Published on 01 September 2017
Working Paper No. 674:
By Chiranjit Chakraborty and Andreas Joseph
We introduce machine learning in the context of central banking and policy analyses. Our aim is to give an overview broad enough to allow the reader to place machine learning within the wider range of statistical modelling and computational analyses, and provide an idea of its scope and limitations. We review the underlying technical sources and the nascent literature applying machine learning to economic and policy problems. We present popular modelling approaches, such as artificial neural networks, tree-based models, support vector machines, recommender systems and different clustering techniques. Important concepts like the bias-variance trade-off, optimal model complexity, regularisation and cross-validation are discussed to enrich the econometrics toolbox in their own right. We present three case studies relevant to central bank policy, financial regulation and economic modelling more widely. First, we model the detection of alerts on the balance sheets of financial institutions in the context of banking supervision. Second, we perform a projection exercise for UK CPI inflation on a medium-term horizon of two years. Here, we introduce a simple training-testing framework for time series analyses. Third, we investigate the funding patterns of technology start-ups with the aim to detect potentially disruptive innovators in financial technology. Machine learning models generally outperform traditional modelling approaches in prediction tasks, while open research questions remain with regard to their causal inference properties.
The crash course on financial portfolio optimization, with application in R. The slides are available here, and R codes from there (in a Markdown). The first and second part is still online, here.
via Optimal Portfolios #2 — Freakonometrics
Optimal Portfolios #1 — Freakonometrics
Last week, we got our first class on portfolio optimization. We’ve seen Markowitz’s theory where expected returns and the covariance matrix are given, > download.file(url=”http://freakonometrics.free.fr/portfolio.r”,destfile = “portfolio.r”) > source(“portfolio.r”) > library(zoo) > library(FRAPO) > library(IntroCompFinR) > library(rrcov) > data( StockIndex ) > pzoo = zoo ( StockIndex , order.by = rownames ( StockIndex ) )…
via Optimal Portfolios, or sort of… — Freakonometrics
(This article was first published on R – Curtis Miller’s Personal Website, and kindly contributed to R-bloggers) Introduction Having figured out how to perform walk-forward analysis in Python with backtrader, I want to have a look at evaluating a strategy’s performance. So far, I have cared about only one metric: the final value of the…
via Stock Trading Analytics and Optimization in Python with PyFolio, R’s PerformanceAnalytics, and backtrader — R-bloggers
(This article was first published on Shirin’s playgRound, and kindly contributed to R-bloggers) In my last two posts (Part 1 and Part 2), I explored time series forecasting with the timekit package. In this post, I want to compare how Facebook’s prophet performs on the same dataset. Predicting future events/sales/etc. isn’t trivial for a number…
via Data Science for Business – Time Series Forecasting Part 3: Forecasting with Facebook’s Prophet — R-bloggers
(This article was first published on Shirin’s playgRound, and kindly contributed to R-bloggers) In my last post, I prepared and visually explored time series data. Now, I will use this data to test the timekit package for time series forecasting with machine learning. Forecasting In time series forecasting, we use models to predict future time…
via Data Science for Business – Time Series Forecasting Part 2: Forecasting with timekit — R-bloggers