2018 Agenda



Toronto, ON, Canada, 8 November 2018


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Nov 809:00
Conference pass

Opening keynote: InvestTech – how is the FinTech movement affecting disruptive change for institutional capital markets?

  • With the rapidly expanding FinTech movement almost exclusively targeting the individual consumer, how will this disruptive technology impact the institutional marketplace?
  • What tools and technologies could drive efficiencies in capital allocation?
  • Artificial intelligence and machine learning – as autonomous machines take on a larger role in the investment process, how can the industry address concerns around trust, accountability and fiduciary responsibility?
  • Democratization, dissemination and disintermediation of data – how can innovation in the way information is controlled, accessed and analyzed empower institutional investors?
Paul Ebner, Head of Quantitative Strategies and Risk Premia, CPP Investment Board
Nov 809:20
Conference pass

The next frontier for asset management – how are industry titans surviving disruptive innovation?

  • D’s of disruption – how are the overarching trends of democratization, decentralization, disintermediation and digitization shaping the future for asset management?
  • Consolidation – how have recent historic levels of merger and acquisition activity in recent years altered the dynamics and competitive landscape of the investment industry?
  • Digital innovation roadmap – potential applications and use cases for robo-advisors, blockchain/distributed ledger technology, machine learning/AI and big data technologies
  • When the dust settles – what will business models of the future look like in investment management? What are the potential implications for managers, intermediaries, asset owners and their beneficiaries?
  • Growth of passive investing – have robo-advisors, smart beta funds and other FinTech/InvestTech innovations already sounded the death knell for traditional forms of active management? Or, are falling correlations between assets an early indicator of an imminent passive investing bubble bursting?
Robert Gouley, Senior Analyst, Trading, OMERS Capital Markets
Imad Ferzli, Senior Principal, Global Systematic Investments, Ontario Teachers' Pension Plan
Nov 810:00
Conference pass

Natural Language Processing: Deciphering the Message within the Message

    Insights from Corporate Earnings Calls
  • A review of the ABCs of natural language processing
  • A look at stock selection using sentiment and behavioral-based signals from earnings call transcripts: economic reasoning, signal construction, and backtesting
Frank Zhao, Senior Director, Quantamental Research, S&P Global Market Intelligence
Nov 811:00
Conference pass

Data sweet spot – how are you finding, evaluating and applying ‘edgeworthy' alternative data sources?

  • Investing in the right datasets – how do you evaluate which data sets are worth adopting in the face of high fees and uncertain value?
  • IT & talent requirements for value extraction – what technology and human capital capabilities do you need in place to effectively analyze the data and realize its value within a reasonable time horizon?
  • Comparing source, speed & edge – real-time market data, historical market data, macroeconomic data, public/private company data, alternative data
  • Alternative data, alternative use cases – what other applications may exist for alt data sets outside of alpha generation?
  • Other nascent data applications – do you see any immediate or future value for trading firms and fund managers in data from nanosatellites, drone imagery and Internet of Things?
Jim Creighton, Chief Investment Officer, Manifold Partners
David Rukshin, Chief Technology Officer, WorldQuant
Ben Rudin, Commercial Business Lead, Orbital Insight
Nov 811:40
Conference pass

Calculated risk – how are leading banks and funds leveraging new developments in computing and analytics to optimize risk management?

  • Risk & complexity – how are portfolio managers accurately assessing risk as modern markets become increasingly complex and volatile?
  • Next-generation data management & analytics – what new tools and strategies can you implement to achieve an integrated view of portfolio risk exposure across time horizons and asset classes?
  • Risk modelling – applying new validation tools to ensure accuracy
  • Predictive analytics – 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?
Nikolay Ryabkov, Quantitative Portfolio Manager, Vestcor Inc.
Jeanine Kwong, Head of Equity Risk, Manulife
Brandon Elsasser, Chief Investment Officer, Victoria Capital Management
Nov 812:20
Conference pass


AI & market analysis – leveraging machine-driven insights to guide investment decision making
Machine learning & credit risk – new techniques for optimizing model development
Felix Kan, Director, Model Risk, RBC
Modeling the big picture – how will macroeconomic and geopolitical developments guide trading decisions in 2019 and beyond?
Nikolay Ryabkov, Quantitative Portfolio Manager, Vestcor Inc.
Red October: The Return of Volatility and Using Macroeconomic News Events
The art of backtesting – best practices and war stories from practitioners
Emre Konukoglu, Quantitative Researcher, CPP Investment Board
Token types for crypto novices – a crash course on digital asset classification
Brandon Elsasser, Chief Investment Officer, Victoria Capital Management
VC 2.0 – navigating ICOs, crowdfunding and the new paradigm for capital raising
Mukhtar Mussabetov, Founder, BlockSpace Labs
Nov 814:10
Conference pass

Market structure, execution, liquidity & regulation – what’s on the minds of Canadian traders and investors heading into 2018?

  • Dark pools, block trading & execution strategy – deploying algorithms in both dark and lit venues to locate the best price with minimal price leakage
  • Rising trading costs – how have recent changes in Canadian equity market structure impacted institutional trading costs?
  • Centralized, multi-asset execution – is simplification on the sell-side or having a single execution provider the answer to navigating asset classes and changing regulatory requirements?
  • Institutional vs. technical – how do execution needs differ between trading styles?
  • TCA & analytics – real-time analytics solutions to improve order routing and enhance execution quality
  • Broker innovation – how are savvy brokers evolving strategies and differentiate algorithms to meet buy-side demands?
Chinmay Jain, Professor of Finance Research, University of Ontario Institute of Technology
Chen Wang, President, HXW Trading
Katya Malinova, Associate Professor, University of Toronto
Nov 814:10
Conference pass

Predict Lifetime PD for IFRS 9 with Machine Learning Models

Quant World & Advanced Analytics
  • Framed lifetime PD as a supervised learning problem
  • Deeper neural networks are not necessarily more accurate than a simple logistic regression
  • How to make the model interpretable
Felix Kan, Director, Model Risk, RBC
Nov 814:30
Conference pass

The Role of Data Science in the Future of Market Regulation

Quant World & Advanced Analytics
  • Overview of IIROC and Market Regulation
  • Explosion of Data
  • Evolution of Machine Learning and Big Data Technologies
  • Data Science in Market Regulation
Jamil Abou Saleh, VP, Data Science & Analytics, IIROC
Nov 814:50
Conference pass

Factors galore – what does it mean for quantitative investing

Quant World & Advanced Analytics
  • The drivers behind factor proliferation
  • Testing for factor significance
  • Why certain factors’ excess returns are likely to persist
  • Who is the loser?
  • Next steps in factor strategy development
Nov 814:50
Conference pass

Meta-Labelling: A key financial machine learning tool

  • An overview and analysis of what Meta-labelling is and how you can apply it
  • Meta-labelling and "quantamental" strategies
  • Applying Meta-labelling for asset allocation and risk management
Nov 815:40
Conference pass

Deeply Learning Derivatives

Quant World & Advanced Analytics
  • Teaching deep neural networks to approximate classical derivatives pricing functions
  • How the trained models are very accurate and very fast.
  • Valuation, risk and PL explain can now be down in real time instead of as EOD batch processes
Ryan Ferguson, Derivatives Trader, Scotiabank Global Banking and Markets
Nov 815:40
Conference pass

The role of Algorithmic trading/High frequency trading in equity markets

  • How does Automated Trading/High Frequency Trading impact market liquidity?
  • Does Automated Trading/High Frequency Trading provide liquidity during times of market distress?
  • Does Automated Trading/High Frequency Trading improve price efficiency?
  • Are ETFs more aligned to NAV because of Automated Trading/High Frequency Trading?
Chinmay Jain, Professor of Finance Research, University of Ontario Institute of Technology
Nov 816:00
Conference pass

Autonomous portfolios – a decumulation strategy that will get you there

Quant World & Advanced Analytics
  • Broke before your croak– how serious is the risk of running out of money for retirees whom saving more and working longer are not options?
  • A new-school approach – applying a dynamic decision theory approach to investment management and adding spending flexibility to improve incomes
  • Engineering income – does a self-driving portfolio, engineered to protect against market risk, deliver more income than other investment approaches while minimizing the risk of ruin?
Mark Yamada, President & CEO, PUR Investing Inc.
Ioulia Tretiakova, VP & Director, Quantitative Strategies, PUR Investing Inc.
Nov 816:20
Conference pass

Enhancing Monte Carlo methods – a multivariate Brownian bridge approach

Quant World & Advanced Analytics
  • The usage of Quasi versus Pseudo Random numbers in MC
  • What are some of the drawbacks of Sobol sequences versus Monte Carlo methods
  • The Brownian bridge for time-dependent drift model and constant volatility
Leon Shegalov, Director, Quantitative Core Analytics, R.B.C. Capital Markets Ltd
Nov 816:20
Conference pass

Using AI to enhance asset allocation strategy in the chief investment office

  • Exploring the successful and unsuccessful techniques used by AI-driven asset managers
  • What are the key bottlenecks in training and inference?
  • Which software frameworks and which hardware platforms have proven most useful for those workloads?
  • What does deployment look like?
  • What are the scaling challenges and the key drivers of the cost?
Brandon Da Silva, Machine Learning Researcher, OPTrust
Calvin Yu, Managing Director - Head of Multi-Asset Solutions, qplum
Nov 816:50
Conference pass

Mastering the quantamental equation – combining depth and breadth to generate higher returns

  • Benefits of a dual approach – how can quantamental style investing emphasize the strengths and diminish the weaknesses of each investment research style?
  • Data integration challenges – how are fundamental investors overcoming the technical and corporate barriers to integrating big data technologies into front-office systems?
  • Accentuating the best of both worlds – what types of insights can fundamental analysis uncover that quantitative models cannot, and vice versa?
  • Human capital dynamics – how have talent and skill sets changed over time in the discretionary world? Is business and economics acumen no longer as important as engineering and computer science skills?
  • Creating rapport between the math whiz & value investor – how can such drastically different schools of thought co-exist in the same shop?
Nov 817:10
Conference pass

Closing Remarks & Networking Cocktail Party

last published: 05/Nov/18 16:45 GMT

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