Financial marketsConstraints as a mechanism of Financial Panics in Africa

Kennnedy MuturiFebruary 1, 202344424 min

NOTE: This is the first of a two parts series titled, “Mechanics of Financial panics in Africa’s Financial Markets”

When your wants overlap with the wants of others, you are in some sense inextricably linked. Linked in the mundane sense that you both want the same thing, but also in a deeper sense that forces you to actually mimic their emotions and even their actions. 

How many of us waited in long food lines at the supermarket in early March as the world came to grips with the coronavirus pandemic’s global spread? Why were you waiting in those food lines? Did you independently come to the conclusion that your family was at risk of starvation if you didn’t rush to the market? More likely, you saw or heard about other people heading to the stores and intuitively you figured “well we better stock up too.” 

You want food, other people want food. It is very likely that there is enough food for everyone, but do people trust that others will behave rationally and show restraint to buy only what they need, or are they fearful that others may look to hoard as much as possible? If others are rushing to get food, then you need to rush to get food. As long as we all want the same things, we are all emotionally linked. 

A good example of this phenomenon in the financial world is a bank run. You have money in the bank, other people have money in the bank. Regardless of whether you independently feel that the bank has solvency concerns, if others start panicking about the bank’s solvency, then in a very real way you need to start panicking too. You need to panic because if others withdraw their money from the bank, then the bank may truly become insolvent, so you need to withdraw as well, hopefully before they do. In a completely rational way, you are forced to mimic others: you are forced to panic. 

Whether at the grocery store, the bank, or in financial markets, panic can be sparked by multiple people desiring the same resource (food, cash, safety). There is certainly a logic to these desires: securing food in bulk is a good idea in a pandemic, the bank really doesn’t have enough money to pay all depositors. But the dynamics favor first movers, which leads to participants leering at each other in a circle of mutual distrust, trying to discern if anybody else might jump–so that they might jump first. And as soon as somebody flinches, the run begins. This is an example of a prisoner’s dilemma, the classic example of this dynamic in game theory, where the very anticipation and fear of what others may do alters the optimal choices of each player individually. Anticipating the panic of others will often lead all players in a prisoner’s dilemma to panic themselves. Financial panics are a specific category of panic that are obviously relevant for investors. What are the mechanics of financial panics? There are two key concepts: constraints and diversification.

Constraints are limitations or restrictions that may get in the way of otherwise optimal choices. Many of the features we will delineate are desirable or even add more flexibility in normal times. They only manifest themselves as constraints when stressed conditions surface. Leverage and liquidity are good examples.

  1. Leverage

Taking leverage first: in normal conditions, leverage allows portfolios to be more flexible and less constrained. Leverage gives long/short equity managers the flexibility to tune their books to the desired size, and add securities without resorting to exiting positions in other parts of the book to fund those purchases. The entire concept of risk parity relies on using leverage to scale up exposure to lower volatility asset classes (in most cases fixed income) to match the volatility of higher volatility asset classes (in most cases commodities and/or equities). Managers also use leverage in basis trades, convertible arbitrage, and the list goes on. 

When financial conditions become stressed, commentators often focus on “leverage” when they are talking about weakness in the financial system. As we will see, leverage can become a source of weakness because it suddenly morphs from a flexibility-enhancer into a constraint for those employing it. During market shocks this sudden switch can lead to panic and a cascade of liquidations.

Start with an external shock to markets: Coronavirus fears send Africa’s equity markets downwards and demand for safe havens like bonds upwards. That’s not a panic, even if the size of those moves is substantial. It’s just a rational move driven by the anticipation of economic damage from a global pandemic. However, things cascade from there, and the initial risk aversion leads to even more selling, or “unwinding” in financial parlance. The unwinding process can quickly snowball, as we’ve seen in recent weeks. Trading floors, chatrooms, and blogs become a hotbed of gossip about unwinds: who’s unwinding? Did you hear firm X is unwinding? There was a big unwind today. 

What causes unwinds? As we’ve stated, one explanation is constraints. Constraints may cause a market participant to feel forced, or actually be forced, to unwind their portfolio, even if they don’t believe it’s the best move from an “optimal” long-term perspective.

Let’s use the above example of leverage. If you are levered and your portfolio takes a large mark-to-market loss, you might receive a margin call. If your losses continue, you get additional margin calls and, eventually, one you can’t meet. You have to sell your position (or your broker does it for you), even if you think it’s going to rebound.

Even in the case where your losses are modest, you fear additional losses in the future and look to reduce your positions and leverage to avoid future margin calls. If you were not levered, you would not be as constrained in your decisions; you might choose to hold onto your investments from a more stable footing. Where leverage once enabled flexibility, it now has the opposite effect of a pernicious constraint.


A second constraint is liquidity. Asset managers provide investors with various degrees of liquidity in their funds to get their capital back. In normal times, liquidity is a highly desirable characteristic for investors, and it factors strongly into the decisions of where institutions place their money. For mutual funds, access to capital is daily and redemption proceeds are determined based on the net asset value at the close of that day’s markets. More complex investments such as hedge funds may have weekly, monthly, or quarterly liquidity with varying notice periods. 

If a manager anticipates a large redemption, it will need to sell the securities in its portfolios to raise cash. The constraint of having to return cash to investors can force the hand of portfolio managers in ways that do not align with the fundamental views they would otherwise adhere to, especially if there is a mismatch between the liquidity provided to investors and the liquidity of the investments held in the fund. In the latter case, the manager may choose to sell the most liquid assets to raise cash, knowing less liquid positions may be even more challenging and punitive to exit. 

Even institutional asset owners that structurally have multi-decade horizons, such as endowments, foundations, and pensions, have ongoing liquidity needs for spending obligations, quarterly reporting requirements, or board pressures. Many college and university endowments, established to support their schools’ operating budgets, may need to raise cash, as they will be unable to cut spending despite recent losses incurred. Pension sponsors may face the reality of challenging contributions to their defined benefit plans, as revenues shrink for corporations and governments (due to tax receipt declines), which increases the need to raise cash to pay necessary retirement benefits. 

Performance of style factors** during Financial crisis- Aug 2007(MSCI*)

Source: Morgan Stanley Capital Index (MSCI)

*MSCI Africa Frontier Market index
**Cumulative returns of MSCI’s Africa Frontier Market Index Style factors in August 2007 and cumulative returns of Select MSCI’s Africa Frontier Market Index Style factors in March 2020. For a given factor, for each day T of the month we compute the arithmetic sum of its daily returns since the start of the month. We then divide this sum by the expected standard deviation of this sum which we approximate by root(T) x stdev where stdev is the daily standard deviation of the factor as reported by MSCI at the beginning of the month.

For example, in South Africa, whose superannuation fund industry is one of the Africa’s more admired retirement systems, the government has recently permitted those under financial stress to access up to $20,000 from their pension savings–another potential need to raise cash. In all of these cases, there are scenarios where managers of funds must anticipate redemptions regardless of their performance, if their funds offer better access to capital than others. Again, an example of a feature, liquidity, which during most times is highly sought after, morphs into a constraint.

Risk Guidelines

Finally, a number of hedge funds operate multi-manager platforms to deliver a diversified array of skill sets and investment styles to their investors. These teams may operate fairly independently from one another in the hopes that competition and economic incentives will reinforce a survival-of-the-fittest mindset. 

A feature of such platforms is often tightly implemented risk limits on the various investment teams, in an attempt to maximize diversification (you don’t want all of the teams expressing bullish healthcare bets at the same time for example) and to limit the downside of underperformers. It is common to see rules in place where a team’s capital allocation is cut by specific percentages at certain loss triggers. For example, if the portfolio is down 5% it needs to reduce in size by 20%, at a 10% loss the book needs to shrink by 50%, and a 15% loss the portfolio is liquidated and the team fired. 

During extreme market conditions, some portfolios (say, fixed income relative value) may experience meaningful mark-to-market losses, but no matter how much conviction one has that these trades will rebound and losses will be recovered, the risk constraints will force the portfolios to be cut during this drawdown and unable to participate if/when the markets normalize. Indeed, the very anticipation of hitting those loss thresholds may lead portfolio managers to cut positions themselves, rather than wait until they are up against more rigid risk limits.

In conclusion, market panics force constrained players to unwind, whether due to leverage, liquidity, risk guidelines, or other external or internal pressures. They simply can’t take any more losses, so they do something they may not believe is the right move in expectation: they unwind. This selling pushes prices down further, and eventually pressures the next layer of constrained players to unwind. And they also unwind, forcing the next, and so on down the line. 

Unwinds in this framework occur in the order of constraints, often eventually impacting even those with the strongest track records, soundest liquidity terms, and best governance: a weakness cascade. Now, this doesn’t mean all selling pressure or event unwinds are caused by constraints: some managers truly do have quite strong fundamental views that would cause the same set of decisions, or perhaps find the market conditions so unusual that they prefer to not express any significant view until normalcy resumes, but constraints are an often overlooked driver of market panics that warrant attention.

Kennnedy Muturi

Ken is a Quantitative Trader with experience in investments, quantitative finance, financial modelling and algorithmic trading in Global Investable Markets (GIM). He enjoys using Bayesian Statistics, Time Series and Machine Learning in developing Robust consistent Alphas in Equities Market, FX, ETPs and Derivatives instruments. He enjoys deep dives in understanding High Frequency Trading infrastructures and improving how the African financial markets work. He holds a Bachelor's in Actuarial Science from Strathmore Institute of Mathematical Sciences : An Executive Program in Algorithmic Trading (EPAT) certificate in Algo Trading from QuantInsti : A current MSc student in Financial Engineering at World Quant University.

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