he final exam includes two parts as follows: This is Part 2(20% of the final exam grading will be your customized example) Topic: "Predicting Credit Default Risk Using Logistic Regression and PCA"

The final exam includes two parts as follows: This is Part 1 1. The final exam practice questions will account for 80% of the final exam grading.

Part 2: Questions using R codes (20%)

Questions from textbook (80%): Ch7: CFA 1, 2, 3, 4, 10 Ch8: CFA 1, 3, 4, 5 Ch9: CFA 8, 9, 10 Ch10: problem set 1, 4 Ch11: CFA 1, 2, 3, 4, 5 Ch12: CFA 1, 2 Ch13: problem set in excel: 1~7, CFA: 2

Sarantuya 113035128 Repeat the codes on Palomar webpage by running single and Fama French factor models.

Sarantuya(113035128) Try the prompt "How to calculate the asset weights of the minimum variance portfolio using R?" on AIs to generate the codes in R.

Homework08_Investment Portfolio Analysis "Backtesting a Momentum Strategy in R (quantRoom)" using R.

Sarantuya Sharavsambuu 113035128_Financial Software package

Sarantuya Sharavsambuu 113035128 Investment Portfolio Analysis

Exercise05_Sarantuya.Sh_Financial Software Package

Try to replicate the examples of Chapter 4 (classification) in this link. https://emilhvitfeldt.github.io/ISLR-tidymodels-labs/04-classification.html

Chapter 2: ex #10 Chapter 3: ex #2, #10 Chapter 4: ex #12

Sarantuya Sharavsambuu Part 2: Complete the questions in the enclosed pdf file. Upload your code link on RPubs.

Part 1: Complete the questions below in the textbook (60%) Chapter 6: problem set 21 and 22. CFA problem: 8. Chapter 7: problem set 11, 12; CFA problem: 1~3. Chapter 8: problem set 17; CFA problem: 1.

Sarantuya Sharavsambuu(113035128) Run the codings in factor models: capm and famaFrench one factor model

Sarantuya Sharavsambuu(113035128) Exercise 05: replicate the examples in chapter 4

Exercise 04: LDA practice example Q1: Run the example on this website Q2: Run the example in section 4.7.3 (linear discriminant analysis) in the textbook.

Used the attached data file myetf4.csv

Chapter 6: problem #10-12, CFA #1-3

Financial Software:Replicate the examples in 4.7.1 and 4.7.2 in the Lab.

Exercise02 113035128

Application of Financial Software Package 3A

Investment Portfolio HW02

HW05 Sarantuya Sharavsambuu 113035128

hw01 sarantuya

Sarantuya HW06

Final exam Sarantuya 113035128

Sarantuya Sharavsambuu Final exam

Sarantuya Sharavsambuu hw05

Sarantuya Sharavsambuu hw05

Sarantuya Sharavsambuu

In-class exercise/Sh.Sarantuya 113035128/

Homework03 Khuslen.B

khuslen.t hw03

Khuslen.T/113035136/

``{r} library(dplyr) ``` ```{r} library(tidyr) ``` ```{r} stock_df <- read.csv("stock_df.csv") ``` ```{r} stock_df_long <- stock_df %>% pivot_longer( cols = starts_with("X2019_week"), names_to = c("year", "week"), names_pattern = "X(\\d+)_week(\\d+)", values_to = "price" ) %>% mutate( year = as.integer(year), week = as.integer(week) ) ``` ```{r} head(stock_df_long) ```

hw02

1. Load up required libraries: tidyverse, lubridate, tidyquant, readxl, writexl. 2. Import the data files using read_excel() to import excel files. 3. Based on bikes.xlsx, show "model" and "price" column with "price" in descending order. (Hint: use select(), arrange(), desc()) 4. Based on bikes.xlsx, show "model" and "price" column with "price" greater than mean value of price. (Hint: use select(), filter())