Stock Market Margin Requirements

The use of margin requirements as a potential policy tool for controlling sudden stock price changes and market volatility has attracted considerable attention in the developed stock markets. However, no consensus has emerged regarding the significance of the impact of changes in margin requirement of the stock market. The existing analyses are extended to test the impact of changes in margin requirements on stock prices in an emerging stock market. Using recent data from the Stock Exchange of Thailand, margin requirements are demonstrated to have an important impact on the behaviour of stock prices. Increases (decreases) in margin requirements usually lead to a downward (upward) trend in stock prices. An interesting aspect of the result is the apparently asymmetric nature of the response of stock prices to changes in margin requirements. The market appears to respond immediately and forcefully to a decrease in the margin requirements; whereas the response to an increase in margin requirements is much slower and weaker.

References and Further Reading

Macroeconomic Impact on Stock Market Returns and Volatility: Evidence from Sri Lanka

This article examines the brand-name effect of airline crashes. These disasters raise several financial issues. A crash represents the loss of a plane, the loss of lives, the loss of scheduling capacity, higher insurance costs, and the potential loss of consumer goodwill. This article focuses on the last of these. Are consumers reluctant to fly with airlines that have poor safety records or do they treat crashes merely as random events that bear no reflection on the quality of the airline?

This paper examines the dynamic and switching effects of volatility spillovers arising from US stock market returns and GDP growth on those of Australia, Canada and the UK. For this purpose, we use quarterly data (1961q1–2013q1) and a constant probability Markov regime switching model. We found that the US stock market volatility significantly affects the stock market volatility of all three countries at least in one of the two specified regimes over time. However, the stock market volatilities in none of the three countries are contemporaneously influenced by the US output volatility even after allowing for two distinct regimes. On the other hand, the US stock market volatility exerts significant influences on the output volatilities of both Australia and the UK. Compared with Australia and the UK, Canada and the US show substantial output volatility co-movements, thereby confirming the close association between the two neighbouring economies through the NAFTA (North American Free Trade Agreement). We conclude that shocks emanating from the US stock market have unequivocal flow-on effects on the output and return volatilities of the other economies.

The business cycle analysis of Arthur F. Burns and Wesley C. Mitchell and the National Bureau of Economic Research presumed that aggregate economic variables evolve on a time scale defined by business cycle turning points rather than by months or quarters. Do macroeconomic variables appear to evolve on an economic rather than a calendar time scale? Evidence presented here suggests that they do. However, the estimated economic time scales are only weakly related to business cycle time scales, providing evidence against the view underlying traditional business cycle analysis. Copyright 1987 by University of Chicago Press.

Using daily prices of indexed bonds between 1970 and 1979, we test whether announcements of the Israeli CPI contain information that is not already reflected in bond prices. The results indicate that bond prices reflect about 85 percent of the new information about inflation as it occurs (i.e., when the Central Bureau of Statistics samples prices). The announcement of the CPI 15 days after the end of the sampling period causes the remaining 15 percent adjustment in bond prices. This evidence raises questions about the empirical importance of misperceptions about inflation as a source of nonneutrality in monetary policy.

By means of a data set previously unavailable for academic research, actual trading histories of individual futures traders are examined. With this more detailed data, the author is able to (1) test the risk/return hypothesis directly; (2) include a much larger segment of the market than before; and (3) use actual instead of hypothetical t rading strategies. It is shown that the commercial (hedging) traders are most profitable, while noncommercial (speculative) traders earn n egative or zero profits. Because speculators are not receiving reward s for the risks they willingly absorb, the theory of normal backwarda tion and its extension can be rejected. Copyright 1987 by University of Chicago Press.

Purpose This paper aims to use three proxy variables – initial public offerings, trading volume and business confidence index (BCI) to construct an investor sentiment index both for the USA and China, taking into account the challenging periods of the COVID-19 pandemic and the Russo-Ukrainian conflict. Design/methodology/approach Based on monthly data for a period from January 2009 to June 2022, this paper uses data of BCI, consumer confidence index (CCI), gross domestic product, trading volume and Fama and French (1993) factor data; linear regression of single and multifactor model; and EGARCH-M model for analyzing the effect of investor sentiment on stock market return and volatility, both in the USA and China. Findings The empirical results indicate the suitability of BCI over CCI as a measure of investor sentiment, both in the USA and China. The results indicate that investor sentiment has a significant positive effect on the excess returns in the stock market in both countries. Moreover, the effect of investor sentiment is higher in China than it is in the USA. Such an effect of investor sentiment is significant and fluctuates asymmetrically in the short run but loses its significance in the long run. Optimistic investor sentiment has a larger effect on the stock market volatility in the USA, while the pessimistic investor sentiment has a larger effect in the Chinese stock market. Originality/value This paper focuses on finding a more suitable proxy for investor sentiment from BCI or CCI. This paper also contributes by including both optimism and pessimism in explaining the stock return and volatility in both markets. The overall findings are important for understanding investor behavior in different market conditions.

In the field of business research method, a literature review is more relevant than ever. Even though there has been lack of integrity and inflexibility in traditional literature reviews with questions being raised about the quality and trustworthiness of these types of reviews. This research provides a literature review using a systematic database to examine and cross-reference snowballing. In this paper, previous studies featuring a generalized autoregressive conditional heteroskedastic (GARCH) family-based model stock market return and volatility have also been reviewed. The stock market plays a pivotal role in today’s world economic activities, named a “barometer” and “alarm” for economic and financial activities in a country or region. In order to prevent uncertainty and risk in the stock market, it is particularly important to measure effectively the volatility of stock index returns. However, the main purpose of this review is to examine effective GARCH models recommended for performing market returns and volatilities analysis. The secondary purpose of this review study is to conduct a content analysis of return and volatility literature reviews over a period of 12 years (2008–2019) and in 50 different papers. The study found that there has been a significant change in research work within the past 10 years and most of researchers have worked for developing stock markets.

This paper investigates the impact of capital structure on firm performance using a sample of 3,122 observations of 446 non-financial listed companies on the Vietnam stock market during 2011-2017. Using firm performance measures, namely ROE and Tobin Q, we examined if higher leveraged firms are more efficient or less in their performance. We employed the fixed effect model to prove that there is an inverse U-shaped relationship between leverage and ROE, and then we can find a preferred capital structure for Vietnam non-financial firms. To deal with endogeneity problem of the leverage variable, we employ two stage least squares (2SLS) regression with instrument variable estimators, which helps us strengthen the above results. Keywords Capital structure, firm performance, leverage, efficiency, instrument variable estimator, agency cost theory References [1] J. Abor, The effect of capital structure on profitability: an empirical analysis of listed firms in Ghana, Journal of Risk Finance. 6 (2005) 438-447.[2] M. Baker, J. Wurgler, The Determinants of Capital Structure: Capital Market-Oriented versus Bank-Oriented Institutions, Journal of Financial and Quantitative Analysis. 43 (2002) 59-92.[3] A.N. Berger, E. Bonaccorsi di Patti, Capital structure and firm performance: A new approach to testing agency theory and an application to the banking industry, Journal of Banking and Finance. 30 (2006) 1065-1102.[4] J. Berk, P. DeMarzo, J. Harford, Fundamentals of Corporate Finance, second edition, Prentice Hall, 2012.[5] A.A. Berle, G.C. Means, The Modern Corporation and Private Property, New York: The Macmillan Company, 1932.[6] V. Dawar, Agency theory, capital structure and firm performance: Some Indian evidence. Managerial Finance. 40(12) (2014) Số trang.[7] T.H.V. Duong, A study of the factors affecting the capital structure of the companies listed on Vietnam stock market, Doctoral thesis - National Economics University, 2014.[8] S.J. Grossman, O.D. Hart, Corporate financial structure and managerial incentives, In M. J.J., The economics of information and uncertainy, University of Chicago Press, 1982.[9] M. Jensen, Agency cost of free cash flow, corporate finance and takeovers, American Economic Review Papers and Proceedings. 76 (1986) 323-329.[10] J.C. Jensen, W.H. Meckling, Theory of the firm: managerial behavior, agency costs and ownership structure. Journal of Financial Economics. 3 (1976) 305-360.[11] A. Kraus, R.H. Litzenberger, A State-Preference Model of Optimal Financial Leverage, Journal of Finance. Tập (1973) 911-922.[12] D. Margaritis, M. Psillaki, Capital structure and firm efficiency, Journal of Business Finance and Accounting. 34 (2007) 1447-1469.[13] D. Margaritis, M. Psillaki, Capital structure, equity ownership and firm performance, Journal of Banking and Finance. 34 (2010) 621-632.[14] F. Modigliani, M. Miller, The cost of capital, corporation finance, and the theory of investment, American Economic Review. 48 (1958) 655-669.[15] F. Modigliani, M. Miller, Corporate income taxes and the cost of capital: A correction, American economic Review. Tập (1963) 433-443.[16] S.C. Myer, Determinants of corporate borrowing, Journal of Financial Economics. Tập (1977) 147-175.[17] S.C. Myers, N.S. Majluf, Corporate Financing and Investment Decisions When firms have information that investors do not have, Journal of financial economics. 13 (1984) 187-221.[18] B. Seetanah, K. Seetah, K. Appadu, K. Padachi, Capital structure and firm performance: evidence from an emerging economy, The Business and Management Review. 4 (2014) 185-196.[19] S. Titman, R. Wessels, The determinants of capital structure choice. Journal of Finance. 43(1) (1988) 1-19.[20] L. Weill, Leverage and Corporate Performance: Does Institutional Environment matter? Small Business Economics. 30 (2008) 251-265.[21] J. Williams, Perquisites, risk and capital structure, Journal of Finance. 42 (1987) 29-49.[22] R. Zeitun, M.M. Haq, Debt maturity, financial crisis and corporate performance in GCC countries: a dynamic-GMM approach, Afro-Asian J. Finance and Accounting. 5 (2015) 231-247.

PurposeThis study aims to investigate the conditional volatility of the Asian stock market concerning Bitcoin and global crude oil price movement.Design/methodology/approachThis study uses the newest Dynamic Conditional Correlation (DCC)-Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to examine the conditional volatility of the stock market for Bitcoin and crude oil prices in the Asian perspective. The sample stock market includes Chinese, Indian, Japanese, Malaysian, Pakistani, Singaporean, South Korean and Turkish stock exchanges, with daily time series data ranging from 4 April 2015−31 July 2023.FindingsThe outcome reveals the presence of volatility clustering on the return series of crude oil, Bitcoin and all selected stock exchanges of the current study. Secondly, the outcome of DCC, manifests that there is no short-run volatility spillover from crude oil to the Malaysian, Pakistani and South Korean and Turkish stock markets, whereas Chinese, Indian, Japanese, Singapore stock exchanges show the short-run volatility spillover from crude oil in the short run. On the other hand, in the long run, there is a volatility spillover effect from crude oil to all the stock exchanges. Thirdly, the findings suggest that there is no immediate spillover of volatility from Bitcoin to the stock markets return volatility of China, India, Malaysia, Pakistan, South Korea and Singapore. In contrast, both the Japanese and Turkish stock exchanges exhibit a short-term volatility spillover from Bitcoin. In the long term, a volatility spillover effect from Bitcoin is observed in all stock exchanges except for Malaysia. Lastly, based on the outcome of conditional variance, it can be concluded that there was increase in the return volatility of stock exchanges during the period of the COVID-19 pandemic.Research limitations/implicationsThe analysis below does not account for the bias induced due to certain small sample properties of DCC-GARCH model. There exists a huge literature that suggests other methodologies for small sample corrections such as the DCC connectedness approach. On the other hand, decisive corollaries of the conclusions drawn above have been made purely based on a comprehensive investigation of eight Asian stock exchange economies. However, there is scope for inclusive examination by considering other Nordic and Western financial markets with panel data approach to get more robust inferences about the reality.Originality/valueMost of the empirical analysis in this perspective skewed towards the Nordic and Western countries. In addition to that many empirical investigations examine either the impact of crude oil price movement or Bitcoin performance on the stock market return volatility. However, none of the examinations quests the crude oil and Bitcoin together to unearth their implication on the stock market return volatility in a single study, especially in the Asian context. Hence, current investigation endeavours to examine the ramifications of Bitcoin and crude oil price movement on the stock market return volatility from an Asian perspective, which has significant implications for the investors of the Asian financial market.

The stock price volatility has been a problem in stock markets affecting the stock returns of firms listed at Nairobi Securities Exchange (NSE). Although some scholars have focused on the issue of high share price volatility, the issue persists and still hinders the development of the market. This study main objective is to determine the effects of macroeconomic factors on volatility of stock prices of stock market in Kenya. The specific objectives are; to establish the effects of inflation on stock prices of stock market in Kenya; to establish the effects of interest rates on volatility of stock prices of stock market in Kenya; and to establish the effects of exchange rates on stock prices of stock market in Kenya. The research was anchored on four theories and two models namely the Arbitrage pricing theory, Modern portfolio theory, information cascade theory, Fisher’s theory, Efficient market hypothesis and present value model. The research applied a causal research design, which followed the positivism philosophy of research and knowledge creation. The study focused on analyzing the effects of macroeconomic factors on volatility of stock prices of the NSE 20 share index in Nairobi Securities Exchange over a period ranging from 2009 to 2021. The design allowed collection of monthly data on the study variables and time series analysis of the hypothesized relationships between independent and dependent variables and conducted both descriptive statistical analysis and inferential statistical analysis. Descriptive statistical analysis involved generation of central tendency statistics such as mean, minimum and maximum points of the data as well as standard deviations to show the spread of the data from the mean. The inferential statistical analysis utilized in this study was Error Correction Model, guided by the results of cointegration tests. From the descriptive statistical analysis, the study found that between 2009 and 2021, interest rates demonstrated an average of approximately 9.155, inflation averaged around 150.916, foreign exchange rate maintained an average of roughly 94.219, and stock price volatility averaged 0.4%. The Error Correction Model revealed that, interest rates have no significant effect on stock price volatility of NSE 20 share index (β = -0.00034, p.value = 0.805). Similarly, inflation has no significant effect on stock price volatility of NSE 20 share index (β = -0.0000394, p.value = 0.218). However, foreign exchange has negative and significant effect on stock price volatility in Kenya (β = -0.0139, p.value = 0.017). The study concluded that interest rates and inflation have no significant effect on stock price volatility in Kenyan stock market, while foreign exchange has inverse and significant effect on stock price volatility. Thus, the study recommends that market participants, including government, investors, traders, policy makers and financial institutions, should incorporate foreign exchange rate movements into their risk management strategies. Adequate measures and policies such as market circuit breakers and enhanced risk management protocols should be in place to ensure smooth market operations and prevent any disruptions due to changing foreign exchange dynamics

Do Macroeconomic Announcements Cause Asymmetric Volatility?

Purpose: The aim of the study was to assess the effect of interest rate changes on stock market volatility in Congo. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Changes in interest rates have a notable impact on stock market volatility. When central banks adjust interest rates, it influences investor sentiment and economic forecasts. Typically, an increase in interest rates makes borrowing more expensive, which can dampen corporate profits and economic growth, leading to higher market volatility as investors reassess the value of stocks. Conversely, a decrease in interest rates generally lowers borrowing costs, potentially boosting corporate earnings and economic expansion, which can initially reduce market volatility. However, the long-term effects might differ as lower rates can also lead to overvaluation concerns, eventually increasing volatility. Additionally, interest rate changes signal monetary policy shifts and broader economic conditions, further influencing investor behavior and stock market dynamics. Overall, interest rate fluctuations are a critical factor contributing to stock market volatility through their direct and indirect effects on economic activities and investor perceptions. Implications to Theory, Practice and Policy: Efficient market hypothesis, portfolio theory and behavioral finance theory may be used to anchor future studies on assessing the effect of interest rate changes on stock market volatility in Congo. For practical implications, investors and financial institutions should develop risk management strategies that account for the short-term volatility spikes following interest rate announcements. From a policy perspective, policymakers should consider the lagged effects of interest rate changes when formulating monetary policy to ensure smoother market transitions.