PROJECTS

Completed projects under the supervision of Prof. Richard Herron, Ph.d.

PROJECT 1 - Portfolio Management- Risk Measurement & Stock Performance

(Above is  a clickable link to view the  entire project)

This project aims to help individuals master data manipulation and visualization while also understanding the risk-return tradeoff for various risk measures. The project consists of nine tasks, each focusing on analyzing stock returns and portfolio performance based on different risk measures such as mean returns, volatility, Sharpe ratios, and CAPM betas. The limitations of the analysis are also discussed, including the limitations of the available dataset, the potential bias of randomly selecting stocks for portfolios, and the potential impact of unique factors on individual stock performance. Despite these limitations, the project provides valuable insights into the relationship between risk measures and stock performance.

PROJECT 2- Relation between Bitcoin & Gold

(Above is  a clickable link to view the  entire project)

This Python project aimed to evaluate whether Bitcoin and gold are effective inflation and market risk hedges. The project consisted of six tasks that involved measuring the price level of Bitcoin and gold, estimating capital asset pricing model (CAPM) regressions for both assets, plotting the mean-variance efficient frontier of Standard & Poor's 100 Index (SP100) stocks with and without Bitcoin and gold, finding the maximum Sharpe Ratio portfolio of SP100 stocks with and without Bitcoin and gold, and comparing the 1-year portfolio with the out-of-sample performance of the previous maximum Sharpe Ratio portfolio. The project's purpose was to help us master data analysis and evaluate articles in the popular media using our data analysis skills. The project required skills in financial analysis, data analysis, and visualization. The project's conclusion was that both Bitcoin and gold have the potential to act as inflation and market risk hedges, but their effectiveness in these roles may vary depending on various factors such as the current economic environment, investor sentiment, and geopolitical events. The project's limitations were also noted, such as the short history of Bitcoin and the variation in the effectiveness of hedges depending on the investor's investment goals, risk tolerance, and diversification needs. 

Personal Projects

Option-Implied Probability Distribution for the Terminal Stock Price

This is a group project opted to complete during the course Quantitative Portfolio Management.

(Above is  a clickable link to view the  entire project)

"Option-Implied Probability Distribution for the Terminal Stock Price" presents a statistical method of utilizing option markets to estimate the likelihood of future stock prices, with practical applications for finance and investment on Python. In this project, assumptions are made regarding the option position, such as a one-week expiry date, low volatility, willingness to take on risk, and expectation to profit from time decay. The investor may need to monitor the position closely, and the investment is made partially with the other half of wealth kept as collateral for shorting the options. The analysis reveals that the QQQ option prices dataset is more volatile than the benchmark, and the short strangle strategy's profitability depends on implied volatility and requires careful risk management. The research process includes seven steps, providing insights into the factors affecting options prices and informing trading decisions. The findings have implications for academic research and practitioners seeking to implement effective risk management strategies. The results can be presented through various mediums to allow others to explore the data and trading strategies. The paper contributes to the existing literature on option implied probability and its usefulness in predicting market movements and risk. 

Market Insights Analysis

(Above is  a clickable link to view the  entire project)

The project was performed on Python. The market insights analysis project provides a comprehensive statistical analysis that incorporates descriptive statistics for three carefully selected stocks to gain valuable insights into their performance and trends. Additionally, it offers an in-depth examination of the Capital Asset Pricing Model (CAPM) and Small-Market Line (SML) on these three stocks, shedding light on their risk-return relationship and potential for future returns. Moreover, the project conducts the Four Factor Fama-French Model regression analysis on two specific industries, the Consumer and Healthcare, unraveling the underlying factors that drive their performance.

Brownian Motion & Bond Portfolio

(Above is  a clickable link to view the  entire project)

The Python project unravels the intricacies of financial dynamics and delivers insightful analysis. This comprehensive tool showcases the mesmerizing world of Brownian motion in one, two, and three dimensions, providing a deep understanding of stochastic processes and their implications in financial modeling. The project incorporates the sophisticated Vasicek interest model, it simulates short paths and yield curves for two bonds. These simulated scenarios provide a valuable lens into the behavior of interest rates and the dynamics of bond prices. With an intuitive interface and robust interpretive capabilities, this project comprehend and navigates the complex world of financial dynamics.

Portfolio Optimizer

(Above is  a clickable link to view the  entire project)

The Python project encompasses optimal weight calculations for two portfolios, one comprising five stocks and the other consisting of five cryptocurrencies. These weights were determined using three approaches: minimum variance, maximum returns, and equal weights. The portfolios were then compared, and histograms and kernel density plots were plotted to facilitate interpretation and gain deeper insights into their risk-return characteristics. Additionally, the project employed utility maximization in Python to assess which investment opportunity presented a superior option. By integrating these comprehensive analyses, the project equips investors with a robust framework for optimizing portfolio allocations and identifying the most promising investment opportunities in the dynamic financial landscape.

ARIMA- Stock Predictions

(Above is  a clickable link to view the  entire project)

The Python project combines ARIMA projections with ADF testing to provide accurate price predictions for a period of 15 days. Through the utilization of the ARIMA model, which captures time series patterns, the project effectively analyzes historical price data to forecast future trends. To ensure the reliability of the model, the project employs the ADF test, which checks for stationarity in the data, a crucial assumption for accurate predictions. By integrating these techniques, the project enables us to make informed decisions by anticipating price movements and potential market opportunities in the short-term horizon.

GARCH & Historical Volatility

(Above is  a clickable link to view the  entire project)

Markowitz Portfolio Optimization

(Above is  a clickable link to view the  entire project)

The paper uses MATLAB to perform Markowitz Portfolio Optimization Theory.

Capstone Projects

Strategic Merger Proposal: P&G and ELF Beauty 

(Above is  a clickable link to view the  entire project)

The project focused on a mock proposal for the merger of P&G and ELF Beauty, highlighting my expertise in key areas crucial to this role. I meticulously created a pro forma, providing comprehensive financial projections for both companies pre- and post-merger. Additionally, I developed an insightful report encompassing a robust business plan, strategic acquisition plan, and implementation plan. Through careful negotiation and deal structuring, I maximized value for both parties involved. Leveraging advanced Excel techniques, I constructed a comprehensive valuation model, including the Discounted Cash Flow and Dividend Discount Models, to assess the merged entity's potential. Furthermore, I forecasted Free Cash Flow for the next five years and utilized Historical P/E modeling. With these skills, I am well-prepared to provide invaluable insights and guidance in complex merger and acquisition transactions.