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Empirical Asset Pricing via Machine Learning
We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premiums. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based strategies from the literature. We identify the best-performing methods (trees and neural networks) and trace their predictive gains to allowing nonlinear predictor interactions missed by other methods. All methods agree on the same set of dominant predictive signals, a set that includes variations on momentum, liquidity, and volatility.
Journal Article
Interpretive social science : an anti-naturalist approach
In this book Mark Bevir and Jason Blakely set out to make the most comprehensive case yet for an 'interpretive' or hermeneutic approach to the social sciences. Interpretive approaches are a major growth area in the social sciences today. This is because they offer a full-blown alternative to the behavioralism, institutionalism, rational choice, and other quasi-scientific approaches that dominate the study of human behavior. In addition to presenting a systematic case for interpretivism and a critique of scientism, Bevir and Blakely also propose their own uniquely 'anti-naturalist 'notion of an interpretive approach. This anti-naturalist framework encompasses the insights of philosophers ranging from Michel Foucault and Hans-Georg Gadamer to Charles Taylor and Ludwig Wittgenstein, while also resolving dilemmas that have plagued rival philosophical defenses of interpretivism. In addition, working social scientists are given detailed discussions of a distinctly interpretive approach to methods and empirical research. The book draws on the latest social science to cover everything from concept formation and empirical inquiry to ethics, democratic theory, and public policy. An anti-naturalist approach to interpretive social science offers nothing short of a sweeping paradigm shift in the study of human beings and society. This book will be of interest to all who seek a humanistic alternative to the scientism that overwhelms the study of human beings today.
Book
Advances in human intracranial electroencephalography research, guidelines and good practices
•We provide good practices for both new and experienced iEEG researchers.•We bridge the patient, clinician and researcher perspective.•Sections include data collection, processing, localization, analysis and statistics.•Each section provides a review, gives recommendations and demonstrates visualization.•We provide an outlook and discuss perspectives on continued iEEG development.
Since the second half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.
Journal Article
Handbook of research methods and applications in spatially integrated social science
\"The chapters in this book provide coverage of the theoretical underpinnings and methodologies that typify research using a Spatially Integrated Social Science (SISS) approach. This insightful Handbook is intended chiefly as a primer for students and budding researchers who wish to investigate social, economic and behavioural phenomena by giving explicit consideration to the roles of space and place. The majority of chapters provide an emphasis on demonstrating applications of methods, tools and techniques that are used in SISS research, including long-established and relatively new approaches.\" --Cover.
Book
International standards for the analysis of quality-of-life and patient-reported outcome endpoints in cancer randomised controlled trials: recommendations of the SISAQOL Consortium
Patient-reported outcomes (PROs), such as symptoms, function, and other health-related quality-of-life aspects, are increasingly evaluated in cancer randomised controlled trials (RCTs) to provide information about treatment risks, benefits, and tolerability. However, expert opinion and critical review of the literature showed no consensus on optimal methods of PRO analysis in cancer RCTs, hindering interpretation of results. The Setting International Standards in Analyzing Patient-Reported Outcomes and Quality of Life Endpoints Data Consortium was formed to establish PRO analysis recommendations. Four issues were prioritised: developing a taxonomy of research objectives that can be matched with appropriate statistical methods, identifying appropriate statistical methods for PRO analysis, standardising statistical terminology related to missing data, and determining appropriate ways to manage missing data. This Policy Review presents recommendations for PRO analysis developed through critical literature reviews and a structured collaborative process with diverse international stakeholders, which provides a foundation for endorsement; ongoing developments of these recommendations are also discussed.
Journal Article
Sociolinguistics and the narrative turn : researching languages and society in contexts of change and transition
In proposing the use of an alternative method for generating knowledge in the field of sociolinguistics, this book presents a fresh point of view. To introduce his argument, the author gives a well-considered critique of some of the guiding principles of traditional sociolinguists. Then, conducting a small-scale study involving seven participants, he shows how a qualitative narrative approach can renew the theorisation of the relationship between language and society.
Book
Variability in the analysis of a single neuroimaging dataset by many teams
Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses
1
. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset
2
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5
. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.
The results obtained by seventy different teams analysing the same functional magnetic resonance imaging dataset show substantial variation, highlighting the influence of analytical choices and the importance of sharing workflows publicly and performing multiple analyses.
Journal Article
Reliability of quantitative multiparameter maps is high for magnetization transfer and proton density but attenuated for R 1 and R 2 in healthy young adults
We investigate the reliability of individual differences of four quantities measured by magnetic resonance imaging‐based multiparameter mapping (MPM): magnetization transfer saturation (MT), proton density (PD), longitudinal relaxation rate (R 1 ), and effective transverse relaxation rate (R 2 *). Four MPM datasets, two on each of two consecutive days, were acquired in healthy young adults. On Day 1, no repositioning occurred and on Day 2, participants were repositioned between MPM datasets. Using intraclass correlation effect decomposition (ICED), we assessed the contributions of session‐specific, day‐specific, and residual sources of measurement error. For whole‐brain gray and white matter, all four MPM parameters showed high reproducibility and high reliability, as indexed by the coefficient of variation (CoV) and the intraclass correlation (ICC). However, MT, PD, R 1 , and R 2 * differed markedly in the extent to which reliability varied across brain regions. MT and PD showed high reliability in almost all regions. In contrast, R 1 and R 2 * showed low reliability in some regions outside the basal ganglia, such that the sum of the measurement error estimates in our structural equation model was higher than estimates of between‐person differences. In addition, in this sample of healthy young adults, the four MPM parameters showed very little variability over four measurements but differed in how well they could assess between‐person differences. We conclude that R 1 and R 2 * might carry only limited person‐specific information in some regions of the brain in healthy young adults, and, by implication, might be of restricted utility for studying associations to between‐person differences in behavior in those regions.
Journal Article