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Symbolic regression is a nonlinear regression method which is commonly performed by an evolutionary computation method such as genetic programming. Quantification of uncertainty of regression models is important for the interpretation of…

Machine Learning · Computer Science 2022-09-15 Fabricio Olivetti de Franca , Gabriel Kronberger

A linear multi-factor model is one of the most important tools in equity portfolio management. The linear multi-factor models are widely used because they can be easily interpreted. However, financial markets are not linear and their…

Machine Learning · Computer Science 2019-02-01 Kei Nakagawa , Tomoki Ito , Masaya Abe , Kiyoshi Izumi

Being able to predict stock prices might be the unspoken wish of stock investors. Although stock prices are complicated to predict, there are many theories about what affects their movements, including interest rates, news and social media.…

Machine Learning · Computer Science 2021-05-05 Roderick Karlemstrand , Ebba Leckström

We consider Wald type statistics designed for joint predictability and structural break testing based on the instrumentation method of Phillips and Magdalinos (2009). We show that under the assumption of nonstationary predictors: (i) the…

Econometrics · Economics 2023-07-31 Christis Katsouris

In Bayesian statistics, the marginal likelihood, also known as the evidence, is used to evaluate model fit as it quantifies the joint probability of the data under the prior. In contrast, non-Bayesian models are typically compared using…

Methodology · Statistics 2019-09-24 Edwin Fong , Chris Holmes

Models for which the likelihood function can be evaluated only up to a parameter-dependent unknown normalising constant, such as Markov random field models, are used widely in computer science, statistical physics, spatial statistics, and…

Computation · Statistics 2016-02-12 Richard G. Everitt , Adam M. Johansen , Ellen Rowing , Melina Evdemon-Hogan

Balancing a rare and serious possibility against a more common and less serious one is a familiar problem in many situations, such as the prediction of rare diseases. The relative costs of forecasting errors can be used for any prediction…

Methodology · Statistics 2009-05-04 Paul T Seed

Macroeconomic nowcasting sits at the intersection of traditional econometrics, data-rich information systems, and AI applications in business, economics, and policy. Machine learning (ML) methods are increasingly used to nowcast quarterly…

Econometrics · Economics 2025-12-02 Luca Attolico

In recent years, there has been a growing interest in statistical methods that exhibit robust performance under distribution changes between training and test data. While most of the related research focuses on point predictions with the…

Methodology · Statistics 2024-06-18 Alexander Henzi , Xinwei Shen , Michael Law , Peter Bühlmann

Predictions of the uncertainty associated with extreme events are a vital component of any prediction system for such events. Consequently, the prediction system ought to be probabilistic in nature, with the predictions taking the form of…

Applications · Statistics 2012-10-26 Petra Friederichs , Thordis L. Thorarinsdottir

The restricted maximum likelihood method enhances popularity of maximum likelihood methods for variance component analysis on large scale unbalanced data. As the high throughput biological data sets and the emerged science on uncertainty…

Computation · Statistics 2018-05-15 Shengxin Zhu , Andrew J Wathen

Metrics of model goodness-of-fit, model comparison, and model parameter estimation are the main categories of statistical problems in science. Bayesian and frequentist methods that address these questions often rely on a likelihood…

Data Analysis, Statistics and Probability · Physics 2019-06-26 Carlos A. Argüelles , Austin Schneider , Tianlu Yuan

Data mining methods have been widely applied in financial markets, with the purpose of providing suitable tools for prices forecasting and automatic trading. Particularly, learning methods aim to identify patterns in time series and, based…

Machine Learning · Statistics 2013-01-22 Marcelo S. Lauretto , Barbara B. C. Silva , Pablo M. Andrade

Using non-linear machine learning methods and a proper backtest procedure, we critically examine the claim that Google Trends can predict future price returns. We first review the many potential biases that may influence backtests with this…

Trading and Market Microstructure · Quantitative Finance 2014-03-10 Damien Challet , Ahmed Bel Hadj Ayed

We develop a novel technique to extract credit-relevant information from the text of quarterly earnings calls. This information is not spanned by fundamental or market variables and forecasts future credit spread changes. One reason for…

General Finance · Quantitative Finance 2023-09-12 Harry Mamaysky , Yiwen Shen , Hongyu Wu

In this paper we consider a variety of procedures for numerical statistical inference in the family of univariate and multivariate stable distributions. In connection with univariate distributions (i) we provide approximations by finite…

Computation · Statistics 2012-09-04 Efthymios G. Tsionas

Marginal-likelihood based model-selection, even though promising, is rarely used in deep learning due to estimation difficulties. Instead, most approaches rely on validation data, which may not be readily available. In this work, we present…

Machine Learning · Statistics 2021-06-16 Alexander Immer , Matthias Bauer , Vincent Fortuin , Gunnar Rätsch , Mohammad Emtiyaz Khan

In this study, we introduce new estimation methods for the required rate of return of the stochastic dividend discount model (DDM) and the private company valuation model, which will appear below. To estimate the required rate of return, we…

General Finance · Quantitative Finance 2022-07-07 Battulga Gankhuu

We propose a new approach to mixed-frequency regressions in a high-dimensional environment that resorts to Group Lasso penalization and Bayesian techniques for estimation and inference. In particular, to improve the prediction properties of…

Econometrics · Economics 2020-06-12 Matteo Mogliani , Anna Simoni

Statistical analysis of extremes can be used to predict the probability of future extreme events, such as large rainfalls or devastating windstorms. The quality of these forecasts can be measured through scoring rules. Locally scale…

Methodology · Statistics 2024-02-22 Helga Kristin Olafsdottir , Holger Rootzén , David Bolin