15 Finance
15.1 Analyzing Financial and Economic Data with R
by Marcelo S. Perlin
Not surprisingly, fields with abundant access to data and practical applications, such as economics and finance, it is expected that a graduate student or a data analyst has learned at least one programming language that allows him/her to do his work efficiently. Learning how to program is becoming a requisite for the job market.
15.2 Machine Learning for Factor Investing
by Guillaume Coqueret, Tony Guida
This book is intended to cover some advanced modelling techniques applied to equity investment strategies that are built on firm characteristics.
Link: http://www.mlfactor.com/
15.3 Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis
by Jonathan K. Regenstein Jr.
A unique introduction to data science for investment management that explores the three major R/finance coding paradigms, emphasizes data visualization, and explains how to build a cohesive suite of functioning Shiny applications. The full source code, asset price data and live Shiny applications are available at reproduciblefinance.com. The ideal reader works in finance or wants to work in finance and has a desire to learn R code and Shiny through simple, yet practical real-world examples.
Paid: $60
15.4 Tidy Finance with R
by Christoph Scheuch, Stefan Voigt, Patrick Weiss
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.
15.5 Tidy Portfoliomanagement in R
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.
Created and maintained by Oscar Baruffa
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