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Machine-Learning the Skill of Mutual Fund Managers

Kaniel, Ron; Lin, Zihan; Pelger, Markus; Nieuwerburgh, … - National Bureau of Economic Research - 2022

We show, using machine learning, that fund characteristics can consistently differentiate high from low-performing mutual funds, as well as identify funds with net-of-fees abnormal returns. Fund momentum and fund flow are the most important predictors of future risk-adjusted fund performance,...

Persistent link: https://www.econbiz.de/10012938692

Selecting Directors Using Machine Learning

Erel, Isil - 2018

Can an algorithm assist firms in their hiring decisions of corporate directors? This paper proposes a method of selecting boards of directors that relies on machine learning. We develop algorithms with the goal of selecting directors that would be preferred by the shareholders of a particular...

Persistent link: https://www.econbiz.de/10012453279

The Technological Elements of Artificial Intelligence

Taddy, Matt - 2018

We have seen in the past decade a sharp increase in the extent that companies use data to optimize their businesses. Variously called the `Big Data' or `Data Science' revolution, this has been characterized by massive amounts of data, including unstructured and nontraditional data like text and...

Persistent link: https://www.econbiz.de/10012453413

Financial Machine Learning

Kelly, Bryan T.; Xiu, Dacheng - National Bureau of Economic Research - 2023

We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping...

Persistent link: https://www.econbiz.de/10014322889

Nowcasting World Trade with Machine Learning : a Three-Step Approach

Chinn, Menzie David; Meunier, Baptiste; Stumpner, Sebastian - National Bureau of Economic Research - 2023

across different horizons and real-time datasets. To further improve performances when forecasting with machine learning, we …

Persistent link: https://www.econbiz.de/10014322806

Machine Forecast Disagreement

Bali, Turan G.; Kelly, Bryan T.; Mörke, Mathis; … - National Bureau of Economic Research - 2023

We propose a statistical model of differences in beliefs in which heterogeneous investors are represented as different machine learning model specifications. Each investor forms return forecasts from their own specific model using data inputs that are available to all investors. We measure...

Persistent link: https://www.econbiz.de/10014337816

Complexity in Factor Pricing Models

Didisheim, Antoine; Ke, Shikun; Kelly, Bryan T.; … - National Bureau of Economic Research - 2023

We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance--in terms of SDF Sharpe ratio and test asset pricing errors--is improving in model parameterization (or "complexity"). Our empirical findings verify the...

Persistent link: https://www.econbiz.de/10014372446

AI and Jobs : Evidence from Online Vacancies

Acemoglu, Daron; Autor, David H.; Hazell, Jonathon; … - National Bureau of Economic Research - 2022 - Revised February 2022

We study the impact of AI on labor markets, using establishment level data on vacancies with detailed occupational information comprising the near-universe of online vacancies in the US from 2010 onwards. We classify establishments as "AI exposed" when their workers engage in tasks that are...

Persistent link: https://www.econbiz.de/10012482476

Using Neural Networks to Predict Micro-Spatial Economic Growth

Khachiyan, Arman; Thomas, Anthony; Zhou, Huye; Hanson, … - National Bureau of Economic Research - 2021

We apply deep learning to daytime satellite imagery to predict changes in income and population at high spatial resolution in US data. For grid cells with lateral dimensions of 1.2km and 2.4km (where the average US county has dimension of 55.6km), our model predictions achieve R2 values of 0.85...

Persistent link: https://www.econbiz.de/10012794597

A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series

Stock, James H. - 1998

A forecasting comparison is undertaken in which 49 univariate forecasting methods, plus various forecast pooling … procedures, are used to forecast 215 U.S. monthly macroeconomic time series at three forecasting horizons over the period 1959 … - 1996. All forecasts simulate real time implementation, that is, they are fully recursive. The forecasting methods are based …

Persistent link: https://www.econbiz.de/10012472204

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