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.     

What is the objective of professional licensing? Evidence from the US market for lawyers

According to public interest theory, professional licensing solves the lemon problem generated by asymmetric information. In contrast, the capture theory claims that licensing aims at increasing professional salaries by restricting supply. This paper shows that the two theories can be identified using data from one regulated profession and provides an empirical application to the US market for entry level lawyers. The empirical results support capture theory.     

Multi-product capacitated facility location problem with general production and building costs

This research introduces the multi-product capacitated facility location problem with general production and building costs (MP-CFLPGC). The MP-CFLPGC extends previous problems found in the literature by including multiple products and general production and building cost functions that allow the modeling of different behaviors like economies of scale and congestion. The MP-CFLPGC is formulated as a mixed integer linear program (MILP). To evaluate the performance of the proposed formulation we analyze the results of a commercial optimizer on a set of 288 randomly generated test instances that resemble the Colombian cement industry supply chain. After one hour the optimizer achieved an optimality gap of 1.0 % or less in 55 % out of the 288 test instances. On average the optimality gap was 3.57 %. Additionally, we propose a randomized mathematical-programming-based heuristic for the test instances where the MILP formulation presents significantly high optimality gaps.     

Composition of trade flows and the effectiveness of fiscal devaluation

We empirically explore whether the magnitude of the effects of fiscal devaluation, which consists of reducing the employers’ social security contribution rate and increasing the value‐added tax rate, depends on the composition of trade flows. Our sample comprises data on bilateral balances of trade between 28 European Union (EU) Member States and their main EU trade partners over the 2000–14 period. We use robust ordinary least squares regressions, controlling for the country‐pair and time fixed effects, to test whether there are differences in the sizes of relationships between these taxation forms and bilateral trade balances for different types of goods, by distinguishing between: (i) consumer, intermediate and capital goods; and (ii) labour and capital‐intensive goods. Our results show that the overall effectiveness of fiscal devaluation depends on the composition of trade flows. Value‐added tax is more strongly (positively) associated with (bilateral) balances of trade in consumer goods, compared to balances of trade in capital and intermediate goods. The employers’ contribution rate, in contrast, is more tightly (negatively) related to balances of trade in capital goods. The latter finding also holds true for trade balances of labour‐intensive goods compared to balances of capital‐intensive goods.     

Magic mirror in my hand … How trade mirror statistics can help us detect illegal financial flows

Misreporting tricks of different sorts applied to the transfer of goods between different countries are typically exploited by criminals worldwide for money laundering ends. The main international anti‐money laundering organisations started paying attention to this phenomenon, dubbed “trade‐based money laundering” (TBML), a long time ago, but the failure to develop appropriate analytical tools has reportedly dogged preventive actions. Nonetheless, literature has widely advocated the possibility that the analysis of inconsistencies in mirrored bilateral trade data could provide some help. By building on previous contributions in the field, this work sets up a model factoring in the main structural determinants of discrepancies between mirrored data concerning Italy's 2010 to 2013 external trade at a highly detailed (6‐digit) level of goods classification for each partner country. Point estimates of freight costs are used to net each observation of the corresponding cif/fob discrepancy. The regression estimates are then deployed in order to compute TBML risk indicators at a country/4‐digit product level. Based on the indicators, rankings of countries and product lines can be compiled, which may be used for a risk‐driven search of potential illegal commercial transactions.     

Investment trust IPOs: Issuing behaviour and price performance Evidence from the London Stock Exchange

In this paper, we document an average first day return of 1.91 percent for the population of 105 investment trust IPOs during the period from January 1984 through August 1992 on the London Stock Exchange. This is the first study that finds evidence of significant first day returns for a sample of closed-end fund IPOs. The results also suggest that investment trust IPOs are subject to ‘hot’ issue periods. These tend to occur when there is a marked narrowing in the discounts of seasoned investment trusts. Initial gains are, however, short lived; by the end of their first year, investment trust IPOs substantially underperform a number of relevant benchmarks and, on average, trade at discounts to their underlying net asset values.     

Heterogeneous Firms,Globalisation and the Distance Puzzle

Despite the strong pace of globalisation, the distance effect on trade is persistent or even growing over time (Disdier and Head, 2008). To solve this distance puzzle, we use the recently developed gravity equation estimator from Helpman et al. (2008) (HMR henceforth). Using three different data sets, we find that the distance coefficient increases over time when ordinary least squares (OLS) is used, while the non‐linear estimation of HMR leads to a decline in the distance coefficient over time. The distance puzzle, thus, arises from a growing bias of OLS estimates. The latter is explained by an increase in the importance of the bias from omitting the number of heterogeneous exporting firms relative to the bias from omitting zero trade flows. Furthermore, we show that including zero trade flows cannot solve the distance puzzle when using HMR. The HMR estimates are strongly correlated with the time pattern in freight costs reported by Hummels (2007).