|Abstract: This paper considers the problem of modeling a firm's expected return as a nonlinear function of its observable characteristics. We investigate whether theoretically-motivated monotonicity constraints on characteristics and non- stationarity of the conditional expectation function provide statistical and economic benefit. We present an interpretable model that has similar out- of-sample performance to black-box machine learning methods. With this model, the data provide support for monotonicity and time variability of the conditional expectation function. Additionally, we develop an approach for characteristic selection using loss functions to summarize the posterior distribution. Standard unexplained volume, short-term reversal, size, and variants of momentum are found to be significant characteristics, and there is evidence this set changes over time.|
The aim of this special issue is to feature research papers on theory, methodology, and applications of models and methods for recent advances in statistical finance. We encourage submissions presenting original works on statistical, computational, and mathematical approaches to modelling and analysis of financial data. Innovative applications and case studies in financial statistics are welcome, especially related to novel methodological challenges in the treatment of big data and high-frequency data.
This special issue will bring together contributions from practitioners and researchers working on different aspects of statistical methods in finance, with methodological interests encompassing, but not limited to, the following domains:The motivating application areas could be: For More Detail ...
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