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Financial-Engineering-Projects

Codes to analyze financial markets: fundamental analysis of listed companies, option pricing techniques, e.t.c.. Written in Python, and R.

Stock Annual Fundamentals [R]

This project focuses on the collection of annual data for all stocks. The data sets come from WRDS. Given the monthly data for PERMNO, tickers, cusip, price, shares outstanding and holding period return, we calculate the annualized marketcap and rate of return.

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Value vs. Growth Studies [R]

This project focuses on the exploration of the relationship between E/P ratios and Earnings Growth. The data sets are hot-loaded using tidyquant to pull data from online database. Given the P/E ratios, and 3-year average lagged growth of Net Income, we truncated the top and bottom quantile as outliers. The correlation turns out to be merely 0.1365359, which is weak evidence to establish a linear relationship between these two stats.

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Fama-Macbeth Regression [R]

In this project, we are running Fama-Macbeth regression to predict cross-sectional stock returns with independent variables: firm-marketcap, price-normalized accruals, the earnings-price ratio, and 1/price. The data sets originally come from WRDS. The prediction of year T+2 returns is based on the year T variable as a firm could end its fiscal year in May and report results in October. The performance of the prediction is not as poor as I expected. The the Fama-Macbeth regression runs on the panel data with 826 samples accross 20 years produces the coefficients of [0.00000133, -0.563, -3.02, 0.484] for the above variables respectively.

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Exotic & Path Dependent Options [Python]

  • Fixed Strike Lookback Option
  • Collateral Loans

Binomial Methods for Option Pricing [Python]

  • Convergence Rate of Different Binomial Mthods
  • Price Options using Real-time Data
  • Greeks Estimation using Binomial Method
  • Binomial Method for Put Option
  • Trinomial Method
  • Binomial Method using Halton's Low-discrepancy Sequence

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