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Articles published in 22 counseling journals from 2010–2019 were compared on 26 author and article characteristics. Women now constitute majorities of authorship and counseling journals are publishing larger proportions of research articles, 63% of which used quantitative approaches and about 11% of which studied counseling interventions. [ABSTRACT FROM AUTHOR]

Credit scoring is the process of determining whether applicants should be granted a financial loan. When a financial institution decides to create a credit scoring model for all applicants, the institution only has the known good/bad loan outcomes for accepted applicants. This causes inherent bias in the model We address a gap in the reject inference literature by developing a methodology to simulate rejected applicants. A methodology to illustrate how to simulate rejected applicants must be developed so that the reject inference techniques can be studied and appropriate reject inference techniques can be selected. This study uses a peer-to-peer financial loan information from accepted and rejected financial loan applicants to perform Monte Carlo simulation of rejected applicants. Using simulated data, the researchers compare the performance of three widely used reject inference techniques. [ABSTRACT FROM AUTHOR]

Our experiments evaluate the role of information presentation in reducing violations of the Law of One Price in individual investor selection of index mutual funds. The results indicate that most individuals fail to minimize fees. However, individuals allocate nearly 27% (43%) more of their investment dollars to the lowest fee index mutual fund when receiving fee information in the form of a table compared to a graph presentation (in ten-year rather than one-year form). Overall, a simple change from table to graph fee presentation results in a statistically and economically significant reduction in the fees paid by investors. [ABSTRACT FROM AUTHOR]

In the last several years, there has been significant research in applying semi-supervised machine learning models to the reject inference problem. When a financial institution wants to build a model to predict the default of credit applicants, the institution only has a known good/bad outcome loan status for the accepted applicants; this causes an inherent bias in the model. Reject inference is used to infer the good or bad loan status of credit applicants that were rejected by a financial institution. This paper presents a reject inference technique in which a semi-supervised framework is developed using a Naive Bayes model. The framework uses the expectation-maximization (EM) algorithm to incorporate rejected applicants into the parameter estimation of the model using a bootstrapping approach. The proposed method has an advantage over traditional reject inference methods because the rejected applicant data will participate in the estimation of the model parameters, thus avoiding the extrapolation problem. The Naive Bayes model using the EM algorithm is compared to logistic regression and several semi-supervised techniques. [ABSTRACT FROM AUTHOR]