## Why Investors Need Multiple Betas

Segmented up side and down side betas can be used to for better risk management and to perform non-linear stress testing

Beta analysis has become a staple of the investment industry because it provides a simple way of encapsulating expectations about both relative return and relative risk.

But virtually all measures of beta assume that the fund and its benchmark have the same relationship when making money as when losing money. Possibly even more egregious is the built-in assumption that the relationship is linear across all returns.

Betas should be measured for different zones of returns to capture differences not only in up markets and down markets but also in extreme markets.

Measuring upside/downside statistics is well established in financial services: downside volatility has been a standard measure for decades, and some firms extend the idea to upside and downside correlation.

But few firms consider upside/downside beta, perhaps because they limit themselves to a fundamental factor framework in which market side plays no role. However, in a statistical or regression approach, computing such betas is rather straightforward, especially when dealing with single factor regressions.

In the case of only one index or benchmark, we could divide the dataset in two parts: one subset covering only those days on which the index suffered a loss for which we compute β-, and another subset covering only those days on which the index returned a gain, for which we might compute β+. This would allow for a comparison of how differently, if at all, the fund is sensitive to the index in up markets and in down markets.

Just as it is desirable to have a relatively large beta to upward markets, it’s also desirable to have a small beta to downward markets. Funds that show larger values of β- than β+, on the other hand, would lose more in downward markets than they make in upward markets.

Taking this concept one step further, we propose computing not two betas but four: β – -, β-, β+, and β++, each of which covers a specific zone of index return.

For normal markets, defined as those within one standard deviation of the index’s average return, we calculate β-, and β+ as described above. But we further segment the index’s returns into extreme markets – those outside the one standard deviation band. For days when the index is up more than one standard deviation, we compute β++ and for those days when the index suffers a loss greater than one standard deviation, we compute β – -.

The light grey lines shows the beta if only one regression were performed, with a value of 0.89. The red lines show the betas of the four separate zones which are significantly different from the overall beta.

Figure 1 shows an example using recent returns of the S&P 500 with returns of a hypothetical fund, demonstrating the zones and values of the four betas. For comparison with our technique, we show the all-inclusive beta of 0.89 in the light grey line going through all the data. Using our segmented beta approach, the ‘normal betas’ have values β- = 0.8 and β+ = 1.05, showing that the fund is slightly less sensitive to benchmark movements on the downside than on the upside. To compute those values, we considered only the days on which the fund was up/down and having returns within one standard deviation.

We further divided the data into extreme zones, defined as returns larger than one standard deviation beyond the mean. While each of those zones has only 11 data points, they visibly demonstrate a different relationship with the fund than the data within the one standard deviation zones. The positive extreme beta, β++, has a value of 1.3 while the negative extreme beta, β – -, has a value of only 0.5. As shown through in multiple beta analysis, this hypothetical fund is more sensitive to index/benchmark movements on the upside than the downside and exhibits non-linear behavior.

While segmenting the data set by standard deviation naturally limits the number of data points in the extreme subsets to only 11% of the total, we believe it is superior to using other methods, for example an equal segmentation of data (e.g. 25% for each zone), because of the canonical nature of standard deviation. Rather than using only 100 days’ history as we did in this example, we suggest using 200 days’ history in practice, giving 22 data points each for the computation of β – – and β++.

Beta analysis is often used in simulating market stresses since, for a given shift in the index’s value, beta can be used to estimate the fund’s likely response. For the example shown above, had only one beta been computed, the estimated result for any shift in the S&P 500 would be 0.89 times the S&P move. For example, for a 1% move up in the S&P, we would estimate a 0.89% rise in the fund. For a -1% move in the S&P, we would estimate a -0.89% move in the fund. Instead if we had used β+ and β-, we would arrive at slightly different answers: -0.8 for the downside and +1.05 for the upside. Similarly, using the four betas would result in still further differences. The table below summarizes the results of using just one beta, two betas and four betas for both small and large movements in the index.

Results of stress simulations using different beta approaches.

Certain investment vehicles, such as hedge funds, are supposed to provide non-linear returns that might be picked up by such a multi-beta analysis. Measuring funds’ responses to the markets with multiple betas has the potential to add a layer of useful analysis both for return generation and risk management.

## I’m Long for a “Big Short” Oscar

[This post was originally published in Risk.net’s Hedge Funds Review]

How would you have reacted if I told you, several years ago, that a Hollywood studio was making a movie about the role hedge funds played in the global financial crisis? That it portrayed hedge funds as the heroes? That within the first few minutes every moviegoer would be paying close attention to a detailed explanation of short-selling? What would an option to invest in such a film have been worth? Simply put: not enough. Today, The Big Short is up for five Academy Awards including best picture, best director and best supporting actor. It does an incredible job of taking a rather dry subject and turning it into an engaging story about a few eclectic characters that millions of people continue to pay good money to see. The two best reasons to encourage others to see The Big Short are straightforward: it entertainingly educates about the complexities of the financial system and, more importantly, it accurately portrays the positive role that hedge funds can play.

The Big Short gets the high-level story right and uses accurate details to support its tale. Hedge funds – indeed all successful investment managers – profit from access to scarcely known information. The Big Short tells the story of a number of persistent investors who didn’t accept the party-line explanation of inconsistencies in the mortgage markets. They dug deeper, asked uncomfortable questions and found opportunity. This is not just the story of a handful of investors who profited from the collapse of the housing market – this is the epic story of how to be successful in life: be different, be bold, find a niche, stick to your convictions and see the job through. Each of the subplots in this movie follow that inspiring David vs Goliath storyline.

Christian Bale’s character, Michael Burry, goes through a meticulous analysis of underlying home loans before deciding to bet against the mortgage-backed securities (MBS) in which they’re collateralised. The movie shows him struggling with his preliminary findings, gathering more data, performing more analysis and finally deciding to bet heavily against the US housing market. Day after day as he waits for the collapse, Blurry records his mounting loses by writing his fund’s negative returns in big numerals on a massive white board for all his employees to see. Chutzpah, writ large. His obsessive-like behaviour endears him to the audience. His lack of experience with MBSs strains his credibility with his mentor and investors. We love an underdog and this part of the story delivers.

Meanwhile, Mark Baum, played by a convincing Steve Carell, discovers the hollowness of the same market as he tours empty Florida housing developments and interviews mortgage brokers who brag about putting modest-income earners into palaces through interest-only loans. Following the very skeptical Baum as he pieces together the underlying fragility in the housing market, you can’t help but root for him. This is great acting and great direction coupled with great writing. It’s one thing to hear Steve Carell’s dripping sarcasm when he repeats what he just learned about “CDO”s but to hear him snide as he slowly pronounces “synthetic…” CDO is what makes movie going worthwhile.

The moral aspect of the entire topic is all too briefly – but quite neatly – addressed by Brad Pitt’s character, reclusive Ben Rickert, who left Wall Street to the comfort of his Rocky Mountain sanctuary. Brought back into investing to help two naïve hedge fund entrepreneurs, he admonishes them, “You just bet against the American economy. And if you win, hardworking people will suffer, so try not to celebrate.” Wall Street could use more conversations like that.

The use of cameos to explain complex financial concepts works rather well. The dialogue between Selena Gomez and Nobel Prize-winning behavioural economist Richard Thaler at a Las Vegas blackjack table is inspired, as is celebrity chef’s Anthony Bourdain’s explanation that subprime mortgages can be made to look appealing through collateralisation, just like last week’s fish can be made into seafood stew. Director and screenplay adaptor/writer Adam McKay makes it clear that these topics deserve to be understood and helps the audience by making the lectures entertaining. He seems to tell us “no shortcuts here – I’m going to force you to pay attention to otherwise boring but important stuff by having celebrities teach you. So there!”

To be sure, The Big Short won’t make everyone in the alternatives industry happy, nor will it change the perceptions of those who believe the solution is a radical reform of Wall Street. But it will explain some of the intricacies of the investment management world and how it operates. And it will allow more people to have an informed dialogue about the proper roles of regulation, free markets, finance and opportunity. And whatever side of the debate you’re on, that’s a good thing.

All in all, this is a movie worth seeing. This is a story worth telling. For those of us in the financial investment industry, this is a movie worth encouraging others to see. It’s entertaining, informative and provocative, just like a good movie should be.

## Why this acquisition was the best decision

### The three reasons Investor Analytics was purchased by StatPro

Lots of contemplation, emotion and cold hard logic go into the decision to have your company acquired by another firm. While even my own 13-year old daughter made the canonical “but it’s your baby” argument in trying to convince me not to, it’s never been about that for me. Investor Analytics has not been the goal but rather the means. IA has always been a vehicle to promote what I fundamentally know to be true: that we can all make better decisions by understanding and using analytics and methodologies that have passed rigorous testing. For me, Investor Analytics has been an expression of the tremendous benefits of the scientific method, as applied to financial markets. It matters because real people’s livelihoods depend on financial decisions. Good financial risk management can mean the difference between retiring early and working until old age, or between living month-to-month and investing for your future. With a nod to Simon Sinek, that is my Why. Analytics improve decision making and improve human lives. I’ve spent my career applying it to financial markets so people can make better investment and risk decisions and avoid catastrophic financial mistakes.

You can read the rest of the post here.

## Did Markets Overreact to China?

My latest Risk.net article, co-authored by the fabulous Kathe Macleod, director of Risk at  Senator Investment Group. My column is now fully branded under the “Riskology” moniker.

Results of different models for US equity response to Chinese drawdown. Click to enlarge. The analysis is in the text.

As international markets gyrated over the past several months it seemed to us that participants possibly adopted a “hair-trigger” or reactionary approach and thereby exaggerated both the impact and contagion of what would otherwise have been localized market events. Specifically, we wanted to examine if markets overreact to single-factor events by evaluating if stocks are oversold relative to single-factor exposure. China’s dramatic and unprecedented June and August sell-offs are good candidates for such a study. As we discuss here, for a variety of reasons we found June more representative of single-factor events than the August sell-off and therefore limit our analysis to the June sell-off.

Beginning with the peak on June 12, the Shanghai index did not reach its local trough for 26 days, for an accumulated 32% drop in the SHCOMP (Shanghai) index. This registered as a -3.6 sigma event given its then recent 180-day historical volatility. We examined the returns of the constituent stocks of the US S&P 500 index during the same period by constructing a model that takes into account each stock’s direct exposure to Chinese markets by incorporating that company’s fraction of revenue from China. We consider this a natural way to construct such a model and expect that this is a common approach to analyzing similar situations.

## Number One Reason China Has Begun to Scare Me

I haven’t really been too concerned with the recent market gyrations the past few days, but yesterday’s news that China is lowering interest rates again and is now easing bank reserve requirements by 0.5% is, quite frankly, troubling.

A few weeks ago I wrote in a LinkedIn Pulse piece that “…confidence in Beijing’s ability to support the equity markets may be fading, and the real issue is the underlying Chinese economy” but that we didn’t expect a sustained wider sell-off. Last week’s downturn, just as many started their end-of-summer vacations, set off what began to look like a wave of follow-the-sun mini-crises. Yesterday’s move shows just how immature China’s economic policy making really is.

## 5 Financial Risks to Keep an Eye On This Summer

This summer is no time to take your eye off the risk management ball as the uncertainty and volatility in financial markets won’t take much of a holiday.

There are five financial risk focus areas we suggest you continue to monitor through the dog days of summer: Oil, China, Rate Hike, Liquidity and Loans. Although Greece seems to have settled down – for now – and the EuroZone has avoided its most recent crisis, it’s likely to come back at some point since the solution kicks the can without really addressing the underlying differences in the EU’s constituent economies and cultures. For now, there are bigger market risks to watch for.

## Risk Management Lessons from the SEC of All Places

Risk Magazine branded my monthly column using the Riskology moniker, and the first article under this branding was entitled “What money markets can teach hedge fund.”

You can read the article on the Investor Analytics site.

Please take a moment to comment on what you liked or didn’t like and let me know what other risk topics are of interest to you. The figure below is explained in the article.

Investor Analytics’s “Topographical Risk Report” shows where the fund is today (grey box) and what the value of the fund would be under different combinations of stress. The yellow zone is the warning track and the red zone is, well, to be avoided at all costs.

## Greece may be the least of our worries…

I’ve just posted another article at the Investor Analytics blog on the developing situation in Greece. Feel free to post comments on the Investor Analytics site directly.

## The Risk of a Greek Tragedy

Rather than duplicate the entire article here, I’ll simply point you to the Investor Analytics Blog, where I posted my article about the Grexit.

## Risk Management on Wall Street and at the Supreme Court

One of the first things I noticed as I started my early morning commute today was the number of men wearing pink shirts. On the train, on the ferry from Hoboken to Wall Street and on the streets of lower Manhattan pink shirts seemed to be everywhere. The gym was packed with guys wearing pink shirts too — and when I commented to one of them that we were both wearing pink shirts, he opined that “it’s a low risk choice: it’s a Friday in the summer.” Doing something ‘daring’ becomes easier the more people who accept the behavior. And if a slight majority is ok with it, it ceases to be daring at all. It becomes a low-risk choice.

Click to Enlarge

Which brings me to today’s Supreme Court decision.  Without commenting on my reaction to the decision, it seems to be a low-risk option for the Justices: US opinion polls shows a slight majority of people across the country supporting marriage equality for the first time about 3 years ago. Among 18 to 29 year old voters, the trend is much stronger with 78% of them in favor of equality according to Gallup. While I don’t pretend to know what goes on inside a Supreme Court Justice’s head (I simply do not think like a lawyer does), each one of them must be fully cognizant that the tide of history is clearly on the side of equality. It had became the low-risk decision.

I think I may start to wear pink shirts to work more often.

## Liquidity-at-Risk

This article originally appeared in Risk.net, co-authored with my good friend Ken Akoundi, President of ASPN Solutions. Ken and I will be speaking at the RiskHedge conference in NYC on July 8 on the topic of Liquidity Risk.

Market liquidity, when not taken for granted, is a complex topic that has no quick and easy explanation, measure or analysis. Without liquidity, a market cannot really exist, so most economic, valuation and risk models assume a high level of liquidity. Any other assumption is just too messy: how exactly does one go about modelling a market when one of its required characteristics – liquidity – is in short supply or non-existent?

Both the Wall Street Journal and Investopedia define liquidity similarly: the degree to which a security can be easily bought or sold without materially changing its price. Liquidity is not just the ability to buy or sell – liquidity is about the ability to do so without moving the market. That last bit means that to be truly liquid, a security needs plentiful buyers and sellers so anyone can transact at nearby prices, leading to one of the most common measures of liquidity: volume. But high volume alone does not mean you can always transact without moving the market: what if all that volume is dominated by buyers when there are few sellers – a strategy used by some hedge funds in frontier markets. Without plentiful sellers, just one buyer can and will move the market. The mirror image is also true. Size matters: small quantities can often be easily bought or sold and therefore some would describe the security as ‘liquid’, but the same security at larger quantities may not find a market at all.

## Fed Rate Hike: it’s how much, not when, that really matters.

This post was originally published in Risk.net.

Volatility is up, correlations are down. Equity prices continue to soar, the US Fed continues to delay its inevitable rise in interest rates, the dollar is up almost 20% since last June, and oil is still cheap. So far, 2015 is proving to be a very different environment than 2014. As one trader recently said to me, “compared to the other world sovereign debt markets, the US can now be thought of as high yield. How often does that happen?” But the last few years have not been kind to hedge funds, and indeed the New York Times headline for May 5 was about how much hedge funds were “paid” for recent lacklustre performance. To say the Times cherry-picked the data is an understatement.

Both Cliff Asness, chief investment officer of AQR, and Matt Levine of Bloomberg critiqued the article quite soundly, pointing out that most of the “earning” that was counted in the managers’ compensation – up to 70% of it – actually came from returns on investing the manager’s own money in their own funds. By this standard, Warren Buffet made almost twice as much as all of the top 10 hedge fund managers combined. But the point of the article was that these managers are being highly compensated despite several of them underperforming the S&P 500 last year, which panders to the popular notion that hedge funds are the devil incarnate – in this case, by unfairly collecting high fees even when they fail to deliver returns. The response from the industry in pointing out the flaws in the article will almost certainly not grace the front page of any newspaper like the article itself did. But the article did point out that more than \$18 billion of new assets have flowed into hedge funds this year. With total assets closing in on the \$3 trillion mark, many people clearly think they are worth it.

The investment environment those hedge funds face this year looks chock-full of opportunities. One of my clients recently told me “I’m having more fun trading today than I’ve had in years” due to the combination of dynamics in equity markets, fixed income, currencies and commodities – all four cornerstones of global macro traders.

Equity

Even while equity indexes break all-time highs, the correlations among equity sectors is quite low, allowing traders to differentiate stocks much more easily than over the past few years when the high correlations made every stock indistinguishable from every other stock. In our analysis, 90-day correlations are lower this year than in 2014 between virtually all sectors. The average correlation between Russell 2000 sectors dropped from 0.71 to 0.58 between the last five months of 2014 and the first five months of 2015. Between the 36 sector-sector correlations, 34 of them dropped. Equity volatility is at a healthy level as well, with the VIX down from earlier this year to near historic norms of around 15%.

Rates and Foreign Exchange

When the Fed raised rates between June 2004 and July 2006 from 1% to 5.25%, it did so in near lockstep: virtually perfectly linearly, as shown in the graph. At the time, it signaled those rate hikes rather clearly so there was really no question about the move well before each rise. The effect was to remove volatility and uncertainty from the markets. This time around, the Fed has been very clear that it has adopted a data-driven decision approach – rates will rise when and if the monitored data says they should. In other words, this time around it is projecting volatility, or at least that it is not going to be doing things “lock-in step” like it did in the past.

Between June 2004 and July 2006, the Fed ‘telecasted’ its intentions about rate hikes and raised them in small steps at almost every opportunity. The result was a virtual elimination of uncertainty. They will not do so this time.

The main question about the Fed should not be when it will start hiking rates, as that only affects short-term traders, but rather how much it will increase rates. Consensus among the traders I spoke with is that the Fed will initially take a cautious approach and likely start with a 12.5bp or 25bp move so as not to spook the markets. The implied volatility of the data-driven approach has already begun to manifest itself: for quite some time, pundits have called for an increase in June 2015, and until recently the options market implied Fed rate probability of a hike in June was above 50%, but the slowdown in US economic growth has seriously put that time frame into jeopardy. Some have suggested that after five years of solid growth, it is time for the Fed to raise rates, but I disagree, in that the time frame is irrelevant. The Fed has projected its intention of following the data closely and that does not mean averaging over the past five years – it means looking only at recent numbers. The Fed has adopted a deep exponential weighting of information in its very Bayesian approach to monetary policy.

A harder question to answer is once the Fed sets out on tightening, when will it stop increasing rates? Some have argued that they see stability with wage growth at 3.5% based on older Fed speeches and recent statements by chair Janet Yellen. But one trader I spoke with feels that wage growth is a red herring: relentless technological advancements have made return on capital/investment a much more important indicator than return on labor.

Another dynamic the Fed is monitoring closely is the US dollar’s rise. Although off about 5% from its high in March, the dollar is up almost 20% from a year ago. The strong dollar does not just affect US exports – a strong US dollar puts increasing pressure on foreign dollar debtors and can have a disproportionate impact on emerging markets. The pace of this dollar rally is worrying to some as it may be reciprocated by an equally steep decline.

Most hedge fund managers I spoke with agree that the dollar is not done rising. The euro has plenty of downward pressure: the European Central Bank is lowering rates, even though some in Germany are already calling for relaxation of their quantitative easing policy by summer’s end. Additionally, there is a real price for Europe to pay if it hopes to avoid a possibly devastating ‘Grexit’. Behind the dollar is the geopolitical stability and military strength of the US. With America’s physical distance from much of the turmoil in the world and its economic diversity, the dollar’s strength appears secure to many traders. Most of those I have spoken with agree that the Fed does not want a rapidly increasing dollar and is also concerned about too strong a dollar.

Contango

That oil is fungible has been put to the test: it actually needs to be stored somewhere and as one trader joked recently, the US has just about run out of places to hide it. This surplus makes an oil spike unlikely even if Saudi Arabia reverses course and backs off production. Every manager I spoke with agreed that the US economy has not seen the benefits of the drop in oil – the extra cash in consumers’ pockets has not yet manifested itself in spending. It seems that Americans have decided to increase savings or pay off debts rather than spend the extra cash, for now. At some point, they will increase spending and that should result in more upward movement in corporate earnings and equity levels.

Risks

Some of the concerning risks mentioned to me include liquidity, an equity correction and that the Fed keeps stringing us along and does not raise rates after all. If the Fed does raise rates in the near term, they will keep a very close eye on liquidity levels. Updated regulatory requirements on US money markets start taking effect later this year, already prompting some of them to close shop. If there is a liquidity crisis following even a small rate rise, we could see a quick reversal of Fed intentions back to near zero rates. Such a move would be consistent with their new approach of “data-driven decisions” – if the market tells them to keep rates low because of liquidity or other issues, they probably will.

## Correlation Doesn’t Measure What You Think!

This article originally appears in my risk.net column in February, 2015, which you can find here.

Take a quick look at the two panels of Figure 1 and estimate the correlation for the two funds in both panels. Really, please do it now. What’s your gut feel of the correlation of each set? If you are like virtually everyone I asked, it is quite obvious that the two funds in the left panel are uncorrelated or possibly negatively correlated while those in the right panel are highly correlated with each other. Estimates for the left range from zero to -0.7, and estimates for the right panel are often above 0.7. In reality, though, the funds in the left pane have a return correlation of +0.95 and the correlation for the set on the right is -0.92. That’s right: the funds on the left are positively correlation, and quite highly, while the funds on the right are negatively correlated.

In the left panel, over this simulated three-year time period, Fund 1 shows a 49% total return corresponding to a 14% annualized return. Fund 2 suffers a 29.6% total loss, or an annualized loss of 11%. Now take another look at that left panel and estimate which of these two funds is more volatile. Some people interpret Fund 2 as more volatile because it suffers a loss while Fund 1 has stellar returns, equating “risk” or “loss” with volatility, but most people recognize that Fund 1’s volatility is at least somewhat greater than the volatility of Fund 2. In fact, the volatility of Fund 1 is 2.5 times greater than the volatility of Fund 2.

Figure 1. Left Pane: two funds diverging in value. The blue fund’s total return is 86% while the red fund’s total loss is 22%. Right pane: Two funds both increasing in value.

## The Beta/Correlation Language Barrier

This post originally appeared in my column in Risk Magazine in October, 2014.

Institutional investors tend to use a different vocabulary when speaking about risk than hedge fund managers. Institutional investors often talk about beta and benchmarks. Hedge funds talk about correlations and absolute returns. Betas and correlations are closely related to each other, but not the way most people think.

Institutional investors as a group first learned about quantifying risk when they were introduced to the concept of beta, probably by a long-only equity manager. The conventional interpretation, they were told a long time ago, is that beta measures the relative risk of their portfolio to their benchmark: if beta is greater than 1, the fund has more volatility than the benchmark. If it’s less than one, the fund has less volatility than the benchmark. This simplification has merit for many long-only funds, but it has led to misinterpretations and a false sense of security when alternatives are included. The issue is compounded by the introduction of a related measure – correlation – from hedge funds’ ubiquitous claims of “uncorrelated returns.” The relationship between beta and correlation is not well understood by many managers and, when misinterpreted, can lead to poor investment decisions.

## Reactions to the Russo Ratio

Investor Analytics’ publication of the Russo Ratio – the new analytic that separates risk into volatility and correlation components – seems to have created a bit of a stir. It’s been downloaded hundreds of times and we’ve gotten comments from many portfolio managers, risk managers and academics including some of the post prominent and well respected names in the industry.

We’ve consolidated those discussions into a blog post on our company’s site and we invite you to join the discussion. You can read about those comments and join the discussion here.

In the original paper, we introduced three new analytics: CCR – the Correlation Contribution to Risk, VCR – the Volatility Contribution to Risk, and the Russo Ratio: CCR/VCR, which provides a compact way of understanding how much diversification benefit is inherent in the portfolio. The short paper we published explains these measures and analyzes three different portfolios using them to show the effects of low and high diversification.

## Decoupling Volatility and Correlation: the Russo Ratio

A few weeks ago, a client asked a deceptively simple question: “You guys tell us how much risk our portfolio has, but can you tell me how much of that risk comes from volatilities and how much comes from correlations?” I sat there a bit dumfounded and finally said “well, that’s a pretty obvious thing to ask. I wonder why I’ve never heard that question before.” In 20 years of professional risk management, that really was the first time I heard that question. Risk attribution is a common thing to analyze, but it’s usually answered in terms of how much risk each part of the portfolio carries: e.g., what fraction of the risk is from stocks, from bonds, etc. or from my technology investments, from my energy sector investoments, and so on. But the basic formulas for calculating portfolio risk take three inputs: the investment amounts into different securities, the volatilities of those securities and the correlations between those investments. It makes a lot of sense to ask how much comes from each part, and I’m quite embarrassed that I never thought about asking it myself. So off we went to solve the problem…

## The Magic of Mean Reversion: More Than Meets The Eye

While each individual flip is random, the aggregate behavior is not.

I flipped three heads in a row on the crowded floor at a conference. No big deal. The next toss comes up…heads. Some eyebrows are raised as I prepare to flip again…heads. That’s 5 in a row. A small crowd gathers. “He’s due for a tail” someone says as my thumb flicks the coin into the air…heads. “What are the odds?!” I hear. Someone chimes in – “the odds are growing that he’ll get tails next time.” The coin flips through the air once more…

## The Pre-Mortem: sidestepping disasters before they happen.

Projects fail for many reasons, even after enormous effort and planning. Pre-mortems can help avoid disaster.

Let’s suppose your team needs to decide whether to pursue a project (investment related or not), and it’s time to discuss the risks. What’s the best way to do it? Gary Klein, a research psychologist and currently Senior Scientist at MacroCognition, found that ‘prospective hindsight‘ — imagining that an event has already occurred — increased the chances of identifying the reasons for failure by 30%! In any risk management context, that’s worth learning more about.

Enter the Pre-Mortem.

## Model Risk and Financial Charlatanism

But when we modeled it, the brakes worked!

One of the hot buzzwords in financial risk management these days is “Model Risk”, as if this concept is in some way new. Unfortunately, it’s only recently – a full 6 years after the onset of the Global Financial Crisis (GFC) – that the idea of Model Risk is getting wide coverage. The concept is rather simple: in essence, it says that a model is, well, just a model. It’s not reality. But since we quants / financial engineers / “rocket scientist” types tend to put things a bit more quantitatively than that, the notion of Model Risk includes what’s called “goodness of fit” or measures that assess the appropriateness of a given model for a given situation. They help you understand when the model may need tweaking, may no longer be appropriate or if it’s predictive value may have fallen too low to use. I take pride that Investor Analytics was the very first risk management specialist firm on Wall Street to actively share the results of our Model Risk measures. It wasn’t easy: for many years before the GFC people thought we were a bit nuts to call attention to the limitations of models in our industry.

## Russian Risk Rising

Sanctions’ Effects on Russian Markets. Source: WSJ

There are between 25,000 and 35,000 Russian troops amassed on the border with Ukraine, just a few hours’ unhindered drive from Kyiv, Ukraine’s capital. Last week, Putin sent a very strong signal about his intentions to invade the rest of Ukraine, but only to those who could hear it. There are two ways to say ‘Russian’ in their language: one way, “Rossisskii,” is used to describe any citizen of Russia regardless of their ethnicity. Mongols, Chechnyans, Russians and Ukrainians can all be “Rossisskii” if they carry a Russian passport.  And that’s how Putin always referred to citizens of his country, until last week. He suddenly switched to using “Russki,” the form that means ethnic Russian. He called Crimea primordial ‘Russki’ land and its main seaport, Sevastopol, a ‘Russki’ city. Most telling, he actually said Ukraine’s capital, Kyiv, is “the mother of Russki cities.” As this Washington Post article points out, this must have grated on the ears of any Ukrainian listening, as it was a revisionist reference to Kyiv’s role as the capital of the ancient Rus’ civilization. Peter the Great, the ruler of what was then the country of Muskovy, wanted to improve his country’s reputation by bolstering its historic credentials. He decided to rename his country by starting with Ukraine’s ancient name of Rus’ and adding a few letters. It caused an uproar among Muskovites at the time, but Peter prevailed and claimed his neighboring country’s richer and far more ancient heritage for his own. This Identify Theft would prove decisive in helping Peter establish his newly renamed country on the European stage. Peter stole Ukraine’s history and name; Putin now aims to take her cities.

Just a few days ago, NATO’s Supreme Allied Commander Europe said “the (Russian) force that is at the Ukrainian border now to the east is very, very sizable and very, very ready.” For their part, Ukrainian troops have begun digging anti-tank ditches and have placed giant jax-like concrete tank-blockers near the border. Today, the Ukrainian government started broadcasting messages preparing people for the likelihood of a massive invasion in the coming days.

The US and the EU have already levied sanctions for Russia’s actions in Crimea, and have threatened more if Russia pushes further. So far, the sanctions target those in Putin’s inner circle and include visa bans and asset freezes that have even impacted credit card transactions. Because significant equity in Bank Rossiya and Sobinbank is owned by those on the US sanctions list, MasterCard and Visa have stopped authorizing transactions for credit cards issued by those banks. In Brussels today President Obama said that Moscow must consider “the potential for additional, deeper sanctions” if it pushes further into Ukraine. He went on to say “we recognize that in order for Russia to feel the impact of these sanctions, it will have some impact on the global economy as well as on all the countries represented here today.” Just a few days ago US Energy Department said it would permit exports of liquefied natural gas from Oregon to help European nations struggling with supporting further sanctions because of their reliance on Russian energy.

The market has not been kind to Russia in the past few weeks, as this Twitter post from the Wall Street Journal shows: equity markets down 13%, interest rates up about 150 bps across the curves, and the Ruble continuing to fall. In addition, Russia has admitted that it expects between \$65B and \$70B of foreign capital to leave Russia before the end of March!

If Russia does indeed invade more of Ukraine, we expect significantly more in terms of sanctions and capital flight. As I pointed out in an earlier post, it’s easy to be fooled into thinking that since a portfolio has no direct exposure to Russia that it won’t be affected by these events. In reality, many countries, including Germany, are significant trading partners of Russia’s and their equity and fixed income markets would certainly suffer from contagion.

Stress Testing is a good way to simulate possible market effects of materially increased sanctions or even open warfare. As I pointed out in the last post, modeling would include spikes in energy prices and agriculture products. I think moves of 10% to as high as 50% are not out of the realm of possibility, depending on how the political and military situation plays out. Also be sure to stress correlations quite high between Russia and its main trading partners – Germany, Italy, and France to a lesser extent. Contagion to the interest rates and bond prices in Russia’s largest trading partners should not come as a surprise to anyone. “Flight to Safety” of capital out of Russia in excess of the \$70B already expected in the coming weeks would also be reasonable. In a full military escalation to open warfare coupled with seriously increased sanctions, prudent managers would also simulate a collapse of the Ruble, hyper-inflation and a modern Russian default.

Late on March 26, CNN reported that US Intelligence analyst say there’s a greater likelihood of a Russian invasion than previously believed. The House Armed Service Committee, when it learned of the report, sent a classified letter to the White House expressing concern. An unclassified version of this report said members feel ‘urgency and alarm’ about the information now in their possession.