Personalized goal-based investing via multi-stage stochastic goal programming

In this paper, we propose a goal-based investment model that is suitable for personalized wealth management. The model only requires a few intuitive inputs such as size of wealth, investment amount, and consumption goals from individual investors. In particular, a priority level can be assigned to each consumption goal and the model provides a holistic solution based on a sequential approach starting with the highest priority. This allows strict prioritization by maximizing the probability of achieving higher priority goals that are not affected by goals with lower priorities. Furthermore, the proposed model is formulated as a linear program that efficiently finds the optimal financial plan. With its simplicity, flexibility, and computational efficiency, the proposed goal-based investment model provides a new framework for automated investment management services.     

Auditors' Responses to Workload Imbalance and the Impact on Audit Quality*

Using detailed data for fieldwork hours and audit hours by rank from audit engagements in Korea, we examine whether audits conducted under workload imbalance, proxied by busy‐season audits, impair audit quality, and how auditors adjust staff assignments for busy‐season audits. We generally find that busy‐season audits are associated with lower audit quality, and that audit firms reduce the involvement of senior auditors during busy‐season audits. In addition, the greater the involvement of senior auditors and junior auditors, the lesser the deterioration in audit quality. Finally, although there is no increase in interim audits in response to workload imbalance during busy seasons, increasing interim audits can mitigate the negative impact of busy‐season audits on audit quality. Our results are relevant to auditors and regulators, who have expressed concerns about the adverse effects of workload imbalance on audit quality.     

Institutional Monitoring: Evidence from the F‐Score

The extant literature shows that institutional investors engage in corporate governance to enhance a firm's long‐term value. Measuring firm performance using the F‐Score, we examine the persistent monitoring role of institutional investors and identify the financial aspects of a firm that institutional monitoring improves. We find strong evidence that long‐term institutions with large shareholdings consistently improve a firm's F‐Score and that such activity occurs primarily through the enhancement of the firm's operating efficiency. Other institutions reduce a firm's F‐Score. Moreover, we find evidence that, while monitoring institutions improve a firm's financial health, transient (followed by non‐transient) institutions trade on this information.     

Strategies for Improving Codes of Ethics Implementation in Construction Organizations

It is necessary to reconsider the assumptions upon which the process of implementing compliance with ethical programs rests, in both theoretical and practical terms. These assumptions should hinge on organizational enablers that allow embeddedness of codes of ethics in the web of an organization's processes. This article sets out to describe an approach that will facilitate implementation of codes of ethics in construction organizations and a comprehensive literature survey approach is adopted to achieve this. The paper equally employs the application of the European Foundation for Quality Management (EFQM) model as a tool to stimulate ethical behavior in an organization, with the focus on the enabler criterion of the model. The authors discuss organizational enablers in relation to the implementation of ethical codes. The study demonstrates how ethics can be managed in an organization by proposing a framework to enhance codes of ethics embeddedness in the web of an organization. The paper indicates current research gaps and future opportunities for both academics and practitioners.     

Design and development of inventory knowledge discovery system

Inventory management (IM) performance is affected by the forecasting accuracy of both demand and supply. In this paper, an inventory knowledge discovery system (IKDS) is designed and developed to forecast and acquire knowledge among variables for demand forecasting. In IKDS, the TREes PArroting Networks (TREPAN) algorithm is used to extract knowledge from trained networks in the form of decision trees which can be used to understand previously unknown relationships between the input variables so as to improve the forecasting performance for IM. The experimental results show that the forecasting accuracy using TREPAN is superior to traditional methods like moving average and autoregressive integrated moving average. In addition, the knowledge extracted from IKDS is represented in a comprehensible way and can be used to facilitate human decision-making.     

CONVERTIBLE BOND PRICING MODELS

Convertible bonds are an important segment of the corporate bond market, with worldwide outstandings approaching US$235 billion. Simple pricing models value a convertible bond as being equivalent to a straight bond with an embedded option that enables the bond holder to convert to a specific amount of common stock. The straight bond is subject to both interest rate and credit risk, whereas the option to convert is dependent on the underlying stock price, which exposes the convertible bond holder to equity risk. The complexity of these features means that convertible bonds tend to be treated casually in major derivatives and corporate finance textbooks. This paper presents a survey of the theoretical and empirical aspects of convertible bond pricing. The limitations of these studies are highlighted to identify those areas of research that may improve the valuation process and facilitate the application of these securities for corporate financing.     

Changing Perceptions of Maturity Mismatch in the U.S. Banking System: Evidence from Equity Markets

We use the sensitivity of bank holding company equity returns to market interest rates as an indicator of perceived maturity mismatch. Based on data from 1990 to 2009, there is only weak evidence that market participants perceived banks to be effectively short‐funded. However, looking at 1990–1996 and 1997–2009 subsamples separately, our results suggest that U.S. commercial banks were perceived as short‐funded during the earlier time period but not the later. During this time of changing perceptions of maturity mismatch, banks were increasing their holdings of real estate loans as a share of total assets. We present evidence that, subsequent to 1996, market participants perceived real estate loans as having become effectively shorter‐term.     

A MCGP decision aid for homebuyers to make the best choice

This study adopts a new approach, the multi-choice goal programming (MCGP), to evaluate houses in order to help homebuyers to find better house based on the residential preferences. According to the function of MCGP, homebuyers can set multiple housing goals with multiple aspiration levels. This increases the flexibility to find a suitable house. Compared with other classical methods such as checklist and analytic hierarchy process, MCGP is more efficient, especially while considering a lot of housing criteria and house alternatives. In order to demonstrate the usefulness of MCGP decision aid for housing selection, a real case study is then provided. Furthermore, ten volunteers are invited to participate in the empirical experiment. The results also validate the effectiveness and efficiency of MCGP decision aid.     

Proving regularity of the minimal probability of ruin via a game of stopping and control

We reveal an interesting convex duality relationship between two problems: (a) minimizing the probability of lifetime ruin when the rate of consumption is stochastic and the individual can invest in a Black–Scholes financial market; (b) a controller-and-stopper problem, in which the controller controls the drift and volatility of a process in order to maximize a running reward based on that process, and the stopper chooses the time to stop the running reward and pays the controller a final amount at that time. Our primary goal is to show that the minimal probability of ruin, whose stochastic representation does not have a classical form as does the utility maximization problem (i.e., the objective’s dependence on the initial values of the state variables is implicit), is the unique classical solution of its Hamilton–Jacobi–Bellman (HJB) equation, which is a non-linear boundary-value problem. We establish our goal by exploiting the convex duality relationship between (a) and (b).