Prediction of stocks: a new way to look at it.

While the traditional $R^{2}$ value is useful to evaluate the quality of a fit, it does not work when it comes to evaluating the predictive power of estimated financial models in finite samples. In this paper we introduce a validated $R_{V}^{2}$ value that is Taylor made for prediction. Based on data from the Danish stock market, using this measure we find that the dividend-price ratio has good predictive power for time horizons between one year and five years. We explain how the $R_{S}^{2}$ s for different time horizons could be compared, respectively, how they must not be interpreted. For our data we can conclude that the quality of prediction is almost the same for the five different time horizons. This is in contradiction to earlier studies based on the traditional $R^{2}$ value, where it has been argued that the predictive power increases with the time horizon up to a horizon of about five or six years. Furthermore, we find that while inflation and interest rate do not add to the predictive power of the dividend-price ratio then last years excess stock return does

Two sided analysis of variance with a latent time series

Many real life regression problems exhibit some kind of calender time dependency and it is often of interest to predict the behavior of the regression function along this calender time direction. This can be formulated as a regression model with an added latent time series and the task is to be able to analyse this series. In this paper we engage this through a two step procedure, firstly we treat the time dependent elements as parameters and estimate them in the two-sided analysis of variance setup, secondly we use the estimated time series as predictor of the latent time series. An application to risk theory is discussed.regression, time series, risk theory

Nonparametric Regression with a Latent Time Series

In this paper we investigate a class of semiparametric models for panel datasetswhere the cross-section and time dimensions are large. Our model contains alatent time series that is to be estimated and perhaps forecasted along with anonparametric covariate effect. Our model is motivated by the need to be flexiblewith regard to functional form of covariate effects but also the need to be practicalwith regard to forecasting of time series effects. We propose estimation proceduresbased on local linear kernel smoothing; our estimators are all explicitly given. Weestablish the pointwise consistency and asymptotic normality of our estimators. Wealso show that the effects of estimating the latent time series can be ignored incertain cases.Kernel Estimation, Forecasting, Panel Data, Unit Roots

Development of fungal cell factories for the production of secondary metabolites: Linking genomics and metabolism

The genomic era has revolutionized research on secondary metabolites and bioinformatics methods have in recent years revived the antibiotic discovery process after decades with only few new active molecules being identified. New computational tools are driven by genomics and metabolomics analysis, and enables rapid identification of novel secondary metabolites. To translate this increased discovery rate into industrial exploitation, it is necessary to integrate secondary metabolite pathways in the metabolic engineering process. In this review, we will describe the novel advances in discovery of secondary metabolites produced by filamentous fungi, highlight the utilization of genome-scale metabolic models (GEMs) in the design of fungal cell factories for the production of secondary metabolites and review strategies for optimizing secondary metabolite production through the construction of high yielding platform cell factories

The Froot and Stein Model Revisited

We investigate the model of Froot and Stein (1998), a model that has very strong implications for risk management. We argue that their conclusions are too strong and need to be qualified. Also, there are some unusual consequences of their model, which may be linked to the chosen pricing formula.G20 and G31 and G32