Trying to explain the stock market: A look at the correlation between commodity prices, monetary supply and S&P 500 index.
The detailed steps to reproducing the results of this project are as followed.
Before following the steps, please make sure to have the tidyverse, lubridate, zoo and ggplot2 packages installed in your R environment.
1. Acquiring Data
The raw data listed below were downloaded from the corresponding links:
1) effective_federal_funds_rate.csv:
Source: https://fred.stlouisfed.org/series/DFF
Explanation: The federal funds rate is the interest rate at which depository institutions trade federal funds (balances held at Federal Reserve Banks) with each other overnight.
2) usex_major.csv:
Source: https://fred.stlouisfed.org/series/DTWEXM
Explanation: A weighted average of the foreign exchange value of the U.S. dollar against a subset of the broad index currencies that circulate widely outside the country of issue. Major currencies index includes the Euro Area, Canada, Japan, United Kingdom, Switzerland, Australia, and Sweden.
3) 3mo_tb.csv:
Source: https://fred.stlouisfed.org/series/DTB3
Explanation: The daily interest rates of 3-month Treasury Bills in the secondary market.
4) gold.csv:
Source: https://fred.stlouisfed.org/series/GOLDPMGBD228NLBM
Explanation: Gold price continues to be set twice daily (at 10:30 and 15:00 London GMT) in US dollars by The London Bullion Market Association (LBMA).
5) crude_oil.csv:
Source: https://fred.stlouisfed.org/series/DCOILWTICO
Explanation: Daily crude oil prices.
6) prime_loans.csv:
Source: https://fred.stlouisfed.org/series/DPRIME
Explanation: Rate posted by a majority of top 25 (by assets in domestic offices) insured U.S.-chartered commercial banks. Prime is one of several base rates used by banks to price short-term business loans.
7) ^GSPC.csv:
Source: https://finance.yahoo.com/quote/%5EGSPC/history/
Explanation: SP500 historical daily data from Yahoo Finance - not the St. Louis Fed this time.
The above data are in the repository. They have been renamed from their names as downloaded from sources for convenience.
2. Data Tidying and Wrangling
Please place the data_wrangling_final.r script in the same directory as the downloaded data above.
Then, run the script to acquire master.csv, which will be the "master table" used for data analysis and visualization.
3. Running Analysis in R Markdown File
Please download the writeup_final.rmd file from this repo and place it in the same directory as master.csv. Make sure to set the working directory to that location.
Knit the writeup_final.rmd file to acquire a clean version of the write up. Most code chunks are hidden for the sake of succinctness and presentation.
4. Final Result
The final result will be the writeup_final.html file knit from the rmd mentioned above. You will find this file in the same directory where the rmd file is located.