Day 1


Breakfast & Registration


Chairman’s Opening Remarks

Mr Bob Litterman

Pricing carbon – a pragmatic approach to address mankind’s greatest risk management problem

  • The difficulties of pricing high-cost, low-probability events – is there a viable economic argument for implementing a costly carbon tax whose benefit may or may not be realized at an unknown point in the future?
  • Today’s bill & tomorrow’s dilemma – how much should society spend today to insure the future against climate risk?
  • Investor & shareholder response – are we approaching a tipping point in public opinion and policy on climate risk?
Panel discussion

Ex-change of the guard – competition, innovation and regulation in the new world order for exchanges

  • Equity markets competition & fragmentation – have endeavors to increase competition, transparency and fairness made the market too crowded? Has this impacted best execution? 
  • Recent mergers – how will the unions of CBOE/BATS, NASDAQ/ISE & LSE/Deutsche Boerse shape the future of the global exchange sector?
  • Consolidation – desirable for global markets?
  • Regulation & oversight – market surveillance, supervision and compliance of cross-asset trading
  • New markets, products & asset classes – what’s on the horizon? 
Bill Chen, PhD

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?

SPEED Networking

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

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

  • 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 unstructured 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?
Dr Arun Verma, Quantitative Researcher, Bloomberg LP
Exchange Technology & Blockchain

Engine for (ex)change – how will technology re-invent the role of market operators in the age of the algorithm?

  • Changing business models – have exchanges morphed into technology vendors? 
  • Vision for blockchain – an end-to-end solution streamlining trading, clearing and settlement?
  • Cloud factor – will venues ultimately move market operations to the cloud?
  • Security & compliance – innovation and initiatives for bolstering security in an increasingly unsecure world
  • Artificial intelligence – what role will smart machines play in tomorrow’s exchanges?
Automated Trading & HPC

FPGAs, acceleration technologies, GPUs and high-performance hardware

  • 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?
Quant World & Big Data in Finance

GPS & location-tracking data – bleeding-edge applications for predicting market behavior (topic tentative)

  • How can trading firms apply data from tracking the real-time movement of goods across land, air and sea?
  • How can real-time information about road traffic be used to provide insights into economic trends?
  • Further applications of real-time GPS – crude oil, crop yields, manufacturing, construction
Exchange Technology & Blockchain

David vs. Goliath – how are savvy smaller exchanges adapting in the consolidation era?

  • How are forward-thinking trading venues using their technology and infrastructure dollars to drive efficiency and performance?
  • Crowdfunding and other unconventional, disruptive business models 
  • Risks and rewards of pursuing partnerships with larger exchanges 
Quant World & Big Data in Finance

Using sentiment analysis to inform trading decisions – a viable long-term strategy?

  • Reliable or liability? Given the inclusive nature of social media, how reliable are datasets in predicting future asset prices? 
  • Positive vs. negative sentiment – do they have different effects on the intensity and duration of price movements?
  • Blogs vs. news vs. social media – do different sources of sentiment impact price movements differently?
Exchange Technology & Blockchain

Blockchain – not a game, but a game changer 

  • Nuts & bolts – why blockchain is nothing more than a new form of database technology 
  • Albatross of legacy infrastructure – why obsolete infrastructure costs financial institutions billions from operational inefficiencies, failures and cyber attacks
  • Further applications for distributed ledger – optimizing back-office processing, digitizing physical transfer of assets, increasing transparency in payments, reducing the risk of fraud and providing digital identities to the unbanked

Networking Lunch

Automated Trading & HPC

Ultra-low latency connectivity – how is the game of speed changing in today’s automated trading environment?

  • Cloud & carrier neutral co-location – selecting the right connectivity for your firm 
  • Fiber vs. microwave vs. millimeter wavelength – comparing and contrasting new developments in network connectivity
  • Benefits beyond latency – how can you parlay low latency into high trading intelligence?
  • Network monitoring – solutions, methods and technologies for identifying trends, detecting outliers and correcting excessive latency and jitter in real time
  • How can you accurately measure latency and jitter in a microsecond-sensitive ultra-low latency networking environment?
  • Next-generation data centers – open infrastructure, software-defined technologies and automation
  • LEO (low-earth orbit) satellites – a connectivity revolution?
Exchange Technology & Blockchain

A healthy dose of blockchain skepticism – what is it good for?

  • Overhype much? Outside of digital cash, why hasn’t blockchain bore any commercial fruit in almost 10 years of existence?
  • Barriers to commercial implementation – comparing blockchain to current best practice technology in banking, contracts and database systems 
  • Horse and engine analogy – why blockchain cannot serve any useful purpose for the financial intermediaries it was designed to replace 
  • Conclusion and prediction for the future – blockchain is unlikely to offer economic advantages for any other commercial problems other than removing third party intermediation to allow for the creation of digital cash
Quant World & Big Data in Finance

How and when machine learning methods can be applied to automated trade decisions

  • How to extract relevant features from level II exchange data and apply machine learning methods to predict near-term directional price movements using scikit-learn and Pandas
  • How to create and perform simple backtests of trading strategies
  • How to navigate the engineering challenges of creating production grade machine learning-based strategies
Exchange Technology & Blockchain

Oxford style debate: Blockchain – disruption or distraction?

  • Blockchain champions rave about its potential to facilitate faster, cheaper, safer and more transparent financial transactions – are you buying into the value statement?
  • Banks are spending extraordinary resources on testing and developing blockchain technology – will their efforts ultimately bear fruit?
  • True or false – the only commercially successful application of blockchain technology will be producing digital cash​
Quant World & Big Data in Finance

Implementing deep learning algorithms to forecast market trends

  • What are deep neural networks (DNNs) and how has their predictive power been harnessed in the speech transcription and image recognition communities?
  • How has the computational complexity of DNNs hindered its adoption to predicting behavior in the financial markets?
  • How can the algorithm be effectively deployed on general purpose high-performance infrastructure?
Exchange Technology & Blockchain

Accelerating blockchain’s performance – current limitations and promising directions

  • Accelerating distributed synchronizations and mining operations across blockchain applications
  • Effective splitting of functions and computational loads between hardware substrates and software
  • Vertical automata partitioning across blockchains' implementation layers for scalable throughput
  • Leveraging proven, massively-parallel, concurrent computing technologies for blockchain performance

Afternoon Networking Break

round tables


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.
  • Backtesting in the cloud securely and at scale
  • Greg Ulepic

    Greg Ulepic, Director, Avere Systems

  • Deep learning – applications for predicting market behavior
  • Non-traditional data & AI – how to leverage unstructured data sourced for alpha generation for market and credit risk
  • Vikram Mahidhar

    Vikram Mahidhar, SVP, AI Solutions, RAGE Frameworks

  • Options trading mistakes – the 5 biggest screw-ups and their solutions
  • Real-time market data – best practices for assessing and responding to data quality in latency-critical trading systems
  • David Taylor

    David Taylor, Chief Technology Officer, Exegy

  • Robo-advisors – threat or opportunity to traditional asset management?
  • Automated Trading & HPC

    Enterprise data management – how are you applying innovative technologies to streamline and modernize the information lifecycle?

    • Warehousing & integration – best practices for building an enterprise data warehouse and unifying data management across different IT teams and departments
    • Standardization – what are the benefits of a common industry language, or financial Rosetta Stone, on operational efficiency and regulatory reporting?
    • Storage – deploying cloud servers and software-defined data centers to maximize storage efficiency and scalability; maintaining storage requirements to accommodate new data sources and exponentially growing data volumes 
    • Governance – how to design an effective data governance strategy for managing regulatory risk and meeting compliance demands 
    • Roadblocks – data quality issues; integrating big data technologies with legacy IT infrastructure
    Quant World & Big Data in Finance

    FinTech Education - leveraging technology to understand finance, analytics and data in theory and practice

    • Say it (Ying & Yang) – hardcore math made easy
    • See it (Visualization) – voiced-over simulations as opposed to lecture recording
    • Do it (Interactive computing) – live data, cloud-based computing, hosted editing for on-the-fly replication 
    • Share it (Community) – slide neutralization, feedback looping
    • Frame it (Cross-linking) – multi-media interconnectivity, spoke-to-hub architecture
    Exchange Technology & Blockchain

    Smart contracts – what’s the deal?

    • Establishing a definition – can “smart contracts” simply be defined as a piece of software stored, verified and executed on the blockchain?
    • Use cases in finance – is clearing and settlement the most promising domain for smart contract automation? What about digital wallets? Loans? Escrow?
    • Adoption challenges – legal/regulatory considerations, integration into business ecosystem, lack of standards/best practices
    • When should companies use blockchain-enabled smart contracts instead of existing technology?
    Quant World & Big Data in Finance

    Using Python for financial data analysis

    • Python basics – development environment and scientific libraries
    • Data management – connecting your Python-based trading strategy to the big data ecosystem
    • Research process – the nuts and bolts of building a quantitative trading strategy using Python
    Exchange Technology & Blockchain

    Algorithmic trading of digital currencies – untapped alpha for prop firms? 

    • Prevalence – how much current volume is created by automated strategies? 
    • Developing a trading strategy – what works best?
    • Digital currency markets vs. traditional markets – liquidity, execution, trading fees
    Automated Trading & HPC

    Re-thinking IT infrastructure for trading – compute, storage and networking 

    • Compute infrastructure – batch vs. stream processing, Apache Storm, Map4
    • Storage infrastructure – cluster computing, in-memory computing
    • NoSQL/NewSQL databases – Hadoop, Spark, MongoDB
    • Process externalization & the cloudification of trading – how should your IT department respond to the “everything as-a-service” (XaaS) revolution? 
    Quant World & Big Data in Finance

    Risky business – how are savvy funds blending analytics, cloud computing and quantitative methodology to supercharge risk management? 

    • How are portfolio managers accurately assessing risk as modern markets become increasingly complex and volatile?
    • Leveraging next-generation data management and analytics 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?
    Exchange Technology & Blockchain

    Beyond Bitcoin – illustrating the landscape for emerging and established digital currencies 

    • Market size, growth, competition & composition – with the cryptocurrency market exceeding $10 billion in 2016, what are the growth expectations in 2017 and 2018? 
    • Comparing & contrasting cryptocurrencies – mining requirements, exchange rates, market cap, payment applications, anonymity and other special features 
    • Bitcoin alternatives – will alternative currencies like Litecoin and Ripple challenge Bitcoin’s dominance? 
    Paulo Sironi

    Surviving disruption – an actionable guide to investment success in the era of FinTech  

    • Robo-advisors – current state and future evolution
    • Adaptation for traditional advisors – how can human financial advisors use evolving regulations, customer demand and technology to optimize their business processes and operations?
    • Gamification – how it can produce better investors?
    • New normal – to what extent is financial technology innovation fundamentally changing the rules of investing? Where is current technology best utilized in new-age investment management? 

    Networking Cocktail Party

    last published: 13/Mar/17 20:05 GMT