Classical meets modern in the IDEA protocol for structured expert judgement

Expert judgement is pervasive in all forms of risk analysis, yet the development of tools to deal with such judgements in a repeatable and transparent fashion is relatively recent. This work outlines new findings related to an approach to expert elicitation termed the IDEA protocol. IDEA combines psychologically robust interactions among experts with mathematical aggregation of individual estimates. In particular, this research explores whether communication among experts adversely effects the reliability of group estimates. Using data from estimates of the outcomes of geopolitical events, we find that loss of independence is relatively modest and it is compensated by improvements in group accuracy.     

Regression Modeling for the Valuation of Large Variable Annuity Portfolios

Variable annuities are insurance products that contain complex guarantees. To manage the financial risks associated with these guarantees, insurance companies rely heavily on Monte Carlo simulation. However, using Monte Carlo simulation to calculate the fair market values of these guarantees for a large portfolio of variable annuities is extremely time consuming. In this article, we propose the class of GB2 distributions to model the fair market values of guarantees to capture the positive skewness typically observed empirically. Numerical results are used to demonstrate and evaluate the performance of the proposed model in terms of accuracy and speed.     

Decomposing the market,industry, and firm components of profitability: implications for forecasts of profitability

Academics and practitioners frequently highlight that overall market and industry performance is an important aspect of a firm’s profitability. However, few studies allow for the decomposition of a firm’s profitability into market, industry, and idiosyncratic components, and those that do often assume that the market and industry components are cross-sectional constants. In this study, we allow for variation in firm-specific sensitivities to market, industry, and idiosyncratic economic shocks, and then assess whether and when this decomposition results in improved forecasts of profitability. For the overall sample, we find significant improvements in terms of the magnitude of forecast errors and the frequency with which forecasts based on the decomposed values are superior versus forecasts using only total profitability. Across the sample as a whole, decomposing profitability in the forecasting process results in more accurate forecasts greater than two-thirds of the time (increasing to almost 80% within certain subsamples). Our results provide strong support for the role that firm-specific measures of market and industry profitability play in predicting a firm’s future performance, as well as highlighting settings where the decomposition provides the greatest benefit in terms of predicting future changes in profitability.     

<Emphasis Type="Bold">Measuring the market response to going concern modifications: the importance of disclosure timing</Emphasis>

Auditor going concern modifications (GCMs) are intended to provide market participants with information related to financial distress, and prior research suggests that the disclosure of a GCM elicits a substantial negative market reaction from investors. In this study, we investigate the market reaction to GCMs in a contemporary disclosure regime and consider whether the observed market reaction is confounded by other material disclosures. We find that the majority of GCMs are issued concurrently with earnings announcements (EAs) and that EAs in the year of new GCMs elicit large negative cumulative abnormal returns (CARs). We also find that CARs surrounding GCMs are significantly more negative when GCMs are disclosed with EAs versus following EAs. We then evaluate whether GCMs convey distress that is incremental to EA disclosures by measuring i) the market reaction to GCMs disclosed following EAs, and ii) whether EA CARs are substantially more negative for companies disclosing GCMs with EAs as opposed to after EAs. In both cases, we find that the incremental market response to GCMs is statistically weak and much smaller in economic magnitude than is suggested by prior research. Finally, we find that management disclosures in EAs, rather than the presence of a GCM, appear to convey information that investors use to anticipate bankruptcy. Taken together, these findings suggest that GCMs are confounded by other significant disclosures and that the informational benefits of GCM reporting are significantly smaller than previously thought.     

Pairs trading under transaction costs using model predictive control

Model predictive control with proportional transactions costs provides a good approximation to the optimal trading strategy     

Behavioral portfolio selection and optimization: an application to international stocks

The behavioral approach of decision making has emerged as a diversified solution in the presence of risk and uncertainty. Using the popular cumulative prospect theory as an objective function for portfolio selection, this study implements the classical mean–variance model to compare the portfolio performance of high behavioral stocks with that of stocks with lower behavioral values. Based on a sample of 37 international stocks over the period from October 1998 to November 2017, empirical results from D-vine pair copula GARCH-GEV indicate that the portfolio of high behavioral prospect stocks outperforms the portfolio of stocks with low behavioral scores. This finding may suggest that portfolios with high behavioral values coincide with rational efficiency sets.     

Business cycles,financial cycles and capital structure

We perform peridogram based cycle analysis of firm capital structure and find evidence that firms’ leverage is both persistent and cyclical. The cyclicality of leverage is supported by the trade-off, pecking order and market timing capital structure theories (Korajczyk and Levy in J Financ Econ 68:75–109, 2003; Bhamra et al. in Rev Financ Stud 23:645–703, 2010). Although market timing theory research supports persistence, previous literature dictates that the trade-off and pecking order theories may predict either persistent or mean reverting leverage. Our tests reject mean reversion in favor of persistent and cyclical leverage. We corroborate pecking order theory literature that predicts leverage is persistent. In these models, when firms’ investment spending is below earnings, leverage decreases. In addition, we examine whether firms change their capital structure as a result of business and financial cycles. Since financial cycles last longer than business cycles, financial cycles should have a long term effect on leverage. Our findings confirm the persistent leverage business cycle models that suggest firms change their capital structure due to financial and credit cycles (Jermann and Quadrini in Am Econ Rev 102:238–271, 2012; Azariadis et al. in Rev Econ Stud 83:1364–1405, 2016). We conclude that leverage is persistent due to the cyclicality of the financing decision.     

How supply chain architecture and product architecture impact firm performance: An empirical examination

In order to enhance performance, manufacturing firms are building various capabilities and utilizing supply chain networks that are geographically dispersed around the globe. This study examines how decisions related to supply chain architecture and product architecture influence organizational competitiveness. Building on the co-specialization perspective, we evaluate the performance implications of product modularity and supply chain agility. While, product modularity constitutes a product configuration related to sourcing and assembling of products, supply chain agility is a supply chain configuration related to velocity (sensing, comprehending, and responding) and visibility (supplier network, internal operations, and external environment). Both the product and supply chain architectural decisions can impact performance, individually and in conjunction with each other. We empirically test the hypothesized relationships using data collected from 103 manufacturing firms. Results show that supply chain agility and product modularity directly enhance responsiveness and enable organizations to reduce cost. Furthermore, supply chain agility partially mediates the relationship between product modularity and both responsiveness and cost reduction. We elaborate on the key contributions of this study for both research and practice, discuss limitations, and also offer various avenues for further research.     

An Approach to Security for Unstructured Big Data

Security of Big Data is a huge concern. In a broad sense, Big Data contains two types of data: structured and unstructured. Providing security to unstructured data is more difficult than providing security to structured data. In this paper, we have developed an approach to provide adequate security to unstructured data by considering types of data and their sensitivity levels. We have reviewed the different analytics methods of Big Data to build nodes of different types of data. Each type of data has been classified to provide adequate security and enhance the overhead of the security system. To provide security to a data node, and a security suite has been designed by incorporating different security algorithms. Those security algorithms collectively form a security suite which has been interfaced with the data node. Information on data sensitivity has been collected through a survey. We have shown through several experiments on multiple computer systems with varied configurations that data classification with respect to sensitivity levels enhances the performance of the system. The experimental results show how and in what amount the designed security suite reduces overhead and increases security simultaneously.