity of CAPM in the Nigerian Stock Exchange (NSE) using monthly stock values of 16 firms from the 20 most capitalised firms in Nigeria between the period of January, 2000 and December, 2009. The empirical findings indicate that CAPM is inadequate to explain the role of asset risk for the determination of expected return on investment in Nigeria’s equity market. They established contrary to the hypothesis of the CAPM that higher risk is associated with higher asset return and asset price.
The privatization wave of stock exchanges is growing and the majority of exchanges across the world are in the process of conversion. A key factor for this wave is new technology and competition which requires more funding and flexibility in decision making. Azzam (2010) summarized the most common challenges faced by government/broker owned exchanges such as smaller size, lower number of listing firms, weak governance structure, low quality of management, inefficient regulation of listed firms, and weak eligibility criteria for brokers and members. It is vital to explore the quality and performance of the market in light of the demutualization wave. Theoretically, Boussetta (2016), analyzed the effect of competition between exchanges on the certification role of listing. She found that the profitability objective of privatized exchanges may lead other objectives and subsequently may negatively affect market quality. Later in another paper, Boussetta (2017) assessed the market performance of demutualized exchanges compared to mutual counterparties during the period from 2004 to 2014. She found that the converted exchanges have better profits, increased trading activities and lower transaction costs. Earlier, Abukari and Otchere (2016) focused on the liquidity of stock exchange after demutualization. They found that the transaction cost in the years after the demutualization is significantly better.
Different researchers have used different event windows to study Turn of the Month Effect. Ariel (1987) while evaluating turn of the month effect, defined his event window as (-1, +4) i.e. last working day of previous month and first four days of upcoming month. Lakonishok and Smidt (1988) analyzed Dow Jones Industrial Average (DJIA) for turn of the month effect with an event window of (-1, +3) i.e. last working day of previous month and first three days of new month. Evidence of turn of the month effect for USA, Canada, Switzerland, Germany, UK and Australia has also been found, however no such effect has been reported fro Japan, Hong Kong, Italy, and France (Cadsby and Ratner, 1992). Existence of turn of month effect has also been proved for stock markets of eighteen countries in 1970s (Agarwal and Tondon, 1994).
Abstract: The present study addresses the research question whether the Indian stock market follows random walk? The study considered BSE-100 index based companies as sample. The analysis is based on daily closing prices of companies over a period of time from 1990 to 2012. The data is collected from the data source PROWESS database. To test the RWH, econometric models are applied such as ADF test, PP test, autocorrelation test, variance ratio test, GARCH (1, 1), EGARCH (1, 1), TGARCH (1, 1), GARCH-M (1, 1) and PARCH (1, 1. The non-parametric Runs test is also applied to examine the randomness of observed series. The results show that Indian stock market exhibits the pattern of evidence in which some of periods show that the market is efficient and other periods show that the market lacks efficiency. This means that Indian stock market exhibits the pattern of somewhat mixed evidence, which indicates lack of efficiency. Our results are useful as a piece of information to the regulatory authorities, speculators and the individual and institutional investors of Indian stock market. The results show the true picture of the market and the investors’ response to the stock prices in emerging markets like India which is going through various phases of financial, economic and regulatory reforms. This study has the global impact as Indian firms are integrated with global capital markets. The online trading in India is started in early 90’s and the usage is increased rapidly. The quality of information available today is much superior to what it was in the past. All these dynamic trends have changed the nature and functioning of capital market. Therefore, the dynamics of market efficiency has also changed. This necessitates a study of market efficiency to understand whether or not historical information has any relevance for the market participants, both domestic and global. The sample is sufficiently large and the time considered is also long enough to draw robust conclusions.
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The results of our study may contribute in both theories and practices. First, this article will enrich the research on signaling theory to a certain extent. While the existing signaling theory focuses on dividend distribution policy, we put the research on the internal shares holding behaviors of the company, expanding the coverage of the traditional signaling theory and laying the foundation for the follow-up study of related problems. Second, this article can effectively help su- pervision department to grasp reaction of stock market to the shares reduction of managers, so as to further improve the relevant rules and regulations, to achieve more efficient and standardized supervision and management. Third, this article also helps investors in Chinese stock market to better identify the in- formation behind the management-holding behaviors of listed companies, and guide investors to make rational decisions and make rational investments. Chi- nese stock market started relatively late, the regulatory system is still in the process of continuous improvement. Investors often lack the professional know- ledge to determine the signals of listed companies and top-level investors, ignore the scientific and objective laws in making investment decisions and lack the knowledge of high risk in stock market.
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The firms included in this research are ISE (Istanbul Stock Exchange) and SSE (Shenzhen Stock Exchange) listed manufacturing companies in 2006 and 2005. Data was collected from CorporateInformation.com. This site holds "Best of the Web" recognition from FORBES Magazine. BARRON's Magazine featured the site as one of the best sources of company information for investors. This site is also one of the few sources in the world for English language reports on many companies in Asia, Latin America and Eastern Europe that do not release their results in English. Data of 166 Chinese and 65 Turkish firms were gathered. After excluding the firms with missing values and as outliers at 5% level of significance by the test of Mahalanobis Distance, the sample for analysis was made up of 126 Chinese and 47 Turkish manufacturing firms.
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Abstract: This study is aimed at identifying the relationship between stock dividend issue and return rate of share of 100 firms from Tehran Stock Exchange during years 2007- 2011 tending to issue stock dividend. Pearson correlation test was used to examine the relationship between stock dividend issues and return rate of share and results showed that there is no significant relationship between share return rate and the amount of stock dividend and also between stock dividend issue percentage and return rate of share.
Investment in stock market or capital market is considered as one of the important destinations for investing one’s funds. The investments made in stock market are considered to be more risky as compared to the other forms of investments such as investments in real estate, gold, bonds, bank deposits etc. But at the same time the investment in stock market are more liquid than investment in real estate and bonds. In this paper an attempt has been made to know the perception of the investors towards investment in stock market. A survey of 200 investors has been done for this purpose.
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Imam and Amin (2004) found that the volatility of the stock return of Bangladesh Capital Market follows a generalized autoregressive conditional heteroskedastic (GARCH) process. It has been observed that the volatility predicted this period has more influence in forecasting volatility for the next period. For the DSE return series volatility clustering is said to be persistence implying that in Dhaka Stock Market today’s return has a large effect on the conditional forecast variance many periods in the future. The GARCH model for the two sub-periods of pre-crash and post-crash shows different result. The reason for such structural shifts in GRACH process may be due to high volatility in return series in the later period and to the change in the behaviour of investor after the stock market crash in 1996. Passive role-played by the institutional investors, lack of confidence by the long-term individual investors and the dominance of speculators, among many, are the notable reasons for the change in investor’s behaviour. Tests for non-stationarity indicated that the conditional volatility of DSE index in post-crash period in mean reverting. This finding suggests that current information has no effect on long run forecasts, rather, volatility shocks (random error) than the volatility estimated at earlier period influence more in estimating future volatility.
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