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The purpose of this paper is to explain what information is contained in mutual funds' trading behaviors and to try to further assess the impact on the stock market.

The objective is achieved by an empirical examination using the high‐frequency intraday data. The main methods used for the research are the autoregressive conditional duration model and the UHF‐GARCH model.

This paper gives an empirical study of mutual funds' behavior on two aspects. The first aspect is the direct impact on micro variables. The results show that mutual funds changing their positions will have different influences to the spread, adding position broadens the spread, while decreasing position makes the spread narrow; behaviors of funds change the clustering characteristic of the duration. The second aspect is the impact on the relationships among micro variables. The results indicate that trading started by liquidity buyers will make volatility larger.

This paper supposes funds as informed traders and individual investors as liquidity traders in China's stock market. If it is not true, some interpretations of empirical results would be wrong. The authors' results may help researchers to understand the information content of funds' trading behaviors in the microstructure aspect.

The paper is an original work, which will be interesting to scholars in market microstructure and to practitioners in the Chinese stock market. The main contributions of the paper are: the use of high‐frequency data to study funds' behaviors and combine the trading duration and investors' trading behavior to analyze the information content of trading behaviors; second, the use of 14 stock samples in the Shanghai Stock Exchange to do the empirical study, which ensures the reliability of the results.

We examine the informational roles of trades and time between trades in the domestic and overseas US Treasury markets. A vector autoregressive model is employed to assess the information content of trades and time duration between trades. We find significant impacts of trades and time duration between trades on price changes. Larger trade size induces greater price revision and return volatility, and higher trading intensity is associated with a greater price impact of trades, a faster price adjustment to new information and higher volatility. Higher informed trading and lower liquidity contribute to larger bid–ask spreads off the regular daytime trading period.

The purpose of this study is to identify appropriate autoregressive conditional duration (ACD) models that can capture the dynamics of tick-by-tick mid-cap exchange traded funds (ETFs) for the period July 2017 to December 2017 and accurately predict future trade duration values. The forecasted durations are then used to demonstrate the practical usefulness of the ACD models in quantifying an intraday time-based risk measure.

Through six functional forms and six error distributions, 36 ACD models are estimated for eight mid-cap ETFs. The Akaike information criterion and Bayesian information criterion and the Ljung-Box test are used to evaluate goodness-of-fit while root mean square error and the Superior predictive ability test are applied to assess forecast accuracy.

The Box-Cox ACD (BACD), augmented Box-Cox ACD (ABACD) and additive and multiplicative ACD (AMACD) extensions are among the best fits. The results obtained prove that higher degrees of flexibility do not necessarily enhance goodness of fit and forecast accuracy does not always depend on model adequacy. BACD and AMACD models based on the generalised-F distribution generate the best forecasts, irrespective of the trading frequencies of the ETFs.

To the best of the authors’ knowledge, this is the first study that analyses the empirical performance of ACD models for high-frequency ETF data. Additionally, in comparison to previous works, a wider range of ACD models is considered on a reasonably longer sample period. The paper will be of interest to researchers in the area of market microstructure and to practitioners engaged in high-frequency trading.

This article will select 25 years of subdivided data to perform Kaplan–Meier survival analysis on the export trade relations of Chinese wooden flooring, use discrete-time cloglog models to analyze influencing factors, use logit and probit models to test the robustness, and try to systematically reveal the duration of China's wood flooring export trade and its influencing factors.

This study used Kaplan–Meier survival function estimation method. In the survival analysis, survival function and hazard rate function are often used to characterize the distribution of survival time.

The continuous average export time of China's wooden flooring is relatively long, about 14 years. China's wooden flooring has a negative time dependency. After the export trade exceeds the threshold value of 15 years, the failure rate of trade greatly decreases, which has a “threshold effect.” Gravity model variables have a significant impact on the duration of China's wooden floor export.

Studying the duration of forest products trade is of great significance for clearing deep-level trade relations and promoting sustainable development of forest products trade.

This paper is aimed to investigate the impact of different categories of traders on price and volume durations at Euronext Paris. The two series are respectively related to the instantaneous volatility and the market liquidity; hence, they are particularly suited to test microstructure hypotheses.

A Log-autoregressive conditional duration model was adopted to include the information on the traders’ identity at the transaction level. High-frequency data were used and how the informed traders and the liquidity provider affect the arrival of market events was studied. The robustness of our results was also checked by testing different distributions and controlling for microstructure effects.

It was found that informed traders and the liquidity provider exert a dominant role in accelerating the market activity. This result depends on the state of the market, i.e. it is effective only during periods of high frequency of transactions. The estimates for price durations show that a high instantaneous volatility can be mainly ascribed to a great concentration of informed traders. Informed traders are also found to shorten volume durations by clustering small-size orders to disguise their private signal. For both durations, the liquidity provider is also found to foster the market activity, likely because of his contractual duties.

The article is of interest for researchers in the field of market microstructure, as well as for specialists in the high-frequency trading. Results provide an empirical confirmation of information models which theorize an accelerating effect for informed trading. To the best of the authors’ knowledge, this is the first contribution to study the impact of traders’categories at the transaction level and with different definitions of durations.

The purpose of this paper is to study whether, of China's food and agriculture (agri-food) export, the trade relationships along the extensive margin can transform into short-term ones, and further convert into long-term ones along the intensive margin with survival and maintenance, especially, under what kind of conditions these transformation realize successfully, and what the factors are and how they impact on the length of the trade relationships duration.

The paper comprises three stages: decomposing China's agricultural export data (1999-2008) into highly disaggregated three distinct parts; with the Kaplan-Meier (KM) nonparametric estimation, discovering the practical source of stable and long-term export growth, and the threshold at which the short-run trade relationships deriving from newly established ones convert into long-run ones successfully; by semi parametric estimation through Cox proportional hazard model, preliminarily examining the possible impact of different factors on the trade relationship's hazard rate and duration.

The paper reveals that China agri-food trade pattern at the product level is surprisingly dynamic with newly established trade relations being more likely to fail. While the frequent volatilities of short-term relations at the extensive margin could be used to evaluate the source of short-term export growth especially and effectively, the pattern of negative dependence together with the threshold effect of duration indicates, only when these short-term relations live longer than four years will they substantially contribute to a stable and prolonged export growth. Simultaneously, trade duration significantly correlates to importing country's development status, region it belongs to, product processing degree, export experience and geographical space between trading partners.

To avoid the likelihood of misleading when estimating quantitatively the source of long-term export growth, researchers should be cautious in accurately evaluating the impacts of all possible factors on the two trade margins' performances, which is beyond the scope of this paper though, and a matter of ongoing work on the research agenda.

The study presents a set of important policy implications. Now that turnovers along the extensive margin have little impact on long-term China agri-food export growth, it turns out that improving export survival would result in significantly higher and stable export growth.

By distinguishing the survival of trade relationship channels from their deepening, the discoveries in the paper are crucial to understand the different role the intensive and extensive margins play in China agri-food export growth. The diversified hazard rates of export relations in different duration intervals suggest that the constant hazard rate assumption in Melitz and Bernard et al. is in some sense not appropriate.

The purpose of this paper is to examine the pattern, duration and country-level determinants of global agri-food export competitiveness of 23 major global agri-food trading countries.

A large panel data set is compiled to facilitate assessment of the pattern, duration and country-level determinants of global agri-food export competitiveness using a revealed comparative advantage index.

The results suggest that the duration of revealed comparative advantage is heterogeneous at the agri-food product level. Long-term survival rates as revealed by the comparative advantage indices are among the highest for the Netherlands, France, Belgium, the USA, Argentina and New Zealand. The level of economic development, the share of agricultural employment, subsidies to agriculture and differentiated consumer agri-food products increase the likelihood of failure in the duration of comparative advantage, while the abundance of agricultural land and export diversification reduce that likelihood.

The framework is conceptually innovative in how it models the likelihood of failure in the duration of comparative advantage and assesses implications. Export competitiveness is a crucial factor in long-term global farm business survival as it fosters opportunities for business prosperity on global markets.

Natural disasters profoundly influence agricultural trade sustainability. This study investigates the effects of natural disasters on agricultural production imports in China within 2002 and 2018. This exploration estimates the mediating role of transportation infrastructure and agriculture value-added and the moderating role of government effectiveness and diplomatic relations.

This investigation uses Probit, Logit, Cloglog and Ordinary Least Squares (OLS) models.

The results confirm the mediating role of transportation infrastructure and agriculture value-added and the moderating role of government effectiveness and diplomatic relations in China. According to the findings, natural disasters in trading partners heighten the risk to the agricultural imports. This risk raises, if disasters damage overall agricultural yield or transportation infrastructure. Moreover, governments’ effective response or diplomatic ties with China mitigate the risk. Finally, the effect of disasters varies by the developmental status of the country involved, with events in developed nations posing a greater risk to China’s imports than those in developing nations.

China should devise an early warning system to protect its agricultural imports by using advanced technologies such as data analytics, remote sensing and artificial intelligence. In addition, it can leverage this system by improving its collaboration with trading partners, involvement in international forums and agreement for mutual support in crisis.

Drawing on the literature that has shown the prevalence of short-lived trade relationships, the purpose of this paper is to provide further understanding about this issue by exploring the impact of engaging in Global Value Chains (GVCs) on the chance of export survival at product-country level, paying special attention to the differences between advanced and developing countries. The authors also investigate whether the type of GVC participation (backward or forward) matters for export survival.

To capture to what extent a country’s exports are integrated in GVCs, the authors use the OECD Inter-Country Input-Output database to estimate value added incorporated in exports. Through the estimation of a discrete-time duration model, the authors explore the impact of engaging in GVCs on export survival using highly disaggregated trade data from the CEPII’s BACI database.

The findings endorse the hypothesis that deeper participation in GVCs is a key factor in explaining stability in trade relationships, mainly for developing countries where the trade flows are especially fragile. The authors also find different effects depending on the type of GVC involvement and on whether the value chain partners are advanced or developing.

The paper contributes to the literature by extending the understanding on the factors that promote the stability of exports, including among them, involvement on GVCs (and its forms) which is one of the most relevant factors to explain recent behavior of trade.

The purpose of this paper is to examine how long a real estate investment trust (REIT) initial public offer (IPO) survives until a merger occurs, and to determine the impact of different firm characteristics that exist at the time of the IPO on that survival in the aftermarket period.

The authors apply an accelerated failure time (AFT) duration model to determine how long the IPO will survive until merger occurs.

The results indicate that the time from the IPO to an eventual merger increases with size, the age of the REIT at IPO, and the percentage of institutional ownership. In contrast, the authors find that the time until merger decreases with increased market performance prior to the time of the offering and with the number of additional IPOs occurring at the time of the IPO.

There is a growing body of research that suggests that IPOs might be motivated by subsequent mergers. An understanding of those characteristics that effect the time until a merger occurs these relationships will enable market participants and capital providers to make better decisions about proceeding with, or evaluating, a REIT IPO.

There is a significant body of research on IPOs in general; however, the findings of this research vary depending upon the industry being examined. Further, there are a limited number of papers on IPO aftermarket survival. This is the only paper on REIT IPO aftermarket survival.