An exciting place to apply your skills!Samir FERRAG

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Introduction

2 messages in this talk:

– Systematica Investments as an example of a Financial employer

– Transition from Particle Physics to Financial world

Why would a financial institution hire someone with no Finance/Business/Economic

degree ?

– Large movement among discretionary hedge funds to become quant-like in their processes

» Systematize an investment approach to reduce human decisional risk from the process

» Asset Under Management (AUM) up-scalability of systematic investment processes that is too complex

and impossible to realize within discretionary approaches.

– Research and Quantitative analysis side requires some advanced mathematical/Statistical skills

while analyzing big quantities of data that Particle Physicist (also computer scientists,

engineers or natural scientists) are well trained for.

– Technology side requires knowledge in computing/databases and computing languages that

are most likely to be found within Scientists (SQL, Java, Python, C++, Grid computing,…)

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What is Systematica Investments ? (I)

Systematica Investments is a hedge fund:

– Investment process is systematized and is managed by computer algorithms

– CTA, Equity Market Neutral, Alternative Markets, Risk Premium

Systematica Investments in a nutshell

– Launched in January 2015 after a decade of experience within BlueCrest Capital Management

– Founded by Leda Braga

– Focus on rigorously applying science and technology to the investment process.

– Manages approximately $9bn across a number of futures and equity based strategies.

– Philosophy of the firm:

» Innovation, excellence in research

» Commitment to fostering strong alignment with investors.

– Global presence with offices in Jersey, Geneva, London, New York and Singapore.

Investors:

– Institutional: Pension funds, Sovereign Funds.

– Private banks and Investment banks.

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What is Systematica Investments ? (II)

Competitors:

– CTA: Winton, MAN-AHL, AQR, Aspect

– Equity Market Neutral: RAM (Geneva), Marshall-Wace, GSA, Old Mutual

– Alternative Markets: MAN-AHL, Florin Court

– Risk Premium: GSA

Systematica Groups:

– Executives and product management (~10)

– Technology (~30)

– Modeling and research (~30)

– Execution and Trading (~10)

– Finance and Accounting, Back-office, Compliance, HR. (~30)

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Work environment @ Systematica Investments

Team Culture & Investment Philosophy

– To be systematic investors and to avoid human intervening with models

» Intervention via research process only

– Collaborative working environment within and across all functional teams

– Significant investment in technology

» Workstations, Servers, Grid and Trading platforms

» Database and software licenses

» Market data feeds & Data purchase, data centers

» Manpower

– All aspects of the investment process and platform are subject to continuous research and

improvement

– Rigorous review by peers throughout the research and development cycle

– The pursuit of excellence permeates all of Systematica activities

Collaborative research environment with a diversity of coworker profiles

Smooth transition from Particle Physics to hedge fund industry

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Innovation in today’s businesses

Huge number of tools (services, analytics, data management,…) can be purchased/hired or downloaded for free and used by anyone to perform impressive tasks

Why hiring a scientist then ?

Most valuable business projects are becoming similar to research projects

– One hand: large number of projects fail to bring the expected value

– Other hand: project run by small team can disrupt an entire industry with big return on investment

– Often needed: design projects that change their goals along the way

Innovation is a key

Not only sophisticated skills but also solid bases

– Use the tools but asses their reliability and limitations

– Continuous learning a adaptation

– Able to see big picture as well as technical details

– Able to judge the right time to change

Particle physicist is well trained for above

– Learn from scratch, build and share

– work under pressure, independence and tolerance of long hours working with code and data.

But First: you need to trig the business world interest by showing experience with some appropriate tools

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What is within a Physicist skills in Systematica

Technology:

– 3 categories: Quant Developers, Data Analysts/Scientists and Business Analysts

– Daily activities:

» Build trading platforms, risk monitoring Platforms, Simulation and Back-Test Platform

» Data and data analysis (see backup slide)

– Skills: Java, Python, SQL (and Vertica), MATLAB, Windows/Linux env, C++,…

Modeling and Research (Quantitative analysis):

– Daily activities:

» Build mathematical and stat models to help decisions about:

prices, risk management and monitoring, Portfolio construction, Investments.

» Test new models, interpret data results, communication and documentation…

– Skills: Mathematics (Calculus, Linear Algebra, Numerical Linear Algebra, Probability and

Statistics…), Statistical data analysis, Market dynamics and securities.

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Data scientist (almost natural for HEP…)

Every company will have a different take on job tasks.

Conduct undirected research and frame open-ended industry questions

Extract huge volumes of data from multiple internal and external sources

Employ sophisticated analytics programs, machine learning and statistical methods to

prepare data for use in predictive and prescriptive modeling

Thoroughly clean and prune data to discard irrelevant information

Explore and examine data from a variety of angles to determine hidden weaknesses,

trends and/or opportunities

Devise data-driven solutions to the most pressing challenges

Invent new algorithms to solve problems and build new tools to automate work

Communicate predictions and findings to management and IT departments through

effective data visualizations and reports

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Backup Slides

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Data scientist Tasks

Every company will have a different take on job tasks. Some treat their data scientists as

glorified

Conduct undirected research and frame open-ended industry questions

Extract huge volumes of data from multiple internal and external sources

Employ sophisticated analytics programs, machine learning and statistical methods to

prepare data for use in predictive and prescriptive modeling

Thoroughly clean and prune data to discard irrelevant information

Explore and examine data from a variety of angles to determine hidden weaknesses,

trends and/or opportunities

Devise data-driven solutions to the most pressing challenges

Invent new algorithms to solve problems and build new tools to automate work

Communicate predictions and findings to management and IT departments through

effective data visualizations and reports

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Quant Analyst Tasks

Research and analyze market trends and statistics to make modeling decisions

Develop and implement complex quantitative models (e.g. models for trading equities) and analytical software/tools

Perform daily statistical analyses (e.g. risk analytics, loan pricing, default risk modeling, etc.) and coding tasks (e.g. pattern recognition or machine learning)

Detail model specifications and methods of data collection

Test new models, products and analytics programs

Maintain and modify analytical models while in use

Apply or invent independent tools to verify results

Collaborate with teams of mathematicians, computer engineers and physicists to develop optimal strategies

Consult with financial industry personnel on trading strategies, market dynamics, trading system performance, etc.

Generate requirement documentation for software developers

Present and interpret data results to senior management and clients

Being able to apply scientific methods to finance and discovering new ways of viewing and analyzing this type of data.

Being able to offer investors an investment approach that seeks a better, more true understanding of markets (in both terms of alpha generation and risk management).

Educating others (especially investors) on the importance of quantitative analysis and why, if used effectively, it is so powerful in comparison to more conventional methods.

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Quant Dev tasks

More advanced object-oriented experience and less math and finance expertise than

quantitative analysts.

Close work with Modelers/Analysts:

– Take MATLAB Prototype, optimize it and make it fault tolerant in production environment

– Statistical coding

Building Infrastructure

– trading platforms, risk monitoring Platforms,

– Simulation and Back-Test Platform

– Maintaining large scale systems

On exciting role that sits in the quantitative development arena is that of the star C/C++

developer who understands Unix network programming, low-latency systems and the

ins and outs of the Linux Kernel. These individuals can often be found working in Ultra

High-Frequency Trading (UHFT), where trade orders are now measured in

microseconds.