Risk Management Los Angeles

Changyu (Richard) Ru, CQF, FRM +*(***) -***-****, ad1etd@r.postjobfree.com

EDUCATION

University of Southern California (USC), Viterbi School of Engineering Los Angeles, US Master of Science in Financial Engineering (STEM) 09/2022-12/2023 (Expected) Zhejiang University of Finance and Economics (ZUFE), School of Business Hangzhou, China Bachelor of Science in Finance, GPA: 4.21/5 (WES: 3.68/4) 09/2017-06/2021 Columbia University in the City of New York (Columbia) New York, US Exchange Program: Options and Futures, Risk Management, etc., GPA: 3.68/4 08/2019-12/2019 London School of Economics and Political Science (LSE) London, UK Summer School Program: Finance 07/2018-08/2018

Certificate in Quantitative Finance (CQF) Online

Certified: Advanced Risk Management; Python; Machine Learning 01/2022-10/2023 COMPUTER SKILL Python (Jupyter, NumPy, Pandas, Matplotlib), Wind, Choice, etc. CERTIFICATION Certificate in Quantitative Finance (CQF) Certified; CFA® (Level 2 Passed); FRM® (All Passed) INTERNSHIP

Frost & Sullivan Shanghai, China

Industry Research Consulting Intern 01/2020-03/2020

Conducted in-depth financial analysis on client Fonterra FCG (NZE) covering 10 key metrics including Revenue, EBITDA, NI, ROE, etc. against 5 leading competitors, evaluating debt capacity, profitability, operational efficiency, and overall development potential

Did a comprehensive analysis of the New Zealand dairy industry using PESTEL, Porter's Five Forces, and SWOT, resulting in a 40- page report detailing competition pattern, marketization degree, market shares of major enterprises and main entry barriers

Drafted and revised interview questionnaires, estimated sample sizes, and collected data through consumer surveys and industry expert interviews to acquire first-hand data across various sectors JIC Trust Co., Ltd. Zhejiang, China

Trust and Wealth Management Intern 07/2019-08/2019

Gathered and analyzed data on financial markets, investment products, and macroeconomics; assisted in structuring credit subscriptions, beneficial rights transfers, repayment plans, and project management;

Engaged with beneficiaries and wealth management clients to review relationships and accounts, discuss needs, analyze issues, and deliver relationship-based solutions; Participated in client meetings to understand financial needs and investment objectives; assessed clients' risk tolerance, provided investment advice, and ensured alignment with clients' risk preferences China Galaxy Securities Co., Ltd. Zhejiang, China

Equity Research Intern 01/2019-03/2019

Utilized major financial databases like Wind and Choice to extract data from over 500 data points for precise model calibration

Developed a comprehensive profit forecast model of Xiabu Xiabu (HK0520), incorporating over 15 key variables including revenue projections, operating costs, EBITDA margins and NI estimates, etc.; Employed a DCF analysis in tandem with the forecast model, predicting its future growth of market capitalization over the next 3 years and anticipated the increment of stock price;

Engaged the Future PE to EPS method, deriving a target PE for Xiabu Xiabu based on growth expectations PROJECT

Advanced Options Pricing & Analysis by Python (CQF Project) 05/2023

Applied Euler-Maruyama and Milstein Scheme discretization in Monte Carlo method to simulate 10000 paths of the underlying stock price (each path has the same 252 timesteps) using continuous sampling methods

Implemented advanced algorithms for Asian Options pricing, covering both continuous and discrete averaging methods; Developed precise pricing models for Binary Options, adapting to various market conditions; Derive the pricing of continuous Lookback Options, resulting in precise option value estimations for realized maximum and minimum values Deep Learning and Prediction of DJIA Time Series with LSTMs by Python (CQF Final Project) 08/2023

Used Jupyter Notebook (Anaconda) to obtain Time Series of DJIA from Yahoo Finance; Computed technical indicators like MACD, ATR, etc. derived from Technical Analysis Library using Ta-Lib Python library

Employed comprehensive Exploratory Data Analysis (EDA) including Features Description, Correlation Matrix, Pairwise Plot, etc.; Implemented Features Selection like K-Means Clustering

Crafted state-of-the-art deep learning models models: Dual Layer LSTM and Bi-Directional LSTM; Compared with simple models: GRU and Single Layer LSTM using plotted AUC, ROC Curves and Confusion Matrix

Used a long-short trading strategy for the prediction of subsequent day stock valuations using models above, resulting in impressive gains: Bi-LSTM led with a stellar 77.52% return, compared with 8.1% from a passive hold strategy; Graded 90/100 by CQF Institute SCHOLARSHIP Zhejiang Provincial Government Scholarship 09/2018; College First-Class Scholarship 09/2018

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