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Nowadays, the field of data analytics is witnessing an unprecedented interest from a variety of stakeholders. The purpose of this paper is to contribute to the subfield of predictive analytics by proposing a new non-parametric classifier.

The proposed new non-parametric classifier performs both in-sample and out-of-sample predictions, where in-sample predictions are devised with a new Evaluation Based on Distance from Average Solution (EDAS)-based classifier, and out-of-sample predictions are devised with a CBR-based classifier trained on the class predictions provided by the proposed EDAS-based classifier.

The performance of the proposed new non-parametric classification framework is tested on a data set of UK firms in predicting bankruptcy. Numerical results demonstrate an outstanding predictive performance, which is robust to the implementation decisions’ choices.

The exceptional predictive performance of the proposed new non-parametric classifier makes it a real contender in actual applications in areas such as finance and investment, internet security, fraud and medical diagnosis, where the accuracy of the risk-class predictions has serious consequences for the relevant stakeholders.

Over and above the design elements of the new integrated in-sample-out-of-sample classification framework and its non-parametric nature, it delivers an outstanding predictive performance for a bankruptcy prediction application.

The purpose of this paper is to investigate the incremental information content of estimates of cash flow components in predicting future cash flows.

The authors examine whether the models incorporating components of operating cash flow from income statements and balance sheets using the direct method are associated with smaller prediction errors than the models incorporating core and non-core cash flow.

Using data from US and UK firms and multiple regression analysis, the authors find that around 60 per cent of a current year’s cash flow will persist into the next period’s cash flows, and that income statement and balance sheet variables persist similarly. The explanatory power and predictive ability of disaggregated cash flow models are superior to that of an aggregated model, and further disaggregating previously applied core and non-core cash flows provides incremental information about income statement and balance sheet items that enhances prediction of future cash flows. Disaggregated models and their components produce lower out-of-sample prediction errors than an aggregated model.

This study improves our appreciation of the behaviour of cash flow components and confirms the need for detailed cash flow information in accordance with the articulation of financial statements.

The findings are relevant to investors and analysts in predicting future cash flows and to regulators with respect to disclosure requirements and recommendations.

The findings are also relevant to financial statement users interested in better predicting a firm’s future cash flows and thereby, its firm’s value.

This paper contributes to the existing literature by further disaggregating cash flow items into their underlying items from income statements and balance sheets.

The purpose of this paper is to enlarge the exposure of the Theil–Sen (TS) methodology to the academic, analyst and practitioner communities using an earnings forecast setting. The study includes an appendix that describes the TS model in very basic terms and SAS code to assist readers in the implementation of the TS model. The study also presents an alternative approach to deflating or scaling variables.

Archival in nature using a combination of regression analysis and binomial tests.

The binomial test results support the hypothesis that the forecasting performance of the naïve no-change model is at least equal to or better than the ordinary least squares (OLS) model when earnings volatility is low. However, the results do not support the same hypothesis for the TS model nor do the results support the hypothesis that the OLS and TS models will outperform the naïve no-change model when cash flow volatility is high. Nevertheless, the study makes notable contributions to the literature, as the results indicate that the performance of the naïve model is at least as good as the OLS and TS models across 18 of the 20 binomial tests. Moreover, the results indicate that the performance of the TS model is always superior to the OLS model.

The results are generalizable to US firms and may not extend to non-US firms.

The TS methodology is advantageous to OLS in that the results are robust to outlier observations, and there is no heteroscedasticity. Researchers will find this study to be useful given the use of a model (i.e. TS) which has to date received little attention, and the provision of the details for the mechanics of the model. A bonus for researchers is that the study includes SAS code for implementing the procedure.

Awareness of alternative forecast methodologies could lead to improved forecasting results in certain contexts. The study also helps the financial community in general, as improved forecasting abilities are important for all capital market participants as they improve market efficiency.

Although a healthy literature exists for examining out-of-sample forecasts for earnings, the literature lacks an answer for a simple question before pursuing additional analyses: Are the results any better than those from a naive no-change forecast? The current study emphasizes the idea that the naïve no-change forecast is the most elementary model possible, and the researcher must first establish the superiority of a more complex model before conducting further analyses.

This paper aims to examine and compare the dividend ratios’ statistical and economic ability to predict the equity premium in the UK and US markets and two US sub-indices (S&P 500 Growth and S&P 500 Value).

In this paper, the authors use the linear regression models to examine the dividend ratios’ statistical ability to predict the equity premium. The in-sample and out-of-sample approaches, including Diebold and Mariano (1995) statistics, and Goyal and Welch’s (2003) graphical approach, are used. Also, the mean-variance analysis is used to test the economic significance.

The paper findings indicate that the dividend ratios have in-sample and out-of-sample predictive abilities in both UK and US markets and both US sub-indices. However, the results show that the dividend ratios have a less impressive predictive ability in the US market compared to the UK market and less in the US value index than the US growth index. This could indicate that there is no relation between the number of companies that distribute dividends in each index and the informativeness of dividends ratios. Furthermore, the tests show the dividend ratios’ predictive ability departure during particular periods and in some indices.

Results and implications of this research are exclusively applied to the US and UK markets. These results can also be applied with caution to other markets, taking into consideration the distinctive characteristics of these markets.

Results revealed in this paper imply that the investors in any of the indices may experience economic gain by adopting a dynamic trading strategy using the information content of the dividend ratios prediction models instead of the benchmark model, which is the prevailing simple moving average model.

This paper adds value through testing the prediction models’ economic significance in two well-developed markets, in addition to exploring the relationship between the number of companies distributing cash dividends and the dividends ratio prediction ability. Unlike most of the previous studies in which dividend ratios’ prediction ability is attributed to the number of companies that distribute dividends in the market, this paper denied this interpretation by studying two S&P 500 sub-indices. To the best of the authors’ knowledge, this is the first study to test the prediction models’ ability for these sub-indices.

This paper is intended to test the robustness of the fitness of nested GARCH models.

Both Monte‐Carlo simulation data and real‐world data are used in the paper. Likelihood‐family tests are used to test in‐sample fitness, while mean‐squared prediction error is employed for out‐sample prediction tests.

The paper finds that, generally, the parsimonious principle is found to work well for both criteria. However, it is found that conflict exists between the two criteria: in‐sample likelihood‐family tests pay more attention to conditional distributions or are more sensitive to fat tail effects; while the out‐sample criteria focus more on the accuracy of parameter estimation.

The paper shows that complexity does not necessarily mean good fitness; sometimes, the simpler model can fit better, especially for real‐world data.

Outlines previous research on company failure prediction and discusses some of the methodological issues involved. Extends an earlier study (Tan 1997) using artificial neural networks (ANN) to predict financial distress in Australian credit unions by extending the forecast period of the models, presents the results and compares them with probit model results. Finds the ANN models generally at least as good as the probit, although both types improved their accuracy rates (for Type I and Type II errors) when early warning signals were included. Believes ANN “is a promising technique” although more research is required, and suggests some avenues for this.

Partial least squares structural equation modeling (PLS-SEM) has become popular in the information systems (IS) field for modeling structural relationships between latent variables as measured by manifest variables. However, while researchers using PLS-SEM routinely stress the causal-predictive nature of their analyses, the model evaluation assessment relies exclusively on criteria designed to assess the path model's explanatory power. To take full advantage of the purpose of causal prediction in PLS-SEM, it is imperative for researchers to comprehend the efficacy of various quality criteria, such as traditional PLS-SEM criteria, model fit, PLSpredict, cross-validated predictive ability test (CVPAT) and model selection criteria.

A systematic review was conducted to understand empirical studies employing the use of the causal prediction criteria available for PLS-SEM in the database of Industrial Management and Data Systems (IMDS) and Management Information Systems Quarterly (MISQ). Furthermore, this study discusses the details of each of the procedures for the causal prediction criteria available for PLS-SEM, as well as how these criteria should be interpreted. While the focus of the paper is on demystifying the role of causal prediction modeling in PLS-SEM, the overarching aim is to compare the performance of different quality criteria and to select the appropriate causal-predictive model from a cohort of competing models in the IS field.

The study found that the traditional PLS-SEM criteria (goodness of fit (GoF) by Tenenhaus, R2 and Q2) and model fit have difficulty determining the appropriate causal-predictive model. In contrast, PLSpredict, CVPAT and model selection criteria (i.e. Bayesian information criterion (BIC), BIC weight, Geweke–Meese criterion (GM), GM weight, HQ and HQC) were found to outperform the traditional criteria in determining the appropriate causal-predictive model, because these criteria provided both in-sample and out-of-sample predictions in PLS-SEM.

This research substantiates the use of the PLSpredict, CVPAT and the model selection criteria (i.e. BIC, BIC weight, GM, GM weight, HQ and HQC). It provides IS researchers and practitioners with the knowledge they need to properly assess, report on and interpret PLS-SEM results when the goal is only causal prediction, thereby contributing to safeguarding the goal of using PLS-SEM in IS studies.

As live streaming becomes a widely used online sales mode, previously content-centred anchors are attempting to switch to e-commerce live streaming. The purpose of this research was to explore the mechanisms that prompt consumers to stay or leave after content anchors transfer to live e-commerce broadcasts. In addition, we explored the factors affecting consumption from the perspectives of anchors, consumers and the external environment.

We distributed questionnaires to a group of fans who had experienced the transition of content anchors to live streaming and received back 375 valid questionnaires. Using psychological contract theory, we constructed a theoretical model for the scenario in which content anchors transition to live e-commerce broadcasting and analysed the data using partial least squares structural equation modelling (PLS-SEM).

The results show that circle culture, mainstream culture, initial trust and live streaming content all positively influenced consumers’ attitudes, whilst consumers’ past shopping experiences negatively influenced consumers’ attitudes. The personal charm of the content anchors did not have a significant effect on consumers’ attitudes. Additionally, we found that only anchors with a significant circle culture and good trust levels amongst fans were able to transition to live e-commerce streaming successfully.

This study extends the application of psychological contract theory to the field of e-commerce and describes the transformation of different types of psychological contracts. The paper’s conclusions provide a reference for decision-making and the implementation of transformation by content-based anchors to live streaming, helping them to coordinate their relationships with fans more effectively.

Partial least squares (PLS) has been introduced as a “causal-predictive” approach to structural equation modeling (SEM), designed to overcome the apparent dichotomy between explanation and prediction. However, while researchers using PLS-SEM routinely stress the predictive nature of their analyses, model evaluation assessment relies exclusively on metrics designed to assess the path model’s explanatory power. Recent research has proposed PLSpredict, a holdout sample-based procedure that generates case-level predictions on an item or a construct level. This paper offers guidelines for applying PLSpredict and explains the key choices researchers need to make using the procedure.

The authors discuss the need for prediction-oriented model evaluations in PLS-SEM and conceptually explain and further advance the PLSpredict method. In addition, they illustrate the PLSpredict procedure’s use with a tourism marketing model and provide recommendations on how the results should be interpreted. While the focus of the paper is on the PLSpredict procedure, the overarching aim is to encourage the routine prediction-oriented assessment in PLS-SEM analyses.

The paper advances PLSpredict and offers guidance on how to use this prediction-oriented model evaluation approach. Researchers should routinely consider the assessment of the predictive power of their PLS path models. PLSpredict is a useful and straightforward approach to evaluate the out-of-sample predictive capabilities of PLS path models that researchers can apply in their studies.

Future research should seek to extend PLSpredict’s capabilities, for example, by developing more benchmarks for comparing PLS-SEM results and empirically contrasting the earliest antecedent and the direct antecedent approaches to predictive power assessment.

This paper offers clear guidelines for using PLSpredict, which researchers and practitioners should routinely apply as part of their PLS-SEM analyses.

This research substantiates the use of PLSpredict. It provides marketing researchers and practitioners with the knowledge they need to properly assess, report and interpret PLS-SEM results. Thereby, this research contributes to safeguarding the rigor of marketing studies using PLS-SEM.