fabriziobasso Goto Github PK

billionaires

closet_index_tracking

Closet Index Tracking (or Closet Indexing and Index Hugging) relates to the practice of a fund manager claiming to actively manage an investment portfolio when in reality the fund closely tracks an index. This paper explores the problem of identifying a Closet Index Tracker Fund. This paper first provides a general overview of the regulatory background and the challenges it poses. Then it moves to analyse a set of funds investing in European Large capitalization Equities according to Morningstar’s classification. After calculating a set of statistics on these funds against a set of indices, the paper outlines a procedure to spot potential passive tracker funds relying on clustering unsupervised machine learning algorithms

course-resources-ml-with-experts-budgets

Further student resources for DrivenData's 'Machine Learning with the Experts: School Budgets' DataCamp course.

data-scaling-strategies

Data Scaling Strategies.

datacamp

🍧 A repository that contains courses I have taken on DataCamp

forecastin-us-recession-chapter-vi

Forecastin US recession Chapter VI

forecasting_a_recession_in_the_usa_chapter_i

Forecasting a Recession in the USA

forecasting_recession_in_us_chapterii

Data_Analysis

kaggle

Kaggle Related Files

machine-learning

machine-learning-project-with-scikit-plot

Scikit-plot is a humble attempt to provide aesthetically-challenged programmers (such as myself) the opportunity to generate quick and beautiful graphs and plots with as little boilerplate as possible.

pcaapplied_and_european_yield_curve

This paper aims to explore the time series’ proprieties of the features extracted by using the Principal Component Analysis (PCA) technique on the European AAA-rated Government Bond Yield curve. The PCA can greatly simplify the problem of modelling the yield curve by massively reducing its dimensionality to a small set of uncorrelated features. It finds several applications in finance and in the fixed income particularly from risk management to trade recommendation. After selecting a subset of Principal Components (PCs), this paper first analyzes their nature in comparison to the original rates and the implications in terms of information retained and lost. Then the time-series characteristics of each PC are studied and, when possible, Auto-Regressive Moving-Average (ARMA) models will be fitted on the data. One hundred observations of the original dataset are set aside as a test set to evaluate the predictive power of these models. Eventually, further analyses are performed on the PCs to evaluate the presence of heteroscedasticity and GARCH-ARCH models are fitted when possible. Tests are performed on the fitted coefficient to investigate the real nature of the conditional variance process.

rec_usa_chapterii

recession_in_the_usa_chapter_ii

Forecasting_a_Recession_in_the_USA_Chapter_I

recession_us_chapter_vi

shap

A game theoretic approach to explain the output of any machine learning model.

test1

time_series_files

Files On Studies on TS