How Does the Stock Market React to the Carbon Policy? The Chinese Experience During 2014–2022

One of the most important issues in the capital market is awareness of the level Risk of Companies, especially “systemic risk (unavoidable risk)” that could affect stock returns, and can play a significant role in decision-making. The present study examines the relationship between stock returns and systematic risk based on capital asset pricing model (CAPM) in Tehran Stock Exchange. The sample search includes panel data for 50 top companies of Tehran Stock Exchange over a five year period from 1387 to 1392. The results show that the relationship between systematic risk and stock returns are statistically significant. Moreover, the nonlinear (quadratic) function outperforms the linear one explaining the relationship between systematic risk and stock returns. It means that the assumption of linearity between systematic risk and stock returns is rejected in the Tehran Stock Exchange. So we can say that the capital asset pricing model in the sample is rejected and doesn’t exist linear relationship between systematic risk and stock returns in the sample.

The Market Value of the Corporate Risk Management Function Introduction While the use of risk management strategies have increased dramatically over the last 30 years, measuring the value of risk management activities has been difficult. Much of this can be attributed to the fact that the impact of losses prevented or reduced through risk management practices cannot be easily measured. Schmit and Roth (1990) provide an explanation of the activities performed and tools used by practicing risk managers. The current study examines the issue of whether shareholders value the risk management function. Previous Work Valuation of the firm can be done through several theoretical models such as the Gordon Dividend Growth Model, the Capital Asset Pricing Model (CAPM), and the Arbitrage Pricing Theory model (APT). Cho (1988) explores the relationship between firm value and risk management activities using Gordon's Constant Dividend Growth Model. With the assumption that risk management activities affect the firm's cost of capital, he shows that under certain conditions, risk management activities lower the cost of capital, thus raising the present value of the firm to investors. This suggests that the investors might place some positive value on the risk management process. If the CAPM is valid, rational investors eliminate firm-specific risk through the process of holding a well diversified portfolio of assets, leaving only systematic or market risk. Other authors have examined the issue of whether risk management can be reconciled with the CAPM (see Diallo and Kim, 1989; Cho, 1988; Cross, Davidson, and Thornton, 1986; Cummins, 1976 and 1983; Doherty, 1984 and 1985; Doherty and Tinic, 1981; Hiebert, 1983; Main, 1983a and 1983b; Mayers and Smith, 1982; MacMinn, 1987; and Spreecher and Pertl, 1983), an issue on which the current article provides empirical evidence. Sprecher and Pertl (1983) provide the only other empirical work that is related to this question of the value of risk management activities. Using an event study methodology, they find that large, firm-specific losses create negative abnormal returns. Further, they argue that because the risk management techniques of loss prevention and control can reduce the negative impact of large losses, risk management activities should be positively valued by stockholders. Research Methods An event study methodology similar to that used by Cross, Davidson, and Thornton (1986 and 1989) and Diallo and Kim (1989) is used in this study. The events of interest are defined as published announcements of the formation or expansion of a risk management department in the sample firms. These announcements appeared in various weekly issues of Business Insurance. Investor reaction to these announcements should indicate the value placed on risk management activities by financial market participants. There were 117 announcements of this type identified during the period from 1980 through 1986. To be included in the final sample, the announcements had to refer to firms for which the Center For Research in Security Prices Daily Stock Return contained the necessary stock return information. The final sample consisted of 80 announcements relating to firms whose common stocks are traded on either the New York Stock Exchange or the American Exchange. Unlike other daily event studies in which events are usually identified as having been reported in a daily publication such as the Wall Street Journal, this study uses announcement dates associated with a weekly publication. Furthermore, the information appearing in the published announcements arrives at the publishers in the form of a press release two to five weeks prior to the publication date. Sometime during this pre-event period the original announcement is released and investors are exposed to this new information. This means that any shareholder wealth effect associated with this new information would be expected to occur sometime during this five-week period prior to the identified announcement date rather than on the actual publication date. …

Purpose: The study explores the relationship between firm size, systematic risk, and stock returns across various industries. The purpose of the study is to analyze how these factors influence stock returns and to provide insights for investors and financial analysts. The theoretical framework is based on the Capital Asset Pricing Model (CAPM) and existing literature on firm size, systematic risk, and stock returns.  Methodology: The research methodology involves quantitative analysis using financial data from companies in different industries. Variables such as firm size, systematic risk, and stock returns are measured and analyzed using statistical techniques and models. The study aims to uncover patterns and relationships that can help in understanding the dynamics of stock returns in diverse industry settings.  Findings: The findings of the study reveal significant correlations between firm size, systematic risk, and stock returns. Larger firms tend to exhibit lower systematic risk and higher stock returns compared to smaller firms. The analysis also highlights industry-specific variations in the impact of firm size and systematic risk on stock returns, suggesting that industry dynamics play a crucial role in shaping investment outcomes.  Originality: This study contributes to the existing literature by providing empirical evidence on the relationship between firm size, systematic risk, and stock returns in companies across different industries. The originality of the work lies in its comprehensive analysis of these factors and its implications for investment decision-making.

Understanding the impact of monetary policy announcements on systemic risk and abnormal returns (ARs) of banking sector indexes is important to assess the changes in the cost of equity capital and arrive at a fair rate of return of bank stocks. Empirical studies of this nature from the Indian context are scarce. The present study analyses the impact of monetary policy announcements on systemic risk and abnormal returns of bank index in India. Capital asset pricing model (CAPM) along with Kalman filter was used to estimate the daily systemic risk (beta) and abnormal returns. Ordinary least squares (OLS) regression and event study methodology were used to assess the impact of monetary policy announcements on systemic risk and abnormal returns.

Understanding the impact of monetary policy announcements on systemic risk and abnormal returns (ARs) of banking sector indexes is important to assess the changes in the cost of equity capital and arrive at a fair rate of return of bank stocks. Empirical studies of this nature from the Indian context are scarce. The present study analyses the impact of monetary policy announcements on systemic risk and abnormal returns of bank index in India. Capital asset pricing model (CAPM) along with Kalman filter was used to estimate the daily systemic risk (beta) and abnormal returns. Ordinary least squares (OLS) regression and event study methodology were used to assess the impact of monetary policy announcements on systemic risk and abnormal returns.

1. Introduction For more than three decades, numerous studies have been devoted to capital asset pricing issues. Identifying key factors influencing returns on capital assets remains the major focus of the studies. According to the first important asset pricing theory, the capital asset pricing model (CAPM) of Sharpe (1964) and Lintner (1965), there is a positive linear relationship between expected stock returns and market betas. This single-factor model is theoretically challenged by the arbitrage pricing theory (APT) developed by Ross (1976). The APT provides a theoretical framework to identify more macro factors that have significant explanatory power for stock returns (Roll and Ross 1980; Chen 1983; Bower, Bower, and Logue 1984; and Chen, Roll, and Ross 1986). In order to be included in a multifactor model, a factor must represent a source of nondiversifiable or systematic risk. In addition to theoretical challenges, the evidence of many empirical studies casts doubts on the adequacy of the long-existing CAPM. For example, the findings of Fama and French (1996) suggest that Ps alone cannot explain expected returns on common stocks. Meantime, other macro factors related to the bond market, such as unanticipated changes in the term structure (Chen, Roll, and Ross 1986), are used to explain stock returns. In recent years, more risk factors have been suggested in the literature. For example, in their time-series study about common risk factors in the returns on common stocks, Fama and French (1993) argue that there are three stock market factors, an overall market factor, a size factor (stock price times number of shares), and a book-to-market equity factor, that have a significant impact on stock returns. In addition, two bond market factors, the term structure and risk factors, can capture common variation in stock and bond returns (Fama and French 1993). Similar to Fama and French's (1993) multifactor models, the present study uses all three stock market factors (an overall stock market factor, a size factor, and a book-to-market equity factor), two bond market factors related to maturity and default risks, and a real estate market factor. The reason for introducing a new risk factor, real estate market factor, although not so obvious, is engendered by the theoretical spirit of the APT Real estate is a major asset class, and as almost all firms have operating expenses under this category, it does not matter if the properties are acquired or rented. According to Zeckhauser and Silverman (1983), a significant portion of corporate assets, on average as high as 25%, is real estate related. Changes in real estate market value, therefore, potentially have a direct impact on the value of corporate assets and operating expenditures. If this impact is significant, as are other systematic risk factors, the real estate market factor must play an important role in stock pricing. In addition, the real estate market risk is a macro factor. Therefore, results about this factor do not have any interpretation problems that are associated with firm-specific variables. In order to identify significant factors in explaining returns for industrial stocks, this study estimates various models based on the following six risk factors: an overall stock market factor, a size factor, a book-to-market equity factor, a term structure factor, a default risk factor, and an unsecuritized real estate market factor. Results of this study provide some empirical evidence about the significance of the six risk factors on excess returns for industrial stocks. In addition, this study also investigates how the inclusion of additional risk factors, for example, the inclusion of the bond market factors or the real estate market factor in the single market-factor model, increases the explanatory power of the model. The remainder of this study is organized as follows. Section 2 describes the data and methodology; section 3 presents the empirical results; and section 4 contains the conclusions. …

PurposeUsing capital asset pricing model (CAPM) and the Z-risk index based on weekly data, this study aims to estimate yearly unsystematic, total, three systematic and insolvency risks in the Gulf Cooperation Council (GCC) countries for the period 2010–2018. The findings of CAPM show positive systematic market risk exposure in all GCC countries for all years, which support the contribution of stock markets to bank prices and returns. The mixed signs of systematic interest rate and exchange rate risks in GCC countries provide hedging opportunities, diversification strategies and regional cooperation, which help risk managers to hedge and stabilize their portfolios against interest rate and exchange rate fluctuations. Therefore, it is necessary that managers and policymakers develop a monitoring system on factors affecting bank insolvency risks to avoid bankruptcies and insolvencies.Design/methodology/approachThis study uses the three-factor CAPM and Z-risk index to measure six types of risks. The CAPM uses market information to estimate the sensitivity of banks to the fluctuations of equity markets, debt markets and foreign exchange markets. Sharpe (1964), Lintner (1965) and Treynor (1965) developed a single-factor CAPM and the coefficient of the model was called systematic market risk. The single-factor CAPM highlights stock markets as the only non-diversifiable source of systematic risks, whereas Stone (1974) and Jorion (1990) highlighted interest rate and exchange rate fluctuations as the other types of non-diversifiable systematic risks. The following functional form in equation (1) estimates five types of risks using CAPM.FindingsThe findings of CAPM show positive systematic market risk exposure in all GCC countries for all years, which support the contribution of stock markets to bank prices and returns based on CAPM theory. The mixed signs of systematic interest rate and exchange rate risks in GCC countries support hedging opportunities and diversification strategies which may help risk managers to hedge and stabilize their portfolios against the fluctuations of interest rate and exchange rate. Although, this policy may decrease the profits of banking sectors but at the same time it would stabilize the portfolios and prevent bankruptcies and big losses because of the fluctuations of interest rate. Moreover, a bank has a better chance to have more liquidity position during financial crises because of the diversifications into different regional markets.Research limitations/implicationsTherefore, this study contributes to the existing literature by using risk measurement by a three-factor CAPM and the Z-risk index as discussed further in methodology.Originality/valueIt is necessary that managers and policymakers develop a monitoring system on factors affecting bank insolvency risks to avoid bankruptcies and insolvencies.

Investors want to be compensated for taking higher risk by a higher expected return. This raises the question of the level of return compensation. In financial market theory, this question is answered by one-factor and multifactor models which determine the expected return of a single asset or a portfolio of assets with one or more systematic risk factors. The most widely used model is probably the capital asset pricing model (CAPM). With this one-factor model, which is typically applied to equity securities, the expected return of a stock or stock portfolio is calculated by adding a risk premium to the risk-free rate. The former is the product of the expected equity market risk premium and the beta of the investment. The higher (lower) the systematic risk or market risk of the investment, the higher (lower) the beta and hence the expected return. However, empirical studies on equity securities demonstrate that stock returns are correlated not only with equity market returns but also with other factors. Two of these risk factors are the size of the firm (measured by market capitalisation) and the book-to-price ratio, which were captured by Eugène Fama and Kenneth French in a multifactor model. The Fama–French model (FFM) is a three-factor model that explains expected returns in terms of risk premiums and the corresponding betas for market, size, and value. Both the CAPM and the FFM are based on the assumption that investors are compensated by a premium when they assume systematic risk. Hence, only systematic risk is relevant to valuation. These two models differ in how systematic risk is measured. In the CAPM, the systematic risk is given by the market portfolio, whereas the FFM uses size and value as systematic risk factors in addition to the market portfolio. This chapter examines these two models.

This paper examined the Price-Earnings anomaly and the Capital Asset Pricing Model (CAPM) for the Colombo Stock Exchange (CSE) in Sri Lanka over the period 2004 to 2013. Stock market anomalies inevitably challenge the validity of the Efficient Market Hypothesis and the accuracy of the asset pricing model used in measuring stock returns. Therefore, the accuracy of the asset pricing model used to measure stock returns is in the centre of the market efficiency debate, and the CAPM is an extensively applied asset pricing model in the Sri Lankan capital market. A stochastic version of the CAPM time-series regression was therefore adopted in this study to estimate portfolio abnormal returns as measured by Jensen’s Alpha. The analysis found systematically high abnormal returns from portfolios of stocks with a low Price-Earnings ratio relative to the portfolios consisting of stocks with a high Price-Earnings ratio. This suggests anomalous pricing behaviour in capital market operations in Sri Lanka. Price-Earnings anomaly hypothesises either the CAPM is mis-specified and/or capital markets are inefficient. If the CAPM appears to be a correctly specified asset pricing model that accurately explains stock returns in response to their true level of systematic risk, Price-Earnings anomaly evidently undermines the semi-strong form stock market efficiency. To shed light on this joint hypothesis problem, the study investigated the accuracy of the CAPM. Results of a two-pass regression approach similar to Black, Jensen and Scholes (1972) and Fama and Macbeth (1973) empirical work indicated that the CAPM-beta does not appropriately explain stock returns in Sri Lanka. As such, it appears that the CAPM suffers from omitted variable bias problem and hence mis-specified. In the absence of statistically significant evidence to constitute accuracy of the CAPM, Price-Earnings anomaly observed in this study appears to have been produced either by measurement inconsistencies of the mis-specified CAPM or by a combined effect of both CAPM-misspecifications and capital market inefficiency. From the policy front, market efficiency is an important attribute and the accuracy of the asset pricing model is a vital strand in market efficiency tests. An investigation of market anomalies using alternative asset pricing models such as Fama-French (2015) five-factor model might be a potential area for future research. Asset pricing models are being constantly investigated and these studies may find further specifications in the contemporary asset pricing model.

Objective in writing this article is to provide an overview of the theories that has been developed for stock returns which is an important area of financial markets’ researches. Since the researches in this field are very active for the past quarter, it is not possible to describe all works that has been done in this area. Most important researches will be discussed without going deeper in mathematical tools and theories.Empirical works have been showing that stock returns are predictable cross-sectional and by time. The discussions about prediction of stock price behavior started with Markowitz with his article –Portfolio Selection-. Markowitz won Nobel Prize in 1990 for his research about portfolio theory. However he criticized by many economists since implementation of the theory requires lots of effort to evaluate data and since it uses historical data the prediction will not be accurate. In addition the assumption that stock returns are normally distributed is not true in reality. Sharpe, Lintner, and Mossin independently developed a model which has come to be known CAPM (capital asset pricing model) in 1964, 1965, and 1966 respectively. Beta coefficient is a key parameter in CAPM world. Beta measures risk of an asset in relation to the market such as S&P500 or an alternative factor. Actually the CAPM is a simple model which is based on sound reasoning and some of the assumptions -all investors have the same information, information is costless, and there are no taxes transactions costs- are unrealistic in market. APT (arbitrage pricing theory) presented for a better estimation for stock returns than CAPM. CAPM is a modified theory while APT is a completely different model. APT’s multiple factors provide a better indication of asset risk and a better estimate of expected return. There are n-factors effecting stock returns in APT but the number of factors are unknown. Furthermore CAPM and APT are single-period models. To get multi-period aspects of market ICAPM was developed. After that CCAPM (consumption-oriented capital asset pricing model) was introduced. It tried to explain behavior of stock returns by a logical extension of APT. A long literature exist on prediction of stock market returns but especially after the latest financial crisis these theories must be analyzed and suggested new ideas for forecasting behavior of stock returns. Keywords: Stock Returns, Markowitz, CAPM, APT, ICAPM, CCAPM, Fama-French 3-factor model.

Capital Asset Pricing Model (CAPM) is one of the most significant finance literature models, which assumes a positive linear relationship between the required rate of return and systematic risk on stocks. The model is frequently used in the business world, but empirical tests repeatedly reject the model's validity in its unconditional form. Pettengill et al. have developed an alternative conditional CAPM approach where the unconditional test procedure developed by Fama & MacBeth, (1973) is improved by taking up and down market conditions. This paper investigates both the conditional and unconditional versions of CAPM in both individual and portfolio stock returns between January 2008 and December 2019 on the stocks listed in the Colombo Stock Exchange (CSE). Population of this research includes all the companies listed on CSE and the top 50 stocks with large market capitalization has been selected as the sample. The results of unconditional test procedure show that there is no statistically significant risk-return relationship is found in any test period in both individual and portfolio stock returns. Thus, this result is similar with the previous literature findings. The results of the conditional tests show that there is no significant positive (negative) risk-return relationship in portfolio stock returns and individual stock returns in CSE during up (down) market months. But findings indicate a significant positive risk-return relationship in individual stock returns in upmarket periods; whereas, a significant inverse risk-return relationship is not provided in down market periods.
 Keywords: Colombo Stock Exchange, CAPM, Conditional Relation, Unconditional Relation

This paper examines the relationship between realized mean returns and alternative measures of risk for samples of life insurance stocks during the 1961-76 period within the framework of the Capital Asset Pricing Model (CAPM). The results provide some evidence of a significant relationship between mean returns and systematic risk, but they also provide evidence of a significant relationship between mean returns and measures of nonsystematic risk, in contradiction to the principal implication of the CAPM. According to normative financial theory, the rate of return required by shareholders is of critical importance to investment and financing decisions of publicly-held firms. Knowledge of factors that determine required rates of return is needed for rational decision-making. Theorists agree that most investors are risk averse so that required rates of return will be positively related to risk, but there is less than complete agreement on the relevant measure(s) of risk. The Capital Asset Pricing Model (CAPM) of Sharpe [31] and Lintner [ 18] and the zero-beta version of the CAPM developed by Black [ 1] imply that the proper measure of risk for an asset is its market (systematic) risk as measured by beta and that diversifiable (nonsystematic) risk will not affect required rates of return. Thus, the CAPM predicts that realized returns on financial assets should be independent of nonsystematic risk measures, such as return variance, once the influence of beta has been removed. The CAPM has been subject to extensive empirical analysis. Studies by Black, Jensen, and Scholes [2], Fama and MacBeth [9], and Foster [13] suggest that mean realized returns on common stocks are significantly related

Based on the type of data used, this research uses a quantitative approach, which places an emphasis on testing theory through measuring research variables with numbers and conducting data analysis with statistical procedures. One of the models used to estimate the rate of return is the Capital Asset Pricing Model (CAPM). The purpose of this study is to examine the level of risk and stock returns during the covid-19 pandemic using the Capital Asset Pricing Model (CAPM) method in companies listed on the Indonesia Stock Exchange (IDX) and to examine differences in the level of risk and stock returns before and during the covid-19 pandemic in companies listed on the Indonesia Stock Exchange. Determination of the sample using the purposive sampling method. There are 488 companies on the Indonesia Stock Exchange (IDX) that meet the criteria, with 98 companies classified as high-risk companies and 390 companies classified as low risk. The results showed that there were no significant differences in risk and stock returns in high-risk companies before and during the pandemic in 2020, 2021, and 2022. There is a significant difference in risk and stock returns in low-risk companies before and during the pandemic in 2021, while there is no significant difference before and during the pandemic in 2020 and 2022. There is a significant difference in risk and stock returns in high- and low-risk companies before and during the 2021 pandemic, while there is no significant difference before and during the 2020 and 2022 pandemics.

This paper investigates the validity of Capital Asset Pricing Model (CAPM) for the West African Economic and Monetary Union (WAEMU) stock market using monthly stock returns of twenty Côte d’Ivoire’s listed firms from January 2002 to December 2011. We split this interval into different time periods. Each one of them has also been divided into two different sub-periods among which one served as estimation mean and the second one helped to test the estimated parameters obtained using a times series regression. Afterwards some statistical tests have been conducted to see whether the CAPM’s hypotheses hold or not. The findings showed that higher risk is not associated with higher level of return within the study area. Also, there was no relation between stock return and non-systemic risk except for one period where we found evidence that stock returns were affected by other risk than the systematic risk. On the contrary the stock expected rate of return had a linear relationship with the systematic risk. The study suggested that the listed companies consider other factors and variables which could explain their returns.

This study is done to analyze and compare the accuracy of Capital Asset Pricing Model (CAPM) and Arbitrage Pricing Theory (APT) Model in predicting stocks’ actual return. The purpose of the study is to find the discrepancy of accuracy of CAPM and APT models in predicting company stocks’ return registered in IDX-30 index from Indonesian Stock Exchange from January 2020-2022. The period is chosen because of the Covid-19 pandemic in Indonesia. The chosen stocks are the stocks which have positive return, never leave the index, never have any changes in stocks’ amount in major and minor evaluation, never do stock split, and have routine dividend payout along the study’s period. The result is there is a significant difference between CAPM and APT models in predicting the actual return based on the result from t-test independent samples. Observed from the Mean Absolute Deviation (MAD) of the two models, CAPM model MAD is smaller than those from APT model, thus CAPM is the more accurate model in calculating return form IDX-30 stocks from January 2020-2022.