scholarly journals Firm Characteristics and Empirical Factor Models: A Data-Mining Experiment

2012 ◽  
Author(s):  
Leonid Kogan ◽  
Mary H. Tian
Keyword(s):  
Data Mining ◽  
Factor Models ◽  
Firm Characteristics ◽  
Empirical Factor

How Robust are Empirical Factor Models to the Choice of Breakpoints?

2021 ◽  
Author(s):  
Fabian Hollstein ◽  
Marcel Prokopczuk ◽  
Victoria Voigts
Keyword(s):  
Factor Models ◽  
Empirical Factor

Firm Characteristics and Empirical Factor Models: A Model Mining Experiment

2020 ◽  
Author(s):  
Mary Tian
Keyword(s):  
Confidence Intervals ◽  
Factor Model ◽  
Factor Models ◽  
Compelling Evidence ◽  
Firm Characteristics ◽  
Random Factor ◽  
Five Factors ◽  
Bootstrap Simulation ◽  
Novel Model ◽  
Three Factor

Abstract In a novel model mining experiment, we data mine hundreds of randomly constructed three-factor models and find that many outperform well-known models from the literature, including those with four and five factors. The results provide compelling evidence that the threshold of factor model success needs to be raised. Confidence intervals for model rankings, derived from a bootstrap simulation, offer new insights into the consistency of a model’s pricing ability. Rankings for some well-known models are unusually volatile, which have wider confidence intervals than that of most of the random factor models.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Heart Rate Variability, Emotions, and Music

2010 ◽  
Vol 24 (2) ◽  
pp. 112-119 ◽  
Author(s):  
F. Riganello ◽  
A. Candelieri ◽  
M. Quintieri ◽  
G. Dolce
Keyword(s):  
Data Mining ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Vegetative State ◽  
Low Frequency ◽  
Emotional Reactions ◽  
Heart Beat ◽  
Healthy Controls ◽  
Frequency Spectra ◽  
Emotional Value

The purpose of the study was to identify significant changes in heart rate variability (an emerging descriptor of emotional conditions; HRV) concomitant to complex auditory stimuli with emotional value (music). In healthy controls, traumatic brain injured (TBI) patients, and subjects in the vegetative state (VS) the heart beat was continuously recorded while the subjects were passively listening to each of four music samples of different authorship. The heart rate (parametric and nonparametric) frequency spectra were computed and the spectra descriptors were processed by data-mining procedures. Data-mining sorted the nu_lf (normalized parameter unit of the spectrum low frequency range) as the significant descriptor by which the healthy controls, TBI patients, and VS subjects’ HRV responses to music could be clustered in classes matching those defined by the controls and TBI patients’ subjective reports. These findings promote the potential for HRV to reflect complex emotional stimuli and suggest that residual emotional reactions continue to occur in VS. HRV descriptors and data-mining appear applicable in brain function research in the absence of consciousness.

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