Towards a new approach to predict business performance using machine learning

Financial ratio plays a crucial role in business performance prediction, but the ability of the decision maker to use this method in adjusting management strategy has been extensively ignored. In this paper we attempt to build a fuzzy chance constrained least squares twin support vector machine (FCC-LSTSVM) to predict the business performance through the financial ratios. Specifically, machine learning techniques are utilized to build the models and 796 listed companies in China are selected as the data set. We find that different efficiencies are performed for different models with the same industry and different effectiveness are shown for different predicting time periods with the same method. In addition, the predicting achievements of business performance depend on the types of industries. This paper has extent significance both in theoretical development and managerial practices.     

Conditions for factor (in)determinacy in factor analysis

The subject of factor indeterminacy has a vast history in factor analysis (Guttman, 1955; Lederman, 1938; Wilson, 1928). It has lead to strong differences in opinion (Steiger, 1979). The current paper gives necessary and sufficient conditions for observability of factors in terms of the parameter matrices and a finite number of variables. Five conditions are given which rigorously define indeterminacy. It is shown that (un)observable factors are (in)determinate. Specifically, the indeterminacy proof by Guttman is extended to Heywood cases. The results are illustrated by two examples and implications for indeterminacy are discussed.     

How do investors make predictions? Insights from analogical reasoning research

Previous work on investor decision making has focused almost exclusively on information specific to the company being judged. Consequently, every decision is viewed as a novel event, disconnected from the investor's existing knowledge. In this study, the analogical reasoning literature provides the theoretical support for arguing that investors frequently utilize existing knowledge as a basis for generating predictions about a company's future. The specific proposal is that investors transfer their existing knowledge via two different forms of analogical reasoning. The first, relational reasoning, is based primarily on structural correspondence between a novel company and an existing schema. The second, literal similarity reasoning, is based primarily on surface correspondence of a novel company and a previously encountered company. Our theoretical framework is tested in a study in which experienced investors predict the outcome of a novel company's strategy after reading about the experiences of other companies who implemented a similar strategy. The results are consistent with the occurrence of both relational and literal similarity reasoning, with relational reasoning emerging as the dominant approach to generating investors' predictions. Copyright © 2000 John Wiley & Sons, Ltd.     

Early Change Trajectories in Cognitive-Behavioral Therapy for Binge-Eating Disorder

Rapid response is considered the most well-established outcome predictor across treatments of binge-eating disorder (BED), including cognitive-behavioral therapy (CBT). This study sought to identify latent trajectories of early change in CBT and compare them to common rapid response classifications. In a multicenter randomized trial, 86 adults with BED (DSM-IV) or subsyndromal BED provided weekly self-reports of binge eating over the first 4 weeks of CBT, which were analyzed to predict binge eating, depression, and body mass index at posttreatment, 6-, and 18-month follow-up. Using latent growth mixture modeling, three patterns of early change—including moderate and low decreasing—as well as low stable binge eating were identified, which significantly predicted binge-eating remission at 6-month follow-up. Other classifications of rapid response based on Receiver Operating Characteristics curve analyses or on the literature (≥ 10% reduction in binge eating at week 1, ≥ 70% reduction in binge eating at week 4) only predicted posttreatment remission or overall depression, respectively. Latent change trajectories, but not other rapid response classifications, predicted binge-eating frequency over time. A fine-grained analysis of change over the first 4 weeks of CBT for BED revealed different trajectories of early change in binge eating that led to an improved prediction of binge-eating outcome, compared to that of common rapid response classifications. Thorough monitoring of early change trajectories during treatment may have clinical utility.     

Contrast effects in judgmental forecasting when assessing the implications of worst and best case scenarios

Two experiments investigated whether individuals' forecasts of the demand for products and a stock market index assuming a best or worst case scenario depend on whether they have seen a single scenario in isolation or whether they have also seen a second scenario presenting an opposing view of the future. Normatively, scenarios should be regarded as belonging to different plausible future worlds so that the judged implications of one scenario should not be affected when other scenarios are available. However, the results provided evidence of contrast effects in that the presentation of a second “opposite” scenario led to more extreme forecasts consistent with the polarity of the original scenario. In addition, people were more confident about their forecasts based on a given scenario when two opposing scenarios were available. We examine the implications of our findings for the elicitation of point forecasts and judgmental prediction intervals and the biases that are often associated with them.