useR! 2024

Pricing analytics is an emerging sector where the use of optimization models and forecasting come hand in hand. I've developed models where we can use proprietary data and market intelligence to keep optimizing market share through pricing. The old tale of Volume vs Margin, which to prioritize. Since the development of the model the team has been able to guide business leaders in increasing profits while minimizing loss. In our talk we will discuss: -What is pricing analytics -Pricing challenges -How to optimize pricing using R

In my journey through data roles in econ research at various universities and non-profits, convincing my teams to hop on the R train has been a bit of a tug-of-war and that’s because in econ there's been a historic preference for proprietary tools like STATA that has resulted in a slower uptake of open-source collaborative practices. But yes, things are starting to change, slowly but surely. R and Python are shaking things up in the econ world and it's exciting to see research groups jumping in and opening up. In my experience, effectively showcasing R’s versatility, open-source foundation, and vibrant community support has proved pivotal in winning teams over. The insights I've gathered are relevant for anyone dealing with similar challenges in convincing their teams and if given the opportunity I'm keen to share these with everyone. Imagine this: an econverse brimming with R packages, as user-friendly and structured as the tidyverse or the pharmaverse. It's a vision worth spreading, and I'm eager to rally others to join the cause.

As developers at a leading economic and financial markets research institution, centralizing our data is crucial for both internal (backtesting, sharing forecasts, model validations) and external (client access) use cases. Our contribution consists of two parts: first, the introduction of our R package, developed using the “Open API generator,” enabling seamless querying of our database via a RESTful API. Additionally, we show the integration of a robust authorization and authentication layer within AWS cloud infrastructure. Second, we have designed a user-friendly R/Shiny-based frontend to facilitate easy access for non-technical users. This interface displays forecasts and historic data for various variables and countries, allowing for data updates and downloads. To mimic familiar Excel operations such as row-wise pasting and data removal, we have successfully utilized the open-source R-wrapper rhandsontable of the JavaScript library handsontable. Both contributions enable our analysts and clients to smoothly interact with our database by providing a convenient, secure, and user-friendly environment, enhancing the accessibility of our macroeconomic data for all stakeholders.

Tidy Finance is a transparent, open-source approach to research in financial economics, featuring multiple programming languages. The {tidyfinance} package is a new addition to the R toolbox and includes helper functions for empirical research in financial economics, addressing a variety of topics covered in Scheuch, Voigt, and Weiss (2023). The package is designed to provide shortcuts for issues extensively discussed in the book, facilitating easier application of its concepts. In this presentation, we focus on the family of functions that download and process financial data. We highlight the concept of tidy data and how it can be applied to compiling multiple data sources for financial applications. We also demonstrate the inner workings of the {tidyfinance} package and provide inspiration for applications in teaching and research.

Several contributed packages on CRAN as well as other package repositories offer tools to carry out empirical and quantitative financial research with R [1]. A very well-recognized collection of such packages is the Rmetrics suite of packages offering functionality for many different aspects of empirical and computational finance. In this talk we focus on the fPortfolio package, in particular on portfolio handling and on solving a wide variety of portfolio optimization problems. We present a prototype of the next major version of the package which will be using ROI [2,3], an extensible framework for modeling and solving linear as well as nonlinear (possibly mixed-integer) optimization problems as a backend. Furthermore, we indicate how this package could be employed in practice to manage model portfolios along various regions, base currencies and/or risk constraints. [1] Dirk Eddelbuettel. CRAN task view: Empirical finance, 2024. https://CRAN.R-project.org/view=Finance [2] Stefan Theußl, Florian Schwendinger, and Kurt Hornik. ROI: An Extensible R Optimization Infrastructure. Journal of Statistical Software, 94(15):1-64, 2020. doi: 10.18637/jss.v094.i15.

In my talk, I explore teaching data science and causal inference in a mandatory undergraduate economics course using R, with no prior student knowledge in programming, causal inference, or project management. To teach these concepts, I opted for a flipped classroom approach and group projects: Initially, students learn theoretical concepts (descriptive statistics, data visualization, and causal inference) through short videos, foster these concepts through individual RTutor Problem Sets, and apply them in a case study, which we discuss in class, using RMarkdown to make the results reproducible. Subsequently, they engage in groups of three students to solve three economic projects in RMarkdown, generating HTML reports. These projects require them to gather data (e.g. via API or web scraping), perform statistical analyses, and infer causal relationships, collaborating on private GitHub repositories. Peer reviews follow each project. Tools used include the R-packages RTutor for Problem Set design, ghclass for GitHub management, and GitHub Actions for report verification.

In the urgent pursuit of climate action, the need for effective financial tools to drive sustainable investment has become paramount.
The Paris Agreement Capital Transition Assessment (PACTA) is a forward-looking, science based analysis helping shift capital flows in greener directions and enabling the financial sector to contribute to the goals of the Paris Agreement.
PACTA offers free tools supporting investors in determining the alignment of their portfolios and loan books with widely accepted climate scenarios.
To date more than 1500 institutions have assessed their portfolios with PACTA, analyzing assets totalling over US$100T.

PACTA also equips governments and regulators to assess the climate alignment of their regulated entities, both individually and at the level of an entire sector.
Our team has supported more than a dozen government entities and regulatory bodies in assessing the climate alignment of their financial sectors.

PACTA, written in R and freely available under the MIT license, stands as a powerful tool in the fight against climate change.
Join us to explore its transformative potential and contribute to the advancement of sustainable finance.