Haoxuan Zhang

Applied Econometrics

Used Stata to perform multiple linear regression analysis and dummy variable modeling.

Conducted normality tests (Shapiro-Wilk, Kolmogorov-Smirnov) to evaluate data distributions and model fit

Business Forecasting

Applied R for time series data visualization and analysis

Built and evaluated ARIMA and GARCH models for financial market forecasting Conducted model diagnostics to ensure accuracy and reliability

Quantitative Methods for Financial Markets

Utilized R for quantitative methods, including time series modeling and financial risk analysis.

Calculated risk indicators like VaR (Value at Risk) and CVaR Conditional Value at Risk).

Applied Bayesian probability models to solve complex financial problems

Business Analytics and Statistics

Leveraged statistical methods to analyze business data and derive insights: Successfully published a research paper on social media marketing and consumer behavior analysis, providing insights into effective marketing strategies

Highest Achieving Graduate of the Business Studies Program, Awarded in June 2023

The Frontier of Digital Social Media 07/2024-08/2024

Supervisor: Professor from The Chinese University of Hong Kong cpci

Purpose: To explore how social media platforms and web data mining can be applied to improve business decision-making, with a focus on marketing strategies

Responsibilities:

Studied social media platforms, user behavior, and marketing models, including the AIDA model (Attention, Interest, Desire, Action)

l Applied the 4P theory (Product, Price, Promotion, Place) to real-world marketing case studies

l Utilized case study and process-tracing methods to research consumer behavior and integrate marketing communication strategies

Outcome: Successfully published a research paper on social media marketing and consumer behavior analysis, providing insights into effective marketing strategies

During my time completing Year 11 and Year 12 at Hallett Cove High School in Adelaide, Australia, I experienced significant improvements in both my academic and personal skills. This experience not only enhanced my academic performance but also played a crucial role in developing my language abilities and teamwork skills. Through the Year 11 and Year 12 courses, I acquired a solid foundation of subject knowledge that laid the groundwork for my future academic development. The course work helped me achieve remarkable progress in my academic results, providing support for my subsequent studies and research.

I am currently pursuing a degree in Business Analytics at Monash University, where I have acquired some skills in R and Stata programming. In addition to my classroom studies, I actively engage in self-directed learning, delving into coding techniques focused on modeling and data statistics in business analytics. These learning experiences enable me to independently complete data analysis projects and apply my skills to solve real-world problems.For example, in my previous education, I majored in courses such as Applied Econometrics, Business Forecasting, and Quantitative Methods for Financial Markets. I independently completed assignments using R for time series modeling applications, including visualizing and conducting preliminary analyses of time series data, establishing and applying ARIMA models, and performing model diagnostics and evaluations. In R, I applied quantitative methods to analyze financial markets, including time series analysis and constructing and evaluating forecasting models like ARIMA and GARCH models. In risk management, I utilized R to calculate risk metrics such as VaR (Value at Risk) and CVaR (Conditional Value at Risk) to assess the risks of financial assets. I am also capable of solving Bayesian probability problems using R. Regarding Stata, I can apply code for multiple linear regression analysis, examining the impact of independent variables on dependent variables while evaluating model fit and significance. Additionally, I use Stata for dummy variable analysis in regression modeling, studying the effects of different categories on regression model outcomes, particularly in policy evaluation and market research.Furthermore, I conduct normality tests using Stata, including the Shapiro-Wilk test and Kolmogorov-Smirnov test, to determine whether data follows a normal distribution.