24 April 2022
This update to Big Book of R happened a little differently than the previous ones in that I livestreamed while I was updating it. During the update I felt that there were enough books for the topic of Finance to warrant it’s own chapter!
An Open-Source Active Learning Curriculum for Data Science in Engineering
by Zachary del Rosario
This work provides open-source content for an active learning curriculum in data science. The scope of the content is sufficient for a full-semester introduction to scientifically reproducible statistical computation, data wrangling, visualization, basic statistical literacy, and data-driven modeling. The content is broken into short exercises that introduce new concepts, and longer challenges that encourage students to develop those skills in an open-ended context.
Computational Analysis of Communication
by Wouter van Atteveldt
Assuming little or no background in data science or computer linguistics, this accessible textbook teaches readers how to use state-of-the art computational methods to perform data-driven analyses of social science issues. A cross-disciplinary team of authors—with expertise in both the social sciences and computer science—explains how to gather and clean data, manage textual, audio-visual, and network data, conduct statistical and quantitative analysis, and interpret, summarize, and visualize the results.
Tidy Finance with R
by Christoph Scheuch
Financial economics is a vibrant area of research, a central part of all businesses activities, and at least implicitly relevant for our everyday life. Despite its relevance for our society and a vast number of empirical studies of financial phenomenons, one quickly learns that the actual implementation is typically rather opaque.
This book aims to lift the curtain on reproducible finance by providing a fully transparent code base for many common financial applications. We hope to inspire others to share their code publicly and take part in our journey towards more reproducible research in the future.
Tidy Portfoliomanagement in R
by Dr. Sebastian Stöckl
The book starts with an introduction to the most important tools for the portfolio analysis: timeseries (mainly xts) and the tidyverse. Afterwards, the possibilities of managing and exploring financial data will be developed. Then we do portfolio optimization for mean-Variance and Mean-CVaR portfolios. This will be followed by a chapter on backtesting, before I show further applications in finance, such as predictions, portfolio sorting, Fama-MacBeth-regressions etc
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