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Collusion by algorithm : does better demand prediction facilitate coordination between sellers?

Miklós-Thal, Jeanine; Tucker, Catherine - In: Management science : journal of the Institute for … 65 (2019) 4, pp. 1552-1561

Persistent link: https://www.econbiz.de/10012022643

Machine Learning Panel Data Regressions with an Application to Nowcasting Price Earnings Ratios

Babii, Andrii - 2020

This paper introduces structured machine learning regressions for prediction and nowcasting with panel data consisting of series sampled at different frequencies. Motivated by the empirical problem of predicting corporate earnings for a large cross-section of firms with macroeconomic, financial,...

Persistent link: https://www.econbiz.de/10012826088

Machine learning for predicting stock return volatility

Filipović, Damir; Khalilzadeh, Amir - 2021

We use machine learning methods to predict stock return volatility. Our out-of-sample prediction of realised volatility for a large cross-section of US stocks over the sample period from 1992 to 2016 is on average 44.1% against the actual realised volatility of 43.8% with an R2 being as high as...

Persistent link: https://www.econbiz.de/10012800743

Lawrence R. Klein’s Principles in Modeling and Contributions in Nowcasting, Real-Time Forecasting, and Machine Learning

Mariano, Roberto S.; Ozmucur, Suleyman - 2020

Lawrence R. Klein (September 14, 1920 – October 20, 2013), Nobel Laureate in Economic Sciences in 1980, was one of the leading figures in macro-econometric modeling. Although his contributions to forecasting using simultaneous equations macro models were very well known, his contributions to...

Persistent link: https://www.econbiz.de/10014093271

PREDICTING STOCK RETURN AND VOLATILITY WITH MACHINE LEARNING AND ECONOMETRIC MODELS : A COMPARATIVE CASE STUDY OF THE BALTIC STOCK MARKET

Nõu, Anders; Lapitskaya, Darya; Eratalay, Mustafa Hakan; … - 2022

For stock market predictions, the essence of the problem is usually predicting the magnitude and direction of the stock price movement as accurately as possible. There are different approaches (e.g., econometrics and machine learning) for predicting stock returns. However, it is non-trivial to...

Persistent link: https://www.econbiz.de/10013305881

Machine Learning in Long-Term Mortality Forecasting

Qiao, Yang; Wang, Chou‐Wen; Zhu, Wenjun - 2023

We propose a new machine learning-based framework for long-term mortality forecasting. Based on ideas of neighbouring prediction, model ensembling, and tree boosting, this framework can significantly improve the prediction accuracy of long-term mortality. In addition, the proposed framework...

Persistent link: https://www.econbiz.de/10014359797

The Predictability of DOJ Cartel Fines

Connor, John M. - 2016

Justice has great latitude in recommending corporate cartel fines to the federal courts, and its recommendations are nearly …-deterrence model is quite good. We find that U.S. corporate cartel fines are strongly directly related to economic injuries from …

Persistent link: https://www.econbiz.de/10012979998

Predicting Cartel Formation

Herold, Daniel - 2016

This article distills insights about cartel formation from 41 cases prosecuted by the European Commission between 2001 … following potential causes for cartel formation: Changes in prices, demand and customer conduct, capacity utilization, increased … imports and entry by competitors, and events in the legal and regulatory environment of the firms. Cartel formation is not …

Persistent link: https://www.econbiz.de/10012997727

Human judgment and AI pricing

Agrawal, Ajay; Gans, Joshua; Goldfarb, Avi - 2018

Recent artificial intelligence advances can be seen as improvements in prediction. We examine how such predictions should be priced. We model two inputs into decisions: a prediction of the state and the payoff or utility from different actions in that state. The payoff is unknown, and can only...

Persistent link: https://www.econbiz.de/10011897906

An Artificial Neural Network Representation of the SABR Stochastic Volatility Model

McGhee, William A - 2018

In this article, the Universal Approximation Theorem of Artificial Neural Networks (ANNs) is applied to the SABR stochastic volatility model in order to construct highly efficient representations. Initially, the SABR approximation of Hagan et al. [2002] is considered, then a more accurate...

Persistent link: https://www.econbiz.de/10012907596

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