Testing assumptions underlying economic research on transgenic food crops for Third World farmers: Evidence from Cuba, Guatemala and Mexico

Transgenic crop varieties (TGVs) are being promoted as essential for improving small-scale Third World (SSTW) agriculture. Most economic research on this topic makes critical, untested assumptions, including that farmers will choose TGVs over other varieties because TGVs are economically optimal and because farmers are risk neutral profit maximizers. We tested these assumptions using data from a survey of 334 farmers in 6 communities in Cuba, Guatemala and Mexico in which farmers ranked 4 real and hypothetical maize varieties for eating and sowing. Our results did not support these assumptions. Most farmers preferred farmer varieties for sowing and especially for eating, avoiding TGVs, a preference associated with being risk averse and with non-monetary preferences. Farmers more integrated into modern agriculture were more likely to choose TGVs. These results suggest that farmers most in need of support and most important for conserving genetic diversity are least favorable toward TGVs, and that alternative ways of improving SSTW agriculture should receive more attention.     

Unemployment Insurance and Unemployment: An analysis of the aggregate demand-side effects for postwar Canada

Most of the theoretical and empirical literature on the effects of unemployment insurance (UI) during the last 25 years has focused on the supply-side implications. The object of this article is to argue that much of those discussions over the disincentive effects of UI is misdirected. Our intention is to bring back to the forefront a discussion of the positive macroeconomic role played by UI net injections in stabilizing aggregate demand. Our empirical results using Canadian data for the postwar period support this Keynesian view of the stabilizing effect of UI in contemporary economies.     

Forecasting high-frequency excess stock returns via data analytics and machine learning

Borsa Istanbul introduced data analytics to present additional information about its market conditions. We examine whether this product can be utilized via various machine learning methods to predict intraday excess returns. Accordingly, these analytics provide significant prediction ratios above 50% with ideal profit ratios that can reach up to 33%. Among all the methods considered, XGBoost (logistic regression) performs better in predicting excess returns in the long-term analysis (short-term analysis). Results provide evidence for the benefits of both the analytics and the machine learning methods and raise further discussion on the semistrong market efficiency.     

Risk‐Adjusted Measures of Value Creation in Financial Institutions

Abstract Many financial institutions assess portfolio decisions using RAROC, the ratio of expected return to risk (or ‘economic’) capital. We use asset pricing theory to determine the appropriate hurdle rate, finding that this varies with the skewness of asset returns. We quantify this discrepancy under a range of assumptions showing that the RAROC hurdle rate differs substantially, being higher by a factor of five or more for equity which has a right skew compared to debt which has a pronounced left skew, and also between different qualities of debt exposure. We discuss implications for both financial institution risk management and supervision.     

Machine learning in individual claims reserving

Machine learning techniques make it feasible to calculate claims reserves on individual claims data. This paper illustrates how these techniques can be used by providing an explicit example in individual claims reserving.     

The Asymmetric path of Economic Long Waves

The purpose of this paper is to analyze the pattern and behavior of Economic Long Waves over time, using the long-wave chronologies of two driving economies, the USA and the UK. The statistical evidence seems in general to support the hypothesis that Long Waves are not symmetric and their regularity is based on longer periods of upwave than downwave (contraction-skewed), which implies a negative asymmetric path of these waves over time.     

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.     

Mittlerer quadratischer Prognosefehler bei der Prognose des Abwicklungsergebnisses mittels der Chain-Ladder-Methode

Often in non-life insurance claims reserves are the largest position on the liability side of the balance sheet. The determination of adequate claims reserves in two consecutive accounting years leads to the so-called development result, which is defined as the difference of two successive predictors for the claims reserves. If the predictors for the claims reserves are unbiased the expected development result is equal to zero. However, since in claims reserving one predicts future payments the observed development result will in general deviate from the expected value. In the present paper we analyze this deviation. In an example we discuss the results.     

Appropriate technology in a Solovian nonlinear growth model

We propose a Solovian growth model with a convex–concaveproduction function and international technological spillovers.We test the empirical implications of the model, analysing theeffects of the productivity slowdown that followed the oil shocksof the 1970s. We argue that this slowdown, altering the worldincome distribution, affected the pattern of international technologicalspillovers, taking the poorest countries further away from thetechnological leaders, and therefore unable to exploit theirtechnologies. The result is the emergence of a poverty trapfor low-income countries.