Eine große Herausforderung der multivariablen Analyse mit bilanziellen Kennzahlen besteht in der Identifikation derjenigen Kennzahlen, die zur besten Modellperformance führen und dabei möglichst leicht interpretierbar und intuitiv bleiben. Die Menge der in Frage kommenden Kennzahlen ist in...

We present a new way to model age-specific demographic variables with the example of age-specific mortality in the U.S., building on the Lee-Carter approach and extendingit in several dimensions. We incorporate covariates and model their dynamics jointly with the latent variables underlying...

Motivated by the recurrent Neural Networks, this paper proposes a recurrent Support Vector Regression (SVR) procedure to forecast nonlinear ARMA model based simulated data and real data of financial returns. The forecasting ability of the recurrent SVR is compared with three competing methods,...

Measuring and modeling financial volatility is the key to derivative pricing, asset allocation and risk management. The recent availability of high-frequency data allows for refined methods in this field. In particular, more precise measures for the daily or lower frequency volatility can be...

Stock picking is the field of financial analysis that is of particular interest for many professional investors and researchers. In this study stock picking is implemented via binary classification trees. Optimal tree size is believed to be the crucial factor in forecasting performance of the...

By using a unique data set of single-family house transactions, we examine theaccuracy of the cost and sales comparison approach over different forecast horizons. We find that sales comparison values provide better long-term forecaststhan cost values if the economic loss function is symmetric. A...

In this paper we adopt a principal components analysis (PCA) to reduce the dimensionality of the term structure and employ autoregressive models (AR) to forecast principal components which, in turn, are used to forecast swap rates. Arguing in favor of structural variation, we propose data...

In recent years, support vector regression (SVR), a novel neural network (NN) technique, has been successfully used for financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH method is proposed and is compared with a...

We use dynamic panel analysis to examine whether credit rating agencies achieve what they claim to achieve, namely, look into the future when assigning their ratings. We find that Moodey's ratings help predict individual financial ratios over a horizon of up to five years. Ratings also predict a...