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For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

This study aims to propose a theoretical model to characterize the optimal forward freight agreement (FFA) procurement strategies and investigate the determinants of FFA trading activities from a new cross-market perspective.

A two-step model specification is used to empirically test the theoretical results for the Capesize, Panamax and Supramax sectors. It is found that spot demand has a positive relation with FFA trading volume for all three sectors. Moreover, spot demand volatility has a negative relation, while the correlation between spot demand and spot rate has a positive relation with FFA trading volume for the Capesize and Panamax sectors.

The results show that the expected spot demand is scaled by a “quantity premium,” which is the product of a demand covariance term, a demand riskiness term and a demand volatility term. This can be used by the traders in the FFA market to construct their hedging strategies.

In this paper, the authors document the existence of price clustering in the US corporate bond market.

Using a sample of 8,422,593 corporate bond trades in 2014, the authors find that over 18% (1,522,284 trades) of all bond trades end in a clustered price, defined as a price ending in 00, 25, 50, or 75.

Overall, the authors find that both bond rating category and risk, as measured by standard deviation of prices, play a role in price clustering; speculative grade bonds account for the majority of clustered prices. Clustered prices are more likely to have higher coupon rates, higher prices, and higher standard deviations of price than bonds with non-clustered prices. Regardless of size, both buy and sell dealer trades with customers (relative to interdealer trading) lead to an increase in price clustering. Dealers appear to use clustered prices when purchasing from and selling to institutions and, therefore, may use a clustered price to insulate themselves from the risk of asymmetric information. Additionally, the prevalence of clustered prices for retail-sized dealer sell trades suggests that dealers exercise dealer power over retail-sized traders.

This paper contributes to the literature on price clustering by examining trade price clustering of corporate bonds. It is different from previous papers on price clustering in equities. Given that bonds tend to be priced off of yield, it is unusual that trade prices cluster. It also demonstrates what kind of bonds cluster and with which customers dealers trade at clustered prices. It parallels other research in demonstrating dealer power over retail-sized traders.

This paper examines implied volatility asymmetries in KOSPI200 option markets. The empirical results show that the unexpected negative return has a more remarkable effect on implied volatility than the unexpected positive one in the early stages of markets. In the recent stages, markets do not show implied volatility asymmetries. These results give and interesting implication to option market participants. In addition, this paper examines whether trading activity in option markets has an effect on implied volatility. The paper finds that in the second stages of markets trading activity has a negative effect on implied volatility while trading activity do not have effect on implied volatility in the early stages of markets. When trading activity is partitioned into expected and unexpected components, the empirical result shows that all trading activities have a significant negative effect on the implied volatility of option markets.

Highly deprived urban contexts, such as informal settlements in the global south, can turn into niches of extreme innovation and sparkle ingenuity out of necessity. But what are the rationales behind the participation of disadvantaged communities in social innovations? Why do they engage in grassroots innovations? What is it that makes these grassroots try novelties and continue experimenting with them, even when the perceived benefits are not clear yet? This paper aims to examine and conceptualize the rationales for engaging in grassroots financial innovations in the context of extremely deprived urban settings.

This paper is based on the case of grassroots organizations which have started experimenting with the development of a community currency in Kisumu, Kenya. This paper is informed by in-depth interviews with members of three grassroots organizations involved in the community currency, together with observations and meeting participation since 2019.

The rationales argued by the participants for engaging in this grassroots innovation are framed in various ways: as a means for seeking poverty alleviation (the development framing); as a challenge to conventional imaginaries of innovations (the digital framing); and as an innovation embedded in community and trust relations (the community framing). These framings have a mobilizing effect that initially draws participants into the innovation. Yet, what explains persistent participation despite the decreasing influence of these framings over time is the organizational space and strategies of incompleteness accommodating these experiments.

This paper contributes to the emerging body of grassroots innovations movements literature. While research has progressed in its understandings of the challenges of scaling up innovative practices, the examination of the grassroots initiatives stemming from extremely deprived settings, and the rationales and framings behind, have been under examined. This paper comes to bridge this gap.

Se estudian los determinantes y la evolución de la actividad bursátil mensual en el mercado accionario colombiano de 2007 a 2016.

Para ello se emplean modelos de series de tiempo tipo ARIMAX y GARCH, incluyendo variables exógenas, recomendadas por la literatura previa.

Encontramos que la actividad bursátil puede ser pronosticada en buena parte por el valor rezagado a un mes y las innovaciones de cinco y 12 meses. También contribuyen a predecirla, como variables exógenas, una dummy de rendimientos positivos en los últimos tres meses, la presencia de emisiones primarias y el índice VIX de volatilidad del SP500. Estos resultados se mantienen en un alto grado al emplear medidas alternativas de actividad bursátil, el número total de operaciones y la rotación.

Se propone un modelo de predicción de la actividad bursátil que puede servir de modelo para otros mercados accionarios de Latinoamérica. El modelo obtenido es altamente predictivo del valor transado total del mercado al siguiente mes. La estimación de la actividad bursátil es de utilidad para instituciones como la Bolsa de Valores de Colombia, reguladores de los mercados financieros, así como para grandes inversionistas institucionales.

El propósito central de los mercados financieros secundarios consiste en facilitar la transacción de activos financieros, lo que debe reflejarse en alta actividad bursátil, tanto en número de operaciones como en valor transado total. La posibilidad de transar altos montos es una medida importante del desarrollo de un mercado financiero. De esta manera, el modelo aquí propuesto puede usarse para monitorizar y explicar el desarrollo del mercado. En particular, se evidencia el nocivo efecto de la debacle de Interbolsa a finales de 2012 y el positivo efecto de las emisiones primarias.

Este es el primer paper en estudiar la actividad bursátil del mercado accionario colombiano en años recientes. Sirve como modelo para el estudio y seguimiento de esta variable en otros mercados accionarios latinoamericanos.

To study the determinants and evolution of the trading activity in the Colombian Stock Market from 2007 to 2016.

ARMA time series models were used, including several explanatory variables recommended by previous literature.

We find that stock market activity can be predicted to a large extent by its lags, and that positive returns in the last three months, emissions and the VIX index are also explicative variables, as suggested by empirical studies in other countries and theoretical models of market microstructure. These results are robust by using alternative measures of trading activity, total number of trades and turnover.

The main contribution of this study is the analysis of the trading activity of the Colombian Stock Market, a critical variable for monitoring the development of any financial market.

The purpose of this paper is to examine the influence of innovation indicators: Internet usage, patent rights, innovation in exporting countries and innovation in the importing country on the export competitiveness of firms in the East African Community (EAC).

The study adopted the structural gravity model and the Poisson Pseudo Maximum Likelihood a nonlinear estimation method that was applied in STATA on balanced panel data from 2007 to 2018. Data were obtained from World Bank International Trade Center and World Bank development indicators.

Results show that innovation in the importing country, innovation in the exporting country and patent rights of exports are positive and significant predictors of export competitiveness in developing countries. While Internet usage is an insignificant predictor in the EAC.

There is a need to examine the complicated nature of the EAC economy to further this study's findings.

Exporting countries need to take deeper reforms as regards structural transformation to enable firms to integrate into the Global Value Chains (GVCs) to enable them to increase their productivity by reviewing the existing policies to match the changes in the market.

This study explains the complex dynamic interactions of technological innovation indicators in the EAC using quantitative data and that this interaction has an effect on the export competitiveness in import-oriented countries with less harmonization in their trade policies.

This study proposes a literature review and, based on the findings, the authors develop a conceptual framework, attempting to explain how technology may influence visitor behavior and eventually trade show performance.

The present research explores the role of visitors in the trade show context. The analysis specifically focuses on the variables that influence visitors’ participation at business-to-business trade shows and how their satisfaction and perception can be related to exhibition performance. The authors also take into consideration technological trends that prior to COVID-19 pandemics were slowly emerging in the trade show industry.

The findings highlight a continuity between pre-, at and postexhibition phases. Visitors’ behavior represents a signal of how a trade show is perceived as postexhibition purchases and next visit emerge as signals of an exhibition evaluation in relation to visitors’ perception. Besides being urgent tools for the continuity of the sector due to the pandemics, emerging technological trends can be key elements in understanding visitors’ behavior and in boosting their interest and loyalty toward trade shows.

The paper proposes a conceptual model including top notch and innovative technological trends to improve the understandment of visitors’ behavior. Both practitioners in companies and academics might find the study useful, given the digital uplift generated by the pandemics.

To interrogate the grand narrative of “entrepreneurship for development” that dominates international development circles, by applying a feminist critical discourse analysis that prioritizes women's situated experiences as local stories.

Two existing frameworks for analysing women's entrepreneurship, namely the 5M (Brush et al., 2009) and the 8M (Abuhussein and Koburtay, 2021) frameworks, are used to examine the local stories of women in rural Ethiopia to provide a counter-narrative to the grand narrative of “entrepreneurship for development”. The local stories are derived from 16 focus group discussions and 32 interviews.

The findings provide a counter-narrative to the grand narrative of “entrepreneurship for development”, evident in Ethiopia and in international development generally, while demonstrating larger structural issues at play. They challenge entrepreneurship's solely positive effects. While women recognize the benefits of having a business, particularly in terms of financial gains, empowerment and social recognition, they also highlight negative consequences, including uncertainty, concerns for their own personal safety, criticism, stress, limited social life and fear of indebtedness and poverty.

Policymakers, scholars and development professionals are urged to reflect on the limitations of “entrepreneurship for development” and to consider the negative effects that promoting an acritical grand narrative of entrepreneurship could have on women's lives.

The article advances an innovative partnership between feminist analysis and established women's entrepreneurship frameworks to contest dominant assumptions in the fields of entrepreneurship and international development studies. It adds to the limited empirical evidence on women's entrepreneurial activity in Ethiopia, tests the adequacy of the 5M and 8M frameworks in the rural low-income context of Ethiopia, and proposes a 7+M framework as an alternative to study rural women's entrepreneurship in low and middle income countries.

We study five exogenous shocks: climate, violence, price hikes, spoilage and the COVID-19 lockdown. We analyze the association between these shocks and trader characteristics, reflecting trader vulnerability.

Using primary survey data on 1,100 Nigerian maize traders for 2021 (controlling for shocks in 2017), we use probit models to estimate the probabilities of experiencing climate, violence, disease and cost shocks associated with trader characteristics (gender, size and region) and to estimate the probability of vulnerability (experiencing severe impacts).

Traders are prone to experiencing more than one shock, which increases the intensity of the shocks. Price shocks are often accompanied by violence, climate and COVID-19 shocks. The poorer northern region is disproportionately affected by shocks. Northern traders experience more price shocks while Southern traders are more affected by violence shocks given their dependence on long supply chains from the north for their maize. Female traders are more likely to experience violent events than men who tend to be more exposed to climate shocks.

The data only permit analysis of the general degree of impact of a shock rather than quantifying lost income.

This paper is the first to analyze the incidence of multiple shocks on grain traders and the unequal distribution of negative impacts. It is the first such in Africa based on a large sample of grain traders from a primary survey.