Designing a reverse logistics network for optimal collection, recovery and quality-based product-mix planning

This study proposes an integrated, reverse logistics supply chain planning process with modular product design that produces and markets products at different quality levels. A mixed integer programming (MIP) model formulates the overall planning process required to maximize profit by considering the collection of returned products, the recovery of modules and the proportion of the product mix at different quality levels. This paper proposes the collection of returnables (end-of life, defective, product under warranty) through retail outlets combined with the recovery of modules from the collected products using a network of recovery service providers. The proposed modular product design approach would create a design criterion that provides an improved recovery process at a lower cost. This study uses a total supply chain view that considers the production, transportation and distribution of products to customers, while a numerical problem illustrates the applicability of the models.     

Has co-movement dynamics in emerging stock markets changed after global financial crisis? New evidence from wavelet analysis

In this article, we revisit the issue of contagion, interdependence and changes in correlation structure after the Global Financial Crisis (GFC) of 2008 between developed and emerging markets in a time-frequency domain using a wavelet-based approach for the period spanning over 1 January 1999 to 8 November 2016. We report evidences of: (a) weaker contagion for Latin American emerging markets during GFC, (b) a strong contagion effect for emerging markets in Europe and the Middle East and (c) a fall in long-run co-movements after GFC, which means by investing in emerging markets, the diversification benefits can be derived in the long run. We report evidence of coexistence of contagion and permanent change in correlation structure.     

Implied recovery

In the absence of forward-looking models for recovery rates, market participants tend to use exogenously assumed constant recovery rates in pricing models. We develop a flexible jump-to-default model that uses observables: the stock price and stock volatility in conjunction with credit spreads to identify implied, endogenous, dynamic functions of the recovery rate and default probability. The model in this paper is parsimonious and requires the calibration of only three parameters, enabling the identification of the risk-neutral term structures of forward default probabilities and recovery rates. Empirical application of the model shows that it is consistent with stylized features of recovery rates in the literature. The model is flexible, i.e. it may be used with different state variables, alternate recovery functional forms, and calibrated to multiple debt tranches of the same issuer. The model is robust, i.e. evidences parameter stability over time, is stable to changes in inputs, and provides similar recovery term structures for different functional specifications. Given that the model is easy to understand and calibrate, it may be used to further the development of credit derivatives indexed to recovery rates, such as recovery swaps and digital default swaps, as well as provide recovery rate inputs for the implementation of Basel II.     

Diversification strategy,capital structure,and the Asian financial crisis (1997–1998): evidence from Singapore firms

We use agency theory to predict the influence of related and unrelated product diversification on a firm's level of debt financing. Further, we argue that the link between diversification and capital structure is moderated by the environment in which firms operate. Using SAS PROC MIXED, we fit a mixed‐effects model to our unique six‐year longitudinal dataset (1995–2000) of 245 publicly listed Singapore firms. Our data spans the period of the Asian Financial Crisis (1997–1998). We find that firms pursuing unrelated product diversification take on less debt financing in stable environments, but more debt financing in dynamic environments. Using longitudinal structural equation modeling, we find a reciprocal relationship between a firm's product diversification strategy and its debt financing level. Copyright © 2009 John Wiley & Sons, Ltd.     

Predicting Downturns in the US Housing Market: A Bayesian Approach

This paper estimates Bayesian Vector Autoregressive (BVAR) models, both spatial and non-spatial (univariate and multivariate), for the twenty largest states of the US economy, using quarterly data over the period 1976:Q1–1994:Q4; and then forecasts one-to-four quarters-ahead real house price growth over the out-of-sample horizon of 1995:Q1–2006:Q4. The forecasts are evaluated by comparing them with those from an unrestricted classical Vector Autoregressive (VAR) model and the corresponding univariate variant of the same. Finally, the models that produce the minimum average Root Mean Square Errors (RMSEs), are used to predict the downturns in the real house price growth over the recent period of 2007:Q1–2008:Q1. The results show that the BVARs, in whatever form they might be, are the best performing models in 19 of the 20 states. Moreover, these models do a fair job in predicting the downturn in 18 of the 19 states.     

Bargaining and Search with Incomplete Information about Outside Options

This paper considers a model of bargaining in which the seller makes offers and the buyer can search (at a cost) for an outside option; the outside option cannot be credibly communicated, and the seller's offer is recallable by the buyer for one period. There are essentially two equilibrium regimes. For sufficiently high search cost, the game ends immediately; otherwise the search occurs in equilibrium. Compared to the case where the buyer can communicate his outside option, the seller is worse off, and the game results in search for a smaller set of values of the search cost, i.e., less equilibrium delay.C72.     

Trade,Technology and Human Capital: Stylised Facts and Quantitative Evidence

Stylised evidence on trade, total factor productivity (TFP) and skill intensity of the labour force is presented. Features emerging as salient are: growing trade in technology‐intensive products from the industrialised nations to the relatively laggard nations leads to embodied technology diffusion; the technology‐intensive sectors have larger shares of skilled workers; countries experiencing TFP growth usually have higher levels of educational attainment; also, the skilled labour payment share for a sector is positively associated with that sector’s regional trade share. These facts together help explain why endowment of more skilled labour facilitates absorption of technology ferried via trade.     

Hedging credit: Equity liquidity matters

We theorize and confirm a new channel by means of which liquidity costs are embedded in CDS spreads. We show that credit default swap (CDS) spreads are directly related to equity market liquidity in the Merton [Merton, R.C., 1974. On the pricing of corporate debt: The risk structure of interest rates. J. Finance 29, 449–470] model via hedging. We confirm this relationship empirically using a sample of 1452 quarterly CDS spreads over 2001–2005. In the model, this relationship is monotone increasing when credit quality worsens. These results are robust to alternative measures of equity liquidity and other possible determinants of CDS spreads.     

An Empirical Examination of the Divergence between Managers’ and Analysts’ Earnings Forecasts

We study circumstances when analysts’ forecasts diverge from managers’ forecasts after management guidance, and the consequences of this divergence for investors and analysts. Our results show that investors’ return response to earnings surprises based on analyst forecasts is significantly weaker when analyst and management forecasts diverge, and that this attenuating effect is stronger when the management forecast is more credible. When the divergent management forecast is more accurate than the analyst consensus forecast, the subsequent‐quarter analyst consensus forecast is significantly more accurate than that of the current quarter, and exhibits less serial correlation. Overall, our findings suggest that, when analyst and management forecasts diverge, investors find the two sources to contain complementary information, and analysts learn to improve their subsequent forecasts.