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This paper examines the relationships between Russian and other equity markets over the period of 1995-2004. To account for potential instability in the market relationships we apply a number of cointegration approaches: Gregory-H...
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This paper examines the relationships between Russian and other equity markets over the period of 1995-2004. To account for potential instability in the market relationships we apply a number of cointegration approaches: Gregory-Hansen [1996. Residual-based tests for cointegration in models with regime shifts. Journal of Econometrics 70, 99-126] test, which allows for a structural break in the relationships, a stochastic cointegration framework by McCabe [2003. Testing for Stochastic Cointegration and Evidence for Present Value Models. Working Paper], the non-parametric test by Breitung [2002. Nonparametric tests for unit roots and cointegration. Journal of Econometrics 108(2), 343-363] and a regime-switching cointegration model in the spirit of Ho [1999. Financial liberalization and international capital mobility of Taiwan: a regime-switching approach. Asian Economic Journal 13(4), 407-417]. The tests point to a significant agreement that the Russian equity market remained isolated from the influence by international markets in the long run and that while a structural break might have occurred in August 1998 this did not alter the nature of long-run relationships.
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We examine the relationship between stock liquidity and returns before, during and after the 2007-2009 financial crisis. We obtain evidence of a positive association for Germany and the UK, whereas China exhibits the opposite resu...
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We examine the relationship between stock liquidity and returns before, during and after the 2007-2009 financial crisis. We obtain evidence of a positive association for Germany and the UK, whereas China exhibits the opposite result and the US provides inconclusive evidence. Crown Copyright (C) 2019 Published by Elsevier B.V. All rights reserved.
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We assess how characteristics of product and forward markets affect levels and volatilities of commodity spot prices. We examine (i) how product market structure and forward market trading affect spot market games, (ii) the links ...
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We assess how characteristics of product and forward markets affect levels and volatilities of commodity spot prices. We examine (i) how product market structure and forward market trading affect spot market games, (ii) the links between product market structure and spot price stability, (iii) whether forward trading destabilizes spot prices, and (iv) how information arrival affects price volatility and the volume of trade. We find that market structure models of the price level but not of price stability receive support, that increased forward trading leads to lower prices, and that the relationship between trading and price instability is indirect via a common causal factor.
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Based on the different research approaches, econophysics can be divided into three directions: empirical econophysics, computational econophysics, and experimental econophysics. Because empirical econophysics lacks controllability...
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Based on the different research approaches, econophysics can be divided into three directions: empirical econophysics, computational econophysics, and experimental econophysics. Because empirical econophysics lacks controllability that is needed to study the impacts of different external conditions and computational econophysics has to adopt artificial decision-making processes that are often deviated from those of real humans, experimental econophysics tends to overcome these problems by offering controllability and using real humans in laboratory experiments. However, to our knowledge, the existing laboratory experiments have not convincingly reappeared the stylized facts (say, scaling) that have been revealed for real economic/financial markets by econophysicists. A most important reason is that in these experiments, discrete trading time makes these laboratory markets deviated from real markets where trading time is naturally continuous. Here we attempt to overcome this problem by designing a continuous double-auction stock-trading market and conducting several human experiments in laboratory. As an initial work, the present artificial financial market can reproduce some stylized facts related to clustering and scaling. Also, it predicts some other scaling in human behavior dynamics that is hard to achieve in real markets due to the difficulty in getting the data. Thus, it becomes possible to study real stock markets by conducting controlled experiments on such laboratory stock markets producing high frequency data.
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Problem statement: This study uses daily data from the Tehran Stock Market (TSM) to illustrate the nature of stock market volatility in an undeveloped and young stock market. Although most studies suggest that a negative shock to ...
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Problem statement: This study uses daily data from the Tehran Stock Market (TSM) to illustrate the nature of stock market volatility in an undeveloped and young stock market. Although most studies suggest that a negative shock to stock prices will generate more volatility than a positive shock of equal magnitude but there is no evidence of asymmetric effect in TSM. Determine the nature of stock market volatility in an oil exporting country. Approach: Trading in Tehran Stock Market (TSM) is based on orders sent by the brokers. The data consist of 2375 daily observations of the closing value of the Tehran stock market from 3/30/1998 to 5/04/2007. Our empirical finding shows that the unconditional variance is 0.18 but visual inspections of the time series suggests that volatility of the stock return rate displays the clustering phenomenon associated with GARCH processes. Results: The estimation and test results for all models suggest that the leverage effect term, γ, is not significant at 5% level. Although, in Asym. CARCH model based on normal distribution for errors, the estimated coefficient on the asymmetry term is -0.066 with a z-statistics of -1.749 recognized as significant at 10% level, but it has the wrong sign. It seems that good news and bad news has the same effect on stock prices in TSM, a result that is contradictory to other studies for developed countries. Conclusion: The estimated models containing TARCH, EGARCH, asymmetric CARCH and PARCH with different assumptions on error distributions suggest no strong and significant asymmetric effect. There are some reasons for this finding: (1) In Iran with Islamic laws, debt contracts are illegal or at least not enforced and Iranian firms do not have any financial leverage. As a result, we would expect to find smaller leverage effects in volatility in Iran than in the United States, for example. In deed the institutional differences with western financial markets manifest themselves in different return characteristics. (2) Stock prices in the TSM by regulation and intervention cannot exceed from some range. The strong serial correlation in returns necessitating long lags in the mean equations is possibly due to such regulations. (3) The history of TSM is very short compared to other stock markets and the information flow in this market is very slow. The estimated coefficients on the expected risk (as a measure of the risk-return tradeoff) are not significant. These findings suggest that the TSM is not efficient.
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Stock Market Forecasting (SMF) has become a spotlighted area and is receiving increasing attention due to the potential that investment returns can generate profound wealth. In the past, researchers have made significant efforts t...
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Stock Market Forecasting (SMF) has become a spotlighted area and is receiving increasing attention due to the potential that investment returns can generate profound wealth. In the past, researchers have made significant efforts to forecast the stock market trends and predict the best time to buy, sell, or hold. The essence of past investigators' various techniques and methods was to maximise the abundant opportunities that abound in the stock market trading and amass huge wealth from it. Over the years, no scientometric review has been conducted to scientifically map out the trends, progress, and limitations in the subject area. In this regard, this paper presents a pioneering scientometric review in SMF. It investigates a total of 220 reputable articles (2001-2021) to identify trends and patterns in stock market forecasting studies. VOSviewer software was used to conduct science mapping analysis. Actionable insights from the analysis explain significant metrics such as the top research outlets, most-cited articles, most co-occurred keywords, most influential countries, and much more. More so, a key finding in this paper is the introduction of a less computational approach that has the possibility of making a better forecast. Yet, past researchers have not thoroughly explored this option. This paper is beneficial to Early Stage Researchers (ESR), governments, funding bodies, managers, analysts, financial enthusiasts, practitioners, and investors, so as to understand the current progress and focus areas in stock market prediction.
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Purpose - The purpose of this paper is to examine what happens to the variance of individual stocks forming the Dow Jones Industrial Average (DJIA) allowing for aggregate uncertainty measured by (ⅤⅨ), the "fear gauge index" of U...
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Purpose - The purpose of this paper is to examine what happens to the variance of individual stocks forming the Dow Jones Industrial Average (DJIA) allowing for aggregate uncertainty measured by (ⅤⅨ), the "fear gauge index" of US options contracts. In examining each individual stock belonging to DJIA in 2011, the authors reconsider aggregate market uncertainty ((ⅤⅨ)) as the mixing variable. In contrast to studies on the effects of (ⅤⅨ) on the aggregate equity market, the data set used in this paper allow a further look at the proposition that market aggregate uncertainty should have varying impact on individual stock variance. Design/methodology/approach - GARCH-M models estimate individual stock returns belonging to the DJIA in 2011 on its lags and on the ARCH-M term in the mean equation linking stock returns to the variance equation. The longest time span has 5,738 observations for most stocks under daily frequency from January 3, 1990 to December 30, 2011. The authors use one lag for the VTX2 term to address simultaneity problems in the variance equation. In order to allow for interactions between volatility and business cycles, the authors include a dummy variable for the three recessions identified by the NBER over the period. Findings - Adding the "fear gauge" (ⅤⅨ) index and a dummy variable for recessions to the variance equation in GARCH-M models, the VDC coefficient always increases variance and the recession dummy has mixed effects. Overall, (ⅤⅨ) acts as expected as mixing variable. Supporting the mixture of distribution hypothesis, the impact of VDC is always positive (1.039 on market variance) and GARCH effects vanish completely for the index and almost as much for 24 stocks. Research limitations/implications - In theory, the effects of VDC on stock variance should be positive and statistically significant, together with reductions of GARCH persistence. The authors find this to be the case for the aggregate stock market and for 24 out of its 29 DJIA stocks. The authors leave for further work extensions to estimating the variance equation for companies very exposed to idiosyncratic changes, such as oil price fluctuations or stock buybacks. The implication of this research for the academic or financial community relies on the estimation of VDC effects on individual stock variance, controlling for business cycles. Originality/value - Due to its benchmark in equities, stocks in the Dow Jones Industrials make it a very interesting case study. This paper reconsiders the aggregate uncertainty hypothesis for two main reasons. First, the financial press and traders keep a very close track on the daily evolution of (ⅤⅨ). Second, recent research emphasizes the formal predictive power of VDC in US stock markets. For the variance equation, existing works report positive values for the VDC-coefficient on the S&P 500 index but they have not examined individual stocks as the authors do in this paper.
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Purpose - The paper aims to study the impact of the introduction of Nifty index futures on the volatility of the Indian spot markets by use of econometric models.
Design/methodology/approach - The study considered six measures of...
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Purpose - The paper aims to study the impact of the introduction of Nifty index futures on the volatility of the Indian spot markets by use of econometric models.
Design/methodology/approach - The study considered six measures of volatility, the dynamic linear regression model, and the GARCH models to investigate volatility in National Stock Exchange (NSE) Nifty prices both before and after the onset of futures trading.
Findings - The GARCH analysis confirmed no structural change after the introduction of futures trading on Nifty, and found that whilst the pre-futures sample was integrated, the post-futures sample was stationary. Spot returns volatility is found to be less important in explaining spot returns after the advent of futures trading in NSE Nifty.
Practical implications - The results imply that futures markets serve their prescribed role of improving pricing efficiency and improve the quality of information flowing to spot markets. This will enable investors to prudently structure their strategies investing in both spot and futures markets. Originality/value - This study is an original piece of work towards exploring the impact of the introduction of futures trading on cash market volatility in an emerging economy like India.
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This paper analyses the effects of stock market turnover and liquidity, as measures of financial deepening, on stock market returns in selected :9 developed and 21 developing countries over 1988-2014 by implementing Pedroni's pane...
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This paper analyses the effects of stock market turnover and liquidity, as measures of financial deepening, on stock market returns in selected :9 developed and 21 developing countries over 1988-2014 by implementing Pedroni's panel cointegration methodology and panel vector error-correction models. Stock market turnover contributes more to stock market returns than stock market liquidity in both selected developed and developing economies. However, the results are much weaker for developing countries than for developed countries.
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Share repurchases are done in order to utilize the free cash reserves of the company. The company may have two options, one is to pay dividends and another is to retain its earnings for future growth. But sometimes, instead of giv...
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Share repurchases are done in order to utilize the free cash reserves of the company. The company may have two options, one is to pay dividends and another is to retain its earnings for future growth. But sometimes, instead of giving out dividends which attract lot of tax, the company decides to go in for a share repurchase. So basically share repurchase is a way to distribute dividends to shareholders. The other prominent reason for a company to announce a share repurchase is undervaluation. The company management feels that the market is undervaluing the company and as a resort to correct this valuation, shares are repurchased at a premium to market price and in most cases, subsequently cancelled out. There are several methods of share repurchase. One of the methods of share repurchase is through tender offers. This study tries to understand the impact of the announcement of repurchase offer through tender offer and its impact on the share price of the tendering company. In this study, it was found that there were abnormal negative returns for the shareholders after the closure of repurchase. It was also found that there were no abnormal returns to the shareholders pre and post announcement of the repurchase programme. In comparing the differences in the returns of the three time periods (namely before announcement, after announcement and post closure of announcement), repeated measures ANOVA was used and it was found that there was no significant difference between the returns to shareholders in the three periods. The returns to the shareholders was affected by the number of shares repurchased by the company but not so much by the amount of money spent by the company in repurchasing the shares. It was also found that the returns to shareholders for tender offer companies are significantly different from the returns to shareholders of companies opting for an open market repurchase.
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