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yitaohu88's Projects

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A Convolutional Neutral Network built using Python Jupiter Notebook For Udacity Machine Learning Engineer Micro-degree

empirical-method-in-finance icon empirical-method-in-finance

Winter 2020 Course description: Econometric and statistical techniques commonly used in quantitative finance. Use of estimation application software in exercises to estimate volatility, correlations, stability, regressions, and statistical inference using financial time series. Topic 1: Time series properties of stock market returns and prices  Class intro: Forecasting and Finance  The random walk hypothesis  Stationarity  Time-varying volatility and General Least Squares  Robust standard errors and OLS Topic 2: Time-dependence and predictability  ARMA models  The likelihood function, exact and conditional likelihood estimation  Predictive regressions, autocorrelation robust standard errors  The Campbell-Shiller decomposition  Present value restrictions  Multivariate analysis: Vector Autoregression (VAR) models, the Kalman Filter Topic 3: Heteroscedasticity  Time-varying volatility in the data  Realized Variance  ARCH and GARCH models, application to Value-at-Risk Topic 4: Time series properties of the cross-section of stock returns  Single- and multifactor models  Economic factors: Models and data exploration  Statistical factors: Principal Components Analysis  Fama-MacBeth regressions and characteristics-based factors

fixed-income-projects icon fixed-income-projects

Basic bond valuation in HW1 and simple strip-principal arbitrage trading algorithm implemented in HW2

ml-and-data-science icon ml-and-data-science

Machine Learning and Data Mining: Regression [Linear (Selection and Shrinkage, Dimension Reduction, Beyond Linearity) & Non-Linear Regressions (Logistics, K-NN, Trees)], Cross Validation (LOOCV, K-Folds, Bias vs. Variance), Classification (LDA, QDA, K-NN, Logistic, Tree, SVM), Clustering (PCA, K-Means, Hierarchical) This course will provide an introduction to main topics in data mining / statistical learning, including: statistical foundations, data visualization, classification, regression, clustering. Emphasis will be on statistical learning methodology and the models, intuition, and assumptions behind it, as well as applications to real-world problems. You may find my final project in the stats 415 project folder. Project Summary  Implemented all the classifier learned throughout the semester to predict obesity rates in America as classified through the BMI with the best classifier as 7-fold KNN and prediction accuracy of 81.54%  Analyzed model selection methods to provide the most optimal model and finding the best predictors; concluded that BMI can be non-parametrically predicted based off income, eating habits, exercise habits and shopping habits

risk-parity-and-minimal-variance-portfolio-based-on-a-regularized-estimate-of-variance-covariance-ma icon risk-parity-and-minimal-variance-portfolio-based-on-a-regularized-estimate-of-variance-covariance-ma

Estimated the Covariance Matrix with the LedoitWolf and Diagonalization shrinkage methods, significantly reducing the out-of-sample estimating errors Constructed a risk parity and a minimum-variance portfolio using Convex optimization and nested clustered optimization algorithm Backtested the risk parity and minimum-variance portfolios with monthly rebalancing and 49 industry portfolios as asset universe, improving the Sharpe Ratio from 0.23 (no shrinkage) to 0.63 

yitao-hu icon yitao-hu

I am a MFE (Master of Financial Engineering) student at UCLA Anderson School of Management, pursuing a career in quantitative finance or data science

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