machine learning, quantitative researcher

Dan Wang Email: **************@*****.***

To obtain a full-time position in the eld of quantitative research eld. Mobile: +1-201-***-**** Education

Stevens Institute of Technology Hoboken, NJ

Doctor of Philosophy in Financial Engineering (Machine Learning in Finance), GPA: 3.96 Expected May. 2021 Beihang University Beijing, China

Bachelor of Science in Optical Information, GPA: 3.71 July. 2015 Skills & Certification

Languages: Python(Advanced), C++, SQL, R, MATLAB, VBA, SAS, Julia Certi cation: Level III CFA candidate; Certi ed Base Programmer for SAS 9 Research

Alpha Driven Trading Research Hoboken, NJ

Building an algorithm trading system driven by Alpha signal using cross-sectional method Nov. 2018 - Mar. 2019

Develop a back-tested platform for alpha signal generation using cross sectional model by machine learning

Construct and solve portfolio optimization problem with Beta target and Dollar neutral using mathematical tools

Develop an automate trading model with risk control and order place system using selenium

Rank top 3 on both absolute return and Sharpe ratio over 200 competitors. Machine Learning in Credit Rating (Joint Work with UBS) Hoboken, NJ Applying Deep Neural Network and NLP to predict quarterly corporate credit rating Aug. 2018 - Present

Study best architecture to assess corporate credit rating using machine learning algorithms (SVM, CNN, LSTM)

Develop a data-driven trading strategy based on the credit event predicted by ML/DL model

Adopted NLP techniques to transform an textual nancial report into a numeric vector as additional features

Constructed a counterfactual explanation to provide insight recommendation for company to improve credit rating Markets Volatility Transmission Hoboken, NJ

Investigating volatility contagion e ect in global markets using high frequency time series model Nov. 2018 - Present

Decomposed BEKK-GARCH model into volatility spillover using Markov Chain Monte Carlo (MCMC)

Analyzed the in uence of volatility spillover for global markets in high frequency (intraday) level Working Experience

Structured Finance, Methodology Development and Researcher New York, NY Morningstar May. 2020 - Present

Built a predictive model to predict Net Cash Flow change for CMBS using statistical method

Responsible for the research about interest rate impact study on ABS (student loan & auto loan) Cash ow

Developed and enhanced analytical features on RMBS internal predictive modeling platform using python Adjunct Python Instructor Hoboken, NJ

School of Business, Stevens Institute of Technology Aug. 2018 - Present

Course Instructor: Introduction to Python for Financial Applications

Teaching Assistant: Machine Learning in Finance; Financial Market Microstructure & Trading Strategies Algorithmic Trader Fall Analyst Hoboken, NJ

Fair Value Partner Inc Sep 2017 - Oct 2017

Developed a system to scrape and clean treasury bond data from Thomson Reuters using python API

Built a hierarchical machine learning models in Python for determining Cheapest To Delivery bond for basis trading, and built a back-tested model to simulate PnL for trading strategy Selected Publication

D. Wang, T. Wang and I. Florescu, \Is Image Encoding Bene cial for Deep Learning in Finance?" IEEE Internet of Things Journal, doi: 10.1109/JIOT.2020.3030492. P. Golbayani, D Wang, and I Florescu.\Application of Deep Neural Networks to assess corporate Credit Rating." arXiv preprint arXiv, 2003.02334 (2020).

Y Zhou, L Han, and D Wang, \A moment-based criterion for determining the number of components in the normal mixture model", Journal of systems Engineering and Electronics, Vol. 28, No. 4, August 2017.

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