Asset Management Capital Markets

Location:

Manhattan, NY, 10007

Salary:

300

Posted:

April 24, 2025

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Resume:

Gregory John Komansky

1-718-***-**** **********@*****.***

Senior Banking and Global Asset Management Executive with extensive product creation, distribution and transactional expertise. Strong communication, sales and trading skills who has the ability to build new and grow existing businesses within Investment Banks and Asset Managers. Highly quantitative and detail oriented who performs collaboratively in a rapid-paced, high pressure atmosphere creating custom investment & trading strategies, developing new products and managing diverse teams around the globe. Core competencies:

Trade Optimization & Analytics Delta-1 Custom Basket Creation & Trading

Algorithmic Trading & Development Macro-Thematic & Factor Research

Portfolio Creation, Management & Optimization Risk Management & Index Trading

Fundamental & Quantitative Analysis Options & Strategic Hedging

Product Development Across Asset Managers & CIB CIB Global Portfolio Sales & Trading CAREER TRACK

MultiStrat New York, NY / Bermuda

Managing Director – Co-Head of Capital Markets, Head of Quantitative Solutions & Portfolio Construction 12/23 – Present

Team Lead for Collateralized Insurance Portfolio Modeling and Structuring

Responsible for Quantitative Structuring & Technology Infrastructure to support, analyze over $100B in multi-line GWP and structuring over $1.5B in Collateralized Insurance Investments

Responsible for MultiStrat’s Capital Markets expansion and overall development J.P. Morgan CIB New York, NY

Executive Director – Global Quantitative Basket Trading, Sales & Trading, Quantitative Development 4/18 – 07/23

Developed a new product offering called APTO - (Advanced Portfolio Trading Optimizer). Responsible for trading over

$112B globally since creation in late 2018 and delivered over $15m+ / yr in revenue

APTO is a Global Portfolio Optimization Process that uses Factor, Impact and Cost Models along with user inputs to create an efficient trading frontier for each asset to minimize risk, impact and reduce variance

Grew the Optimized Portfolio Trading Business from $6B Globally in 2017 into a $40B+ /yr trading business

APTO is being used across Risk, Agency and Delta-One trading to allow the user to understand baskets in a more detailed manner by decomposing stocks into key quantitative factors, PCR statistics, correlations and overall impact

Global Portfolio Sales & Trading ~ Lead Quantitative member that was responsible for covering global clients across Global Program and Delta-1 verticals handling agency trading, ETF’s, index events, custom baskets and risk

Created “Market-MuZings,” a factor trading focused note that highlights trading ideas and helps clients make informed trading decisions by highlighting factor correlations, spreads and other unique aspects of the factor landscape

Delta-1 Team member that was instrumental in creating custom factor baskets and creating efficient trading strategies for clients to help minimize impact and preserve client alpha. Trading revenue grew 10-15% y/y

Built an Index Tracking / Monitoring tool used to create trading strategies based on Index events

Led a team of Global Quants who developed pre/post-Trade analytics, optimized trading schedules & hedging tools used by agency and risk trading teams. The tool allows the user to compare optimized strategies across the trading spectrum

Key member of J.P .Morgan's Quant conference schedule. Conducted numerous Global Macro Quant Conferences / Webinars / Break-Out Sessions on Trading Quant Factors, Creating The Efficient Trading Frontier, Optimal Scheduling, Delta- 1 and Basket Creation

Clearbridge Investments ($125Billion AUM) New York, NY Director – Co-Portfolio Manager, Senior Trader & Head of Quantitative Solutions 06/05 -8/17

Lead trader and quantitative analyst for the International Growth Product offering. During 2014-17 using a model driven stock selection approach the International Growth product grew to over $2.5B from $475m in AUMs

Developed and Co-Managed an MSCI global Smart Beta portfolio, minimizing sector and factor exposures while maximizing alpha. Seeded with $50m

Formulated and Co-Managed an SPX hedged portfolio which utilized a proprietary dynamic hedging model to monitor and trade based on a zero-cost collar structure. This new product was based on an SPX product with over $10B in AUMs

Created the ClearBridge Quantitative research team responsible for all quant screens, risk analytics and stock selection tools. Conducted monthly global trading and quantitative meetings discussing what worked fundamentally and factorially to PMs, analysts and board members and how we could recalibrate if needed

Lead trader for Large Cap, Small Cap Growth & International Core Value portfolios with over $25B under management..

Developed a global quantitative multi-factor stock selection model which was used to generate investment ideas, monitor portfolio factor exposures and provided a backtesting environment

Responsible for all global programs, hedging, options trading as well as all risk bids and negotiations

Designed an algorithmic trading application that increased liquidity while minimizing impact by utilizing a “round-robin” approach where the parent algorithm would search for liquidity across multiple child algorithms Citigroup Asset Management ($645 Billion AUM) New York, London and Asia

Director – Senior Trader & Quantitative Analyst 08/96 – 06/05

Promoted to Lead trader for both the Salomon Brothers Small Cap Growth and CGAM Mid Cap Core portfolios

Promoted to Senior trader after spending 1.5yrs in London and Asia trading Equities, FX and Options

Lead quantitative analyst for the Citigroup Growth and Core product offerings. Responsible for model creation and backtesting

Selected as the sole trading team member to develop global trading requirements to serve as the foundation for Citigroup Asset Management’s global trading platform. This new platform was installed within 2yrs across 5 global locations supporting Fixed Income, Equities, FX, Derivatives supporting over 400,000 accounts

Developed a process to handle Global Program Trading prior to the implementation of CGAM’s global trading platform. This new process automated program trading so that the traders could view their entire baskets performance, monitor risk on one screen, trade multiple baskets and increase alpha. This new process was instrumental in helping CGAM win several billions of dollars of transition trades

Created a new unit called Portfolio Analytics and led a group of six members that were responsible for BARRA risk reporting, producing detailed attribution on a monthly basis and holding monthly PM performance meetings

Member of the Global Performance Team responsible for performance monitoring, attribution, risk and calculations to become AIMR compliant

EDUCATION

Villanova University – Villanova, PA

B.A., Mathematics June 1996

Men’s Golf Team ~NCAA Third Team Honorable Mention Polytechnic University - Brooklyn, NY

M.S., Financial Engineering Dec 2002

Series 7, 63

J.P. Morgan Certificate in A.I. Search

Global Quantitative Webinars / Speaking Events 2023: Held BreakOut Session at APAC/EMEA Macro Conference on Trading the Efficient Frontier Virtual BreakOut Session on Portfolio Optimization (due to COVID-19) A P T O

2023

Trading and Quantitative Research

Introduction to APTO – Basket Decomposition / Optimal Trajectory

APTO aims to create an efficient trading schedule for each stock within the basket by accounting for Factor Risk, Volatility, Correlations, ADV For illustrative purposes only

Efficient Frontier Analytics

Optimization Layer

Market Impact & User Constraints

The result is a Risk AWARE set of trading

vectors that are optimized to reduce Portfolio

Level Risk Adjusted Costs vs. a Naïve Volume

centric profile

The Building Blocks of Risk Decomposition / Why Correlations Matter

We attempt to minimize the overall cost and risk of the basket by understanding the correlations and risks associated with each name and their relationship to the entire basket

Understanding the correlations as well as the Percent Contribution to Risk (PCR) allows the model to make a more informed decision on how to create an optimized trading trajectory

Notional POV 30D Vol. PCR 30D Corr

MSFT ($8.42MM) 0.30% 20 -10 0.7

NEWR $21.1MM 24.00% 35 110 0.7

$1,000,000

$2,000,000

$3,000,000

$4,000,000

$5,000,000

$6,000,000

$7,000,000

$8,000,000

$9,000,000

MSFT_APTO Residual Notional MSFT_VWAP Residual Notional

$5,000,000

$10,000,000

$15,000,000

$20,000,000

$25,000,000

NEWR_APTO Residual Notional NEWR_VWAP Residual Notional Open Close Open Close

For illustrative purposes only

The Building Blocks of Risk Decomposition / Why Correlations Matter We are buying both MSFT and NEWR in a dollar balanced basket

Understanding the correlations as well as the Percent Contribution to Risk (PCR) allows us to make a more informed decision on how to create an optimized trading trajectory

In the prior (long-short) example used MSFT to “hedge” or reduce our risk. Here in this long only basket, MSFT is additive to the risk of the overall basket and as a result treats MSFT more like an In-Line

The model understands that MSFT is extremely liquid, highly correlated and offers no reduction in risk by holding on to the name

$2,000,000

$4,000,000

$6,000,000

$8,000,000

$10,000,000

$12,000,000

$14,000,000

$16,000,000

NEWR_APTO Residual Notional NEWR_VWAP Residual Notional

$2,000,000

$4,000,000

$6,000,000

$8,000,000

$10,000,000

$12,000,000

$14,000,000

$16,000,000

MSFT_APTO Residual Notional MSFT_VWAP Residual Notional Notional POV 30D Vol PCR 30D Corr

MSFT $14MM 1% 20 35 0.7

NEWR $14MM 18% 35 65 0.7

Open Close Open Close

For illustrative purposes only

Factor Awareness & Risk Reduction

For illustrative purposes only

Factor Exposure & PCR help create the Trading Vectors that min Risk & Cost

Reducing Risk helps reduce PnL distributions

Open Close

Factor Awareness & Risk Reduction

For illustrative purposes only

Now we can see across the trading horizon how an Optimized Schedule differs than a schedule that is not Risk Aware

Decomposing into Factors, Understanding Correlations, Detailed Analysis of PCR all helps create a new schedule Optimized Trading Schedule

Output is aimed to reduce risk &

variance thru the entire trading

schedule

Monotonically Reduces Risk

Naïve – VWAP Factor Trading Schedule

As w can see in this volume drive example the

Factor

Risk is held thru the trading session

Factor Awareness & Risk Reduction- Putting it all Together For illustrative purposes only

StrategyName RiskBps CostBps SpreadCostBps ImpactCostBps RiskAdjCostBps APTO_0.05 55.23 37.76 3.12 34.64 95

APTO_0.25 50.6 38.41 3.4 35.01 90.85

APTO_0.5 48.02 39.32 3.51 35.81 89.09

APTO_1.0 45.93 40.77 3.58 37.2 88.37

APTO_2.0 44.67 42.52 3.61 38.91 88.82

Naive_POV_15.0 37.66 68.73 3.51 65.22 107.76

Naive_POV_20.0 23.03 80.41 3.91 76.51 104.29

Naive_VWAP 56.50 48.74 2.76 45.98 107.29

sym LECO.OQ MSFT.OQ NYCB.N AMED.OQ KD.N NOW.N SI.N LECO.OQ 1.0 0.5 0.4 -0.3 -0.4 -0.5 -0.2

MSFT.OQ 0.5 1.0 0.4 -0.4 -0.5 -0.6 -0.3

NYCB.N 0.4 0.4 1.0 -0.3 -0.4 -0.5 -0.3

AMED.OQ -0.3 -0.4 -0.3 1.0 0.3 0.4 0.2

KD.N -0.4 -0.5 -0.4 0.3 1.0 0.5 0.2

NOW.N -0.5 -0.6 -0.5 0.4 0.5 1.0 0.3

SI.N -0.2 -0.3 -0.3 0.2 0.2 0.3 1.0

BUY SELL SUM

$ 25,313,104 $ 57,739,935 100.00

sym Shares Gross Notional Gross Pct Gross PCR pctAdv avgSpread ADV 30D* MSFT.OQ 55000 $ 15,428,050 18.58 -10.75 0.25 0.94 0.18 25 NYCB.N 590719 $ 5,399,172 6.5 -4.09 5.51 12.50 4.3 106 LECO.OQ 26986 $ 4,485,883 5.4 -2.38 9.04 18.19 7.19 20 KD.N 7140 $ 102,031 0.12 0.12 0.82 7.62 0.45 34

SI.N 74893 $ 137,803 0.17 0.20 0.31 45.68 0.5 175

AMED.OQ 36703 $ 2,695,101 3.25 2.33 16.24 26.03 8.98 35 NOW.N 125000 $ 54,805,000 65.99 114.57 11.29 12.12 8.45 35

PreTRADE Analytics / Detailing

Optimized vs. Naïve Trading Costs

Think of the Basket in 3 Clusters

Risk Additive

Risk Reducing “Synthetic Hedge”

“Noise” / The middle layer

The “Noise” are names that have an

optimal trajectory that is determined

more by the impact f(x) and Market

model as they offer no hedge and thus

will increase Risk if they are not

executed in that manner

Basket analysis at the stock level. Risk

Model outputs such as PCR, CORR,

Volatility etc.

Putting it all Together- Creating / Understanding & Reacting to the Output For illustrative purposes only

VWAP

sym Open Auction 9:30 10:00 10:30 11:00 11:30 12:00 12:30 1:00 1:30 2:00 2:30 3:00 3:30 Close Auction ExecQty NYCB.N 4600 492**-***** 420**-***** 363**-***** 300**-***** 354**-***** 363**-***** 107***-***** 590719 MSFT.OQ 600-****-**** 410*-****-**** 260*-****-**** 250*-****-**** 310*-****-**** 55000 LECO.OQ 100-****-**** 160*-****-**** 110*-****-*** 140*-****-**** 180*-****-**** 26986 KD.N 0 -600 -500 -400 -400 -400 -300 -300 -400 -300 -400 -400 -400 -1440 -900 -7140 AMED.OQ -200 -2100 -2800 -2200 -2000 -1700 -1600 -2000 -1800 -1700 -2100 -2300 -2700 -8203 -3300 -36703 SI.N -800 -11300 -8500 -6500 -5700 -4300 -4300 -3900 -3800 -4200 -4100 -4200 -4200 -8093 -1000 -74893 NOW.N -1000 -14200 -11400 -9300 -8100 -6400 -6700 -5800 -4900 -5700 -5800 -6300 -6900 -20100 -12400 -125000 POV 16

sym Open Auction 9:30 10:00 10:30 11:00 11:30 12:00 12:30 1:00 1:30 2:00 2:30 3:00 3:30 Close Auction ExecQty NYCB.N 180**-****** 158***-****** 62519 0 0 0 0 0 0 0 0 0 0 590719 MSFT.OQ 4700 50300 0 0 0 0 0 0 0 0 0 0 0 0 0 55000 LECO.OQ 200-****-**** 330*-****-**** 230*-****-**** 2700 118*-*-*-*-*-***** KD.N -700 -6440 0 0 0 0 0 0 0 0 0 0 0 0 0 -7140

AMED.OQ -300 -3200 -4300 -3200 -3000 -2600 -2500 -2900 -2700 -2600 -3100 -3600 -2703 0 0 -36703 SI.N -5000 -69893 0 0 0 0 0 0 0 0 0 0 0 0 0 -74893 NOW.N -1500 -20400 -16600 -13400 -11700 -9200 -9600 -8500 -7000 -8300 -8300 -9100 -1400 0 0 -125000 APTO 1

sym Open Auction 9:30 10:00 10:30 11:00 11:30 12:00 12:30 1:00 1:30 2:00 2:30 3:00 3:30 Close Auction ExecQty NYCB.N 3900 412**-***** 415**-***** 331**-***** 273**-***** 313**-***** 330**-***** 139***-***** 590719 MSFT.OQ 0 0 0 0-600*-****-**** 570*-****-**** 320*-****-**** 870*-****-***** LECO.OQ 0-600-****-**** 150*-****-**** 100*-***-**** 160*-****-**** 708*-****-***** KD.N -700 -6440 0 0 0 0 0 0 0 0 0 0 0 0 0 -7140

AMED.OQ -400 -4000 -3700 -2300 -2000 -1500 -1500 -1700 -1400 -1400 -1600 -1900 -1900 -8203 -3200 -36703 SI.N -5000 -69893 0 0 0 0 0 0 0 0 0 0 0 0 0 -74893 NOW.N -1800 -25500 -20600 -14700 -9300 -6600 -5600 -4600 -3500 -3800 -3700 -3800 -4200 -10700 -6600 -125000 APTO POV 15 VWAP

BUY SELL SUM

$ 25,313,104 $ 57,739,935 100.00

SI.N 74893 $ 137,803 0.17 0.20 0.31 45.68 0.5 175

AMED.OQ 36703 $ 2,695,101 3.25 2.33 16.24 26.03 8.98 35 NOW.N 125000 $ 54,805,000 65.99 114.57 11.29 12.12 8.45 35

Now we can compare the actual trading

schedules

If we see that we have correlation break-down or a synthetic hedge is not providing the “hedge” we

modeled, we can “RE-Plan” / Take action on

those names

VWAP

POV 15

OPTIMIZED

Optimized Trading

Trajectory has been

restructured to provide a

hedge and take action

on names that are not

Risk Reducing but only

increase Timing Risk

Disclaimer

P O R T F O L I O T R A D I N G A N D Q U A N T I T A T I V E R E S E A R C H

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