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The purpose of this paper is to examine the ability of hedge funds and funds of hedge funds to generate absolute returns using fund level data.

The absolute return profiles are identified using properties of the empirical distributions of fund returns. The authors use both Bayesian multinomial probit and frequentist multinomial logit regressions to examine the relationship between the return profiles and fund characteristics.

Some evidence is found that only some hedge funds strategies, but not all of them, demonstrate higher tendency to produce absolute returns. Also identified are some investment provisions and fund characteristics that can influence the chance of generating absolute returns. Finally, no evidence was found for performance persistence in terms of absolute returns for hedge funds but some limited evidence for funds of funds.

This paper is the first attempt to examine the hedge fund return profiles based on the notion of absolute return in great details. Investors and managers of funds of funds can utilize the identification method in this paper to evaluate the performance of their interested hedge funds from a new angle.

Using the properties of the empirical distribution of the hedge fund returns to classify them into different absolute return profiles is the unique contribution of this paper. The application of the multinomial probit and multinomial logit models in the fund performance and fund characteristics literature is also new since the dependent variable in the authors' regressions is multinomial.

The purpose of this paper is to investigate the relationships among choice of risk management tools, perceived effectiveness of risk management tools, self-reported risk attitude, and farm and farmer characteristics.

A mail survey was used to collect information about utilization of risk management tools, perceived effectiveness of risk management tools, and factors that could influence choice of risk management tools by Illinois farmers. Cluster analysis, one-way ANOVA, χ2 tests of independence, and multinomial logistic regression were utilized to detect possible relationships among choice of risk management tools, perceived effectiveness of risk management tools, self-reported risk attitude, and farm and farmer characteristics.

Multinomial logistic regression analysis revealed that age and gross farm income (GFI) were the strongest predictors of the risk management tool utilization group to which an individual would be assigned. The number of risk management tools utilized decreased with age but increased with GFI. Neither self-reported risk attitude nor education was a significant independent variable in the multinomial logistic regression model, but both were strongly impacted by age. Younger farmers with higher GFI were the most likely users of hedging.

The results of this study provide support for the idea that farmers who are better able to generate revenue are better able to manage risk, but the direction of causality was not investigated.

Risk management service providers could benefit from this study as a benchmark for understanding their current and potential farmer clients’ risk management strategies.

This study used cluster analysis and multinomial logistic regression to address the complexity of decisions regarding multiple risk management tools. The number of tools utilized by individuals was investigated.

For firms listed on the New Zealand Stock Exchange, which is a relatively thinly traded market, the purpose of this paper is to examine the nature of stock returns associated with a dividend omission announcement when computations specifically address thin trading, and whether specific firm characteristics affect the likelihood and nature of a dividend omission.

First, event study analysis is used to check if dividend omissions actually do impact share prices in terms of short-term abnormal returns and longer-term cumulative abnormal returns (CARs) in a thinly traded market. Second, binomial logistic regression analysis is used to determine what, if any, company characteristics are associated with the decision to omit a dividend. Third, multinomial logistic regression analysis is employed to determine what firm characteristics are associated with continuing (or ending) a phase of no dividends before a dividend resumption.

Dividend omissions generate immediate negative abnormal returns, and there is a longer-term persistence of negative CARs. The size and duration of these abnormal returns are smaller, but still significant, when thin-market-specific methodology is employed. With respect to firm characteristic, smaller firms, firms with decreased earnings, a higher level of extraordinary charges, greater leverage and firms with a higher book-to-market value are associated with a greater likelihood of making an omission. With respect to the length of time between an omission and resumption of dividend payments, earnings decreases, a higher book-to-market value, a higher level of extraordinary charges and a decrease in firm debt level become significant.

This paper adds value in two dimensions. First, it considers dividend omissions in three different, but inter-connected ways. Second, the use of multinomial logistic regression to examine an aspect of the non-payment hiatus breaks new ground.

Artificial neural network (ANN) models, part of the discipline of machine learning and artificial intelligence, are becoming more popular in the marketing literature and in marketing practice. This paper aims to provide a series of tests between ANN models and competing predictive models.

A total of 46 pairs of models were evaluated in an objective model-building environment. Either logistic regression or multiple regression models were developed and then were compared to ANN models using the same set of input variables. Three sets of B2B data were used to test the models. Emphasis also was placed on evaluating small samples.

ANN models tend to generate model predictions that are more accurate or the same as logistic regression models. However, when ANN models are compared to multiple regression models, the results are mixed. For small sample sizes, the modeling results are the same as for larger samples.

Like all marketing research, this application is limited by the methods and the data used to conduct the research. The findings strongly suggest that, because of their predictive accuracy, ANN models will have an important role in the future of B2B marketing research and model-building applications.

ANN models should be carefully considered for potential use in marketing research and model-building applications by B2B academics and practitioners alike.

The research contributes to the B2B marketing literature by providing a more rigorous test on ANN models using B2B data than has been conducted before.

The purpose of this study is to understand the effect of price promotions, venue and place of residence on low‐risk, risky and high‐risk alcohol consumption behaviour of young women between 18 and 24 years of age who attend university in Australia, Wales and Germany.

The quantitative, self‐administered questionnaire collected data from a convenience sample of three universities in three OECD countries with high alcohol consumption being: a regional Australian university (n=305), a city Welsh university (n=354) and a rural German university (n=325).

First, the multinomial logistic regression results revealed that price promotions and venue influenced alcohol consumption in Wales alone while place of residence influenced alcohol consumption in Australia; however, price promotions, venue and place of residence had no effect on young women attending university in Germany. Second, the binomial logistic regression results for Wales reported a sensitivity to price promotions for all three alcohol consumption risk classifications; however, location was of little consequence to risky drinkers when compared to high risk drinkers. For Australia, the place of residence did not influence alcohol consumption for both risky and high‐risk drinkers.

The value of this study lies in the examination of three levels of alcohol consumption – low‐risk, risky and high‐risk – for the same cohort across three countries using the same test instrument and standard alcohol consumption metrics. As such, this study provides a more meaningful macro view of alcohol consumption; thus has the capacity to contribute to effectual intervention strategies.

This study aims to develop a regression-based machine learning model to predict housing price, determine and interpret factors that contribute to housing prices using different data sets available publicly. The significant determinants that affect housing prices will be first identified by using multinomial logistics regression (MLR) based on the level of relative importance. A comprehensive study is then conducted by using SHapley Additive exPlanations (SHAP) analysis to examine the features that cause the major changes in housing prices.

Predictive analytics is an effective way to deal with uncertainties in process modelling and improve decision-making for housing price prediction. The focus of this paper is two-fold; the authors first apply regression analysis to investigate how well the housing independent variables contribute to the housing price prediction. Two data sets are used for this study, namely, Ames Housing dataset and Melbourne Housing dataset. For both the data sets, random forest regression performs the best by achieving an average R² of 86% for the Ames dataset and 85% for the Melbourne dataset, respectively. Second, multinomial logistic regression is adopted to investigate and identify the factor determinants of housing sales price. For the Ames dataset, the authors find that the top three most significant factor variables to determine the housing price is the general living area, basement size and age of remodelling. As for the Melbourne dataset, properties having more rooms/bathrooms, larger land size and closer distance to central business district (CBD) are higher priced. This is followed by a comprehensive analysis on how these determinants contribute to the predictability of the selected regression model by using explainable SHAP values. These prominent factors can be used to determine the optimal price range of a property which are useful for decision-making for both buyers and sellers.

By using the combination of MLR and SHAP analysis, it is noticeable that general living area, basement size and age of remodelling are the top three most important variables in determining the house’s price in the Ames dataset, while properties with more rooms/bathrooms, larger land area and closer proximity to the CBD or to the South of Melbourne are more expensive in the Melbourne dataset. These important factors can be used to estimate the best price range for a housing property for better decision-making.

A limitation of this study is that the distribution of the housing prices is highly skewed. Although it is normal that the properties’ price is normally cluttered at the lower side and only a few houses are highly price. As mentioned before, MLR can effectively help in evaluating the likelihood ratio of each variable towards these categories. However, housing price is originally continuous, and there is a need to convert the price to categorical type. Nonetheless, the most effective method to categorize the data is still questionable.

The key point of this paper is the use of explainable machine learning approach to identify the prominent factors of housing price determination, which could be used to determine the optimal price range of a property which are useful for decision-making for both the buyers and sellers.

The purpose of this paper is to assess the efficiency determinants of mergers and acquisitions (M&A) in the context of Latin American airlines based on business-related variables commonly found in the literature. The idea is to identify preferable potential airline matches in light of fleet mix, ownership structure and geographical proximity.

In order to achieve the objective, all possible combinations of M&A pairs are considered in the analysis, which is developed in a two-stage approach. First, the M&A Data Envelopment Analysis model efficiency and returns-to-scale estimates are computed. Then, robust regression and multinomial logistic regression are respectively used to discriminate these estimates in terms of such business-related variables.

The results reveal that these different contextual variables significantly impact virtual efficiency and returns-to-scale levels. Private ownership, passenger focus and a better match between aircraft size and demand for flights appear to be key drivers for merged airline efficiency.

The study makes theoretical contributions, though limited to analyzing Latin American airlines only. The use of bootstrapped robust/multinominal logistic regression, compared to the methods adopted by previous literature studies, generates more accurate and robust results related to the efficiency drivers due to its special feature and ability to allow the discrimination of increasing, decreasing, and constant returns to scale in light of a given set of contextual variables.

This study examines the pure effect of the merging activity on efficiency gains. Not only private ownership but also a hybrid public–private ownership has a positive influence on virtual efficiency, suggesting an important governmental role in promoting M&A in the airline industry.

The authors present an original take on the issue of airline mergers by exploring what are the major drivers possibly involved in efficiency gains of potentially merged (virtual) airlines. The authors identify preferable potential airline matches where efficiency gains would be positive in light of business-related variables such as fleet mix, ownership structure and geographical proximity. The analysis also includes an assessment of the impact of contextual variables such as cargo type, ownership structure and geographical proximity in relation to the strategic fit of mergers considering the resulting efficiency and returns-to-scale scores of virtually merged airlines. To the authors’ knowledge, no previous research has addressed these issues in Latin American airlines. Further research directions for this industry are also discussed.

Malnutrition is one of the serious public health problems especially for children and pregnant women in developing countries such as Bangladesh. This study aims to identify the risk factors associated with child nutrition for both male and female children in Bangladesh.

This study was conducted among 23,099 mothers or caretakers of children under five years of age from a nationally representative survey named Bangladesh Multiple Indicator Cluster Survey, 2019. This study used chi-square test statistic for bivariate analysis and multinomial logistic regression was used to evaluate the adjusted effects of those covariates on child nutritional status.

The prevalence of severely malnourished, nourishment was higher for males than females (5.3% vs 5.1%, 77.4% vs 76.8%) while moderately malnourished were higher for females (18.1% vs 17.4%). The findings from the multinomial model insinuated that the mother’s education level, wealth index, region, early child development, mother’s functional difficulties, child disability, reading children's books and diarrhea had a highly significant effect on moderate and severe malnutrition for male children. For the female children model, factors such as mother’s education level, wealth index, fever, child disability, rural, diarrhea, early child development and reading less than three books were significant for moderate and severe malnutrition.

There is a solution to any kind of problem and malnutrition is not an exceptional health problem. So, to overcome this problem, policymakers should take effective measures to improve maternal education level, wealth status, child health.

This paper explores which fundamental aspects of US insurance firms are significant factors in determining whether a firm will be a target or acquirer firm.

By focusing on 251 mergers and acquisitions (M&A) deals (119 target firms and 132 acquirer firms) over the period between 1990 and 2019, multinomial logistic regression results identify the determinants associated with becoming targets or acquirers.

US insurance firms are more likely to become targets if they are smaller, have lower cash holdings, are non-life, and do not have environmental, social and governance (ESG) scores. Insurance firms are more likely to be acquirers if they have higher profitability, higher cash flow and higher intangibles, and if they are non-life and do not have ESG scores. Moreover, the likelihood of becoming an acquirer decreases in times of global financial crises (GFCs) as compared to non-GFC times.

This paper is the first to utilize multi-period multinomial logistic regression analysis to investigate the determinants of selection decisions of M&A targets and acquirers in the US insurance industry.

The purpose of this paper is to apply Poisson generalized linear model (PGLM) with log link instead of multinomial logistic regression to monitor multinomial logistic profiles in Phase I. Hence, estimating the coefficients becomes easier and more accurate.

Simulation technique is used to assess the performance of the proposed algorithm using four different control charts for monitoring.

The proposed algorithm is faster and more accurate than the previous algorithms. Simulation results also indicate that the likelihood ratio test method is able to detect out-of-control parameters more efficiently.

The PGLM with log link has not been used to monitor multinomial profiles in Phase I.