An exciting place to apply your skills! Samir FERRAG 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 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,…)

1 What is Systematica Investments ? (I)

. Systematica Investments is a : – Investment process is systematized and is managed by computer algorithms – CTA, Equity , 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, , New York and Singapore. . Investors: – Institutional: Pension funds, Sovereign Funds. – Private banks and Investment banks.

2 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)

3 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

4 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

5 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.

6 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

7 Backup Slides

8 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

9 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 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.

10 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.

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