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Despite the limitations of its bilateral trade volume and security cooperation with China, Egypt was the first Middle Eastern and Arab country that established a strategic partnership with China in 1999 and the second to build a comprehensive strategic partnership in 2014. This article employs role theory to unpack this seeming paradox, and argues that it is largely because of the compatibility of their national role conceptions that China and Egypt have managed to broaden and deepen their cooperation in the Xi-Sisi era. A thorough examination of the two countries' official documents and a variety of other related literature reveals that China and Egypt are determined to build a comprehensive and complementary partnership with the strategic docking of China's Belt and Road Initiative (BRI) and Egypt's 'Vision 2030' plan. As pivotal states in Asia and the Middle East respectively, China and Egypt aim to reshape the global power structure through the pursuit of multipolarity and solidarity of rising powers. [ABSTRACT FROM AUTHOR]

Financial markets are complex systems, which can be studied under the lens of econophysics, including the study of phenomena such as power laws. We test the hypothesis of a power law in Ibovespa, the main Brazilian stock market index, and cannot dismiss the hypothesis that its stocks are distributed following a power law. This is most interesting, as these stocks are ordered by individual trading volumes, thus a similar pattern always emerges organically even though companies are always exchanging positions and being excluded or added to the index. [ABSTRACT FROM AUTHOR]

The research seeks to identify the impact of exchange rate fluctuations on the most prominent indicators of the Iraqi stock market, such as market value and trading volume in the market, as these variables affect the activity of various productive institutions by providing the necessary funding to carry out new investment projects or sustain ongoing projects, as well as the possibility of confusing their plans and disrupting the achievement of their set goals in the event of inability to provide the necessary funding. The research studied the theoretical aspect of the exchange rate in general and in Iraq in particular, and the market value and volume of trading in the financial markets and the Iraq stock exchange. As for the analytical side, it relied on the annual data of the exchange rate, which is issued in the form of periodic bulletins by the Central Bank of Iraq, and the market value index and trading volume in the Iraqi Stock Exchange in Iraq for the period 2009-2019. The research reached a set of conclusions, the most prominent of which is that there is a long-term two-way reciprocal relationship between the exchange rate and the indicators of the mentioned financial markets in Iraq, despite the existence of an imbalance in the short time, the research also found that there is a possibility to consider the market value and trading volume indicators as general indicators about the direction of future economic activity in any national economy. [ABSTRACT FROM AUTHOR]

Higher consumption and increased import requirements for the South Asian Association for Regional Cooperation (SAARC) region can be catered through neighboring trade partners if resources are optimally utilized. The purpose of this research is to analyze the connection between regional trade of SAARC countries and the food security challenges faced by the region. The study uses data from 1990-2018 for Pakistan, India, Sri Lanka, and Bangladesh to econometrically analyze the determinants of the volume of food trade. The results show that the gross domestic product of importing or exporting countries and foreign direct investment (FDI) have positive impact on regional trade. The bilateral exchange rate between trading partners has a negative impact on the trade volume. The results also showed the absence of a long-run relationship between volume of trade and food security using Johansen's cointegration test. Our analysis suggests that policy makers should focus on the means for creating favorable environment in Pakistan and India to not only meet the increasing global demands for food but also increasing their competitiveness for high-quality and low-quality priced products in major exports markets. [ABSTRACT FROM AUTHOR]

Abstract In an order-driven and strictly regulated stock market, illiquidity risks' effects on asset pricing should be highlighted, particularly in such extreme market conditions as those in China. This paper utilizes panel data from China's stock market in an attempt to answer whether the illiquidity risk in various dimensions—including price impacts, the transaction speed, trading volume, transaction costs, and asymmetric information—can explain stock returns. We find that almost all dimensions of stock illiquidity are positively associated with excess stock returns. More importantly, smaller, less-liquid stocks suffer more liquidity costs, providing a strong evidence for "flight-to-liquidity." Additionally, the transaction costs and asymmetric information, denoted by bid-ask spreads, robustly account for these illiquidity effects on stock pricing and differ from the findings in the U.S. market. We also find that the "flight-to-liquidity" can partially explain the idiosyncratic volatility puzzle, investors' gambling, and herding psychologies. This study provides substantial policy implications in regulation and portfolio management for emerging markets. Highlights • Illiquidity risk in various dimensions positively explains stock returns. • The less-liquid stock suffers more illiquidity cost, showing "flight-to-liquidity". • Bid-ask spreads better suggest the illiquidity's effects on stock returns. • "Flight-to-liquidity" can partly explain idiosyncratic risks and momentum. [ABSTRACT FROM AUTHOR]

Abstract We present a novel approach in the research field of price–trading in stock markets. We consider the effect of trading volume (related with liquidity) on the dynamic dependence of time series, using data from the Portuguese stock market, with both listed and non-listed firms in the main index. We use detrended fluctuation analysis, with a sliding windows approach, to estimate the dynamic dependence of shares. We found two interesting and related features: firms listed in the main index have results more centred at the 0.5 level; exponents' volatility is less in the second half of the sample, after the most severe period of the Eurodebt crisis. Then, we relate this dependence with trading volume, with detrended cross-correlation analysis and the respective correlation coefficient. We found that for firms listed in the main index, some show evidence of negative correlations while for non-listed firms, most of the shares have the same results. We conclude that the lack of liquidity could be related to increased dependence. An interesting feature found is that some firms have a positive correlationship between the variables, but all those firms work with natural resources in their activities, which could mean that other factors influence their dependence patterns. Highlights • A novel approach is presented to analyse the relationship between price and trading. • We used a sliding windows approach, to evaluate dependence evolution. • We make the correlation between (dynamical) dependence and trading volume. • Results show the importance of lack of liquidity in stock market dependence. [ABSTRACT FROM AUTHOR]

Abstract The trading volume in stock markets is known as an important variable which reflects the liquidity of the financial markets and therefore is regarded to be greatly important for the measurement of market liquidity risk. In this work, a new concept called recurrence duration is introduced for study of daily trading volumes, which is inspired by idea of the volatility duration that was proposed and studied in our previous work. The recurrence duration is thought as the shortest passing time that the following days' trading volume takes to exceed or go below the current trading volume which is time-varying. Similar to the volatility duration distribution of the price returns, the power-law function could describe the empirical probability distribution of recurrence durations of trading volumes, and their tail distributions can be fitted by two stretched exponential functions. Further, the correlation relationships of trading volumes between Chinese stock indices as well as the correlations of recurrence durations are investigated. One approach employed is a recently proposed method, time-dependent intrinsic correlation (TDIC), which is based on the empirical mode decomposition (EMD) to decompose nonlinear and nonstationary signals into the intrinsic mode functions (IMFs), the instantaneous periods of which are used then in determination of the sizes of sliding windows to compute the running correlation coefficients for the multiscale signals. The empirical results reveal rich patterns of correlations for both trading volumes and recurrence durations at different scales for different modes. Another approach is the widely-used DCCA cross-correlation coefficient, by which the level of cross-correlation is measured for both original series and IMF modes of the stock indices. Highlights • A concept called recurrence duration is introduced for study of stock trading volumes. • Prob-distributions of recurrence durations for Chinese stock indices are analyzed. • Relationships between Chinese stock indices are investigated from trading volumes and recurrence durations aspects. • The TDIC method and DCCA cross-correlation coefficient are observed a well practical application. [ABSTRACT FROM AUTHOR]

Plenty of literature has proven the existence of local media bias in stock market from the perspective of trading volume. Given to the cross-listings of AH stocks in China, we explore the dynamics relationship between stock prices and media coverage by means of MF-DCCA. We mainly find that H-share prices seem to move more consistently with foreign media coverage in contrast to mainland media coverage, while A-share prices react to different media coverage at the similar intensity. These findings are robust to alternative measurements of returns. Generally speaking, our findings support the existence of local bias in media choosing. [ABSTRACT FROM AUTHOR]

We investigate the dynamics of efficiency and long memory, and the impact of trading volume on the efficiency of returns and volatilities of four major traded currencies, namely, the EUR, GBP, CHF and JPY. We do so by implementing full sample and rolling window multifractal detrended fluctuation analysis (MF-DFA) and a quantile-on-quantile (QQ) approach. This paper sheds new light by employing high frequency (5-min interval) data spanning from Jan 1, 2007 to Dec 31, 2016. Realized volatilities are estimated using Andersen et al.’s (2001) measure, while the QQ method employed is drawn from Sim and Zhou (2015). We find evidence of higher efficiency levels in the JPY and CHF currency markets. The impact of trading volume on efficiency is only significant for the JPY and CHF currencies. The GBP currency appears to be the least efficient, followed by the EUR. Implications of the results are discussed. [ABSTRACT FROM AUTHOR]

Emission trading can not only significantly improve the environment quality, but also save the total social cost. The emission trading market can only overcome the market failure caused by externalities only if it has good transaction efficiency, and the market pricing system is the most direct factor that affects transaction efficiency. This paper constructs a three-party dynamic game model for market maker’s quotation trading to study the forming mechanism of the market maker’s optimal quotation, and constructs an institutional cost model for market maker’s quotation trading to compares the institutional costs of the market maker system. The study found that: (1) The optimal quotation of market makers is affected by marginal information costs, marginal pollution control costs, and initial allocation of emission permits. (2) The high cost of market information, the extremely uneven initial allocation of emission permits, and the free allocation scheme based on historical emissions may all lead to inefficient transactions. (3) When the trading volume is the same, compared with the competitive bidding mechanism, market maker quotes can save social information costs in the large-scale market. The study results of this paper can provide a theoretical basis for the development of China’s emission permits trading. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to investigate the stock market response to dividend announcements in high growth emerging markets of Gulf countries.Design/methodology/approach The sample includes 1,092 dividend announcements from 299 listed firms over the period 2010-2015.Findings In the environment where there is an absence of capital gain and income tax, the authors find some evidence for the stock price reaction that partly supports the signaling hypothesis. The findings show that the Gulf Cooperation Council (GCC) market is inefficient because of the leakage information before the announcement in bad news, and the delay of share price adjustment in good news. In addition, the authors report significant trading volume (TV) reaction in all the three announcements clusters, where dividends increase, decrease, and are constant, lending support to the hypothesis that the dividend change announcements have an impact on the TV response due to different investors’ preferences.Originality/value This is the first empirical paper on market reaction in share price and TV around dividend announcement using data for the majority of GCC countries. [ABSTRACT FROM AUTHOR]

On the negative side, price limits are criticized for increasing stock price volatility and hindering the price discovery process. On the positive side, price limits are argued to give panicky investors additional time to reassess their judgments and thus provide an opportunity for correcting the element of overreaction in pricing stocks. This study analyzes the effectiveness of price limits in Borsa Istanbul by utilizing a propensity-matched control sample in addition to the traditional benchmarks used in the literature. Similar to recent research, we find strong evidence that price limits lead to increased and persistent price volatility and decreased liquidity. We also provide evidence that price limits interfere with the price discovery process. Results show that smaller stocks with larger volatility and higher trading volume are more likely to experience limit hits. Furthermore, the difference in the findings from the matched control sample and the traditional benchmarks points out the importance of accounting for firm- and market-related characteristics when analyzing the effect of price limits. [ABSTRACT FROM AUTHOR]

Empirical market microstructure literature widely employs the non-linear and non-Gaussian multiplicative error class of models (MEMs) in modelling the dynamics of trading duration and financial marks. It routinely maintains the weak exogeneity of duration vis-à-vis marks in estimations. However, microstructure theory states that trade duration, volume and transaction prices are simultaneously determined. We propose Lagrange-multiplier (LM) tests for weak exogeneity for the MEMs. Our LM tests are extensions of the weak exogeneity tests applicable to VAR or VECM models with Gaussian distributions. Empirical assessments show that (i) weak exogeneity is widely rejected by the data in the MEMs and (ii) the failure of weak exogeneity seriously biases parameter estimates. We hope our tests will be of interest in future empirical applications. [ABSTRACT FROM AUTHOR]

The moving average strategy is a technical indicator that can generate trading signals to assist investment. While the trading signals tell the traders timing to buy or sell, the moving average cannot tell the trading volume, which is a crucial factor for investment. This paper proposes a fuzzy moving average strategy, in which the fuzzy logic rule is used to determine the strength of trading signals, i.e., the trading volume. To compose one fuzzy logic rule, we use four types of moving averages, the length of the moving average period, the fuzzy extent, and the recommend value. Ten fuzzy logic rules form a fuzzy set, which generates a rating level that decides the trading volume. In this process, we apply genetic algorithms to identify an optimal fuzzy logic rule set and utilize crude oil futures prices from the New York Mercantile Exchange (NYMEX) as the experiment data. Each experiment is repeated for 20 times. The results show that firstly the fuzzy moving average strategy can obtain a more stable rate of return than the moving average strategies. Secondly, holding amounts series is highly sensitive to price series. Thirdly, simple moving average methods are more efficient. Lastly, the fuzzy extents of extremely low, high, and very high are more popular. These results are helpful in investment decisions. [ABSTRACT FROM AUTHOR]

We run an experiment to investigate the emergence of excess and synchronised trading activity leading to market crashes. Although the environment clearly favours a buy-and-hold strategy, we observe that subjects trade too much, which is detrimental to their wealth given the implemented market impact (known to them). We find that preference for risk leads to higher activity rates and that price expectations are fully consistent with subjects’ actions. In particular, trading subjects try to make profits by playing a buy low, sell high strategy. Finally, we do not detect crashes driven by collective panic, but rather a weak but significant synchronisation of buy activity. [ABSTRACT FROM AUTHOR]

This paper develops a new mechanism that takes into account the fast change in behaviours of futures returns and trading volumes in order to model the time-varying and quantile-varying dependence between return and volume for energy-related futures products traded on TOCOM, NYMEX and ICE Futures Europe. A logistic function with the product of one-step-ahead expectations of return and volume as a transition variable is used to depict the time-varying weight of a mixture copula. This paper then employs a mixture copula of a Gumbel copula and a rotated Gumbel copula to detect the asymmetric V-type pattern and uses a mixture copula of a Gumbel copula and a survival Gumbel copula to measure the asymmetric increasing-type pattern. Empirical results demonstrate that the asymmetric V-type pattern is a more appropriate specification to characterize the return–volume nexus than the asymmetric increasing-type pattern, irrespective of the types of energy-related futures products and futures exchanges. The time-varying dependence has greater dependence in the lower–upper corner of the joint distribution than in the upper–upper corner of the joint distribution, implying that market participants believe that market reversals are more likely during periods of price declines than in periods of price increases. Moreover, this paper shows the inappropriateness of the two-step estimation method that has been widely used in the existing literature. [ABSTRACT FROM PUBLISHER]

This paper proposes a unified approximation method for various options whose pay-offs depend on the volume weighted average price (VWAP). Despite their popularity in practice, very few pricing models have been developed in the literature. Also, in previous works, the underlying asset process has been restricted to a geometric Brownian motion. In contrast, our method is applicable to the general class of continuous Markov processes such as local volatility models. Moreover, our method can be used for any type of VWAP options with fixed-strike, floating-strike, continuously sampled, discretely sampled, forward-start and in-progress transactions. [ABSTRACT FROM AUTHOR]

Following its introduction in the seminal study of Osborne (1959), a voluminous literature has emerged examining the returns–volume relationship for financial assets. The present paper revisits this relationship in an examination of the FTSE100 which extends the existing literature in two ways. First, alternative daily measures of the FTSE100 index are used to create differing returns and absolute returns series to employ in an examination of returns–volume causality. Second, rolling regression analysis is utilised to explore potential time variation in the returns–volume relationship. The findings obtained depict a hitherto unconsidered complexity in this relationship with the type of returns series considered and financial crisis found to be significant underlying factors. The implications of the newly derived results for both the understanding of the nature of the returns–volume relationship and the development of theories in connection to it are discussed. [ABSTRACT FROM AUTHOR]

Taking Baidu Index as a proxy for abnormal investor attention (AIA), the long memory property in the AIA of Shanghai Stock Exchange (SSE) 50 Index component stocks was empirically investigated using detrended fluctuation analysis (DFA) method. The results show that abnormal investor attention is power-law correlated with Hurst exponents between 0.64 and 0.98. Furthermore, the cross-correlations between abnormal investor attention and trading volume, volatility respectively are studied using detrended cross-correlation analysis (DCCA) and the DCCA cross-correlation coefficient ( ρ D C C A ). The results suggest that there are positive correlations between AIA and trading volume, volatility respectively. In addition, the correlations for trading volume are in general higher than the ones for volatility. By carrying on rescaled range analysis (R/S) and rolling windows analysis, we find that the results mentioned above are effective and significant. [ABSTRACT FROM AUTHOR]

Automated high-frequency trading has grown tremendously in the past 20 years and is responsible for about half of all trading activities at stock exchanges worldwide. Geography is central to the rise of high-frequency trading due to a market design of “continuous trading” that allows traders to engage in arbitrage based upon informational advantages built into the socio-technical assemblages that make up current capital markets. Enormous investments have been made in creating transmission technologies and optimizing computer architectures, all in an effort to shave milliseconds of order travel time (or latency) within and between markets. We show that as a result of the built spatial configuration of capital markets, “public” is no longer synonymous with “equal” information. High-frequency trading increases information inequalities between market participants. [ABSTRACT FROM AUTHOR]

In this paper, we seek to demonstrate the predictability of stock market returns and explain the nature of this return predictability. To this end, we introduce investors with different investment horizons into the news-driven, analytic, agent-based market model developed in Gusevet al. [Algo. Finance, 2015,4, 5–51]. This heterogeneous framework enables us to capture dynamics at multiple timescales, expanding the model’s applications and improving precision. We study the heterogeneous model theoretically and empirically to highlight essential mechanisms underlying certain market behaviours, such as transitions between bull and bear markets and the self-similar behaviour of price changes. Most importantly, we apply this model to show that the stock market is nearly efficient on intraday timescales, adjusting quickly to incoming news, but becomes inefficient on longer timescales, where news may have a long-lasting nonlinear impact on dynamics, attributable to a feedback mechanism acting over these horizons. Then, using the model, we design algorithmic strategies that utilize news flow, quantified and measured, as the only input to trade on market return forecasts over multiple horizons, from days to months. The backtested results suggest that the return is predictable to the extent that successful trading strategies can be constructed to harness this predictability. [ABSTRACT FROM PUBLISHER]

Water quality trading (WQT) could be an innovative policy to incentivize farmers to implement best management practices (BMPs) for their activities. This study focused on assessment of involving unregulated agricultural nonpoint sources (NPS) into the WQT market in Gharesoo watershed in the west of Iran. It also proposes a methodology to determine location-based trading ratios as well as environmental penalty cost to achieve a more well-designed market structure. Trading activities in different scenarios were described by trading volume (TV), participation rate (PR), total exchanged value (TEV), and other market parameters in order to achieve a better comparison of market performance. Results showed that, by applying NPS to the Gharesoo watershed, total phosphorous (TP) trading market could increase TV, PR, and TEV up to 11, 1.7 and 7.5 times, respectively, depending on which level of BMPs are implemented by them. Additionally, it could save 29% of the total cost of implementing a TP total maximum daily load in this watershed compared to the 'command and control' approach. Furthermore, the agricultural sector could profit by $5.49 million (or $75/ha) by choosing solutions such as terrace systems and filter strips to register into the market. This profit can be allocated to the development of new agricultural technologies. [ABSTRACT FROM AUTHOR]

For a single market, the cross-correlation between market efficiency and trading volume, which is an indicator of market liquidity, is attentively analysed. The study begins with creating time series of market efficiency by applying time-varying Hurst exponent with one year sliding window to daily closing prices. The time series of trading volume corresponding to the same time period used for the market efficiency is derived from one year moving average of daily trading volume. Subsequently, the detrended cross-correlation coefficient is employed to quantify the degree of cross-correlation between the two time series. It was found that values of cross-correlation coefficient of all considered stock markets are close to 0 and are clearly out of range in which correlation being considered significant in almost every time scale. Obtained results show that the market liquidity in term of trading volume hardly has effect on the market efficiency. [ABSTRACT FROM AUTHOR]

Do spikes in Twitter chatter about a firm precede unusual stock market trading activity for that firm? If so, Twitter activity may provide useful information about impending financial market activity in real-time. We study the real-time relationship between chatter on Twitter and the stock trading volume of 96 firms listed on the Nasdaq 100, during 193 days of trading in the period from May 21, 2012 to September 18, 2013. We identify observations featuring firm-specific spikes in Twitter activity, and randomly assign each observation to a ten-minute increment matching on the firm and a number of repeating time indicators. We examine the extent that unusual levels of chatter on Twitter about a firm portend an oncoming surge of trading of its stock within the hour, over and above what would normally be expected for the stock for that time of day and day of week. We also compare the findings from our explanatory model to the predictive power of Tweets. Although we find a compelling and potentially informative real-time relationship between Twitter activity and trading volume, our forecasting exercise highlights how difficult it can be to make use of this information for monetary gain. [ABSTRACT FROM AUTHOR]

Price impact is an important area of research in market microstructure. Previous studies have examined the relationship between trade size and price impact on a number of equity markets. In this study, using recent order book data from the London Stock Exchange, we examine the immediate price impact function for all stocks from FTSE 100 and novelly we investigate whether the function displays time-of-day effects. The results show that price impact exhibits a power-law scaling that price impact is highest in the first hour of the trading day, and lowest in the 90 minutes before market close. [ABSTRACT FROM PUBLISHER]

We match daily data on newspaper coverage of a sample of Italian listed companies with monthly data on the amount of advertising that a given company has purchased on a given newspaper. Controlling for time-invariant features of each newspaper and of each company – and for ownership links between companies and newspapers – we show that newspaper coverage of a company is positively and significantly related with advertising expenditure by that company on that newspaper. The magnitude of this correlation is quite large: when controlling for ownership links, a standard deviation increase in monthly ads expenditure (i.e. 75,000 euros) is on average associated with 8 additional articles per month mentioning that company. We also find that coverage of a company is higher the day after a press release, but especially in newspapers where more ads are purchased. This result on press releases is robust to controlling for time invariant features of each company–newspaper pair, i.e. for (company × newspaper) fixed effects. Moreover, coverage is correlated with past day absolute return and trading volume, and this relationship appears to be steeper for those newspapers where more ads are purchased, especially in the case of positive returns. [ABSTRACT FROM AUTHOR]

The positive relation between stock price changes and trading volume (price–volume relationship) as a stylized fact has attracted significant interest among finance researchers and investment practitioners. However, until now, consensus has not been reached regarding the causes of the relationship based on real market data because extracting valuable variables (such as information-driven trade volume) from real data is difficult. This lack of general consensus motivates us to develop a simple agent-based computational artificial stock market where extracting the necessary variables is easy. Based on this model and its artificial data, our tests have found that the aggressive trading style of informed agents can produce a price–volume relationship. Therefore, the information spreading process is not a necessary condition for producing price–volume relationship. [ABSTRACT FROM AUTHOR]

We investigate market microstructure with high frequency data for all stocks that participate in the Ibovespa index, traditional and influential Latin American index. Market microstructure has been widely investigated in developed markets however not many studies focus on developing countries such as Brazil. We modeled volatility with a straightforward method using standard deviation as proxy, and classified the volume traded of all Ibovespa stocks in every 10 minutes interval of the trading days sampled. In the end, we found similarities of previous investigations documented in financial literature such as the stylized facts L-shape pattern for volatility and the U-shape for volume. One interesting finding is that during the most active trading time, the interval that comprises the closing call, volatility slumps; on the other hand, the second most active trading time, the opening, displays extreme volatility. [ABSTRACT FROM AUTHOR]

How trading volume responds to price return is investigated by the return–volume correlation dynamics, based on the daily data of two typical financial markets, i.e., the Chinese and the United States stock markets. Whereas the price returns being observed to be positively correlated with the trading volumes for the Chinese stock markets, a significant transition from the positive correlation to negative correlation is revealed for the United States stock markets. Leverage and anti-leverage effects of the return–volume correlations are found for the last decade of the United States stock markets and the Chinese stock markets, respectively. Nonlocal dynamics further suggests an adverse correlation for the two markets. A retarded herding model is introduced to describe the leverage and anti-leverage effects. Our work may shed a new light on the financial dynamics differing in the mature and emerging markets. [ABSTRACT FROM AUTHOR]

Knowing the characteristics of news in numerical indices one can use them in mathematical and statistical models and automated trading systems. Currently, the tools of the news analytics have been increasingly used by traders in the U.S. and Europe. The interest in news analytics is related to the ability to predict changes of prices, volatility and trading volume on the stock market. The emphasis of the paper is on assessing the added value of using news analytics data in improving the explanatory power of the GARCH-Jump model. Based on empirical evidences for some of FTSE100 companies, the paper examines two GARCH models with jumps. First we consider the well-known GARCH model with autoregressive conditional jump intensity proposed in [1]. Then we introduce the GARCH-Jumps model augmented with news intensity and obtain some empirical results. The main assumption of the model is that jump intensity might change over time and that jump intensity depends linearly on the number of news (the news intensity). The comparison of the values of log likelihood supports the hypothesis of impact of news on the jump intensity of volatility. [ABSTRACT FROM AUTHOR]

Thinly traded securities exist in both emerging and well developed markets. However, plausible estimations of market risk measures for portfolios with infrequently traded securities have not been explored in the literature. We propose a methodology to calculate market risk measures based on the Kalman filter which can be used on incomplete datasets. We implement our approach in a fixed-income portfolio within a thin trading environment. However, a similar approach may be also applied to other markets with thinly traded securities. Our methodology provides reliable market risk measures in portfolios with infrequent trading. [ABSTRACT FROM AUTHOR]

Highlights: [•] We study the effect of short selling restrictions on asset prices in a series of market experiments. [•] Asset prices are higher when short selling constraints are imposed. [•] Variations of the degree of divergence of opinion do not appear to affect price levels. [•] Short selling constraints reduce trading volume and tend to increase bid-ask spreads. [ABSTRACT FROM AUTHOR]

Purpose – The aim of this paper is to measure and evaluate the relationship between returns-volatility and trading volume and returns and volatility of financial market indexes using time-varying copulas. Design/methodology/approach – The time dynamic dependence parameter is allowed to evolve according to a restricted ARMA-type equation which includes a constant term that is driven by a hidden two-state first-order Markov chain. Findings – In using this time dynamics in conjunction with non-elliptical distribution functions and tail dependence measure, the authors are allowing for (and focusing on) non-linearities in the returns-volume-volatility relationship. The results support the assumption that current trading volume provides information about future volatility as well as that there is a negative relationship between returns and their volatilities in financial market indexes. Originality/value – The authors provide an interesting empirical interpretation for the regimes the authors have identified: in the high dependence regime the sequential information arrival hypothesis and/or noise trading hypothesis are valid, consequently future volatility prediction is possible and persistent but does not last indefinitely; in the low dependence regime, the future volatility prediction is more unlikely to occur, since both trading volume and return negatives have a low (near zero) relation with future volatility. [ABSTRACT FROM AUTHOR]

High-frequency return, trading volume and transaction number are digitally coded via a nonparametric computing algorithm, called hierarchical factor segmentation (HFS), and then are coupled together to reveal a single stock dynamics without global state-space structural assumptions. The base-8 digital coding sequence, which is capable of revealing contrasting aggregation against sparsity of extreme events, is further compressed into a shortened sequence of state transitions. This compressed digital code sequence vividly demonstrates that the aggregation of large absolute returns is the primary driving force for stimulating both the aggregations of large trading volumes and transaction numbers. The state of system-wise synchrony is manifested with very frequent recurrence in the stock dynamics. And this data-driven dynamic mechanism is seen to correspondingly vary as the global market transiting in and out of contraction-expansion cycles. These results not only elaborate the stock dynamics of interest to a fuller extent, but also contradict some classical theories in finance. Overall this version of stock dynamics is potentially more coherent and realistic, especially when the current financial market is increasingly powered by high-frequency trading via computer algorithms, rather than by individual investors. [ABSTRACT FROM AUTHOR]

We consider a two-period model in which a continuumof agents trade in a context of costly information acquisition and systematic heterogeneous expectations biases. Because of systematic biases agents are supposed not to learn from others' decisions. In a previous work under somehow strong technical assumptions a market equilibriumwas proved to exist and the supply and demand functions were proved to be strictly monotonic with respect to the price. Here we extend these results under very weak technical assumptions. We also prove that the equilibrium price maximizes the trading volume and further additional properties (such as the antimonotonicity of the trading volume with respect to the marginal information price). [ABSTRACT FROM AUTHOR]

Based on efficient importance sampling (EIS) technique, we propose the maximum likelihood (ML) method to estimate the stochastic volatility (SV) models based on four different return distributions. Taking Shanghai Stock Exchange composite and Shenzhen Stock Exchange component indices as an example, we empirically test the performance of the SV models based on different return distributions, and aim to find the appropriate return distribution for China's stock market. Empirical results demonstrate that the SV model based on the skew student's t-distribution (SVSKt model) which can account for skewed and peaked and heavy-tailed returns provides significant improvement in model fit over the SV models based on the normal, the student's t and the generalized error distributions (GED). [ABSTRACT FROM AUTHOR]

This paper is concerned with a dependence analysis of returns, return volatility and trading volume for five companies listed on the Vienna Stock Exchange and five from theWarsaw Stock Exchange. Taking into account high frequency data for these companies, tests based on a comparison of Bernstein copula densities using the Hellinger distance were conducted. The paper presents some patterns of causal and other relationships between stock returns, realized volatility and expected and unexpected trading volume. There is a linear causality running fromrealized volatility to expected trading volume, and a lack of nonlinear dependence in the opposite direction. The authors detected strong linear and nonlinear causality from stock returns to expected trading volume. They did not find causality running in the opposite direction. In addition, the existence of fractional cointegration was examined. Despite the equality of the long memory parameters of realized volatility and trading volumes, they do not move together in the long term horizon. [ABSTRACT FROM AUTHOR]

Abstract: This paper examines the price impact of trading intensity on the MexDer TIIE28 interest rate futures contract, one of the world's most actively traded contracts. A novel volume-augmented duration model of price discovery decomposes trading intensity into liquidity and information components. Duration between transactions exerts a positive influence on price changes, while increases in order flow and trade volume exert positive and negative influences, respectively. The liquidity component dominates the information measure, suggesting that liquidity considerations dictate trade timing. These findings are rationalized with reference to MexDer's organizational structure, specifically the affirmative obligations placed upon marketmakers to trade a minimum volume. [Copyright &y& Elsevier]

The purpose of the paper is to measure whether corporate governance rating affects market value of the firms involded in corporate governance index. In the paper firstly informed about principles of corporate governance and then tried to expose regulations and legislation about corporate governance made in Turkey in the last years. To achive the purpose of the paper, ratings of the firms involved in coprporate governance index in 2007 - 2011 are determined. The stock exchange values of the firms 30 days prior to and 30 days after the disclosure date of the ratings are determined then these values are compared with the corporate governance ratings and a set of statistical analysis applied to data. With the Wilcoxon Signed Rank Test and Paired Samples T - Test, it is proved that there is a statisticaly significant relation between corporate governance rating and market values. [ABSTRACT FROM AUTHOR]

Using empirical data from a social media site (Twitter) and on trading volumes of financial securities, we analyze the correlated human activity in massive social organizations. The activity, typically excited by real-world events and measured by the occurrence rate of international brand names and trading volumes, is characterized by intermittent fluctuations with bursts of high activity separated by quiescent periods. These fluctuations are broadly distributed with an inverse cubic tail and have long-range temporal correlations with a 1=f power spectrum.We describe the activity by a stochastic point process and derive the distribution of activity levels from the corresponding stochastic differential equation. The distribution and the corresponding power spectrumare fully consistent with the empirical observations. [ABSTRACT FROM AUTHOR]

According to the theory proposed by Acerbi and Scandolo (2008) [Quant. Finance, 2008,8, 681–692], an asset is described by the so-called Marginal Supply–Demand Curve (MSDC), which is a collection of bid and ask prices according to its trading volumes, and the value of a portfolio is defined in terms of commonly available market data and idiosyncratic portfolio constraints imposed by an investor holding the portfolio. Depending on the constraints, one and the same portfolio could have different values for different investors. As it turns out, within the Acerbi–Scandolo theory, portfolio valuation can be framed as a convex optimization problem. We provide useful MSDC models and show that portfolio valuation can be solved with remarkable accuracy and efficiency. [ABSTRACT FROM PUBLISHER]

This paper presents a quantitative analysis of the relationship between the stock market returns and corresponding trading volumes using high-frequency data from the Polish stock market. First, for stocks that were traded for sufficiently long period of time, we study the return and volume distributions and identify their consistency with the power-law functions. We find that, for majority of stocks, the scaling exponents of both distributions are systematically related by about a factor of 2 with the ones for the returns being larger. Second, we study the empirical price impact of trades of a given volume and find that this impact can be well described by a square-root dependence: r(V) ~ V1/2. We conclude that the properties of data from the Polish market resemble those reported in literature concerning certain mature markets. [ABSTRACT FROM AUTHOR]

This paper proposes a new panel quantile regression model with change points at unknown positions, which parameters are estimated by Bayesian inference and MCMC sampling. By an empirical study to explore structural changes in the high-frequency dependence from trading volume and market trend to IPO's initial return in China's growth enterprise market, it is found that: structural change points are near the market opening for higher quantiles of returns, and close to the median trading time for lower quantiles; the IPO's trading volume gives a greater impact on IPO's return before change points than after change points on listing date, while the volume of Shenzhen composite index contributes larger impact on IPO's return after change points than before change points; the separation of return and volume of IPOs is detected both at lower quantiles before the change points and at any quantile after the change points; the volume of Shenzhen composite index is positively correlated to IPO's return through all quantiles before or after change points, moreover, the correlation coefficients after change points are stronger than ones before change points, and the same is true at extreme quantiles than at median quantiles. [ABSTRACT FROM AUTHOR]

This paper provides the first evidence for empirical sensitivity of trading volume to human psychological factors. We review therefore extensive evidence about how human psychology affects investor behavior and trading volume. Using the data for individual stocks listed on the CAC40 Stock Market over the period 2005-2011, results based on a fuzzy logic approach indicate a high sensitivity of trading volume to investor sentiments and beliefs. The sensitivity increases abnormally for the pessimism input variable. Taken together all results indicate that economy is highly driven by human psychologies, a result which is in conformity with the prediction of Keynes (1936) and Akerlof and Shiller (2009). [ABSTRACT FROM AUTHOR]