Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed

This paper studies the role of fluctuations in the aggregate consumption–wealth ratio for predicting stock returns. Using U.S. quarterly stock market data, we find that these fluctuations in the consumption–wealth ratio are strong predictors of both real stock returns and excess returns over a

The share of equity issues in total new equity and debt issues is a strong predictor of U.S. stock market returns between 1928 and 1997. In particular, firms issue relatively more equity than debt just before periods of low market returns. The equity share in new issues has stable predictive power

the respondents' remembered utility. The evaluation of remembered utility requires the individual to remember a stream of experiences and to aggregate them in some way. Ideally, one would hope that the individual who reports his or her overall remembered utility for a period performs the task of summing

Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed

Abstract: Bond yield and retail interest rate spreads are presumed to lead real activity on the basis of financial accelerator mechanisms, markup cyclicality or simply because they are forward-looking. Empirical results for Austria show that retail rate spreads outperform many other indicators in this respect. Nevertheless, there is no evidence for a financial accelerator being behind this finding.

Abstract: This paper discusses a detection mechanism for malicious code through statistical analysis of opcode distributions. A total of 67 malware executables were sampled statically disassembled and their statistical opcode frequency distribution compared with the aggregate statistics of 20 non

We address the problem of forecasting a time series meeting the Causal Bernoulli Shifts model, using a parametric family of predictors. The aggregation technique provides a predictor with well established and quite satisfying theoretical prop-erties expressed by an oracle inequality

of the time series variation in post 1990 aggregate stock market returns, with high (low) premia predicting high (low) future returns. Our empirical results depend crucially on the use of “model-free, ” as opposed to Black-Scholes, options implied volatilities, along with accurate realized variation measures

both the process structure and the involved documents ’ semantics. Here we propose a self-learning mechanism which dynamically aggregates a process-based document prediction with a semantic analysis of documents. We present a set of experiments testing the prediction accuracy of the approaches