Part 7: Conclusions

Daniel Zwirn, Jim Kyung-Soo Liew and Ahmad Ajakh | Published in The Journal of Fixed Income, Fall 2019


Given the less-transparent and less-liquid nature of the OTC trading markets, and given participants’ limited knowledge of the embedded risk therein, market-making is dramatically different than in stock markets. Price discovery, one of the most important features of an efficient capital market, presumes liquidity and sizeable volumes that are available on both the bid and ask side. Additionally, continuous and consistent liquidity is typically provided by a diverse set of actively trading participants. If the number of counterparties that trade becomes limited, markets begin to thin, and a “ghost market” eventually emerges in times of crisis; this is the potentially precarious outcome of the risks in the fixed income OTC markets.

Additionally, just because we did not experience a complete meltdown in a given sector of the bond markets, such as in auto loans during the 2008 financial crisis, does not preclude a meltdown for that sector during the next crisis. The auto loan market proved resilient during the 2008 financial crisis, but this sector has undergone secular changes that have significantly increased its risk profile:

  1. The large amounts of capital chasing yields that have flooded this market due to cheap loans; and
  2. Originators, responding to the increase in capital, utilizing aggressive techniques to make more loans (e.g., providing consumers longer payment periods or lower rates so they can acquire vehicles they would otherwise be unable to afford).

Lending money to weak borrowers with low credit scores and insufficient means to make payments was the original sin of the subprime crisis in 2008, and similar financial consequences will likely play out again.

Furthermore, these credit crisis indicators are not presently isolated to only the auto sector. As the number of corporate borrowers with a BBB rating rapidly grows and middle-market company leverage increases, there are an ever-growing number of ominous signs. Retail investors’ persistent appetite for innovative products that provide diversification benefits and increased yield has led to the proliferation of corporate bond mutual funds and ETFs. Although the precise allocation to corporate bonds should be calculated carefully, because many of these corporate mutual funds hold a portion of government bonds, the pattern of behavior is clear. Daily liquidity of mutual funds shares may appear to contradict the underlying OTC corporate bond trading liquidity, but the truth will be felt most painfully when we enter a material downturn. The promise of a liquid structure will disappear to the shock and dismay of most retail investors as they find themselves with no exit.

The takeaway here for retail investors is the immeasurable importance of comprehending the hidden dangers in products with asset-liability mismatches. Regulators also need to shoulder the responsibility of taking a proactive stance, rather than merely examining a financial crisis post mortem.

Another ominous sign in the present market is that credit agencies, tasked with accurately evaluating the risks in bond investments, continue to be shadowed by conflicts of interests, because they receive compensation for providing high ratings. Although the Big Three have moved into less controversial and easier to understand credit ratings methodologies, the new rating agencies have entered the markets and are moving towards those riskier issuances. These players are more than eager to gain market share. As a result, BBB securities comprise companies whose rating may not accurately reflect their true risk profile. These companies are likely closer to a junk rating rather than an investment-grade rating.

New regulations of financial institutions from a compliance standpoint, which gained strong momentum after 2008, may prove to ultimately be ineffective. This could be due to the fact that these newly minted compliance officers generally lack the requisite understanding of the market dynamics associated with OTC-trading bond markets and, therefore, possess an inadequate radar to detect and monitor risks.

Given the sheer size of the bond market and its concentration among several players, even the smallest perturbations of uncertainty in the economy or within this market will have substantial detrimental impact. Many pundits believe that financial crises arise from a run on the banks (Gorton 2018). These so-called runs on the bank arise from a lack of trust in the system (Akerlof and Shiller 2009). Essentially, once a counterparty or a client believes that a bank cannot be trusted, they will stop lending to that bank, as happened to Lehman Brothers during the most recent crisis, or the clients will withdraw their money from the bank. It is not a leap of the imagination to believe that a sudden inability to redeem mutual funds and ETFs will rapidly compound the mistrust of financial markets during market distress or give rise to the next enraged Occupy Wall Street movement.

To conclude our examination of some of the most important secular changes in the financial industry since the crisis of 2008, we particularly want to highlight how a lack of trust in the system could accelerate a financial downturn once it has been triggered. These important secular changes can be summarized, from a larger perspective, as a lack of liquidity in the bond market, because each of the secular changes that we examined will be a barrier to price discovery and investor confidence in the fixed income markets.

In this article, we highlighted five areas of secular changes that we believe will exacerbate the next inevitable downturn. What is not inevitable, however, is a financial catastrophe for investors; understanding certain risks can help investors navigate to safer harbors. Although we believe these excesses will fuel many losses in the next downturn, the consequence for investors can be manageable if they carefully consider (and act upon) the secular changes examined herein.


The authors would like to thank Bo Bao, Machine Learning/AI Engineer at, for her helpful editing and technical assistance.


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BRICS Emerging Markets Linkages: Evidence from the 2008 Global Financial Crisis
The Journal of Private Equity

ABSTRACT: The objective of this article is to examine the market integration among the BRICS (Brazil, Russia, India, China, and South Africa) emerging markets with respect to the global financial crisis of 2008 using cointegration analysis and factor analysis. The study finds that the BRICS emerging markets are cointegrated with each other during the pre- and post-2008 financial crisis period, and the markets have been moving toward greater integration after the 2008 financial crisis. The Granger causality and Johnsen cointegration test show that the stock markets of China, India, and Russia have strong short-term and long-run relationships. Factor analysis reveals that the BRICS markets have become closer after the financial crisis. Brazil, Russia, India, and China appear to have close causal linkages. The market integration among BRICS markets is tested using correlation, Granger causality test, Johansen’s cointegration test, and factor analysis. The correlation coefficient among the stock markets of Russia, India, and China increases as markets increase in trade and geographical closeness. This article has practical implications for investors, governments, and multinational companies.

Forecasting ETFs with Machine Learning Algorithms
The Journal of Alternative Investments

ABSTRACT: In this article, the authors apply cutting-edge machine learning algorithms to one of the oldest challenges in finance: predicting returns. For the sake of simplicity, they focus on predicting the direction (either up or down) of several liquid exchange-traded funds (ETFs) and do not attempt to predict the magnitude of price changes. The ETFs serve as asset class proxies. The authors employ approximately five years ( from January 2011 to January 2016) of historical, daily data obtained through Yahoo Finance. Using supervised learning classification algorithms, readily available from Python’s Scikit-Learn, they employ three powerful techniques: (1) deep neural networks, (2) random forests, and (3) support vector machines (linear and radial basis function). They document the performance of the three algorithms across four information sets: past returns, past volume, dummies for days/months, and a combination of all three. They use a gain criterion to compare classifiers’ performance. First, they find that these algorithms work well over one- to three-month horizons. Short-horizon predictability, over days, is extremely difficult, and thus the results support the short-term random walk hypothesis. Second, they document the importance of cross-sectional and intertemporal volume as a powerful information set. Third, they show that many features are needed for predictability because each feature makes very small contributions toward predictability. The authors conclude that ETF returns can be predicted with machine learning algorithms, but practitioners should incorporate prior knowledge of markets and intuition on asset class behavior.

The “Sixth” Factor—A Social Media Factor Derived Directly from Tweet Sentiments
The Journal of Portfolio Management

ABSTRACT: Institutional investors may have an unclear understanding of the role of social media in asset price determination. Although some of the top quant hedge funds use crowd-based information gleaned from tweets, this relationship may be opaque to the rest of our community. In this article, the authors attempt to clarify the confusion regarding social media sentiment and security return behavior. They show that, surprisingly, Tweet sentiments have significant power in explaining the time-series contemporaneous variation in daily stock returns, even in the presence of well-known equity factors. By examining direct tweet sentiments as provided by StockTwits, the authors claim to have identified a Social Media Factor, the “sixth” factor, and they highlight the distinctions vis-à-vis Fama–French’s factors.