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

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

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

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

This paper aims to examine the short-term structure of the impact of R&D investments on turnover growth, indicating differences between tangible and intangible investments. The main questions are whether R&D and capital investments accompany firms' growth in the subsequent periods and how this relationship depends on other characteristics of the firms, such as size and industry. In addition, the authors study the relationship between R&D investments and the autocorrelation dynamics of firm growth.

The paper uses the European Industrial R&D Investment Scoreboard as data source. This data source includes 1,000 European companies with information on employees, turnover, sector affiliation and details on capital expenditure and R&D expenditure.

The authors find that R&D activities have, on average, a positive effect on turnover growth, while capital investments show both, positive and negative, relationships with firm growth. The relationship and its temporal structure strongly depend on firm size and industry affiliation as well as whether investments are considered as one-time or permanent activities.

Usually, the impacts of firm characteristics on firm growth are studied without explicitly considering time. Firm characteristics and firm growth are usually measured and examined at the same point in time. In contrast, the study will focus on the short-term structure of the influence of firm characteristics on turnover growth, especially the impact of R&D investments.

Demand for express parcel delivery has been greatly increasing in South Korea due to the growth of B2C e-commerce activities. It is imperative that parcel carriers have good insights into the performance of their distribution networks with different levels of demand and plan ahead for the adaptations in order to be able to meet future changes in demand. This paper proposes a framework to evaluate the likelihood of parcels arriving on-time to their destinations (local service reliability) with a strong focus being placed on their spatial distribution. The resulting maps of local service reliability allow us to identify specific locales that would suffer the most from a capacity overflow in the networking system, thus pointing to areas that need immediate attention. Furthermore, this paper attempts to identify potential factors which could affect the spatial variation of local service reliability.

The purpose of this paper is to investigate the effects of crop insurance premiums being determined by small samples of yields that are spatially correlated. If spatial autocorrelation and small sample size are not properly accounted for in premium ratings, the premium rates may inaccurately reflect the risk of a loss.

The paper first examines the spatial autocorrelation among county-level yields of corn and soybeans in the Corn Belt by calculating Moran's I and the effective spatial degrees of freedom. After establishing the existence of spatial autocorrelation, copula models are used to estimate the joint distribution of corn yields and the joint distribution of soybean yields for a group of nine counties in Illinois. Bootstrap samples of the corn and soybean yields are generated to estimate copula models with the purpose of creating sampling distributions.

The estimated bootstrap confidence intervals demonstrate that the copula parameter estimates and the premium rates derived from the parameter estimates can vary greatly. There is also evidence of bias in the parameter estimates.

Although small samples will always be an issue in crop insurance ratings and assumptions must be made for the federal crop insurance program to operate at its current scale, this analysis sheds light on some of the issues caused by using small samples and will hopefully lead to the mitigation of these small sample issues.

This paper employs three months of observations sampled at 60‐second intervals to analyze the behavior of two basket indices from the emerging Athens Stock Exchange: the General Index of the Main (Listed) Securities Market and the Index of the Secondary (Unlisted) Securities Market. The empirical analysis employs robust regression using dummy variables to uncover a rich variety of time‐of‐day regularities in the first four moments of the distribution of returns, the tail behavior, and the dynamic and cross‐dynamic behavior of the two markets. Markets tend to behave differently during their opening and closing, while results are invariably sensitive to outliers. Overall, the results are comparable to those reported for developed equity markets. However, in contrast to other studies, we find no conclusive evidence of long‐memory in either the mean or variance process. ARMA models of seasonally differenced absolute returns were used as a simple but effective way of dealing with the strong regularity in volatility.

The purpose of this paper is to comprehensively review a large and heterogeneous body of academic literature on investors' feedback trading, one of the most popular trading patterns observed historically in financial markets. Specifically, the authors aim to synthesize the diverse theoretical approaches to feedback trading in order to provide a detailed discussion of its various determinants, and to systematically review the empirical literature across various asset classes to gauge whether their feedback trading entails discernible patterns and the determinants that motivate them.

Given the high degree of heterogeneity of both theoretical and empirical approaches, the authors adopt a semi-systematic type of approach to review the feedback trading literature, inspired by the RAMESES protocol for meta-narrative reviews. The final sample consists of 243 papers covering diverse asset classes, investor types and geographies.

The authors find feedback trading to be very widely observed over time and across markets internationally. Institutional investors engage in feedback trading in a herd-like manner, and most noticeably in small domestic stocks and emerging markets. Regulatory changes and financial crises affect the intensity of their feedback trades. Retail investors are mostly contrarian and underperform their institutional counterparts, while the latter's trades can be often motivated by market sentiment.

The authors provide a detailed overview of various possible theoretical determinants, both behavioural and non-behavioural, of feedback trading, as well as a comprehensive overview and synthesis of the empirical literature. The authors also propose a series of possible directions for future research.

Recent research into enterprise performance has focussed on the importance of firm proximity to total productivity. Using spatial correlation of firm performance as a proxy for knowledge transfers and diffusion, the purpose of this paper is to examine the evidence for these spatial effects in non-farm enterprise performance in Uganda, across space and time.

The author uses data from the geo-referenced Uganda National Panel Survey from 2010 to 2012, and employs explicit spatial techniques in the analysis of rural non-farm enterprise performance. Spatial autocorrelation of firm performance are used as proxies for knowledge transfers and information flows among enterprises across space and over time.

The study finds evidence of spatial spillover effects across space and time in Uganda. This implies that, as existing studies of developed countries have found, social infrastructure and firm proximity contribute significantly to the performance of rural economies, through information exchange and knowledge transfers.

Given the communal nature of rural households in the African setting, knowledge exchange and transfers among neighbouring firms should be encouraged as studies have found they have strong effects on business performance. Additionally, business “leaders” could also be useful in disseminating useful new technologies and applications to neighbouring enterprises in order to boost performance and productivity.

There should be better targeting of policy interventions to clusters of particularly needy enterprises.

To the best of the author’s knowledge, this is the first time that spatio-temporal effects of business performance have been explored. While spatial analyses of business performance have been carried out in developed countries, studies using explicit spatial techniques in the developing country setting have been conspicuously absent.

The purpose of this paper is to examine industry herding among the institutional investors and to find whether their herding behaviour is intentional or unintentional.

The study uses Lakonishok et al. (1992) model to examine the presence of industry herding behaviour among institutional investors. To determine whether the herding observed is intentional or unintentional, herding measure is regressed with volatility, volume, beta and return. The period of the study is from 1 April 2005-31 March 2015.

The findings of the study showed that though institutional investors have herding tendency towards most of the industries, in the overall period industry herding was not significant. The herding found in some industrial sectors was linked to economic performance of those sectors in India during the period of study and hence the herding was unintentional in nature.

This is the first attempt to study industry herding among institutional investors and their intent in Indian market ever since the country opened its market to foreign investors in a big way. Present study is limited to the use of only bulk/block data instead of the entire trading data for the period.

This study is the first attempt to investigate industry herding behaviour of institutional investors in the market using their bulk and block trading data. The herding observed in well performing industries has been shown to be unintentional and hence rational. The results indicate that the entry of big institutional investors, including foreign institutions into the Indian market has not destabilised the market by irrational herding.

Air quality, noise and proximity to urban infrastructure can arguably have an important impact on the quality of life. Environmental quality (the price of good health) has become a central tenet for consumer choice in urban locales when deciding on a residential neighbourhood. Unlike the market for most tangible goods, the market for environmental quality does not yield an observable per unit price effect. As no explicit price exists for a unit of environmental quality, this paper aims to use the housing market to derive its implicit price and test whether these constituent elements of health and well-being are indeed capitalised into property prices and thus implicitly priced in the market place.

A considerable number of studies have used hedonic pricing models by incorporating spatial effects to assess the impact of air quality, noise and proximity to noise pollutants on property market pricing. This study presents a spatial analysis of air quality and noise pollution and their association with house prices, using 2,501 sale transactions for the period 2013. To assess the impact of the pollutants, three different spatial modelling approaches are used, namely, ordinary least squares using spatial dummies, a geographically weighted regression (GWR) and a spatial lag model (SLM).

The findings suggest that air quality pollutants have an adverse impact on house prices, which fluctuate across the urban area. The analysis suggests that the noise level does matter, although this varies significantly over the urban setting and varies by source.

Air quality and environmental noise pollution are important concerns for health and well-being. Noise impact seems to depend not only on the noise intensity to which dwellings are exposed but also on the nature of the noise source. This may suggest the presence of other externalities that arouse social aversion. This research presents an original study utilising advanced spatial modelling approaches. The research has value in further understanding the market impact of environmental factors and in providing findings to support local air zone management strategies, noise abatement and management strategies and is of value to the wider urban planning and public health disciplines.

As the contradiction between economic development, resource and environment has become increasingly prominent, low-carbon competitiveness has received worldwide focus. This study aims to examine low-carbon competitiveness in 31 provinces (cities and regions) of China.

An evaluation index system for low-carbon competitiveness in China has been constructed, which is composed of 25 economic, social, environmental and policy indicators. To study the state of low-carbon competitiveness and resistance to China’ development of low-carbon competitiveness, this study uses a combination of the catastrophe progression model, the spatial autocorrelation model and the barrier method.

China’ low-carbon competitiveness gradually decreases from coastal to inland areas: the Tibet and Ningxia Hui autonomous regions are the least competitive regions, while the Shandong and Jiangsu provinces are the most competitive areas. The spatial correlation of the 31 provinces’ low-carbon competitiveness is very low and lacks regional cooperation. This study finds that the proportion of a region’ wetland area, the proportion of tertiary industries represented in its GDP and afforestation areas are the main factors in the development of low-carbon competitiveness. China should become the leader of carbon competitiveness by playing the leading role in the Eastern Region, optimizing the industrial structure, improving government supervision and strengthening environmental protection.

The paper provides a quantitative reference for evaluating China’ low-carbon competitiveness, which is beneficial for environmental policymaking. In addition, the evaluation and analysis methods offer relevant implications for developing countries.