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.     

Predicting remembering and forgetting of autobiographical memories in children and adults: a 4-year prospective study

Preservation and loss to forgetting of autobiographical memories is a focus in both the adult and developmental literatures. In both, there are comparative arguments regarding rates of forgetting. Children are assumed to forget autobiographical memories more rapidly than adults, and younger children are assumed to forget more rapidly than older children. Yet few studies can directly inform these comparisons: few feature children and adults, and few prospectively track the survival of specific autobiographical memories over time. In a 4-year prospective study, we obtained autobiographical memories from children 4, 6, and 8 years, and adults. We tested recall of different subsets of the events after 1, 2, and 3 years. Accelerated rates of forgetting were apparent among all child groups relative to adults; within the child groups, 4- and 6-year-olds had accelerated forgetting relative to 8-year-olds. The differences were especially pronounced in open-ended recall. The thematic coherence of initial memory reports also was a significant predictor of the survival of specific memories. The pattern of findings is consistent with suggestions that the adult distribution of autobiographical memories is achieved as the quality of memory traces increases (here measured by thematic coherence) and the rate of forgetting decreases.     

Everyday‐like memory for objects in ageing and Alzheimer's disease assessed in a visually complex environment: The role of executive functioning and episodic memory

To investigate everyday memory, more and more studies rely on virtual‐reality applications to bridge the gap between in situ approaches and laboratory settings. In this vein, the present study was designed to assess everyday‐like memory from the virtual reality‐based Human Object Memory for Everyday Scenes (HOMES) test (Sauzéon et al., 2012 , Exp. Psychol., 59, 99) in ageing and in Alzheimer's disease (AD). Two aims motivated this study: the first was to assess multiple processes of episodic memory (EM) functioning embedded within contexts closely related to real life in ageing and AD using the multi‐trial free‐recall paradigm, and the second aim was to evaluate the mediating effects of executive functioning (EF), EM, and subjective memory complaints (SMCs) on age differences in the HOMES measures and in AD. To this end, the HOMES test and neurocognitive tests of EF and EM were administered to 23 younger adults, 23 older adults, and 16 patients with AD. The results were: firstly, compared to young adults, elderly adults presented only free‐recall decline that almost disappeared in recognition condition whereas AD patients exhibited a poor clustering, learning, and recognition performance, and also a high amount of false recognition; secondly, age differences as well as AD related deficits on the HOMES test were mediated by both memory and EF measure while those observed on false memory indices were only mediated by EM measure; thirdly, the HOMES indices are related to SMCs even when episodic or EF measures are controlled. Overall, the results supported the fact that the VR‐based memory test is an appropriate device to capture age‐related differences as well as the AD effect with respect to both in situ and laboratory settings.     

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.