Investor heterogeneity and the cross-sectional stock returns in China

We examine the relationship between divergence of opinion and the cross-sectional stock returns in Chinese A share market where short-selling of stocks is prohibited by law. Using a proxy for divergence of opinion among the entire investor base, we document a positive relationship between divergent beliefs and future stock returns. This is in sharp contrast to Miller's (1977) prediction of a negative relationship between the two. The result is likely to be driven by the dominance of individual investors and their speculative trading behaviors in China. Miller's prediction is confirmed when divergence of opinion is measured using data on mutual fund holdings. Our results are robust to a number of common return predictors. We also find a significantly negative relationship between the fraction of tradable shares in listed Chinese companies and future stock returns. Increase in the fraction of tradable shares tends to reduce the predictability of stock returns using divergence of opinion.

Projections of the future path of the gender wage gap in Great Britain

Purpose – The paper attempts to project the future trend of the gender wage gap in Great Britain up to 2031.
Design/methodology/approach – The empirical analysis utilises the British Household Panel Study Wave F together with Office for National Statistics (ONS) demographic projections. The methodology combines the ONS projections with assumptions relating to the evolution of educational attainment in order to project the future distribution of human capital skills and consequently the future size of the gender wage gap.
Findings – The analysis suggests that gender wage convergence will be slow, with little female progress by 2031 unless there is a large rise in returns to female experience.
Originality/value – The paper has projected the pattern of male and female skill acquisition together with the associated trend in wages up to 2031.

Design of multiple sequence alignment algorithms on parallel, distributed memory supercomputers

The challenge of comparing two or more genomes that have undergone recombination and substantial amounts of segmental loss and gain has recently been addressed for small numbers of genomes. However, datasets of hundreds of genomes are now common and their sizes will only increase in the future. Multiple sequence alignment of hundreds of genomes remains an intractable problem due to quadratic increases in compute time and memory footprint. To date, most alignment algorithms are designed for commodity clusters without parallelism. Hence, we propose the design of a multiple sequence alignment algorithm on massively parallel, distributed memory supercomputers to enable research into comparative genomics on large data sets. Following the methodology of the sequential progressiveMauve algorithm, we design data structures including sequences and sorted k-mer lists on the IBM Blue Gene/P supercomputer (BG/P). Preliminary results show that we can reduce the memory footprint so that we can potentially align over 250 bacterial genomes on a single BG/P compute node. We verify our results on a dataset of E.coli, Shigella and S.pneumoniae genomes. Our implementation returns results matching those of the original algorithm but in 1/2 the time and with 1/4 the memory footprint for scaffold building. In this study, we have laid the basis for multiple sequence alignment of large-scale datasets on a massively parallel, distributed memory supercomputer, thus enabling comparison of hundreds instead of a few genome sequences within reasonable time.

Does the choice of estimator matter when forecasting returns?

While the literature concerned with the predictability of stock returns is huge, surprisingly little is known when it comes to role of the choice of estimator of the predictive regression. Ideally, the choice of estimator should be rooted in the salient features of the data. In case of predictive regressions of returns there are at least three such features; (i) returns are heteroskedastic, (ii) predictors are persistent, and (iii) regression errors are correlated with predictor innovations. In this paper we examine if the accounting of these features in the estimation process has any bearing on our ability to forecast future returns. The results suggest that it does.

Regulatory theory insights into the past, present and future of general purpose water accounting standard setting

Purpose – Accounting and water industry experts are developing general-purpose water accounting (GPWA) to report information about water and rights to water. The system has the potential to affect water policies, pricing and management, and investment and other decisions that are affected by GPWA report users' understanding of water risks faced by an entity. It may also affect financial returns to accounting and auditing firms and firms in water industries. In this paper the authors aim to examine the roles of the accounting profession, water industries and other stakeholders in governing GPWA. Recognising that the fate of GPWA depends partly upon regulatory power and economics, they seek to apply regulatory theories that explain financial accounting standards development to speculate about the national and international future of GPWA.

Design/methodology/approach – Official documents, internal Water Accounting Standards Board documents and unstructured interviews underpin the authors' analysis.

Findings – The authors speculate about the benefits that might accrue to various stakeholder groups from capturing the GPWA standard-setting process. They also suggest that internationally, water industries may dominate early GPWA standards development in the public interest and that regulatory capture by accounting or water industry professionals will not necessarily conflict with public interest benefits.

Practical implications – Accounting for water can affect allocations of environmental, economic, social and other resources; also, accounting and water industry professional standing and revenues. In this paper the authors identify factors influencing GPWA standards and standard-setting institutional arrangements, and thereby these resource allocations. The paper generates an awareness of GPWA's emergence and practical implications.

Originality/value – This is an early study to investigate water accounting standard-setting regulatory influences and their impact.

A modelling framework for optimisation of commodity production by minimising the impact of climate change

The agricultural sector is vulnerable to the impact of climate change due to decreasing rainfall, increasing temperature, and the frequency of extreme weather events. A modelling framework was developed and applied to identify issues, problems and opportunities arising in regional agricultural systems as a consequence of climate change. This integrated framework blends together land suitability analysis, uncertainty analysis and an optimisation approach to establish optimal agricultural land-use patterns on a regional scale for current and possible future climate scenarios. The framework can also be used to identify (i) regions under threat of productivity decline, and (ii) alternative crops and their locations that can cope better with changing climate. The methods and contents of the framework are presented by means of a case study developed in the South West Region of Victoria, Australia. The results can be used to assess land suitability in support of optimised crop allocations across a local region, and to underpin the development of a regional adaptation policy framework designed to reduce the vulnerability of the agriculture sector to the impacts of climate change.