代写COMP0050、代写C++，Java编程

日期：2022-03-23 11:04

COMP0050 Assignment

Data

Download from moodle the file COMP0050CourseworkData.zip.

This contains two datasets:

1- peerToPeerLoans.csv: The data come from George, N. (2018) (All

Lending Club loan data version 6, February 2018, www.

kaggle.com/wordsforthewise/lending-club). This is a subset of the

datasets used in Turiel, J. D., & Aste, T. (2020). The variable of

interest is charged_off, which takes value 1 if a debtor is not repaying

the loan (0 otherwise).

2- stockReturns.csv: this dataset contains 500 daily percentage stock

returns for 50 assets.

Tasks

There will be two tasks corresponding to the two datasets:

1. The task is to build a model to predict whether a customer will default

on their loan. You should compare the performance of different

methods (e.g. logistic regression, classification trees/forests) in terms

of their ability to correctly predict loan defaults. You are free to focus

on a subset of the data (e.g. a reduced set of features, or a subset of

the loans) and to manipulate the data as you like, but you should

explain your rationale.

2. Focus on the global minimum variance portfolio. Compare the

portfolio variance using two different regularizers. Use validation

methods to find the optimal values of the parameters.

For both tasks, justify whether you want to focus only on subsamples of the

data. You are also free to explore questions related to the data and the

tasks you think are interesting, as long as your analysis includes the

development of predictive models of defaults for what concerns task 1 and

regularized portfolio optimization for task 2.

Useful references in relation to the above tasks are the following

Turiel, J. D., & Aste, T. (2020). Peer-to-peer loan acceptance and default

prediction with artificial intelligence. Royal Society open science, 7(6),

191649.

Fastrich, B., Paterlini, S., & Winker, P. (2015). Constructing optimal sparse

portfolios using regularization methods. Computational Management

Science, 12(3), 417-434.

Brodie, J., Daubechies, I., De Mol, C., Giannone, D., & Loris, I. (2009).

Sparse and stable Markowitz portfolios. Proceedings of the National

Academy of Sciences, 106(30), 12267-12272.

Written report

A brief written report (maximum 8 pages, with a maximum 4 pages for each

task) containing the justification of the approach, the results of your

analysis, and a discussion of your results should be submitted to Moodle

before the deadline of Wednesday 06/04/2022 at 16:00.

Marking?

This assignment is worth 100% of the overall mark (50% for each task).

The marking will be based on the following criteria (with uniform weights):

1) Clarity of presentation and explanations?

2) Validity of results ?

3) Critical interpretation of the results?