Qiyu Yang
About Me
My name is Qiyu Yang. I am majoring in Financial Engineering and I will graduate from Lehigh University with a master of science degree in May 2023. I seek to be part of a team that reflects my ambitious and driven personality.
Portfolio
By clicking on the 10-Ks analysis, a detailed report can be accessed. The primary objective of this analysis is to establish the correlation between various variables and returns. In terms of innovation, two noteworthy outcomes(negative/positive) have been identified and are listed below:
Negative:
Positive:
This document focuses on the interpretation of linear regression coefficients in house price forecasting and aims to identify the variable with the strongest relative correlation. It should be noted that the original data contains outliers and missing values, which have not been addressed in this analysis. Please click the Regression Practice to see the whole analysis.
The goal of this analysis is to predict the house prices accurately using various regression algorithms and data preprocessing techniques. To achieve this, the data was cleaned by removing values with a missing values greater than 80% and selected a few variables that I thought were more informative to make a graph to see if they had outlier, and removed them. Selected lasso, lassoCV, RandomForestRegressor, LGBMRegressor, and XGBRegressor for the prediction models. Compared the respective R2 and selected the highest value for the holdout prediction. (To avoid overfitting the model as much as possible)
XGBRegressor is the one I chose at last. The R2 score is 0.9072.
these are the top 10 predictor variables for the model:
Pandemic effect on Supply chain 2019-2022
We have developed a comprehensive dashboard that provides insightful visualizations of how the COVID-19 pandemic has affected the supply chain industry. Our dashboard encompasses a variety of supply chain companies that have been merged with the S&P 500, providing users with a broad perspective on the market trends. The dashboard enables users to evaluate the level of participation for each seller industry and provides access to the accounting messages of individual seller companies.
Our team has leveraged analytics and data visualization techniques to develop an intuitive and user-friendly dashboard that enables users to navigate and interpret the data with ease. The dashboard provides real-time updates and is a valuable resource for investors and analysts seeking to monitor the supply chain industry’s impact during these challenging times.
Conclusion: Health care sector increased 20%, Consumer Staples sector increased 30%, Information Technology sector increased 40%.
The primary objective of this analysis is to forecast housing prices. To achieve this, various regression models, including Linear, Decision Tree, Random Forest, and Gboost, were compared. Based on the evaluation of the mean squared error (MSE), the linear regression model was selected as the optimal predictor. The predicted results are presented below:
- Qualitative and quantitative analysis of compelling common features for top technology stocks within the field.
- Data cleaning and transferring and detected autocorrelation analysis of cryptocurrency and inflation.
- Predicted and analyzed stock prices based on falling knife trends. Reported that both VaR and CVaR are calculated based on the marginal and joint distribution respectively. Measured the risk of falling and rising probabilities.
Career Objectives
Financial analyst or risk analyst seeking a challenging role in a dynamic organization where I can utilize my skills in finance, business and analytics to develop innovative financial strategies and establish a meaningful career focused on societal impact.
Skills and Hobbies
Skills: Python(Pandas, streamlit, sklearn), Tableau, Microsoft Suits, Eviews
Hobbies: Photography, music
Page template forked from evanca