Market Outlook: Dive Deeper


Dive 1: Yield curve flattening

Back in 1980s, Professor of Duke University Cam Harvey, pioneered the idea that when yield curve inverts (the ten year treasury yield is lower than the three month) a recession in the economy follows. Typically we would need to see the curve inverted on a quarterly basis to expect a recession in the next 12 to 18 months.

As can be seen on the graph, at current levels, the ten year minus the three month rate is close to zero (38 basis points) but not below. Nevertheless it is very low number by historical standards and one oughts to be cautious going forward.

Dive 2: Performance of US Banks vs S&P since the financial crisis

The graph shows the performance of US banking stocks in relation to the S&P 500 since the last recession. Since then, banks have hardly managed to make new highs, a clear laggard in this decade.

Dive 3: Broken Liquidity

The nature of liquidity in today’s markets and the role of High Frequency trading is a topic of large debate in the financial industry. In addition, the dominant presence of passive investing creates another source of risk.

It is both unclear and untested what is going to happen in a market downturn.

The graph below shows the book depth in S&P e-mini futures since 2017. Even for such a small sample, one can see the remarkable drop in liquidity for  2018.

Source: CME Liquidity tool

Dive 4: CAPE ratio

The cyclically adjusted price-to-earnings ratio, commonly known as CAPE, Shiller P/E, is a valuation measure usually applied to the US S&P 500 equity market. It is defined as price divided by the average of ten years of earnings (moving average), adjusted for inflation. As such, it is principally used to assess likely future returns from equities over timescales of 10 to 20 years, with higher than average CAPE values implying lower than average long-term annual average returns.

Dive 5: Why managers fail to produce uncorrelated alpha?

The active management industry has been under criticism for several years now for a premise that cannot easily be defend: uncorrelated risk adjusted returns that beat traditional assets. Adjusted for fees it seems that active managers do not stand up to their promise.

But why is this happening?

In our view we highlight the two most important reasons:

  • The role of passive investing:

As passive investing becomes dominant, price discovery for individual stocks weakens due to “blind” index participation. As a result, it is of no surprise that large activist funds have massively underperformed the last few years (e.g. Pershing Square, Einhorn, Ichan).

  • The role of the Federal Reserve:

Since the financial crisis,  markets have had a remarkable increase under an extremely low volatility regime, a phenomenon never seen before. Additionally, during this period, drawdowns have been very small and their subsequent recoveries abnormally fast.

One reason is the faith that markets put in the Fed to support the system with extraordinary measures such as quantitative easing, verbal communications or other ways.

In such an environment, an active manager underperforms the index as rapid sell-off will make the manager to rebalance his portfolio that will eventually recover very fast (that is selling lower to buy higher).

Dive 6: Why don’t you use artificial intelligence and machine learning in your strategies?

Machine learning is nowadays heavily used in Wall Street and especially so in high frequency trading. In our view the application of machine learning, especially unsupervised, in portfolio management is destined to be limited, because:

  1. a) Strategies need to rely on sound economic and academic rational
  2. b) Black box models that rely solely on machine learning will always be skeptical with investors
  3. c) Such algorithms are notorious in over fitting as there are many degrees of freedom

Dive 7: What is systematic investing

In general there are two types of investing or trading: Discretionary and Systematic. In discretionary investing decisions are based on subjective criteria that cannot be quantified in a clear and consistent way. It relies on one’s own experience in the markets, her knowledge of the economy and her subjective view of what is expected from the markets going forward. On the other hand, systematic investing is a way of defining trade goals, risk controls and rules that investment decisions are made in a methodical way.

There are two important elements in systematic investing:

  1. One or more trading rules that produce a trading signal to buy or sell one or more assets. This can range from very simple to very complicated. It can be mathematical, statistical or heuristic in nature. The important key is that one can clearly identify (and repeat) the decision process in detail.
  2. A  risk or sizing methodology that allocates capital for a given investment decision produced by the ruleset

Typically, systematic investing is employed as a way to harvest risk premia or risk factors. The promise is that by applying systematic investing in risk factors one can be successfully exposed to alternative sources of risk.

It is important to note that systematic investing can be factor-based but not necessarily (e.g. high frequency, clone strategies, etc).

According to BarcleyHedge as of 2nd Quarter 2018, total assets under management for the hedge fund industry was $3014.3 billion, and the managed futures (systematic funds) industry was $369.5 billion. Note that total assets under management worldwide is around $135 Tr.

Dive 8: Factor based investing and alternative risk premia (history in a nutshell)

Financial markets are very different today than they were 10 or 20 years ago. As with every industry, technology and innovation (financial as well) have had their fair share in the financial markets. The  speed and availability of information, on a global scale and accessible to everyone, challenged traditional approaches in investing such as stocks and bonds. The financial crisis in 2008 have also shown that the classic “traditional portfolio” can be a source of great risk as correlation in periods of crisis increase substantially.

As a result of the financial crisis, what we call “traditional alpha” has been greatly challenged. A relentless search of new sources of alpha, what is often called “alternative alpha” or “alternative risk premia” started. The promise was that alternative premia will expose other sources of return and ideally uncorrelated sources of risk.

The idea was not new. During the nineties several researchers, mainly not economists, showed that markets are not efficient and that asset returns can be explained by other “factors” such as value, momentum, quality, carry (and more). The idea of factor-based investing was employed back then by few market participants mainly the more sophisticated and the ones that could take advantage of the advancements in computing and hardware. During this period, investing in these factors can be considered as “alpha” given that they were not widely known to the investment public.

The rise of ETFs since 2000 have allowed greater adoption of other risk factors and this was greatly intensified after the financial crisis. By 2010 a simple form of factor based investing was easily accessible to retail investors and the greater adoption of these factors gave another big advantage in the ETF industry (low fees, passive investing).

The popularity of ETFs and the accessibility of factor based investing to the wider audience reduced somewhat the returns of these factors what can be considered as “enhanced” or “alternative beta”.

How can popular factors such as momentum and value still work?

This happens because they bear uncertainty that many investors are not willing to take or be patient enough to withstand this risk. It is simply the same reason why investors will sell low and buy high.

Factor investing is not as exotic as one might thing. The advantage of a risk factor approach is lower correlation between alternative risk factors compared to correlation of traditional risky asset.

Historically, a portfolio of alternative risk factors (equally weighted, risk adjusted) outperformed a traditional portfolio of stocks and bonds.

Dive 9: What is hidden or shadow convexity

The term shadow convexity was originally opted by Chris Cole in 2015. The term convexity is used in derivatives to describe the nonlinear payoff an investor is exposed when buying or selling options. Positive convexity means profits are accelerated more than a linear increase in the underlying asset while negative convexity is the the opposite (losses are accelerated disproportionally).

“Shadow” or hidden convexity was used by Chris Cole to describe the non-linear risks our financial system is exposed to due to FED’s unconventional monetary policy for many years, the perception that central banks will always be there to support investors and other incentives given to investors to sustain disproportionate risk.

Simply speaking: “things will most probably be fine but if they go wrong the results will be catastrophic”

The ultimate non-market example of shadow short convexity is the failure of communism and the fall of the Berlin Wall in 1989 while a market example of shadow convexity is the role of portfolio insurance in the 1987 Black Monday crash. The portfolio insurance strategy relied upon selling increasing amounts of financial futures to protect against drawdowns in equity markets. The greater the decline in the market the more financial futures were sold to offset the loss. As a result, a non-linear feedback loop was created causing a -20% single day decline in the S&P 500 index.