Abhinay Korukonda's Resume
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Abhinay Korukonda
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Abstract:
Abhinay Korukonda's Resume
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Discover why 18 million people worldwide trust Overleaf with their work.
Abhinay Korukonda's Resume
\begin
Discover why 18 million people worldwide trust Overleaf with their work.
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\textbf{\LARGE Abhinay Korukonda}\\[0.5ex]
\textscale{1.05}{Queen Lane, Philadelphia, PA 19129\\
\Letter\hspace{0.1ex} \href{mailto:r_korukonda@mfe.berkeley.edu}{r\_korukonda@mfe.berkeley.edu}
\hfill
\Mobilefone\hspace{0.1ex} +1 (510) 570-4828}
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\roottitle{EDUCATION}
\headedsection
{University of California, Berkeley | Haas School of Business}
{Berkeley, CA}
{Master of Financial Engineering}
{\period{Mar}{2018}{Mar}{2019}}
{GPA: 3.88 / 4.00}
\headedsection
{Indian Institute of Technology, Bombay}
{Mumbai, India}
{Bachelors of Technology in Chemical Engineering}
{\period{Aug}{2011}{Apr}{2015}}
{Awarded Full-Tuition Scholarship\\
All India rank 610 among half a million students appearing for entrance examination to IITs}
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\roottitle{WORK EXPERIENCE}
\headedsection
{\href{https://www.pimco.com/en-us/}{PIMCO}}
{Newport Beach, CA}
{Rotational Internship in Quantitative Portfolio Management \& Quantitative Research}
{\period{Oct}{2018}{Dec}{2018}}{
\vspace{-2.4ex}
\begin{circlist}
\item Constructed successful systematic equity strategies adjusting for exposures to countries, market, sectors and T-costs.
\item Implemented Carry, Rolldown signals for repo-funded off-the-run US treasuries in Python for production
\end{circlist}
}
\headedsection
{\href{https://www.linedata.com/}{Linedata}}
{Mumbai, India}
{Analyst}
{\period{May}{2015}{Mar}{2018}}
{\vspace{-2.4ex}
\begin{circlist}
\item Managed portfolio analytics \& market risk models for global macro funds. Clients: {\href{https://www.bloomberg.com/news/articles/2018-06-12/citrone-s-discovery-strikes-a-comeback-with-italian-short-wager}{Discovery Capital}}, {\href{https://www.businessinsider.com/scott-bessent-launches-key-square-group-2016-1}{KeySquare Group}}
\item Supported Investment team by providing statistical insights on structured products and macro indicators. Clients: {\href{https://www.globalatlantic.com/}{GAFG}}
\item Presented performance attribution, market views based on P\&L drivers, and risk reports to clients
\end{circlist}
}
\spacedhrule{0.8ex}{0.0ex}
\roottitle{QUANTITATIVE RESEARCH PROJECTS}
\headedsectiontwo
{\href{https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3359464}{Factor Timing and Sector Allocation using Regime Switching Models}}{\period{Jan}{2019}{Mar}{2019}}
{\href{https://www.blackrock.com/institutions/en-us/biographies/ronald-kahn}{Dr. Ronald Kahn, MD, BlackRock}}
{\vspace{-2.4ex}
\begin{circlist}
\item Used Hidden Markov models for asset allocation in sectors and dynamic factor ETFs under the assumption of 2 regimes
\item Backtested portfolios had higher Sharpe, lower skew, kurtosis, drawdown compared to benchmark and baseline models.
\end{circlist}
}
\headedsectiontwo
{Style Investing in Corporate Bonds}{\period{Jul}{2018}{Oct}{2018}}
{\href{http://www.weatherstormcapital.com/}{WeatherStorm Capital, San Fransisco, CA}}
{\vspace{-2.4ex}
\begin{circlist}
\item Generated uncorrelated signals using Python in IG \& HY constituent bonds based on bond and fundamental data
\item Backtested portfolios ($>$1.2 IR) based on single \& combined signals of Value, Momentum, Carry, and Defensive
\end{circlist}
}
\headedsectiontwo
{Systematic Underpricing in Convertible Bonds}{\period{Jul}{2018}{Oct}{2018}}
{\href{https://www.blackrock.com/us/individual/investment-ideas/systematic-fixed-income}{Systematic Fixed Income, BlackRock}}
{\vspace{-2.4ex}
\begin{circlist}
\item Built trading strategy based on convertible bond mispricing by implementing trinomial tree pricing models in Python
\end{circlist}
}
\spacedhrule{0.8ex}{0.0ex}
\roottitle{DATA SCIENCE PROJECTS}
\headedsectionthree
{Forecasting Magnitude of Corporate Spread Changes}{\period{Jan}{2019}{Feb}{2019}}
{\href{https://www.iaqf.org/}{IAQF Academic Paper Competition, 2019}}
{\vspace{-2.4ex}
\begin{circlist}
\item Achieved out of sample 0.2 $R^2$ on corporate spread change forecasts using Elastic Net models by using artificially constructed, and known market features.
\end{circlist}
}
\headedsectiontwo
{Machine Learning for Asset Allocation}{\period{Sep}{2018}{Oct}{2018}}
{\href{https://www.linkedin.com/in/carolinagalleguillos/}{Dr. Carolina Galleguillos}}
{\vspace{-2.4ex}
\begin{circlist}
\item Determined asset allocation weights of S\&P 500 stocks using clustering methods on returns correlation matrix
\item Backtested strategy had lower variance and higher returns compared to Markowitz or Risk Parity methods
\end{circlist}
}
\headedsectiontwo
{Machine Learning Models for Recovery Value}{\period{Sep}{2018}{Feb}{2019}}
{\href{https://www.linkedin.com/in/terry-benzschawel-64998413/}{Dr. Terry Benzschawel, Benzschawel Scientific LLC}}
{\vspace{-2.4ex}
\begin{circlist}
\item Improved error on prediction of recovery value using ensemble methods on corporate defaults data
\end{circlist}
}
\spacedhrule{0.8ex}{0.0ex}
\roottitle{SKILLS \& OTHERS}
\begin{indentsection}
\skill{Mathematics}{Econometrics, Probability Theory, Optimization, Stochastic Calculus and Monte Carlo Simulation, }
\skill{Machine Learning}{Generalized Linear Models, Dimensionality Reduction, Neural Networks, and Ensemble Methods.}
\skill{Finance}{Portfolio Management, Quantitative Investing, Backtesting, and Volatility Modeling.}
\skill{Computer}{Excellent in Python, and Bloomberg. Proficient in R, C++, SQL, and VBA.}
\skill{Certifications}{FRM by GARP}
\skill{Interests}{Strategy Games, Music, Adventure Sports}
\end{indentsection}
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