2016 Agenda 




Chairman's Opening Remarks

Panel discussion

A brave new world for HFTs – going public, attracting talent and battling public perception

  • Will Virtu’s IPO in 2015 set a precedent for other HFT IPOs in the coming years?
  • Is regulatory scrutiny, coupled with a lukewarm public perception of HFT, just short-term noise or a significant concern for firms considering launching an IPO?
  • Regulation AT – how would the CFTC’s landmark proposal affect proprietary traders, market fairness and systemic risk?
  • Do the potential benefits justify significantly lowering the bar for government access to intellectual property?
  • Can the long-term investment styles employed by institutional investors benevolently co-exist with microsecond-sensitive strategies used by HFTs?
  • Are we seeing an exodus of top technology talent from finance to other verticals and industries?
  • What can the trading and investing industry do to attract, retain and develop the next generation of leadership?
  • How can the industry effectively educate the “average” investor about the merits of HFT?

Quantum quants – the future of finance

  • Why many of today’s popular optimization techniques, like mean-variance optimization and co-integration models, fail to perform as advertised and are actually detrimental to finance
  • Why today’s models are not felicitous to real-world applications, which require a degree of complexity and robustness that simple models cannot satisfy
  • How quantum computers can allow us to develop models cognizant of reality’s complexity and free us from an overreliance on dumb-downed models and heuristics


Automated Trading & HPC

Untapped alpha – the origins of quant trading and future of alpha

  • What role does quantitative trading have in maintaining efficient, liquid markets for the short and long-term future?
  • The importance of understanding the sources of returns when designing trading strategies
  • How will advances in quantitative research help uncover new sources of alpha?
Quant World & Big Data in Finance

Deactivating “active share”

  • What is active share, and how are mutual funds and institutional money managers using this metric to influence asset allocation decisions?
  • How, if at all, does it correlate to benchmark returns, fund returns and overall performance?
  • Should institutional money managers emphasize active share as a manager selection tool or an appropriate guideline for portfolios?
Automated Trading & HPC

HPC strategy for capital markets – how are you modernizing storage, software and server technology to meet current demands?

  • Data analytics solutions  – how to deploy Hadoop, Spark, Cassandra and other software frameworks for data-intensive workloads
  • Total IT virtualization & software-defined data centers  – can you automate your data center by using policy-based software to manage pooled networking, server and storage computing resources?
  • OpenStack  – working with vendors to streamline installation, configuration, deployment and maintenance
Dino Vitale,   Director, Distributed Platform Engineering,  TD Securities
Quant World & Big Data in Finance

Risk analytics – how are you applying big data technologies to manage and mitigate risk?

  • Predictive analytics  – how are you using predictive tools to measure risk?
  • Automation & real-time analysis  – what hardware, software and computing resources are necessary to automate risk management in real time?
  • Barriers to implementation  – data security, budgetary limitations, unorganized and siloed data
Pouya Taaghol, PhD,   CEO,  Big Data Federation, Inc
Automated Trading & HPC

Next-generation infrastructure – FPGAs, acceleration technologies and low-latency networks

  • Budget balancing  –speed vs. capacity and latency vs. performance
  • Parlaying speed into intelligence  – how are new classes of server technology enabling deterministic performance in addition to cutting latency?
  • Measuring the value of FPGA enhanced functionality  – how can they optimize trade workflow and be applied beyond the traditional domains of market data acquisition and distribution?
  • Key differentiators for high-performance servers  – what features can provide latency-sensitive trading firms with superior performance in connectivity, data access and computation?
  • Scalable flash storage  – is the growing market for flash storage an optimal alternative for data-driven hedge funds, trading firms and banks using traditional performance-optimized HDDs?
  • What advantages can flash-based configurations offer in terms of flexibility, efficiency and scalability for large-scale big data and analytics applications?
  • Deploying Java virtual machines (JVM) for enterprise workloads to improve deployment time for new algorithms, maximize operational efficiency and remove tail risk
Quant World & Big Data in Finance

Hedge funds in the doldrums – will underperformance, high fees and disruptive technology spell the end of traditional active management?

  • Exiting pensions –  do recent hedge fund divestitures by NYCERS and CALPERS spell imminent transformative change for active management, or is this nothing more than a blip on the long-term radar?
  • Compensation structure –  is it time to re-think the fee structure for active managers?
  • Alternative options –  how much are smart beta funds, robo-advisories and other low-cost actively managed ETFs threatening the traditional fee model? How can institutional asset managers respond to the increasing threat of disruptive investment technology?
  • Man vs. machine –  will AI-driven investing make discretionary investing obsolete or complement the traditional model? What would a hybrid approach look like?
  • Regulation and performance –  how has Dodd-Frank, MiFID and other regulatory initiatives hampered hedge fund performance?


Automated Trading & HPC

‘Best ex’ standards – what is the buy-side looking for from brokers?

  • Buy-side control –  what is driving buy-side firms to seek more power in the execution process? How would more buy-side control affect clearing costs?
  • 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?
Quant World & Big Data in Finance

Sharing is caring – how can firms capture ‘alpha’ through open source technology?

  • Are cost pressures the only driving force behind the open source movement among trading firms, hedge funds and banks?
  • Market participants are highly protective over the source code powering their trading systems, but are human factors equally important to a firm’s competitive advantage?
  • How can we create an environment that encourages firms with proprietary technology to contribute back to open source projects?
  • Docker and open-source container applications – how do these new technologies benefit enterprise infrastructure development?
  • How will open source solutions shape the future of quant and algorithmic trading?
Automated Trading & HPC

Quit yankin’ my blockchain – will distributed ledger technology actually live up to its enormous expectations for disruption?

  • Blockchain champions rave about its potential to facilitate faster, cheaper, safer and more transparent financial transactions – are you buying into the value statement?
  • Measuring the response from banks and governments around the world –   are key market players encouraging the development and integration of distributed ledger technology?
  • Regulatory considerations and industry standards – how should this technology be governed to both minimize transactional risks and encourage mass-market adoption?
  • Which existing pain points, business applications and revenue-generating use cases are most favorable for blockchain adoption and integration?
Quant World & Big Data in Finance

Combining Black-Litterman, exotic beta and risk parity – a unique approach to portfolio optimization

  • Creating a robust, flexible framework for portfolio construction by integrating three traditionally alternative techniques, Black-Litterman optimization, exotic beta and risk parity
  • Implementing exotic beta as a prior alpha model in the classic Black-Litterman approach
  • Using the risk parity portfolio as the efficient starting portfolio for Black-Litterman optimization on both theoretical and practical grounds
  • How can this integrated methodology create a robust, flexible framework beneficial to a wide range of investors?
Quant World & Big Data in Finance

‘The present of futures’ – new findings on pricing derivatives

  • The convexity conundrum in the old world
  • The multi-curve framework
  • The convexity conundrum in the new world
  • Numerical examples




These small group discussions don’t require any presentation or preparation, and are simply designed to serve as platforms for networking, collaboration and information exchange between like-minded professionals.

Since roundtable sessions run concurrently, you can choose 1 session that is most interesting.

Click on above text to expand list of roundtable topics and the respective discussion leaders.
Automated Trading & HPC

How are you leveraging the financial data explosion to create a faster, more intelligent front office?

  • How are you minimizing latency in active decision making
  • How are you deploying analytics (both visual and statistical) to facilitate profitable trading decisions?
  • Regulation and execution – data-savvy approaches to achieving best execution while meeting transparency requirements mandated by MiFID II and Reg NMS
  • Connecting to the big or tick data ecosystem – how are you integrating Hadoop, Spark, Cassandra, Kafka compared to tick databases, messaging and CEP engines into your technology stack?
Moderator:   Michael Dobrovolsky,   Lead Architect, Enterprise Big Data Solutions & Advanced Analytics,  Morgan Stanley
Drew Carey,   Sales Director,  BATS Global Markets
Quant World & Big Data in Finance

Programming wars – which language is the best for quant developers?

  • C++, Java, C#, Python, MATLAB, R, Julia – is there a clear winner?
  • Which languages would you recommend early-stage quants to master?
  • What criteria should lead developers and CTOs consider when committing to a particular language?
Automated Trading & HPC

Fully autonomous trading – how next-generation AI systems can trade like humans without any manual intervention

  • Evaluating the impact of human emotions, biases and errors on returns
  • To what extent are human psychology factors introduced into trading algorithms?
  • Quant and high frequency vs. fully autonomous trading – what are the differences?
  • Forecasting future developments in artificial intelligence and implications for financial markets – have we entered a second machine age?
Quant World & Big Data in Finance

Unspanned stochastic volatility and conformal symmetry

  • Combining calibration flexibility of market models with tractability and computational efficiency of shot rate models
  • Enabling robust calibration to the whole variety of caps and swaptions with various expirations, strikes and tenors
  • How Backward induction via low dimensionality allows for efficient valuation of Bermudan swaptions without resorting to suboptimal American Monte Carlo

Trading, poker and game theory

  • Game theory versus random walk models
  • Strategic positions versus strong positions
  • Winning frequently versus winning big pots


last published: 03/Oct/16 18:25 GMT