2016 Post-Summit Report

The 2016 time Summit was held April 27-28, 2016 at The Hyatt Regency Greenwich in Old Greenwich, CT. More than 100 leaders from the tactical investment industry gathered for focused education and dedicated time Connect one-on-one meetings.

In numbers

managers

investors

billion dollars (in manager assets)

meetings scheduled

Who attended?

  • Zbigniew Hermaszewski, Altis Investment Management
  • Jean Jacques Duhot, Arctic Blue Capital
  • Marcos Bueno, Argon Capital
  • Martin Lueck, Aspect Capital
  • Linus Franngard, Blackrock SAE
  • Ryan LaFond, Blackrock SAE
  • Chris Howland, Blue Sky Alternative Investments
  • Simon Kitson, Blue Sky Alternative Investments
  • Chris Mellen, Boronia Capital
  • Ray Carroll, Breton Hill Capital
  • Jerry Parker, Chesapeake Capital
  • Philippe Laraison, Duet
  • Ian McIntosh, Edesia Asset Management
  • Adrian Eterovic, Episteme Capital Partners
  • Suhail Shaikh, Fulcrum Asset Management
  • Bob Murray, Graham Capital Management
  • Andrew Kaleel, Henderson Global Investors
  • Robin van Boxsel, Independent View
  • Alex Greyserman, ISAM
  • John Moody, J E Moody & Company LLC
  • Tony Kaiser, Kaiser Trading Group Pty Limited
  • Mark Carhart, Kepos Capital
  • Giorgio De Santis, Kepos Capital
  • Robert Hillman, LDF Advisers LLP
  • Jesper Sandin, Lynx Asset Management
  • Michael Sheehan, Orion Commodities Management, LP
  • Pierre Villeneuve, Q Capital Management
  • Michael Brandt, QMS Capital Management
  • Roy Niederhoffer, R.G. Niederhoffer Capital Management
  • Michael Mundt, Revolution Capital Management
  • Vasant Dhar, SCT Capital/New York University
  • Larry Smith, Third Wave Global
  • Walt Weissman, Tradelink
  • Steve Evans, Tudor Investment Corporation
  • 40 North
  • 50 South Capital (Northern Trust)
  • Abbey Capital
  • Aberdeen Asset Management
  • Abbington Investment Group
  • Abu Dhabi Investment Authority (ADIA)
  • Albourne Partners Limited
  • Alternative Investment Group, LLC
  • BBR Partners
  • Bessemer Trust
  • Blackrock
  • Cambridge Associates LLC
  • Cliffwater
  • Commonfund Asset Management Company Inc.
  • Credit Suisse Asset Management
  • Efficient Capital Management
  • First State Super
  • Fischer and Company
  • FRM Investments
  • General Motors Asset Management
  • Global Asset Management (GAM)
  • Grosvenor Capital Mgmt
  • Investcorp
  • K2 Advisors
  • K2 Advisors LLC/Franklin Templeton
  • LGT Capital Partners
  • Lighthouse Investment Partners, LLC
  • Lyxor Asset Management
  • Massachusetts Pension Reserves Investment Management Board
  • Michigan State University Investment Office
  • Morgan Stanley Investment Management
  • Neuberger Berman Alternatives
  • New Jersey Department of the Treasury
  • Ontario Teachers’ Pension Plan
  • PivotalPath
  • Ramsey Quantitative Systems Inc. (RQSI)
  • Russell Investment Group
  • Salient Partners
  • Steben & Company, Inc.
  • Summit Strategies Group
  • T2AM
  • Tages
  • The Canadian Medical Protective Association
  • Titan Advisors, LLC
  • UBS Financial Services, Inc.
  • UNC Management Company
  • UPS Group Trust
  • UPS Investment Group

Speakers

Daniel Kahneman, Nobel Laureate

Daniel Kahneman Nobel Laureate     Author, Thinking Fast and Slow

Michael Brandt, QMS Capital Management

Michael Brandt    QMS Capital Management     **********

George Coplit, LGT Capital Partners

 George Coplit       LGT Capital Partners               *********** ********

Giorgio De Santis, Kepos Capital

  Giorgio De Santis        Kepos Capital         ******* ***       ***********

Vasant Dhar, New York University / SCT Capital Management

     Vasant Dhar        NYU, SCT Capital Management ***********

Steve Evans, Tudor

     Steve Evans    ***      *  Tudor                 ****** *******   ***********

Ryan LaFond, Blackrock Scientific Active Equity

Ryan LaFond Blackrock Scientific Active Equity    *********

Duet Asset Management, Philippe Laraison

 Philippe Laraison    Duet Asset     **Management **** *******

Ian McIntosh, Edesia Asset Management

 Ian McIntosh    ** Edesia Asset   *    *   Management          ******

Roy Niederhoffer, R. G. Niederhoffer Capital Management

Roy Niederhoffer R.G. Niederhoffer Capital Management  *********

Topics

Thinking (and Allocating), Fast and Slow

Daniel Kahneman, Nobel Laureate | Roy Niederhoffer, R.G. Niederhoffer Capital Management

In an interview with Galen Burghardt and Brian Walls, Daniel Kahneman (Nobel laureate in Economic Science, author of Thinking, Fast and Slow, and founding partner of TGG Group) explained how prospect theory and broad versus narrow framing causes us to shy away from diversification and to replace assets too quickly on the basis of information that has little or no predictive value.

Roy Niederhoffer (R.G. Niederhoffer Capital Management) followed with an application of Kahneman’s and Tversky’s research to the question of why, when hedge funds face a much higher performance hurdle than CTAs because of their relatively high correlation with the returns on a conventional portfolio, institutional investors have invested at least 20 times as much in hedge funds. His presentation took us through applications of prospect theory and its foundation in loss aversion, the attraction of simple versus complex explanations of the sources of returns, and the practice of comparing the performance of separate components of a portfolio rather than focusing on the performance of the entire portfolio.

Commodities. Is there any hope on the horizon?

Ian McIntosh, Edesia Asset Management | Philippe Laraison, Duet Asset Management

The commodity hedge fund landscape has been fraught with poor performance and numerous fund closings over the past several years. We at COEX have seen some investor interest in the space recently and asked Ian McIntosh (CIO of Edesia Asset Management) and Philippe Laraison (CIO, Duet Commodities Fund) to provide their outlook across the commodity markets they cover.

Ian focused on the agricultural commodity markets and the demand side Edesia is forecasting the world’s population to exceed 9 billion by 2040 – 2050. Rising incomes will lead to increased per capita caloric intake and move greater proportion of diets toward meat and dairy products. Urbanization will compound this effect through additional shifts in consumptive baskets. On the supply side, greater demand requires supply expansion through increases in area, yield, feed and supply chain efficiency. Currently, the majority of current crop production is in the Northern Hemisphere. Supply expansion can only occur in the Southern Hemisphere as there is limited land available in the North due to temperature and water issues. Expansion in the Southern Hemisphere is compromised by political / developmental risk, environmental issues and lack of market access / infrastructure.

Philippe focused on the crude markets and started his presentation discussing the drawdown of commercial crude inventory – shifting the curve structure into backwardation. He sees the front part of the curve shifting higher while the back-end will remain anchored around full-cycle cost of North American shale production. Refinery margins continue to be under pressure which will ultimately lead to lower refined product stocks. Philippe warned of additional drivers that could impact the above. Namely, the forthcoming change in Saudi leadership, E&P companies access to the capital markets, future investment plans and production profile as well as unanticipated weather events.

The Quality of Decision Making

Michael Brandt, QMS Capital Management | Giorgio De Santis, Kepos Capital | Steve Evans, Tudor

The academic research engine is biased toward producing false positives, especially when the hypotheses or trading rules being examined number in the hundreds or thousands. This session focused on the challenge of knowing how to make good decisions when one is swimming against a tide of confirmation and optimism biases and a flawed academic research industry.

Steve Evans (of Tudor), who manages both internal and external allocations for Tudor, took us through a fascinating catalogue of the research problems he faces on a daily basis, not the least of which included a tendency to throw away discouraging results and to use data that were not available at the time one was making investment decisions.

Giorgio de Santis (of Kepos Capital) followed this with his own take on the problem by explaining his work with bagging and boot strapping that he believes provides much more sober insights into what one can realistically expect a trading strategy to produce. He also warned us about our tendency to find stories to explain what we see, when in fact there is none — something that Kahneman would call our tendency to think causally rather than statistically.

Michael Brandt (of QMS Capital Management) closed this session with an impassioned discussion on the importance of knowing whether the data you work with comes from the same distribution, or whether the world has shifted in fundamental ways at key points in history. The reason we should care, he argued, is that the quality and quantity of our data can radically change the mix of false positives and false negatives that our research produces.

Can big data move the needle?

George Coplit, LGT Capital Partners | Ryan LaFond, BlackRock Scientific Active Equity | Vasant Dhar, New York University/SCT Capital Management

Many managers and institutional investors in the space have been discussing the impact that big data and machine learning may have on CTA and systematic macro strategies. George Coplit from LGT Capital Partners moderated a session which included Ryan LaFond from Blackrock and Vasant Dhar from SCT Capital/NYU. Each presenter was asked to discuss their views on the implications these technologies pose on various investment strategies.

George Coplit introduced the session by first revealing how dynamic the term “big data” is. Over time and across different industries the phrase has meant very different things. In the hedge fund space, it is often intertwined with discussions around “non-traditional” datasets. George was quick to temper the hype around these datasets; their cost is often prohibitively high for smaller firms and larger firms have yet to allocate significant capital towards models employing them. But this will eventually change. George reviewed the large (and growing) number of technology firms creating unique data solutions for the financial services industry. George summarized that while progress in these strategies has been slow, there remains tremendous potential.

Ryan LaFond began by defining big data as being less structured, larger in volume, higher in frequency and having digital traces of human behavior. Opportunities are presenting themselves in a number of different ways as “90% of the data in the world today has been created in the last 2 years.” Ryan went on to describe ways in which learning from data has changed our approach to analyzing and measuring various things. As an example, the traditional approach to coming up with the Michigan Consumer Sentiment results is to survey a few hundred consumers via their land line. The big data approach is to analyze internet searches of which 3.5 billion are done in realtime. Blackjack found that their internet search index more closely tracked harder retail sales which was a better predictor of consumer sentiment. In addition to this example, Ryan showed how google search interest is a leading indicator of consumption, how data satellite images can forecast economic activity and how alpha can be extracted from China’s social media. In short, the factors that we need to consider in order to answer key investment questions are expanding and providing possible investment opportunities.

Vasant Dhar presented the research behind his upcoming Harvard Business Review article entitled “When to Trust Robots with Decisions, and When Not To.” He provided the audience with a new framework for thinking about which decisions we should turnover to machines and which should be retained by humans. He presented a “Decision Automation Map” which breaks down the problem across two dimensions: predictability and cost per error.