Asymmetric effects of housing wealth on college enrolment

We investigate the impact of housing wealth, credit availability and financial distress on college enrolment decisions. We find that housing wealth is negatively related to enrolment in public schools and positively related to enrolment in private schools. This evidence suggests that, on average, students substituted away from private schools towards public institutions during the recent financial crisis.     

Reference points for measuring social performance: Case study of a social business venture

We present a longitudinal qualitative case study to elaborate on how a social venture forms reference points for social performance. Although organizations increasingly use various social performance targets to direct their operations, the scholarly knowledge on social performance reference points remains limited. We make use of the prior accounting literature and draw on the idea of compromising accounts to discuss how provisional and performative metrics can have a significant role in how organizations develop new ways to evaluate their social performance. Given that the social performance reference point criteria are ambiguous and the corresponding referents malleable, performative accounts are helpful as they can intervene in the organizational life by making particular things visible, providing space for interpretations, and facilitating discussion, thus creating temporary settlements and enabling opportunities for productive compromises between different organizational groups and evaluative principles. The recursive feedback loops between reference point referents, criteria and accounting artefacts help the organization to make sense of its own social performance and interpret the associated performance feedback, and thereby provide ground for organizational decisions on further action. Moreover, we discuss how imperfect accounts can be useful for social businesses in their pursuit of developing their activities and achieving social impact.     

Does educational diversity of managers matter for the performance of team‐managed funds?

We study the effect of the educational diversity of managers on the performance of team‐managed mutual funds using a large sample of U.S. equity funds from 1994 to 2013. We consider diversity in terms of both final educational degree and field of educational specialisation. We find that, in general, both types of diversity have a positive impact on fund performance, and our results are robust over a wide range of performance metrics and changes in market conditions.     

Can investors time their exposure to private equity?

Private equity performance, both for buyouts and venture capital, has been highly cyclical: periods of high fundraising have been followed by periods of low performance. Despite this seemingly predictable variation, we find modest gains, at best, to pursuing realistic, investable strategies that time capital commitments to private equity. This occurs, in part, because investors can only time their commitments to funds; they cannot time when commitments are called or when investments are exited. There is a high degree of time-series correlation in net cash flows even across commitment strategies that allocate capital in a very different manner over time.     

Correction: Speaking Truth to Power: Twitter Reactions to the Panama Papers

Journal of Business Ethics -     

Introduction to symposium: Smallholders in communist and postcommunist societies

The primary contributions of smallholders during the communist and early postcommunist periods have been food production and labour for large farms. Those conditions are changing, however, as modern farms require less labour and food supply may be imported. For most smallholders in Central and Eastern European and former Soviet Union countries, the postcommunist neoliberal environment has not brought significant improvement, and strong arguments can be made that land grabbing, social and economic exclusion, and rural poverty are worse than regime bias during the communist period. Cooperatives, which have empowered smallholders in other parts of the world, have not been as well developed in postcommunist nations.     

Multi-level and mixed-level <Emphasis Type="Italic">k</Emphasis>-circulant supersaturated designs

Supersaturated designs (SSDs) constitute an important class of fractional factorial designs that could be extremely useful in factor screening experiments. Most of the existing studies have focused on balanced designs. This paper provides a new lower bound for the \(E(f_{NOD})\)-optimality measure of SSDs with general run sizes. This bound is a generalization of existing bounds since it is applicable to both balanced and unbalanced designs. Optimal multi and mixed-level, balanced and nearly balanced SSDs are constructed by applying a k-circulant type methodology. Necessary and sufficient conditions are introduced for the generator vectors, in order to pre-ensure the optimality of the constructed k-circulant SSDs. The provided lower bounds were used to measure the efficiency of the generated designs. The presented methodology leads to a number of new families of improved SSDs, providing tools for directly constructing optimal or nearly-optimal k-circulant designs by just checking the corresponding generator vector.     

Machine learning for financial transaction classification across companies using character-level word embeddings of text fields

An important initial step in accounting is mapping financial transfers to the corresponding accounts. We devised machine-learning-based systems that automate this process. They use word embeddings with character-level features to process transaction texts. When considering 473 companies independently, our approach achieved an average top-1 accuracy of 80.50%, outperforming baselines that exclude the transaction texts or rely on a lexical bag-of-words text representation. We extended the approach to generalizes across companies and even across different corporate sectors. After standardization of the account structures and careful feature engineering, a single classifier trained on 44 companies from 28 sectors achieved a test accuracy of more than 80%. When trained on 43 companies and tested on the remaining one, the system achieved an average performance of 64.62%. This rate increased to nearly 70% when considering only the largest sector.