• No time for complacency

    Andy Sparks, Managing Director and Head of Portfolio Management Research

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  • Two crises, countless lessons

    Chin Ping Chia, Head of Equity Solutions Research, Asia Pacific

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  • Staying ahead of the index evolution

    Sebastien Lieblich, Managing Director and Global Head of Equity Solutions Research

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  • Of financial models, reality and the bumblebee

    Peter Shepard, Head of Fixed Income, Multi-Asset Class and Real Estate Research

    Read more

The Global Financial Crisis: Ten years on

The global financial crisis of 2008 fundamentally changed global markets. Its impact on investors cannot be overstated. Here we share reflections from leading MSCI researchers discussing what we learned about risk and markets and where they see the investment world heading over the next 10 years.
 

AN INTERACTIVE TIMELINE OF THE GLOBAL FINANCIAL CRISIS

Click through for a brief history of the crisis, from early warning signs in 2007 to the market bottom in 2009.


7 Feb
2007

HSBC says U.S. subprime mortgage losses worsen

27 Feb
2007

Freddie Mac stops buying most risky subprime loans

2 Apr
2007

New Century Financial files for bankruptcy

31 Jul
2007

Bear Stearns liquidates 2 hedge funds backed by subprime mortgage loans

6 Aug
2007

Week-long quant liquidity crunch begins

9 Aug
2007

American Home Mortgage Investment files for bankruptcy

13 Aug
2007

Fitch cuts Countrywide Financial's rating

17 Aug
2007

U.S. Fed cuts bank lending rate by 0.5%

28 Aug
2007

Sachsen Landesbank rescued by Baden-Wuerttemberg Landesbank

14 Sep
2007

U.K. government stops run on Northern Rock

11 Jan
2008

Bank of America buys Countrywide Financial

18 Jan
2008

Scottish Equitable says withdrawals can take 12 months

17 Feb
2008

U.K. nationalizes Northern Rock

3 Mar
2008

HSBC declares $17.2 billion credit crisis loss

3 Mar
2008

JPMorgan Chase buys Bear Stearns with Fed's $30 billion guarantee

11 Jul
2008

U.S. seizes IndyMac Federal Bank

7 Sep
2008

U.S. takes over Fannie Mae and Freddie Mac

15 Sep
2008

Lehman Brothers files for bankruptcy, Bank of America buys Merrill Lynch

16 Sep
2008

U.S. bails out AIG for nearly 80% ownership, Asian markets plummet

19 Sep
2008

Treasury creates temporary backstop for money market funds

21 Sep
2008

Goldman Sachs and Morgan Stanley become bank holding companies

25 Sep
2008

Washington Mutual Bank sold to JPMorgan Chase, Ireland in recession

3 Oct
2008

U.S. Congress passes $700 billion bailout, Wells Fargo buys Wachovia

6-9 Oct
2008

Iceland nationalizes its three largest banks

8 Oct
2008

Global central banks coordinate actions

4 Nov
2008

WestLB taps German rescue package

9 Nov
2008

$586 billion stimulus package in China

20 Nov
2008

U.K. takes 58% stake in Royal Bank of Scotland

23 Nov
2008

U.S. rescues Citigroup

19 Dec
2008

Big 3 automakers bailout

19 Jan
2009

Second bank rescue plan in U.K.

17 Feb
2009

$787 billion stimulus package in U.S.

A tectonic shift in risk appreciation - Peter Zangari

https://www.youtube.com/embed/N8cIDmfLKCw?autoplay=1

Staying ahead of the index evolution - Sebastien Lieblich

https://www.youtube.com/embed/TtWOB5I5vfM?autoplay=1

Looking at the bright side of booms and busts - Jorge Mina

Interviews

Two crises, countless lessons


True to its name, the global financial crisis touched every region in the world. Yet, having gone through a major financial crisis in 1997, Asia fared relatively better than most places, says Chin Ping Chia. Meanwhile, one positive trend he sees as stemming from the 2008 crisis is a more long-term approach to investing. That's giving rise to better stewardship practices and more interest in environmental, social and governance (ESG) factors in Asia.

 
“I think the pace of ESG adoption in Asia is remarkably fast."
Chin Ping Chia, Head of Equity Solutions Research, Asia Pacific
 

What is your most poignant memory of the global financial crisis?

I was in Hong Kong, and was following the news as it happened globally. One defining moment, I would say, was the collapse of Lehman Brothers. It was significant for me not just because it was largest bank collapse during that time but because I had friends who worked there. So, overnight, they found themselves with no job. And these were people who had been with the bank for 10 or 15 years. It made it very real.

How did that experience compare with the 1997 Asian financial crisis?

During the Asian crisis, I was the director of the Department of Statistics with the Ministry of Trade and Industry of Singapore. My job was related to finance but not in finance. Having experienced the Asian financial crisis in 1997, though, there was a kind of déjà vu when the 2008 crisis came. The difference, of course, was that the ’97 crisis was contained within Asia, more or less. When the 2008 crisis happened, it involved firms with a global presence, and the world was more integrated, generally, than 11 years earlier, so there was a bigger impact.

Were Asian policymakers and investors better positioned for 2008 because of 1997?

For policymakers and investors who were directly impacted by the '97 financial crisis, there was a lasting fear of another crisis coming. That had its negatives, but it also meant they learned to be a lot more prudent in terms of managing common monetary policy and leverage. Financial institutions had also learned important lessons in the '97 crisis that probably helped minimize some of the impact of 2008. This is not to say none of the Asian countries or markets were affected by the global financial crisis. It’s more a matter of degree.

How did either crisis change your own perspective?

Well, I would say if you can live through a financial crisis, it's actually a good thing because you do learn from it. The important thing is to try to look back and understand what happened, because it is really very easy to forget the causes and effects in detail.

Right after, you tend to see a more conservative approach. As time goes on, the caution level goes down and people start to take on more risk. That's why we’ve seen excessive risk tending to accumulate during long periods of market calm and stability.

What are some of the trends you see shaping up for the next 10 years?

There is no crystal ball here. But I always believe that no matter how much experience you have accumulated over years, there are bound to be some places that you don't pay enough attention to. There will be another crisis. We just don't know from where or how it will happen.

On a positive note, I think investors have a stronger focus on taking a long-term perspective. This is true globally and in Asia, where we see a growing interest in environmental, social and governance issues, or ESG.

How is that playing out in Asia?

We have seen a few large asset owners in the region starting to take a firmer stand. They are making sure that they can put in place much more sustainable investment processes. I think the pace of ESG adoption in Asia is remarkably fast. And this is all driven by some of the largest investors in the country, which have hundreds of billions of dollars in assets. At the same time, you have other pretty good supporting factors, like the implementation of stewardship codes that are driving investors to become more active owners. This adds up to what I think are all the right conditions for ESG to be successful in the region.
 

Of financial models, reality and the bumblebee


As the financial crisis unfolded, Peter Shepard witnessed reality confronting theory. "There was a mounting series of events that weren't what anyone expected," says Shepard, who holds a Ph.D. in theoretical physics from the University of California, Berkeley. "It defied everything I read in the textbooks." More than a decade later, Peter has led research and development of many models at MSCI, where he instills in his team a healthy skepticism for the limits of their equations: Even the best financial models, he says, cannot fully account for human behavior.

 
“You don't necessarily get diversification by investing in lots of different investment vehicles.”
Peter Shepard, Head of Fixed Income, Multi-Asset Class and Real Estate Research


From where you sit, what has changed since the crisis?

One big change that we're seeing across the board is the squeeze on traditional active management. Clearly, that's pushing a lot of investors into passive strategies, and into factor investing. At the same time, it's also pushing a lot of investors into private assets which they believe may offer a premium or a better opportunity for skilled managers. And so a lot of conversations that we have with people thinking about private assets go back to the crisis; specifically making sure the lessons about liquidity are front and center. Some very important institutions really got pinched by liquidity during the crisis. The capital calls kept on coming even as the cash flows dried up. It’s important for today’s investors to keep the past in mind.

The crisis certainly challenged the conventional wisdom on asset allocation. Is the 60/40 portfolio still relevant?

Now it's more like 40/40/20 where that 20 is private assets. But there is also recognition that many of these assets, whether private or public, have a lot in common as far as the drivers of returns. You don't necessarily get diversification by investing in lots of different investment vehicles.

That leads us to factor-based allocations. Increasingly, our clients want to make strategic asset allocation decisions based on these underlying drivers of returns — and then they're determining which investment vehicles are the most efficient way of getting those factor exposures.

Has the proliferation of data made investing more science than art?

The short answer is no. If anything, the crisis taught the danger of mistaking finance for science. One contributor to the crisis, in my view, was former physicists taking their equations far too seriously, and thinking that what they found in an equation was the truth. It’s that kind of reductionist view that I think is really dangerous. We need to understand that math and models are important tools, but ultimately we're trying to describe people. And people's behavior is far more complicated than any equation that I'm able to solve.

How do you make sure that you and your team stay grounded in the real world?

One of the most important challenges in my role now is to instill in people a healthy fear of their models.  Most of the work we do is based in scientific practices that make sure the models are robust; for example, we have various techniques for backtesting and stress-testing our models. But then we also try to look at problems from many angles and get a more complete picture.

Maybe I sound like my grandfather, but there are a lot of young people in the industry today who weren’t around during the crisis. They may not realize the extent to which the future might not look like the past. So, as a leader, I need to make sure I help them keep that firmly in mind.

What trends will impact your research over the next decade?

I won't claim to know how a lot of trends will play out, but I do think a number will have an impact. Interest rates have been a one-way bet for decades, and so the prospect of a low-return environment for the next decade seems very real. I think that coincides with some demographic changes that will be very challenging as asset owners work to meet their liabilities.

I also think technology is going to have macro effects. For example, what will the effect of technology be if a lot of jobs are automated? Will it be like the invention of the power loom, where it wasn't so great for the weaver but the people who could operate the looms had better lives? Or will it really leave people behind and change the structure of the economy?

What do you think this means for your industry over the next decade?

It's very likely that the role of technology, big data and artificial intelligence will have a large impact in areas where scale is the primary impediment. But for problems that smart people have struggled with for decades, such as, “What is the optimal asset allocation?” I'm not convinced a machine will be able to take that over.

Think about the fact that the most powerful artificial intelligence networks recently surpassed the brain power of a bumblebee, and they're closing in on a tree frog. Bumblebees are very impressive — and that level of intelligence may be enough for, say a self-driving automobile — but I’m not sure I’d ask a bumblebee to manage my mother's retirement income.
 

https://www.youtube.com/embed/p6fbmacenNA?autoplay=1

Looking at the crisis through the lens of governance -
Ric Marshall

https://www.youtube.com/embed/2x0RYLXsONw?autoplay=1

No time for complacency - Andy Sparks

Interviews

Finding the right formula: The rise of factor investing


George Bonne admits his timing hasn't always been perfect. The Harvard University physical chemistry Ph.D. joined the semiconductor industry near the height of the tech bubble. Then, in 2006, he shifted into finance, where he created novel quantitative models for what is now Thompson Reuters StarMine—just in time for the financial crisis.

 
“Investors became more aware of the potential for ‘extreme events’ [after the crisis], and the need to diversify across factors, not just across asset classes.”
George Bonne, Executive Director, Equity Core Research


For starters, how would you describe factor investing to, say, your mother?

It's taking the emotion out of the investment decision and buying securities based on some systematic, generally simple rules that have statistically been shown to have generated excess return over long periods of time. That might be, for example, buying securities that are cheap relative to their earnings. Factors are predominantly used in equity investing, but are expanding to other areas, such as fixed income. I hope Mom can understand that.

The truth is, it can be difficult for people to wrap their heads around. Quant folks like me have been using factors for years, but factor investing has only recently become more widely used.

You came to finance from academia. How did you make the leap?

My graduate work was modeling chemical reactions taking place in the atmosphere. Then, when I was in semiconductor equipment engineering, a good chunk of my time was spent modeling the performance of the equipment, trying to predict when it would need to be serviced before it actually broke. Now at MSCI, my team and I build models used in analytics and index products. At first glance my career trajectory might seem meandering, but the common thread has been building models and conducting analysis with large amounts of data. The fundamental skills and techniques required in all of these areas are all very similar.

Did the financial crisis make you second guess that last career move?

Not quite. I certainly got in at a high point for quantitative investing, but I think it has since passed that high water mark. The financial crisis actually served as a positive for factor investing, increasing people’s awareness of factors as drivers of risk and return.  

Would you say the financial crisis sparked interest in factor investing?

There is some relationship. Investors became more aware of the potential for “extreme events,” and the need to diversify across factors, not just across asset classes. For example, funds tracking MSCI's Minimum Volatility Indexes increased in assets and popularity after the crisis. I think, more importantly, though, there is that greater recognition of factors as risk and return drivers, and that there are systematic factors that have demonstrated long-term risk premia over time. If you can create a strategy — and at low cost — to capture this, why wouldn't you?

What is the Holy Grail for factor investors?

People are always seeking ways to identify and understand new and uncorrelated information and sources of alpha [excess returns]. The explosion in data science and different types of data has made people scramble to try to be unique in what they do, and not miss out on information their peers may be looking at.

How has factor investing evolved over the last decade?

It started out with the most well-known, well-studied academic factors based on very traditional data — company fundamentals, earnings estimates, pricing. Now you're seeing more variety. For example, we recently built a model for sentiment factors, such as options, short interests and social media.

There are clearly pockets of value in alternative data, and we are looking at them. However, investors need to consider the complete picture. For example, with traditional data you get coverage of about 30,000 companies, but with some alternative data sets you may cover only 30. Also, machine learning algorithms have been around for many years, but recently we’re seeing a lot of hype about using them and AI (artificial intelligence) in the investment process. Using these terms with VCs (venture capitalists) has been a great way to get funding, but I think thus far, the hype mostly has outweighed the value.

What about the next 10 years? Where is factor investing headed?

As people get more comfortable with factors, we’ll see a continuation of the trend toward the “quantification” of investing. Even fundamental managers are realizing they need to somehow incorporate factors and quantitative tools into their investment process. We’re already starting to see specialized indexes based on alternative data, and I think we’ll see more factors and indexes based on them as alternative data becomes more mainstream.  We will see factors constructed from non-linear techniques, and those dynamic strategies I mentioned may increase in complexity, and will likely be driven by more complicated algorithms or machine learning as technology advances.
 

Rethinking what it means to be globally diversified in real estate


The financial crisis underscored the importance of taking a more nuanced approach to diversification and risk management. Yet, when it comes to real estate, investors still tend to paint the asset class with a broad brush. Part of the issue, says Will Robson, is accessing and analyzing data that can be translated across global real estate markets, and applied to the total portfolio.

 
“Real estate has always had a high home bias, and so global diversification is a relatively new thing in real estate compared to say, equities."
Will Robson, Executive Director and Global Head of Real Estate Applied Research


What was happening in real estate at the start of the financial crisis?

The U.K. commercial real estate market had just opened up to daily-priced real estate funds, and there were huge inflows from retail investors. You had these quite liquid investment vehicles for investing directly in real estate, which is in itself an illiquid asset class. Still it was relatively easy to raise capital and everyone wanted in. Then the financial crisis hit, and everything turned negative very quickly.

How would you characterize the market today?

One thing we've seen in the recovery is that investors are much more focused on prime high-quality real estate in high-quality cities and locations. In the heady days of 2005 and 2006, there was a lot more investment volume spread across the whole quality spectrum, and debt was much easier to come by than today.

What other lessons did real estate investors learn?

Pre-financial crisis, people talked about real estate as being a diversifier for a portfolio, but they focused on valuations rather than transaction pricing. This tends to underestimate the volatility and correlations with other asset classes. People thought real estate provided more of a diversification benefit to their portfolios than perhaps it did.

Up until the crisis, I don't think people were really analyzing their real estate exposures in any systematic way as part of a multi-asset portfolio. The fact that real estate was such a big part of the story really highlighted the need to understand the interaction of real estate with other asset classes.

Is that easier to do today than a decade ago?

It's still a work in progress because most real estate-focused investors still are operating in a world where everything is based on their valuations. They think of risk in terms of individual buildings, things like lease agreements and tenant mixes. And that’s hard to get away from. Unlike in equities, where you’re not involved in day-to-day management of the company, per se, with real estate, you’re managing a business plan, as well as a portfolio.

You’re also dealing with a very broad spectrum in terms of risk-return characteristics, ranging from very safe, long-lease properties, all the way to speculative ground-up developments in emerging economies. Today, people are interested in understanding in a bit more detail what kind of real estate they're investing in and identifying the common risk factors. Still, the challenge is finding a way to analyze risk in real estate that speaks the same language as other asset classes.

How about geographically? Are investors more diversified globally?

Real estate has always had a high home bias, and so global diversification is a relatively new thing in real estate compared to say, equities.

You can see the potential for diversification when you just look back at returns around the crisis. You had markets like the U.K., Ireland and the U.S. that had quite a violent response, whereas a lot of European markets had a more delayed and measured response in terms of their real estate valuations. Then, some parts of Asia didn't really have a downturn at all in their commercial real estate market, on a valuation basis at least.

Why have investors been slow to adopt a more global approach?

Real estate is such a local business — even the definition of a yield can vary from one market to another — so having data that is comparable is actually quite a challenge. We’ve worked for the last few years trying to bring as much standardization as possible to the way that real estate returns are measured and the methodologies behind that. We’re trying to build a bridge where there is more information about the specifics of an investor’s portfolio, but also allow them able to consider risk from a macroeconomic perspective.

What trends will change real estate investing over the next decade?

Because such a big part of data analysis in real estate is actually getting the data, cleaning it and making it usable, if technology can advance to facilitate that, maybe that will encourage more people to share data. When more data is available, the analysis becomes more sophisticated. Hopefully, that will allow us to apply similar factors, terminology and analysis techniques from the equities markets to private real estate and have everybody talking a similar language.

As that happens we should see more and more standardization in other areas, such as legal or contractual aspects. Things like blockchain may help make it a more global and liquid market. That could have massive implications, improving real estate's potential for diversification and increasing its role in multi-asset portfolios.

Know what you own


As the world saw during the financial crisis, the nature of securitization – bundling financial assets, such as home loans, and turning them into tradeable securities – makes analyzing these investments extremely complex. David Zhang has dedicated most of his career to helping investors get to the bottom of what they're buying. It's a constant work in progress, he says, given that changes in regulations, risk regimes and borrowers’ behavior, to name a few, can throw a wrench in even the best models.

 
“The crisis drove home that you cannot look at the data in isolation.”
David Zhang, Head of Securitized Products Research


Where were you when it became clear the housing crisis was becoming a global financial crisis?

I was working at Credit Suisse as head of securitization quantitative research. So I had a front-row seat in the bubble years, the mortgage and financial crises and the subsequent recovery and reforms. The first sign of trouble showed up in the second half of 2007 when the mortgage market experienced a sharp increase in so-called early delinquencies in the private label MBS [mortgage-backed security] market. This was unprecedented, as borrowers hadn’t been delinquent that quickly after loans closed. This revealed serious problems in mortgage underwriting and led to a sharp depreciation of private label MBS. Initially this was an isolated issue, but in the next six to eight months, banks whose assets were tied to these securities and had weak capital and liquidity positions started experiencing severe stress. Remember, the MBS market back then was larger than the Treasury market.

For me, the defining moment of a mortgage crisis becoming the financial crisis was in March 2008, when Bear Stearns was sold to J.P. Morgan, with funding support from the Federal Reserve Bank of New York. I remember I was in a Tokyo hotel room, packing to go to the airport when I saw the news that J.P. Morgan was paying $2 per share for Bear Stearns.

How has the industry changed in the last decade?

The reforms of the last decade have touched nearly every aspect of mortgages and securitization in general; there have been changes in accounting rules, disclosure rules and capital and liquidity requirements. They have profoundly changed and improved the mortgage and securitization market. Meanwhile, whole industries have sprung up to increase data and analytics abilities for mortgage investors to look at MBS with much more in-depth analysis.

What are some of the key lessons that came out of the crisis?

Before the crisis, investors, modelers and rating firms relied heavily on data from the bubble years and on assumptions that proved wrong. For example, the mantra was that there never had been a national U.S. housing downturn, and that correlations between the performance of various asset types were low. All these assumptions were held forth as unchallenged truths until the crisis exposed them as inadequate.

Fortunately, investment technology has also advanced significantly post crisis. Big data analytics is one of the most positive trends emerging from that time. Overall, there is a sense that investors need do more and better analysis to “know what they own,” especially in the securitization space. The crisis drove home that you cannot look at the data in isolation.

How are investors better positioned to know what they own?

Post crisis, the industry disclosed a huge amount of new data, especially in mortgage issuance and performance. MSCI was a leader in the research community in terms of using these data to understand the causes of the mortgage crisis and the potential impact of newly proposed government policies. We also suggested new policies and practices. For example, we did a pioneering study that combined mortgage loan data with consumer credit bureau data to gain unique insight on why – contrary to conventional wisdom – borrowers who weren't underwater experienced large default rates. We also researched collateral composition and performance for other securitizations. These include [government] agency backed securities and collateralized loan obligations, both of which have boomed in recent years.

In addition to understanding collateral better, investors have been doing more analysis on the structuring side, relying less on rating agencies.

What are the biggest trends in securitization you see for the next 10 years?

One big trend may be the growth of securitization in Asia. In the next five to 10 years, the Asian securitization market may rival the U.S. market. Since the crisis, many Asian countries have been keen to learn the lessons that came out of the U.S. crisis so they can avoid a similar fate.

Big data and artificial intelligence (AI), meanwhile, should be very important in terms of investment and risk management. These could lead to profound changes in how we research investments, how we build risk analytics and for overall operations.

How can AI improve securitization research?

One issue with analyzing securitized products is the large amount of data required and the large amount of risk factors. AI can give you a much better, more efficient way to deal with the kind of data we're analyzing and find new ways to analyze risk, such as linking public securities data with consumer data, and with other non-traditional data.
 

https://www.youtube.com/embed/ZmZYRTNKbzI?autoplay=1

Taking a more holistic view of risk and opportunity -
Laura Nishikawa

https://www.youtube.com/embed/gbumh6ofGM8?autoplay=1

Turning a crisis into opportunity - Dimitris Melas