2017 Agenda

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New York, 3 October 2017


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Oct 308:45
Conference pass

Chairman's Opening Remarks

Welcome to Trading Show New York 2017!
Michael Oliver Weinberg, CFA, Partner & Chief Investment Strategist, Protégé Partners
Oct 308:50
Conference pass

The Mathematics of Poker – quantitative methodology for the Hold’em table and trading desk

  • What lessons can be applied from poker to quantitative trading, and vice versa?
  • Online vs. live poker and pit trading vs. screen trading 
  • Exploitative play vs. optimal play 
  • Poker finance – portfolio theory, risk of ruin and the Kelly Criterion
  • Psychological components of trading and poker
  • Will quantitative skills and knowledge ultimately become a necessity for poker players?
Oct 309:10
Conference pass

The D’s of disruption – how will democratization, disintermediation and decentralization shape the future of capital markets?

  • Democratization of investment research – does crowdsourcing earnings estimates from anonymous contributors, professionals and non-professionals from the buy-side, sell-side and academia lead to more accurate forecasting than traditional sell-side research?
  • Real-time information – how are social networks, blogs and other digital mediums offering instantaneous access to news and information filling a void left by traditional investment research?
  • Decentralization, disintermediation & distributed ledger – how will blockchain-based technologies redefine regulation, oversight and the provision of trust in financial transactions?
  • The dark side of decentralization – what are the hidden risks in a blockchain revolution? 
  • Data democracy – is the trend toward equal access to information eroding alpha? If so, how can active managers pivot to regain a competitive edge? 
  • Cryptocurrency market cap, growth, competition & composition – with the total market cap nearing $90 billion in mid-2017, what are the growth expectations for cryptocurrencies heading into 2018? 
Oct 310:50
Conference pass

HPC & infrastructure strategy – aligning compute, storage and networking for real-time data consumption and analysis

Automated Trading & HPC
  • Design & engineering developments – how are new technologies driving trading firms from proprietary software and hardware to open source, automated, software-defined models?
  • ‘Big data’ architecture – how are you integrating Hadoop and deploying big data technologies into the enterprise?
  • Real-time insights – how can traders use visualization technology to aid in real-time pattern discovery and outlier detection?
  • High performance storage – achieving performance, density and scale; assessing performance upside for large-scale big data and analytics applications in the growing flash storage market ‘
  • Customizing trading applications in the cloud – how to deliver maximum performance, ultra-low latency and advanced functionality
  • Budgetary blinders – why has technology spending remained so nebulous at a time when capital markets firms have widely embraced measuring, analyzing and benchmarking so many different aspects of their operations?
  • The power of TCO (total cost of ownership) – why understanding technology TCO is a must to remain profitable in today’s challenging and competitive market conditions 
  • Factoring in human capital – the importance of factoring the cost of employees (compensation & benefits) along with tech spend (hardware, software & data) into TCO calculations 
  • The power of measurement – how can a complete and accurate picture of TCO help firms slash costs, eliminate redundancies and inform strategic decision making?  
David Rukshin, Chief Technology Officer, WorldQuant
Tony Rea, HPC Business Development, Dell EMC
James O'Shea, Head of Reengineering, RBC Capital Markets
Oct 310:50
Conference pass

Re-thinking risk – how are savvy funds using new technologies to supercharge risk management?

Quant World & Big Data in Finance
  • Complexity & volatility – how are portfolio managers accurately assessing risk in unpredictable modern market conditions?
  • Next-generation data management & analytics – leveraging new tools to achieve an integrated view of portfolio risk exposure across time horizons and asset classes
  • Risk modelling – how can new validation tools be applied to ensure accuracy? 
  • Predictive analytics – how are you using predictive tools to measure risk?
  • Compute-intensive risk – how are quants and risk managers using the cloud to scale up modeling capacity without breaking the bank on building new data centers?
  • Alternative data for risk management – how can “alt” data be leveraged as an indicator for measuring risk, anticipating volatility and extracting unique market insights?
Oct 311:30
Conference pass

Fixed income revolution – how can the buy-side make a seamless transition to an electronic marketplace?

Automated Trading & HPC
  • Drivers of change – regulation, higher capital requirements for banks, illiquidity, technological innovation, increasing institutional investor appetite for data-driven insights
  • New electronic platforms – are all-to-all trading platforms a panacea for bond market illiquidity? 
  • HFT & fixed income – are HFTs valuable liquidity providers?
  • Analytics, TCA & best execution – capitalizing on newly available order book and market data to enhance operational efficiency and lower costs
  • Deregulation – how, if at all, will any efforts by the Trump administration to undo many of the restrictions mandated via Dodd-Frank, impact the evolution of fixed income market structure?
  • Learning from history – what lessons from other asset classes, particularly equities, should be heeded during this crucial transformational period for the fixed income market?
Moderator: Ashok Mittal, President & Co-Founder, The Beast Apps
Vladimir Danishevsky, Head of Corporate Bonds Flow eTrading IT, Citi
Oct 311:30
Conference pass

Evaluating time-series momentum in non-liquid markets – risk-adjusted performance vs. diversification

Quant World & Big Data in Finance
  • Trend strategy performance – how does time-series momentum perform across markets and asset classes?
  • Trader behavior – how are traders, especially smaller ones, evaluating the merits of trend strategies across markets?
  • More than meets the eye – why trend strategies can offer significant portfolio diversification benefits despite seemingly unattractive risk-adjusted performance 
Oct 311:50
Conference pass

Partial correlations – a physicist's approach to portfolio diversification

Quant World & Big Data in Finance
  • Markowitz’s mean-variance portfolio – overviewing the widely-used theory for constructing portfolios reflecting investors’ desire for both higher returns and lower risk
  • Application challenges – estimation errors of the portfolio covariance matrix, low diversification on Markowitz-derived portfolios
  • Revised Markowitz theory – why replacing Pearson’s correlations with partial correlations in the Markowitz theory is a promising concept for constructing optimal portfolios
Alec Schmidt, Lead Research Scientist, Kensho
Oct 312:10
Conference pass


Automated Trading & HPC
1.) Beyond Bitcoin – evaluating trading opportunities in the growing cryptocurrency market (2x tables)
  • Martin Garcia, Vice President, Genesis Trading (Leader  Table 1)
  • Garrett Nenner, Head of Trading Technologies, Linedata (Leader Table 1)
  • David Namdar, Co-CIO, Galaxy Investment Partners (Leader Table 2)
3.) Real-time market data – best practices for assessing and responding to data quality in latency-critical trading systems 
  • David Taylor, CTO, Exegy
4.) Fear gauge flatline – how to overcome low volatility in the post-election era 
  • Derek Wang, CEO, Bell Curve Capital
5.) Fixed income leaders – making a seamless transition to an electronic, data-driven marketplace 
  • Ashok Mittal, President, The Beast Apps
6.) Hybrid cloud in trading networks – how does quality data collection interact with other business workflows?  
  • Michael Wright, Manager, Napatech
  • Patrick Flannery, CEO, MayStreet
Oct 312:10
Conference pass


Quant World & Big Data in Finance
1.) Complex data sets – new mining approaches and efficient strategies for improving data discovery, utility & sustainability 
  • Jonathan Greenberg, Senior Solutions Architect, Kinetica
2.) AI & deep learning – technology trends, challenges, opportunities & implications 
  • Stef Weegels, Director, Verne Global
3.) Alternative data – using non-traditional sources to harvest trading signals
  • Miquel Noguer Alonso, Executive Director, UBS
4.) Emerging managers – how are allocators evaluating machine learning and big data investment strategies?  
  • Michael Oliver Weinberg, CFA, Chief Investment Strategist, Protégé Partners
  • Michal Dziegielewski, Director of Investment Research, FQS Capital Partners
Michael Oliver Weinberg, CFA, Partner & Chief Investment Strategist, Protégé Partners
Jonathan Greenberg, Senior Solutions Engineer, Kinetica
Bert Mouler, President & CEO, Profluent Capital
Howard Getson, CEO, Capitalogix Trading
Oct 314:00
Conference pass

Big data & AI strategies – a framework for machine learning and alternative data-based investing

Quant World & Big Data in Finance
  • Classifying nascent datasets - assessing the relevance and applications of various datasets generated by individuals, businesses and machines
  • Advanced machine learning algorithms - supervised learning, unsupervised learning, deep learning, reinforcement learning
  • Alternative data universe - roadmapping the current landscape for alternative data sources and associated technology providers
Oct 314:00
Conference pass

Defending algorithmic trading systems against cyberattacks

Automated Trading & HPC
  • Defining the cyber-threat landscape for live automated trading systems – why are they such a valuable target?
  • The DNA of cyber weapons and potential impacts on trading systems
  • How can you sufficiently protect and defend automated trading systems against the dynamically evolving arsenal of cyber weapons?
Oct 314:20
Conference pass

Buy-side discussion: AI in trading & investing –  debating the risks, rewards and reality of robo-advisors and artificial intelligence in capital markets

Quant World & Big Data in Finance
  • AI & your investment strategy – the advantages and disadvantages of trading on probability and using AI to enhance your investment strategy
  • Markets for machines – how will increased usage of machine learning & AI alter market dynamics? 
  • Fiduciary rule fallout – how the new financial investment rules for advisors to act in their clients’ best interest impact robo-advisors?
  • AI-associated risks – what risks should financial firms be aware of as smart machines become more fundamental in modern markets? Do the biggest risks lie in man’s misuse of technology or machine learning techniques themselves?  
Oct 314:20
Conference pass

Deep learning in trading – the next 10 years

Automated Trading & HPC
  • HFT (high-frequency trading) case study – how HFT became the first end-to-end application of computer science to trading through flawless engineering (big data technologies) and faster interference (FPGA & low latency programming)
  • Job trends – declining demand for trader and quant jobs, skyrocketing demand for machine learning, AI & FinTech jobs
  • A deep learning future for trading – how is it being used, why is it being used and who is using it? Why is deep learning so far behind in asset management?
Oct 314:40
Conference pass

Spotlighting ‘RegTech’ – how can cutting-edge technology transform GRC (governance, risk and compliance) from a back-office burden into an enterprise opportunity?

Automated Trading & HPC
  • Real-time risk – how much hidden value exists in real-time risk data, and how can you unlock it to maximize operational efficiency?
  • Automating the back office – leveraging the power of data analytics, cloud computing and machine learning to cut costs and enhance regulatory compliance 
  • Regulatory change management – how are trading firms, banks and funds maximizing GRC technology to keep up with the flood of regulatory changes and revisions? How are firms conquering the challenge of integrating RegTech solutions across legacy systems and infrastructure? 
  • Innovation through collaboration – how can market regulators, financial institutions, technology vendors and other key stakeholders in the RegTech ecosystem work together to promote industry standards, seamless systems integration and innovation? 
Moderator: Michael Beller, CEO, Thesys Technologies
Mostafa Raddaoui, VP, Capital Market Systems Technology, MUFG Americas
Rajeev Ranjan, SVP, Operational Risk Management - Electronic Trading, Citi
Oct 314:45
Conference pass

Sell-side discussion: AI & machine learning technology solutions – what new tools, strategies and products are on the horizon?

Quant World & Big Data in Finance
  • Data warehousing revolution – how are vendors transforming data warehouses to serve a growing buy-side need to analyze huge volumes of disparate unstructured data sources?
  • Smart data & real-time indicators – have technology providers solved the challenge of enabling the real-time delivery of accurate and relevant macroeconomic indicators? 
  • AI & machine learning solutions for emerging data sources – using the latest advances in cognitive computing, deep learning and neural networking to extract unique insights from images collected by satellite, drone and HAPS (High Altitude Pseudo-Satellite) imagery
Oct 315:10
Conference pass
Oct 315:50
Conference pass

Next-generation data – what tools, technologies and applications will be most critical for enabling alpha discovery?

Quant World & Big Data in Finance
  • Is it sustainable to sell a packaged alpha product? If so, how are vendors offering packaged alpha products accounting for crowding and the resultant decay of the value with time?
  • Will the ability to manage emerging non-traditional data sources (i.e. nanosatellites, drone imagery, Internet of Things) ultimately become the most critical ingredient for alpha discovery?
  • How are vendors helping firms de-silo and integrate enterprise data into a unified environment?
  • Underlying infrastructure – how will firms maintain the necessary computational resources to uncover alpha-generating signals from expanding and diversifying data sources? Will in-memory applications become a critical component in all data management and analytics platforms?
Oct 315:50
Conference pass

The alpha of trade performance – how to harness the power of real-time TCA to lower execution costs, optimize trading algorithms and predict market behavior 

Automated Trading & HPC
  • Decaying alpha, tighter spreads, thinner margins & increased risk aversion – how must quant funds, trading firms, asset managers and banks adjust when implementing a market data strategy?
  • Performance measurement solutions – post-trade, intra-day and real-time cost analysis 
  • Capturing, cleansing, storing & analyzing market data – how to enable alpha discovery across asset classes and geographies in turbulent market conditions
  • Vendor relationships – the build vs. buy puzzle and working with your provider to improve performance, capacity and cost efficiency
  • Real-time insights – how can traders use visualization technology to aid in real-time pattern discovery and outlier detection?
  • Real-time pattern detection – how to use low-latency, complex event processing (CEP) technology to interpret live data streams
Nikhil Singhvi, Global Head Market and Client Connectivity Technology, Credit Suisse
Maria Belianina, Director, OneMarketData
Oct 316:30
Conference pass

Passive aggressive – what’s next for active management amidst the seismic investor shift into ETFs & index funds? 

Quant World & Big Data in Finance
  • Cost factor – should the timeless passive vs. active debate be recast as high vs. low-cost investing?
  • Compensation structure – is the 2 & 20 fee structure officially extinct?  What other viable compensation structures could replace it? 
  • Regulation & consolidation – should we expect margin compression and increasing regulatory burdens to bring more consolidation to the asset management industry?
  • Passive investing bubble? Are falling correlations between assets an early indicator of an imminent passive investing bubble bursting? Will such a bubble ultimately create a fertile environment for an active management comeback? 
  • Dead, dying or evolving? Have robo-advisors, smart beta funds and other FinTech/InvestTech innovations already sounded the death knell for traditional forms of active management? If so, how will active management be defined in the future? Will the systematic methods employed by quants evolve to replace the elements of human intuition? 
Oct 316:30
Conference pass

The quest for ‘best ex’ – how are buy-side firms enhancing execution quality amidst changing market dynamics? 

Automated Trading & HPC
  • Buy-side control – what is driving buy-side firms to seek more power in the execution process? 
  • Cross-asset market microstructure – how are Reg NMS and MiFID changing firms’ approach to sourcing liquidity? Has regulation ultimately improved or hindered execution?
  • Execution strategy – how are firms deploying algorithms in both dark and lit venues to locate the best price with minimal price leakage? 
  • Broker innovation – how can savvy brokers evolve strategies and differentiate algorithms to meet buy-side demands?
  • Clearing & settlement – settlement services for securities lending transactions? what is the importance of central counterparty clearing and 
  • Institutional vs. technical – how do execution needs differ?  
  • TCA & analytics – real-time analytics solutions to improve order routing and enhance execution quality
Sean Hendelman, CEO, T3 Trading Group LLC
Michael Warlan, Head of Global Trading, Third Avenue Management
Nataliya Bershova, Head of Execution Research, AllianceBernstein
Oct 317:10
Conference pass

How will cryptocurrencies impact modern investment portfolios?

  • Good as gold? Should investors buy cryptocurrencies or gold as a safe-haven asset? How will the emergence of digital currencies impact the value of gold, silver and other stores of value in the short and long term?
  • ICOs taking off – with over $1 billion raised through ICOs (initial coin offerings) in 2017, how should institutional investors weigh the risks against the revolution? 
  • Regulatory risk – what developments do risk-averse institutions need to see on the regulatory front to quell concerns about compliance?  
  • Ripple effect – how will the democratized, decentralized and disintermediated foundation of digital currency markets affect change across the entire capital markets ecosystem? 
  • Crypto block trading – current and future developments for facilitating the execution of large institutional digital currency trades
  • Non-correlation effect & institutional portfolios – how can institutional investors leverage Bitcoin’s extremely low correlation with traditional asset classes to optimize their portfolios?
last published: 03/Oct/17 03:15 GMT