Wednesday, May 9, 2018

The National Pension Hub Gears Up?

The National Pension Hub (NPH) for Pension Knowledge and Research is gearing up:
Through collaboration with leading Canadian pension plans, service providers, academia, and policy makers, the Global Risk Institute has established the National Pension Hub for Pension Knowledge and Research.The purpose of the NPH is to provide a sustainable pipeline of independent and objective pension research that, among other things, will lead to innovative solutions to pension design, governance and investment challenges. It leverages the global leadership of Canadian pension plans and consulting plans and engages the academic community on complex research topics to produce objective pension-focused and industry-relevant research and insights. We strive to offer local pension design insights as well as globally-relevant pension investment and governance research to establish Canada as a source for leading pension research.

Interested in contributing your research? Please visit our Call for Proposal page for more information (submission deadline: May 11, 2018).
I had a brief chat with Barbara Zvan, Chair of the NPH, and Chief Risk & Strategy Officer at Ontario Teachers' Pension Plan, to go over the latest developments. For some background on the NPH, see an earlier comment of mine here.

Barb basically told me that they are in the process of awarding two contracts to academics at different universities and she told me to circle back in June when she will be able to provide more details.

She told me the NPH is up to 21 members which includes all the major Canadian pensions and even a large well-known insurance company, Great West Life (click on image):


Great West Life is a holding of the Desmarais family and seniors and juniors sit on its board of directors (click on image):


Desmarais' Power Corporation is also a major sponsor of the Montreal Conference which includes the Montreal Pension Conference.

Who else sits on the Board of Great West Life? A fellow my blog readers will surely recognize (click on image):


Yes, that's Don Raymond, the former CIO of CPPIB who is now Managing Partner and Chief Investment Officer, Alignvest Management Corporation and Alignvest Investment Management Corporation.

By the way, for those of you who don't know, Alignvest Personal Pension Plan helps incorporated professionals retire with more assets versus RRSPs through higher contributions and lower taxes while offering world-class investment management. Learn more about this personal pension plan here.

Anyway, back to the National Pension Hub (NPH). It's gearing up, evaluating submissions and proposals from academics. The major themes are the following:
  • Technology Disruption and Innovations
  • Cyber Security and Fraud
  • Climate Change and Environmental Risk
  • Regulatory Compliance and Financial Stability
  • Systemic Risk
  • Macroeconomic Risk
  • Risk Management Practices
I told Barb Zvan that it's too bad the research mandates only target academics because I know highly qualified industry professionals who can really add meaningful value here. She told me they have to be an adjunct professor or team up with an academic to submit a research proposal.

Barb also told me the grants vary depending on the scope of the research project and it can be broken down into phases if needed. But everyone needs to provide details on the project they'd like to work on, how much it will cost, and strict deadlines for deliverables (all common sense).

Since I had Barb on the phone, I also mentioned a recent comment of mine looking at whether Vestcor's benchmarks are a joke where I noted the following:
Somebody told me that Ontario Teachers' has a "Benchmark Committee" steered by its CEO, Ron Mock, and is made up of him, the CIO and Barbara Zvan, the head of Strategy & Risk. This committee makes sure nobody is gaming their benchmark in any investment activity.

I asked him why doesn't anyone from the Board sit on this committee and he replied: "The Board approves the benchmarks but it's up to management to make sure they are strictly adhered to in terms of risk. If management doesn't do its job, the Board can change the benchmark and even fire the CEO."

Good point. This person also told me that CPI + 400 or 500 bps is a fine benchmark to use in private markets and most deals aim to ensure CPI + 700 to have an "extra cushion". He added: "Private markets aren't liquid, there is a lot of time and energy involved in deals, so it's ok to want an extra premium over benchmark in deals."

As far as the risk, he stated: "The biggest risk in private market deals is permanent loss of capital but if the compensation is structured over a four or five-year rolling return period, the manager is aligned with the organization's objective not to take excessive risk by gaming the benchmark."

That is an important point, there are no perfect benchmarks in alternative investments, you want pension fund managers to take risk but not to go crazy and risk losing a ton of money on any given year. If the compensation is structured to primarily reward long-term performance, you can do away with a lot of these private markets benchmark gaming issues.

And remember, benchmarks can be gamed everywhere, including public markets and hedge funds, it's not just a private markets problem. If a manager is taking excessive or stupid risks, be it liquidity or leverage or whatever, it should be reflected in their benchmark. Period.
So, Barb clarified some things. OTPP's Benchmark Committee includes the CEO, CIO, her (Chief Risk & Strategy Officer), as well as the CFO and Head of HR. The CFO provides performance figures and the Head of HR links performance to compensation.

In terms of risk, Barb's team assigns a risk budget to all investment groups across public and private markets, and they make sure the risk budget is respected and that risk parameters of the investment activities are well captured in the benchmark.

She did say that typically private market deals look for a premium over benchmark and "that's fine because it aligns interests with the long-term return objective of the plan."

She agreed with me that private market benchmarks aren't obvious and that T-bills + 500 basis points may be an appropriate benchmark for one pension plan for some private markets and entirely inappropriate for another depending on the risk budgets being allocated.

In other words, assigning benchmarks for private (or public) markets isn't a straightforward endeavor, it really depends on the underlying risks being taken.

I also asked Barb about events taking place, including one that took place last Friday on alternative data.

Barb told me she couldn't go to that event but luckily one of my blog readers, Lisa Brown who consults pensions and is the founder of Gorgon Capital Research, was kind enough to send me her notes:
Is alternative data a thing now? Dr. Ashby Monk the Executive and Research Director of the Stanford Global Projects Center believes it is. At a roundtable discussion last week at the Global Risk Institute’s headquarters in Toronto, he delivered a persuasive admonishment to the pension executives present. His message was: give serious  consideration to the emerging role of alternative data.

This was the National Pension Hub’s inaugural Roundtable discussion. It was well attended by the biggest pension funds in central Canada. Following a few words of introduction by the GRI’s president Mark Caplan and the institute’s executive in residence, Gareth Whitten, Dr. Monk, proceeded with a technological history lesson designed to highlight the disrupting technologies that have become commonplace over the last 15-20 years alongside the fact that, as financial analysts, we are still using Yahoo Finance, a technology that was available over 20 years ago. We’re now accessing it on our phones which is new, but the underlying data is old hat.

The point was well taken. Surely, the cutting edge of financial analysis could use more edge. Dr. Monk went on to explain that advances in artificial intelligence (AI) and machine learning (ML) were beginning to do our jobs faster and more comprehensively than we ever could. This is no surprise given that a large part of our jobs is sifting through information and raw data to arrive at a conclusion. With parameters and strict rules, machines could certainly arrive at conclusions from a set of data far more efficiently than any human but… what does that mean?

Asset managers that currently make use of AI and ML are grappling with this question right now. Bloomberg has reported that at one of Man Group’s biggest funds, the AHL Dimension Programme, approximately 50% of the fund's profits were attributed to AI strategies. Man Group is not alone. Many other large asset managers are experimenting with AI technology and building expert teams. The issue, however, is this: Are the managers that deploy AI technology to manage their assets fulfilling their fiduciary duty? If they cannot explain the positions or the trades that the algorithms execute, are they really responsible stewards of their client’s assets?

I’m very wary of predictions of the future of technological disruption. It’s been my experience that change follows its own meandering path that somehow always seems to elude even the closest watchers. As such, I’m not convinced that our jobs are at imminent risk. I am convinced though, that Alternative Data will become increasingly essential to those who manage long-term assets.

Alternative Data is often called “big data” or “metadata”. It is typically collected as a by-product of providing some other service like banking, e-commerce, social media or satellite imagery. Because of what this data has the potential to reveal, if it can be organized and analyzed, these data sets have value. It seems that organizing and analyzing this data would be an excellent application of AI and ML.

Dr. Monk’s point concerning alternative data is that currently, the biggest beneficiaries of the value created by alternative data are the world’s largest hedge funds and other institutional investment companies. He believes that pension funds ought to get in on the action, especially considering the value to be gained from insights into the revenue streams of their longer-term investments. Anything that informs the bidding process on longer-term projects, like toll roads or parking lots, or helps with liability management, is something that pension funds might want to sit up and pay attention to.
I thank Lisa for kindly sharing her synopsis and thoughts on this event, it's greatly appreciated.

I wasn't invited to this event and even if I was, I'm too busy looking at stocks and markets during the day using good old free Yahoo Finance data (thank you Neil Cunningham).

Every day, I slice and dice data with a few easy keystrokes and can see which stocks are moving in each industry I track and on my watch list.

For example, here are the stocks moving up and down on my watch list late Wednesday afternoon (click on each image):



I can then look at the daily and weekly charts of each stock, as well as who are the top holders of each stock and tell you if I think there's more upside or not.

It's laborious work and that's only half the job. More importantly, I read macro research from many places, especially good places like Cornerstone Macro, to get a good understanding of the macro environment and which sectors I should be focusing on.

I then email or call my trading buddy Fred Lecoq and go over charts and trading ideas with him. Fred usually (but not always) knocks some sense into me but unlike me, he will never buy big dips (his system is more geared to buying big breakouts after long consolidation on the weekly and monthly charts).

In my opinion, there is no perfect system for swing trading stocks. Sure, I can ask my other buddy, Derek Hulley who is an expert in data analytics to program a bunch of my ideas and make the process more efficient but I doubt any computer would have told me to buy the big dip on Solid Biosciences (SLDB) and hold it (click on image):


I didn't pull the trigger on this one but my point is I see things every single day and track moves on stocks that swing a lot, like Esperion Therapeutics (ESPR), another one that had a big dip recently (click on image):


Don't worry, I'm not invested in this one either, but tracking it just like I'm tracking hundreds of stocks on my watch list, most of which you haven't heard of but some brand names too, like Walmart (WMT) which got hit today (click on image):


I told my 31,000 followers on StockTwits this morning (follow me here), don't rush to buy it because while it's oversold on the daily chart, it can get more oversold on the weekly chart and you have to know your key long-term levels ($78 and $73) and understand stocks can overshoot on the upside and on the downside.

For example, have a look at shares of Tesaro (TSRO), one of the biotech companies I track which made a huge gains in 2016 before peaking early in 2017 and then just getting hammered ever since (click on image):


Believe it or not, there were some big hedge fund quants taking over the world playing this stock on the way up and down, but my point is you cannot simply buy big dips, most of the times, you will get eviscerated!!

There are a lot of things in my head which are programmable but there are other things that aren't, like getting a good "feel" for markets and that is often a judgment call that comes with trading experience.

So, I  guess I agree with Lisa, I'm all for alternative data and machine learning but count me as a bit of a skeptic. Dollar for dollar, I can use my free old Yahoo Finance data and trample a lot of these sophisticated quants who think they're the next Soros or Jim Simons.

I can also sit down with any top hedge fund manager and grill them on each and every position in their portfolio, telling them whether I agree or not based on my macro outlook and technical analysis. Sure, they have the pedigree and are thus able to command huge fees for managing money, but that doesn't impress or intimidate me.

Of course, to be fair, managing your own money is very different than managing other people's money and hedge fund managers are fiduciaries that need to worry about portfolio construction and managing downside risks very carefully. They cannot afford to take huge risks and be wrong or else they're out of business.

For them, it makes perfect sense to invest in alternative data and machine learning as they're constantly looking for an edge.

But alternative data isn't just for hedge funds. Lisa Lafave, a senior portfolio manager at HOOPP, posted an interesting article on LinkedIn, Who Cares About Climate Risk?, which shows you how by using climate data you can become a better real estate investor.

The possibilities are endless but I believe you need good old fashion qualitative analysis to complement any quantitative approach you take to investing.

And my biggest fear at these large hedge funds and pension funds is they're hiring super smart quants with technical skills but they know nothing about how to research things qualitatively before they start programming their code.

Anyway, that's my opinion, you don't have to agree with me.

I thank Barb Zvan for the quick update on the National Pension Hub and Lisa Brown for her resume and insights on the recent event on alternative data.

By the way, it takes a lot of time writing these comments which most of you read for free. I want to thank the few who are decent enough to donate and contribute to this blog via PayPal on the top right-hand side, under my picture. It's greatly appreciated.

Below, Morgan Slade, CEO of CloudQuant, discusses whether data scientists can save hedge funds. He notes the following:
The hedge fund industry, once known for innovation and high absolute returns now lags the passive investing indices (e.g. SPY, during the period from 2009 to 2016). Most hedge funds trade the same strategies, have the same risk factor exposures, and have effectively replicated each other. In recent years, systematic hedge funds have sought to automate these well-known hedge fund strategies and have substantially done so. Research teams for hedge funds have not generated enough ideas to keep pace with asset growth. Annual returns without the deduction of management fees have declined to 5.43% per year on average from 2011 to 2015. Recently startups have created crowd research tools to address this gap between alpha production and alpha demand. We believe they will be successful if the crowd researchers are not required to become domain experts in trade execution. Cloudquant using Anaconda and other open source python toolsets provides research tools to simulate execution and a business model that enables data scientists to generate alpha ideas at scale by allowing them to focus on data science.
It's a tongue in cheek presentation well worth watching but I have a lot of comments which I cannot go over here. This guy should spend a week with me and Fred, we'll blow his quant socks off!-:)

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