Search results

The main objective of this study is to obtain new empirical evidence on non-synchronous trading effects through modelling the predictability of market indices.

The authors test for lead-lag effects between the Indian Nifty and Nifty Junior indices using Pesaran–Timmermann tests and Granger-Causality. Then, a simple test on overnight returns is proposed to infer whether the observed predictability is mainly attributable to non-synchronous trading or some form of inefficiency.

The evidence suggests that non-synchronous trading is a better explanation for the observed predictability in the Indian Stock Market.

The indication that non-synchronous trading effects become more pronounced in high-frequency data suggests that prior studies using daily data may underestimate the impacts of non-synchronicity.

The originality of the paper rests on various important contributions: overnight returns is looked at to infer whether predictability is more attributable to non-synchronous trading or to some form of inefficiency; the impacts of non-synchronicity are investigated in terms of lead-lag effects rather than serial correlation; and high-frequency data is used which gauges the impacts of non-synchronicity during less active parts of the trading day.

Reviews previous research based on event study methodology, pointing out that events can influence returns in many ways, and applies the method to a sample of mergers and acquisitions in the thinly traded Norwegian market 1983‐1994. Explains how the classic market model can be adjusted to control for non‐synchronous trading and changing/asymmetric volatility; and how the event and non‐event periods can be combined into a single model. Applies two different models to the data, compares the results and finds the ARMA‐GARCH approach superior to the OLS. Discusses the implications of this for researchers.

The purpose of this paper is to examine the stock price movements for existence of informed trading prior to a merger announcement for the companies listed on the emerging markets of India for the period from 1996 to 2010.

This study applies several event study methodologies and regression analyses to analyze the stock price movement surrounding a merger announcement. The paper divides mergers in two different types: industry merger cases and non-industry merger cases and in two different time periods: recession and boom.

The results show that the information held only by insiders’ works its way into prices. The paper finds strong evidence of insider trading in the case of industry mergers and mergers during recessions.

The results from this study have immediate policy implications for India and other developing markets as the paper provides the type of mergers and time periods when merger announcements are more susceptible to insider trading.

The paper extends the literature on mergers and insider trading by analyzing firms trading on a developing capital market, which, unlike the developed markets, is characterized by inadequate disclosure and a weaker enforcement of securities regulations. The results support this notion and recommend Indian securities market regulators to tighten the lax regulations. In addition, the author document the divergence in price reaction to the merger announcements for different types of mergers: industry mergers and non-industry mergers, as well as for mergers during different market conditions: recession vs booming capital markets.

Prior work claims that the CAPM is mis‐specified based on evidence that beta and idiosyncratic variables such as size, book‐to‐market, and price‐earnings ratios combine to explain average cross‐sectional variation in stock returns. This paper set out to reexamine this research by employing more rigorous statistical methods to control for beta shifts.

TIMVAR program is employed to control for risk shift. Clean betas are used to reexamine the methods used in Fama and French (1992).

The paper shows that, even if the CAPM generates expected returns, the mentioned idiosyncratic variables may be correlated with expected returns. Moreover, if beta is measured with error, then it is possible for idiosyncratic variables to enter as explanatory variables. Evidence is provided indicating that, as measurement error in beta is reduced, then the role of beta in explaining cross‐sectional returns increases, while the role of idiosyncratic variables diminishes.

Beta continues to be a significant variable to measure the risk of securities.

This paper employs a novel approach (TIMVAR program) to detect and control for structural changes in linear regression. This approach has been widely used in quality control by industries. The audience of the paper includes both academics and practitioners.

The purpose of this paper is to estimate the Beta Risk Coefficient of 32 listed companies (shares), which are included in the Ghana Stock Exchange (GSE All Share Index).

This research investigated some of the issues that can affect beta estimates (the measurement of returns, the choice of market index used, the length of estimation period, the sampling interval, the issue of normality, autocorrelation, the effect of thin trading, seasonality and stability) by using 32 listed companies. The methodology used was the Market model and some of the beta estimation techniques used included Scholes‐Williams' beta, Dimson's beta, and Fowler‐Rorke's beta.

The empirical results generally confirm the evidence by various researchers in the literature reviewed. However, the tests for the effect of thin trading and the effect of seasonality reject the null hypothesis (H_o: β_Monday=β_Tuesday=β_Wednesday=β_Thursday=β_Friday).

The study has about 95 per cent originality since the authors went into the field to gather all the data needed and did all the analysis.

This paper aims to assess, from an empirical perspective, the research question if public media reports which relate concrete banks to concrete allegations of money laundering have an adverse impact on banks stock prices and what are the drivers of such impact?

The paper makes use of event study methodology and uses the constant mean and the market model. The event window is calibrated towards a five-day window, and the estimation window has a length of 90 days, in line with best academic practices. Drivers are identified by correlation analysis. and the market model uses ordinary least squares regression.

The application of event study methodologies yields the results that stock prices of affected banks generate, at the date of the news appearance, statistically significant negative abnormal returns under both the market model and the constant mean model. As negative abnormal returns have been mainly found at the date of the event itself, the findings confirm that the impacts of money laundering may be severe but short natured. In addition, the paper finds that the identified negative abnormal returns may be driven by the banks’ size in terms of total assets, by the bank’s profitability in terms of return on assets and by the bank’s sustainability risk.

The findings have implications in terms of banking and supervisory practices. In specific, the findings help to argue that banking consolidation is needed to lower the impacts of AML cases, as stock prices of larger banks show less sensitivity. In addition, the findings could be used to determine financial sanctions against banks violating AML regulation. Finally, the findings imply that AML news can have severe and fast-moving financial stability considerations and are, therefore, important in crisis situations.

As there appears to be no substantial research that applies event study methodology to the money laundering context, the combination of research question and methodology has an innovative character. In addition, there is no clear literature on media and money laundering.

Though stock portfolio return autocorrelation is well documented in the literature, its cause is still not clearly understood. Presently, evidence of private information induced stock return autocorrelation is still very limited. The difficulty in obtaining foreign country information by small investors makes the private information of institutional investors in the ADR (American Depository Receipt) market more significant and influential. As such, the ADR market provides a favorable environment for testing the effect of private information on return autocorrelation. The purpose of this paper is to address this issue.

In this paper, ADRs are sorted annually into three groups based on market equity capitalization. Within each capitalization group, ADRs are further sorted into three groups based on the fraction of shares held by institutional investors. Each ADR is assigned to one of the nine groups and group membership is rebalanced each year. The return autocorrelation of individual ADR securities and ADR portfolios for each group are then calculated.

The results demonstrate that ADR individual stock and portfolio daily return autocorrelations are positively related to institutional ownership. It is also found that other explanations, such as non‐synchronous trading, bid‐ask spread and volatility of ADR, cannot explain the positive relation between daily return autocorrelations and institutional ownership of ADR.

Since ADR market is more suitable than other markets for testing the role of private information, stronger and clearer results are got accordingly. This paper suggests that trading strategy based on private information of institutional investors can lead to stock return autocorrelation in ADR daily returns.

A number of Chinese firms have dual-listed in USA and China. The US listing takes the form of American Depositary Receipts (ADRs) whereas the China listing in the form of A-shares. Though ADRs and their underlying A-shares lack full fungibility due to regulatory constraints, they nevertheless represent the same claiming rights and hence should be affected by the same fundamentals or news. The purpose of this paper is to examine the mutual return influences between ADRs and A-shares of dual-listed Chinese firms, and whether and how the recent global financial crisis has altered the mutual feedback dynamics.

The paper uses the bivariate VAR approach to model the returns of ADRs and A-shares. The model is jointly estimated with the three-stage least squares (3SLS) method. It also accounts for the non-synchronous trading problem caused by the fact that the Chinese and US markets are located in different time zones and that the two market observe different national and religious holidays.

The authors find significant mutual return transmissions between ADRs and their A-share counterparts. In the absence of local market sentiments, the return transmission is more prevalent going from USA to China than it is the other way around. After the market factors are included in the models, the information flows between A-shares and ADRs become stronger and bidirectional. Additionally, both ADR and A-share returns are strongly affected by the market sentiment of the marketplace where they are traded. Lastly, the authors find evidence showing that the recent global financial crisis has enhanced the linkage between ADRs and their underlying A-shares.

This paper adopts a more rigorous approach to overcome the potential issue caused by non-synchronous trading. It investigates how the global financial crisis has altered the ADR and A-share return feedback dynamics.

It is known that the National Pension Service (NPS) of Korea contributes to the market stability because it tends to pursue the negative feedback trading strategy in the Korean stock market. While many studies deal with institutional investors’ trading in the financial derivatives market, the NPS’s trading in the derivatives market is rarely studied. Using the NPS’s trading data for the period from January 2010 to March, 2020, the authors examine the transactions of the NPS in the KOSPI200 futures market. We find that the NPS’s net investment flow (NIF) in KOSPI200 futures is negatively associated with the past returns of KOSPI200 futures and the KOPI200 index. However, we also find that the NPS’s NIF of KOSPI200 futures is positively associated with its NIF in KOSPI200 stocks. Along with the legal restriction on the NPS’s trading in the derivatives market, the result suggests that the NPS uses KOSPI200 futures to deviate the problems related to non-synchronous trading in the spot market. To the best of our knowledge, this paper is the first study of the NPS’s transactions of KOSPI200 futures. The paper suggests that the NPS does not trade KOSPI200 futures for hedging or arbitrage profit but for complementing its transactions in the spot market of KOSPI200 stocks.

The main purpose of this paper is to investigate autocorrelation structure of stock and portfolio returns in a unique market setting of Saudi Arabia, where nearly all active traders are the retail individuals and the market operates under severe limits to arbitrage. Specifically, the authors examine how return autocorrelation of Saudi Arabian stock market is related to factors such as the day of the week, stock trading, performance on the preceding day and volatility.

The sample consists of the daily stock price and index data of 159 firms listed in Tadawul (Saudi Arabian Stock Exchange) for the period from January 2004 through December 2015. The methodology of Safvenblad (2000) is primarily used to investigate the autocorrelation structure of individual stock and index returns. The authors also use the Sentana and Wadhwani (1992) methodology to test for the presence of feedback traders in the Saudi stock market.

Results show that there is significantly positive autocorrelation in individual stock, size portfolio and market returns and that the last two are almost always larger than the first. Return autocorrelation is negatively related to firm size. Interestingly, return autocorrelation is positively related to trading frequency. For portfolios, autocorrelation of returns following a high absolute return day is significantly higher than that following a low absolute return day. Similarly, return autocorrelation during volatile periods is generally larger than that during tranquil periods. Return correlation between weekdays is usually larger than that between the first and last days of the week. Overall, the results suggest that the possible reason for positive autocorrelation in stock returns could be the presence of negative feedback traders who are engaged in frequent profit-taking activities.

This is the first paper that thoroughly investigates the autocorrelation structure of the returns of the Saudi stock market using both index and individual stock returns. As this US$583bn (as of August 21, 2014) market opened to foreign institutional investors in June 2015, the results of this paper should be of significant value for the potential uninformed foreign investors in this relatively lesser known and previously closed yet highly prospective market.