Regret in health insurance post‐purchase behavior

Unlike other forms of insurance, individuals with health insurance generally expect to make claims through the policy period. Selecting an appropriate level of cost‐sharing is difficult and individuals may, ex‐post, regret the choice of a less‐than‐suitable coverage amount. Using a national health insurance survey of private market consumers from 2013 to 2017, we evaluate the potential for post‐purchase regret in the health plan purchasing decision. We employ an ordered logistic model and find that consumers whose plan choices were likely financially dominated by a foregone alternative are significantly more likely to express regret through reporting significantly lower likelihood of renewal, even when controlling for confounding considerations including affordability, self‐assessed risk, and satisfaction with the plan.     

Adverse selection in the used‐car market: evidence from purchase and repair patterns in the Consumer Expenditure Survey

We analyze adverse selection in the used‐car market using a new approach that considers a car as an assemblage of parts, some with symmetric information and others with asymmetric information. Using data from the Consumer Expenditure Survey and Consumer Reports, we examine turnover and repair patterns. We find evidence of adverse selection due to the conditions of the transmission, engine, and, during colder months, air‐conditioning; and sorting due to the conditions of the vehicle body and, during warmer months, air‐conditioning. Our quantification exercises indicate that adverse selection may have a meaningful effect on trade volume and quality in the secondhand market.     

A multi‐agent‐based hybrid framework for international marketing planning under uncertainty

The increased complexity and competition in the global marketing environment present new challenges to decision‐makers. The characteristics of the international marketing planning problem are clarified in this paper. The advantages and disadvantages of relevant techniques and technologies that may be applied to deal with the planning problem are analysed. A multi‐agent‐based hybrid intelligent framework for international marketing planning and associated Internet strategy formulation is then established, with underlying techniques, technologies, software architecture and integration method outlined. In addition, a software prototype of the hybrid framework, called AgentsInternational, is created and presented, with initial evaluation results reported. Further work on this topic is also planned. Copyright © 2009 John Wiley & Sons, Ltd.     

The Adequacy of Pre‐purchase Due Diligence in Independent Small Business and Franchising

This research reports an in‐depth study of the due diligence activities that prospective independent small business operators and franchisees in Australia undertake prior to purchasing or starting up their businesses. Although academic literature and industry publications promote undertaking ‘proper due diligence’, there is a lack of empirical research into the nature of due diligence and its effect on business outcomes. Using a qualitative approach, 60 currently and formerly operating independents and franchisees were personally interviewed, exploring the diversity of approaches to undertaking due diligence prior to entering business. The research revealed that differences occur in both the type and amount of due diligence undertaken by independents and franchisees and highlighted further differences between current and former operators. In general, the due diligence undertaken by participants was relatively unsophisticated with few exceptions of rigour and planning. Where prospective independents and franchisees were entering business for the first time, their appreciation of business was naïve. A steep learning curve followed during which they often recognised flaws in their initial research. As a result of this qualitative in‐depth research, we present a set of propositions regarding due diligence and a model for future testing on a large sample.     

A two‐step system for direct bank telemarketing outcome classification

A two‐step system is presented to improve prediction of telemarketing outcomes and to help the marketing management team effectively manage customer relationships in the banking industry. In the first step, several neural networks are trained with different categories of information to make initial predictions. In the second step, all initial predictions are combined by a single neural network to make a final prediction. Particle swarm optimization is employed to optimize the initial weights of each neural network in the ensemble system. Empirical results indicate that the two‐step system presented performs better than all its individual components. In addition, the two‐step system outperforms a baseline one where all categories of marketing information are used to train a single neural network. As a neural networks ensemble model, the proposed two‐step system is robust to noisy and nonlinear data, easy to interpret, suitable for large and heterogeneous marketing databases, fast and easy to implement.     

Development of a case‐based decision support system for process modeling in BPR

This paper presents a knowledge‐based methodology for business process reengineering that uses a case‐based reasoning paradigm to provide decision support to its users in the modeling of a current problem and a redesign of critical business processes. As a process modeling tool for representing the business process, the event process chain (EPC) modeling method is used in this paper. We developed a CAPMOSS (CAse‐based Process MOdeling Supporting System) to support our proposed methodology. To reengineer a new business process problem, CAPMOSS retrieves from its case base the case that is most similar to the current problem. CAPMOSS uses a retrieved case to guide the structuring of AS‐IS models and TO‐BE models of a target business process. Using the transformational knowledge of a retrieved case, CAPMOSS helps the user to transform an AS‐IS model into a TO‐BE model for the target process with ease and the purchasing process in a government R&D institute is explained as an application of this approach. Copyright © 2000 John Wiley & Sons, Ltd.     

The role of attribute selection in Deep ANNs learning framework for high‐frequency financial trading

In financial trading, technical and quantitative analysis tools are used for the development of decision support systems. Although these traditional tools are useful, new techniques in the field of machine learning have been developed for time‐series forecasting. This paper analyses the role of attribute selection on the development of a simple deep‐learning ANN (D‐ANN) multi‐agent framework to accomplish a profitable trading strategy in the course of a series of trading simulations in the foreign exchange market. The paper evaluates the performance of the D‐ANN multi‐agent framework over different time spans of high‐frequency (HF) intraday asset time‐series data and determines how a set of the framework attributes produces effective forecasting for profitable trading. The paper shows the existence of predictable short‐term price trends in the market time series, and an understanding of the probability of price movements may be useful to HF traders. The results of this paper can be used to further develop financial decision‐support systems and autonomous trading strategies for the financial market.     

Feature selection methods involving support vector machines for prediction of insolvency in non‐life insurance companies

We propose two novel approaches for feature selection and ranking tasks based on simulated annealing (SA) and Walsh analysis, which use a support vector machine as an underlying classifier. These approaches are inspired by one of the key problems in the insurance sector: predicting the insolvency of a non‐life insurance company. This prediction is based on accounting ratios, which measure the health of the companies. The approaches proposed provide a set of ratios (the SA approach) and a ranking of the ratios (the Walsh analysis ranking) that would allow a decision about the financial state of each company studied. The proposed feature selection methods are applied to the prediction the insolvency of several Spanish non‐life insurance companies, yielding state‐of‐the‐art results in the tests performed. Copyright © 2005 John Wiley & Sons, Ltd.     

Capital gains tax,supply‐driven trading and ownership structure: direct evidence of the lock‐in effect

This study investigates the effect of differential capital gains tax rates on investor trading and share prices in a unique market setting that facilitates the resolution of conflicting prior evidence of holding period tax incentives. In particular, we examine whether the concessionary tax treatment of long‐term capital gains increases the supply of shares that qualify for long‐term status, thereby causing downward price pressure. We find evidence of abnormal seller‐initiated trading following the 12‐month anniversary of listing for IPO firms that appreciate in price (‘winners’) and report no such evidence for firms that decline in price (‘losers’). Consistent with the tax concessions being greater for individual than institutional investors, we report that abnormal seller‐initiated trading is mitigated by higher levels of ownership by institutional investors. We also report limited evidence, for winners, of declining share prices upon qualifying for long‐term tax status.     

Defining personalized concepts for XBRL using iPAD‐drawn fuzzy sets

An efficient and effective analysis of business data requires a better understanding of what the data represents, and to what degree. A human‐like way of accomplishing that without being too detailed yet learning more about data content is to summarize and map the data into concepts familiar to a person performing analysis. Processes of summarization help identify the most essential facts that are embedded in the data. All this is of significant importance for analysis of large amounts of business data required to make good and sound financial decisions. There are two aspects enabling more comprehensive yet easier processing of data: a standardized representation format of financial data; and a human‐friendly way of defining concepts and using them for building personalized models representing processing data. The first of the aspects has been addressed by the eXtensible Business Reporting Language (XBRL)—a standardized format of defining, representing and exchanging corporate and financial information. The second aspect is related to providing individuals with the ability to gain understanding of data content via determining a degree of truth of statements summarizing data based on their own perception of concepts they are looking for. In this paper, we introduce a tablet application—Tablet‐based input of Fuzzy Sets (TiFS)—and demonstrate its usefulness for entering personalized definitions of concepts and terms that enable a quick analysis of financial data. Such analysis means utilization of soft queries and operations of aggregation that extract and summarize the data and present it in a form familiar to analysts. The application allows for defining concepts and terms with ‘finger‐made’ drawings representing a person's perception of concepts. Further, these definitions are used to build summarization statements for exploring XBRL data. They are equipped with ‘drawn’ definitions of linguistic terms (e.g. LARGE, SMALL, FAST) and linguistic quantifiers (e.g. ALL, MOSTLY), and enable summarization of data content from the perspective of a user's interests. The ‘drawn’ linguistic terms and quantifiers represent membership functions of fuzzy sets. Utilization of fuzzy sets allows for performing operations of data summarization in a human‐like way. The application of TiFS illustrates ease of inputting personalized definitions of concepts and their influence on the interpretation of data. This introduces aspects of personalization and adaptation of artificial intelligence systems to perceptions and views of individuals. The proposed application is used to perform a basic analysis of an XBRL document.     

Nonlinearity and Flight‐to‐Safety in the Risk‐Return Trade‐Off for Stocks and Bonds

We document a highly significant, strongly nonlinear dependence of stock and bond returns on past equity market volatility as measured by the VIX. We propose a new estimator for the shape of the nonlinear forecasting relationship that exploits variation in the cross‐section of returns. The nonlinearities are mirror images for stocks and bonds, revealing flight‐to‐safety: expected returns increase for stocks when volatility increases from moderate to high levels while they decline for Treasuries. These findings provide support for dynamic asset pricing theories in which the price of risk is a nonlinear function of market volatility.     

The Context for Cross‐Border Insolvency Law Reform in Sub‐Saharan Africa

Cross‐border insolvency literature has developed significantly in recent years. However, the scholarship that has evolved lacks an insight from the perspective of Sub‐Saharan Africa (SSA). Existing theories on cross‐border insolvencies, and the global insolvency benchmarks that emerged in the recent years, have almost exclusively been developed from the best practices obtained in advanced economies. Accordingly, the context within which SSA cross‐border insolvency reform may be undertaken must be determined and explored given the pressure towards globalisation and the potential for the pressure to result in unsuitable legislative reform. This article sets out the context for cross‐border insolvency law reform in SSA. It raises issues that are likely to arise during the reform process and challenges that may be faced. Copyright © 2014 INSOL International and John Wiley & Sons, Ltd     

Who Will Pay for Long‐Term Care in the UK? Projections Linking Macro‐ and Micro‐Simulation Models

The long‐term care funding system continues to attract much debate in the UK. We produce projections of state and private long‐term care expenditure and analyse the distributional impact of state‐financed care, through innovative linking of macro‐ and micro‐simulation models. Variant assumptions about life expectancy, dependency and care costs are examined and the impact of universal state‐financed (‘free’) personal care, based on need but not ability to pay, is investigated. We find that future long‐term care expenditure is subject to considerable uncertainty and is particularly sensitive to assumed future trends in real input costs. On a central set of assumptions, free personal care would, by 2051, increase public spending on long‐term care from 1.1 per cent of GDP to 1.3 per cent, or more if it generated an increase in demand. Among the care‐home population aged 85 or over, the immediate beneficiaries of free personal care would be those with relatively high incomes.     

Liquidity in a Market for Unique Assets: Specified Pool and To‐Be‐Announced Trading in the Mortgage‐Backed Securities Market

Agency mortgage‐backed securities (MBS) trade simultaneously in a market for specified pools (SPs) and in the to‐be‐announced (TBA) forward market. TBA trading creates liquidity by allowing thousands of different MBS to be traded in a handful of TBA contracts. SPs that are eligible to be traded as TBAs have significantly lower trading costs than other SPs. We present evidence that TBA eligibility, in addition to characteristics of TBA‐eligible SPs, lowers trading costs. We show that dealers hedge SP inventory with TBA trades, and they are more likely to prearrange trades in SPs that are difficult to hedge.     

Voluntary Disclosures as a Mechanism for Defining Entity Status in Australian Not‐for‐Profit Organisations

This study examines managerial efforts to portray an entity's not‐for‐profit (NFP) status based on voluntary disclosure practices. The annual report text of 61 NFPs are analysed in accordance with Salamon and Anheier's (1997) NFP definitional framework. Results indicate a predominant application of the structural‐operational definition. Furthermore, the ‘organised’ attribute of this definition prevails over the ‘non‐profit‐distributing’ criterion that has been advocated by various parties. Standard‐setting bodies may want to consider: (1) NFP management perspectives in any revised NFP definition; and (2) greater clarity in conceptual framework and standard‐setting arrangements to improve overall transparency in NFP reporting practices.     

Mean Reversion in Profitability for Non‐listed Firms

The presence of mean reversion in profitability at the firm level is important for valuation and prediction of growth and earnings. We investigate the mean reversion in accounting profitability for Norwegian non‐listed firms for the period 1988–2006. We find a mean reversion rate of about 0.44 per year. This is higher than found in other studies. We also find that small firms have a higher mean reversion rate than large firms. Our results should have important practical implications for the difficult task of valuing non‐listed firms. Previously, price‐to‐book ratios have been used to investigate changes in profitability over time for listed firms. We examine bankruptcy risk as an alternative variable for unlisted firms. We find that bankruptcy risk may help explain changes in profitability, but the results are not as strong as found in previous work.     

Making trading decisions for financial‐engineered derivatives: a novel ensemble of neural networks using information content

Over the last decades, there has been a growing interest in applying artificial intelligence techniques to solve a spectrum of financial problems. A number of studies have shown promising results in using artificial neural networks (ANNs) to guide investment trading. Given the expanding role of ANNs in financial trading, this paper proposes the use of a hybrid neural network, which consists of two independent ANN architectures, and comparatively evaluates its performance against independent ANNs and econometric models in the trading of a financial‐engineered (synthetic) derivative composed of options on foreign exchange futures. We examine the financial profitability and the market timing ability of the competing neural network models and statistically compare their attributes with those based on linear and nonlinear statistical projections. A random walk model and the option pricing method are also included as benchmarks for comparison. Our empirical investigation finds that, for each of the currencies analysed, trading strategies guided by the proposed dual network are financially profitable and yield a more stable stream of investment returns than the other models. Statistical results strengthen the notion that diffusion of information contents and cross‐validation between the independent components within the dual network are able to reduce bias and extreme decision making over the long run. Moreover, the results are robust with respect to different levels of transaction costs. Copyright © 2009 John Wiley & Sons, Ltd.     

The Retail Price Index and the Cost‐of‐Living Index: testing for consistency in theory and practice

This paper tests the published section‐level price and weight data used in the compilation of the UK retail price index (RPI) for consistency with the theory of the cost‐of‐living index. We use a non‐parametric test of theoretical consistency and bootstrap statistical methods to estimate the probability of consistency.     

Research horizons for public and private not‐for‐profit sector reporting: moving the bar in the right direction

The examination of public and private not‐for‐profit sector financial reporting has been a topic of interest on a cyclical basis in Australia over the last 30 years. Traditional topics have included examinations of the intended and unintended consequences of specific standards, the accountability value of financial reports, transaction neutrality, compliance with the accounting standards, and more recently, the prospective implications of new, differently focused reporting standards considering such issues as income measurement and outcomes reporting. With increased recent attention from standard setters and regulators, and greater data availability, the opportunities for undertaking impactful research in these and related areas are increasing. In this paper, we focus on research that has examined the following questions: (i) Which private and public NFPOs lodge financial reports and what is reported; (ii) Who are the users and what are their information needs? (iii) Which private and public NFPs should lodge financial reports and what should be included in them; and (iv) How should the accounting frameworks for NFP sector reporting be set? For each of these issues, we identify the research gaps and opportunities for further research.     

Value for Money? Neo‐Liberalism in New South Wales Prisons

The NSW prison sector has undergone considerable reform over the last ten years. The NSW government now oversees the operation of publicly managed prisons, one privately managed prison and a number of new public prisons operating under the new 'Way Forward' management model. In order to establish which approach to prison management offered the best value for money, the NSW government undertook a 'value for money' assessment in 2005. In this article, we argue the cost accounting information is privileged in the assessment process. However, we contend that this information was limited and partial, and provided a poor basis on which to form policy. Even so, the NSW government has proceeded on this basis. In order to explain this, we position the report within the wider neo-liberal turn in policymaking and the workplace reforms that have accompanied it.