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AUEB-Stats Short Course: “Machine Learning in R: Applications in Finance”  by Prof.Ionut Florescu (Stevens Institute of Technology) Forumgrstats

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AUEB-Stats Short Course: “Machine Learning in R: Applications in Finance”  by Prof.Ionut Florescu (Stevens Institute of Technology) Forumgrstats
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AUEB-Stats Short Course: “Machine Learning in R: Applications in Finance”  by Prof.Ionut Florescu (Stevens Institute of Technology) Empty AUEB-Stats Short Course: “Machine Learning in R: Applications in Finance” by Prof.Ionut Florescu (Stevens Institute of Technology)

Sat 12 Nov 2022 - 0:15
AUEB-Stats Short Course: “Machine Learning in R: Applications in Finance”  by Prof.Ionut Florescu (Stevens Institute of Technology) 2022_110


AUEB STATISTICS SEMINAR SERIES NOVEMBER 2022

SHORT COURSE

“Machine Learning in R: Applications in Finance”

Ionut Florescu

 Research Professor, School of Business
Stevens Institute of Technology, USA

  • Lecture 1 - Monday 21 November 2022  Room E609* 16.00-18.00
  • Lecture 2 - Tuesday 22 November 2022 Room E609* 12.00-15.00
  • Lecture 3 -  Wednesday 23 November 2022 E802** 12.00-15.00


* 6th floor of the Postgraduate Building of Athens University of Economics and Business (Evelpidon & Lefkados). 
** 8th floor of the Postgraduate Building of Athens University of Economics and Business (Evelpidon & Lefkados). 

The course is financed by the M.Sc. in Statistics of Athens University of Economics and Business.
Certificate of attendance will be provided (electronically) to all participants attending at least 2 out of 3 lectures.
All M.Sc. in Statistics (2022-23) students will follow the short course
All other students should register here https://forms.gle/9nEU4BLaR4w6LrvEA since a limited number of positions is available

Directions about the Course

All students except the current M.Sc. in Statistics (2022-23) should register here https://forms.gle/9nEU4BLaR4w6LrvEA
Please register at the FREE OPEN ECLASS course here https://free.openeclass.org/courses/SC1153/ Password AUEB-Stats2022. Material will be available there.
All participants should bring their laptops with them, fully charged. R and R-Studio should be already installed
Detailed description of the course lectures is attached.

Description of Lectures

Lecture 1: What is Corporate Credit Rating? Comparing basic Machine Learning techniques.

In this lecture we will discuss how machine learning techniques may be used to assess corporate credit ratings. We will discuss details of the article Golbayani, Parisa, Ionut¸ Florescu, and Rupak Chatterjee (2020). “A comparative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees”. In: The North American Journal of Economics and Finance 54, p. 101251.

In the practical session, we will introduce the data used throughout the lectures and learn how the Support Vector Machine algorithm may be used in this area.

Lecture 2: Corporate Credit Rating. Comparing more advanced Machine Learning Techniques.

In this lecture we will discuss deep network architectures specifically LSTM and CNN. We will discuss details of the article Golbayani, Parisa, Dan Wang, and Ionut Florescu (2021). “Application of deep neural networks to assess corporate credit rating”. In: International Journal of Mechanical and Industrial Engineering 14(1). url: https://arxiv.org/abs/2003.02334.

In the practical section we will discuss the most basic Artificial Neural Network architecture (Multi Layer Perceptron) and apply it to financial data.

Lecture 3: Corporate Credit Learning. Understanding how inputs change the output: Counterfactual Explanation.

In this lecture we will discuss the results obtained in our most recent research Wang, Dan, Zhi Chen, and Ionut Florescu (2021). A Sparsity Algorithm with Applications to Corporate Credit Rating. url: https://arxiv.org/abs/2107.10306.

We will present an algorithm that may be used to determine the smallest modification to input variables to change the classification given an existing ML algorithm.

In the practical session we will discuss Decision trees, particularly Random forest. We will apply RF to financial data.

All participants should bring their laptops in all three lectures and have R and Rstudio installed.

Install R from the University of Crete mirror: https://ftp.cc.uoc.gr/mirrors/CRAN/ (only base)
Download and install Rstudio Desktop https://posit.co/download/rstudio-desktop/
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