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Math Finance, MC Simulation, Fixed Income, Python, MATLAB, SQL

Location:
Chicago, IL
Posted:
March 10, 2020

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Resume:

Yiqing Liu

312-***-**** adb769@r.postjobfree.com

EDUCATION

Illinois Institute of Technology Master of Mathematical Finance Chicago, IL. May 2019

Core Coursework: Stochastic Analysis, Math Finance, Monte Carlo, Fixed Income, Num. method for PDE GPA: 4.0/4.0

Wuhan University Bachelor of Economics in Finance Wuhan, China. Jul 2016

Core Coursework: Corporate Finance, Bank Credit Risk Management, Financial Engineering GPA: 3.5/4.0

PROFESSIONAL EXPERIENCE

Envestnet Financial Technologies Quantitative Research Intern Chicago, IL. Jun 2018 – May 2019

Implemented TSMOM (see details at Projects part below) for index, accounts level, adjusted original TSMOM with some realistic arrangement, and researched on more sensitive signals to compensate for the lagging effect of the TSMOM signal.

Processed on daily basis (ESG, UMA/SMA account position, ADR local volume, AUM, etc.) data from data sources (API, SQL DB, FTP, etc.) in (json, xlsx, xml, etc.) form, generated reports or inserted into SQL database.

Huainan Mining Group Shanghai Asset Management Research Intern Shanghai, China. Jun 2015 – Aug 2015

Researched on the thermal coal price change in the Yangtze River region in the short-term and long-term based on inventory change and price trend. Demonstrated the feasibility to utilize the thermal coal futures to hedge market risk using OLS, B-VAR and VECM model, compared the risk reduction and Sharpe Ratio improvement under each model.

PROJECTS

Option Daily Volume Forecasting by Expiration (Python) with CTC Trading Group Feb 2019 – May 2019

Researched on the Days-to-Expiration, Ex-Div. Date, Financials effect on the volume, based on the property of stationary, lognormal distributed Daily Volume(DV) to Average DV ratio, designed adjustment scheme as a log shift to original option volume: using in-sample average/fitted adjust factor to construct adjusted volume timeseries and predict future volume.

Compared model performance for -adjusted and Multilinear model. adjusted models achieved 0.86 out-of-sample R2 and performed relative more accurate prediction(40% RMSE) for close-to-expiration period; Multilinear model (using 8 factors chosen from PCA) makes better volume prediction for all period, with 90% R2 and 65% RMSE.

Time Series Momentum (TSMOM) Strategy for Account (MATLAB) at Envestnet Sep 2018 – Nov 2018

Implemented paper Time Series Momentum (Moskowitz et al. 2012) at index level for Treasury Bill 3 Month, Russell 1000/2000/3000. The adjusted TSMOM perform well as a catastrophe protector for the stress period, with a decreased P&L volatility but sacrifice the return at the normal period in the form of the frequently changed signal even with a signal buffer.

Applied TSMOM for accounts as a target weight shift between bond and equity position due to the signal change. Designed signal-action mapping for converting signals into daily target weights with(out) cash position, designed several adjustments for decreasing rebalancing frequency. On average, TSMOM would boost 2% cumulative portfolio return (1990 – 2018) with annually rebalance frequency compared to the benchmark, and observed similar shortcomings as TSMOM for indices.

Risk Allocation and Performance Optimization (Python) with ARB Trading Group Jan 2018 – Apr 2018

Analyzed real trading data of 68 accounts in 6 years, derived total portfolio risk under std. dev, VaR and ES measure, allocated to each account using Euler Principle for each measure to demonstrate how each account contributes to total portfolio risk, conducted sensitivity test and compared the result of sensitivity test with risk allocation result.

Derived explicit formula for total std. dev risk minimization using Lagrange Multiplier, utilized gradient descent to get optimal initial weight (local solution, with boundary) for optimizing total portfolio performance (SR, Gain Loss Ratio).

SKILLS & CERTIFICATIONS

Computer Languages: MATLAB, Python, SQL, R, LaTeX

Certifications: CFA Level II passed June 2019; FRM candidate(Part II passed Nov 2019);



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