Price distortions

Price distortions are apparent price-value gaps. Trading strategies that are based on such distortions rely less on information advantage than on consistent price monitoring, flexibility of trading, privileged market access, superior financial product knowledge and –  most of all – rational discipline in turbulent times. Price distortions arise from inefficient flows and prevail as long as a sizable share of market participants is either unwilling or unable to respond to obvious dislocations. There are many causes of such inefficiencies, including risk management rules, liquidity disruptions, mechanical rebalancing rules and government interventions.

The basics

What are price distortions?

In the present context price distortions are defined as deviations of quoted prices from a level that would clear the market if all participants were trading for conventional risk-return optimization. In short, they measure gaps between mark-to-market prices and a plausible range of economic values of a contract. The occurrence of distortions implies that market prices can deviate from fundamental value and evidently so.

Like information inefficiency, price distortions lead to a mispricing of financial contracts relative to their fundamental value. Unlike information inefficiency, this mispricing is not based on ignorance, but on ”inefficient flows”. These are transactions in financial markets that are motivated by objectives other than return optimization. In practice, one can observe many market flows and transactions that obstruct the alignment of price and value. Common causes or triggers for such “inefficient flows” include:

  • formal and rigid risk management rules across that apply to many institutions,
  • liquidity shocks, i.e. a sudden deterioration of the tradability of assets or the risk thereof,
  • mechanical allocation rules, for example of exchange-traded funds, indexed fund and related structured products, and
  • government intervention and regulation.

“Financial markets are open, adaptive, out-of-equilibrium systems that are subject to nonlinear dynamics, created particularly but not only by imitation and herd behaviour.”

Sornette and Cauwels, 2014

“The tendency for experimental markets for long-lived assets to price at levels that differ from intrinsic values is one of the most robust and puzzling results from research in experimental markets.”

Breaban and Noussair, 2015

Detecting price distortions

Unlike information-based trading, price distortion-based strategies do not require information advantage in respect to the traded contract. They do not focus on in-depth research of its expected value. Instead, these strategies ascertain some apparent price-value gap and market inefficiency. They subsequently use advantages in market access or in pricing know-how to extract value. Sometimes, trading speed (view post here) and financial leverage can be of the essence.

Detecting inefficient flows and related distortions is not trivial. Most of what is commonly called “market noise” is actually rational trading disguised by complexity (view post here). This makes it easy to underestimate markets. However,  price distortions frequently do arise pursuant to major information or price shocks that create a state of confusion or even panic. Moreover, trading in times of turmoil often bears high transaction cost, which deters market participants from immediately taking advantage of price-value gaps. In order to detect price distortions systematically one can take three different angles:

  • The first is to understand and identify the causes of distortions, such as institutional risk management constraints, market liquidity problems and so forth, which are explained in the sections below. If a market is being heavily influenced by any of these causes it is more probable that prices will be regularly distorted and that there will be payback subsequently.
  • The second angle are metrics of misalignment between prices and fundamental value, such as in financial bubbles (view post here). Diagnosing price distortions this way is not the same as estimating price-value gaps, as the latter would require superior information efficiency. Price distortions can be detected by conventional valuation metrics but with a focus on extreme price value gaps that associated with obstacles to arbitrage or trading.
  • The third approach is to investigate the time series pattern of asset prices. For example, higher-than-exponential asset price growth with apparent feedback loops is often an indication of an unsustainable asset price bubbles (view post here). Also, temporary mild explosiveness in asset prices or exchange rates in conjunction with relative stability in underlying fundamentals is usually indicative of short-term distortions (view post here). Generally, a self-reinforcing price dynamics that is not a reflection or cause of underlying value changes is prone to producing price distortions.

Price distortions prevail because most investors are either unable or unwilling to exploit them. This is very realistic.

  • The vast majority of investors is unable to exploit relative price distortions because their access to arbitrage capital and leverage is restricted. These restrictions can hamper even to sophisticated investors, particularly in times of financial turmoil. They are the very cause of persistent relative value opportunities, particularly in the fixed income space (view post here).
  • Meanwhile, many investment strategies explicitly disregard price distortions, and their flows may for some time overpower more subtle relative value flows. Simple trend following has been a common and successful algorithmic investment strategy (view post here) that deliberately blanks out the fundamental value of a contract altogether. Similarly, many discretionary traders follow “momentum strategies” based on a perceived dominant information flows (trading on news). Moreover, herding is a well documented  behavioural pattern in the investment industry (view post here) that can be efficient from the individual portfolio manager’s perspective, because it saves research costs (view post here). Herding can, however, lead to price distortions, particularly if its motivated by non-fundamental shocks in markets with limited liquidity and a homogeneous investor community, such as in corporate credit markets (view post here). Moreover, there is also reason and evidence of so-called “beta herding”, which means convergence of market betas of individual assets that arises from investors’ biased perceptions, such as overconfidence in predicting directional market moves (view post here). If assets have the ‘wrong’ beta subsequent market moves lead to price bias relative to underlying value and trading opportunities.

“Asset prices in some markets have persistently deviated from levels that would be consistent with the absence of arbitrage opportunities. Such distortions can occur when scarce funding or limited balance sheet capacity prevents investors from taking advantage of the resulting trading opportunities.”

BIS Quarterly Review, September 2015

Price distortions and risk management rules

Risk (management) shocks

The risk management rules of most institutional investors follow widely accepted standards. Yet, similar rules often coerce similar flows.  And one-sided flows in markets with limited liquidity can push prices far from fundamental values. In this way, conventional risk management rules can be a cause of distortions and even set in motion self-reinforcing feedback loops.

Prominent concepts for risk management are value-at-risk (VaR), a statistical measure of expected maximum losses at a specific horizon within a specific confidence interval and expected loss, a measure of expected drawdown in a distress case. These statistical assessments of risk rely on historical variances and covariances, and can be subject to sudden major revisions.

  • The half-time of many VaR measures is no more than 11 days, making them subject to sudden drastic re-assessments.
  • Even risk measures that rely on longer historical simulations have only limited data on actual crises and hence are very vulnerable to changes in assumptions and new crisis experiences (view post here).
  • Different types of statistical risk models tend to diverge during market turmoil and hence become themselves a source of fears and confusion (view post here).

Reliance on statistical metrics can give rise to so-called ‘VaR shocks’: If the estimate of the risk metric-surges, VaR-sensitive institutions automatically recalibrate the risk of their existing positions and subsequently have to reduce their holdings of assets (view post here). Put simply, if an institution has a fixed risk budget a doubling of the estimated value-at-risk or expected loss requires it to liquidate half of its nominal positions.

Analogously, many trading desks or asset managers set “drawdown limits”. These are loss thresholds for a portfolio’s net asset value beyond which traders must liquidate part of all of their positions. Managers are typically under obligation to cut risk regardless of asset value and return prospects. Hence, once the common drawdown limits are broken additional flows ensue in the same direction of the original loss, accentuating price movements for no fundamental reason.

“VaR is an untrustworthy measure of future market risk for one main reason: it is calculated by looking at the past.”

Pablo Triana, 2010

“We need half a century of daily data for the estimators to reach their asymptotic properties, with the uncertainty increasing rapidly with lower sample sizes.”

DNB statistical paper on VaR and expected loss (2016)

Feedback loops

Initial shocks to risk metrics can team up with other reinforcing dynamics to form feedback loops:

  • Dynamic hedging: Many institutions run explicit or implicit “short volatility” positions. Indeed, explicit and implicit short-volatility strategies seem to have expanded strongly in the wake of declining fixed-income yields. They pay steady positive risk premia in normal times at the peril occasional outsized losses. Short-volatility strategies give rise to feedback loops in two ways. From a macro perspective, there is a positive reinforcement between actual volatility and the scale of short-volatility strategies (view post here). From a micro or trading perspective, managers attempt to contain losses through dynamic hedging. These are transactions in underlying assets that reinforce initial market moves.  Dynamic hedging is common practice for option books but is applied widely in other markets. For example, U.S. financial institutions have historically been “short volatility” in respect to long-term interest rates, because of home owners’ option to repay mortgages early (view post here). In times of declining yields delta and probability of execution of this implied option is increasing, forcing institutions to hedge by further extending duration exposure. The probability of severe “convexity events” has been reduced since the Federal Reserve has bought a sizable share of mortgage backed securities from the market (view post here).
  • Credit risk: Risk management can also form feedback loops with credit risk, particularly country risk and counterparty risk.  A good illustration for this is the Credit Default Swaps (CDS) market. CDS are assumed to represent a measure of default risk. In practice this (less liquid) market can gap in large moves, simply as a consequence of one-sided institutional order flows, which themselves could be motivated by risk management or regulatory considerations (view post here). As CDS spreads themselves are used as a measure of credit risk, institutional flows and spreads can reinforce each other to form escalatory dynamics.
  • Public fear: Financial market turbulences and related publicity increases awareness of crisis risk.Fear of disasters, such as economic depressions or war, is more frequent than the actual occurrence of these events (view post here). In normal or good times, people tend to ignore disaster risk. As economic or political conditions deteriorate, people begin to consider disaster risk and the subsequently raise their concerns of disaster. If public fear of crisis is rising, financial risk managers experience pressure from investors, shareholders and even governments to position more defensively.
  • Redemptions: Significant declines in the net asset values of investment vehicles usually give rise to redemptions, often from investors that cannot afford or bear watching wealth dwindling beyond certain thresholds. This is supported theoretically and empirically for equity, bond and credit markets (view post here). In many cases funds provide daily liquidity and costs of redemptions are effectively borne by investors that do not redeem or redeem late. This creates incentives for fire sales and causes of price distortions (view post here). Indeed, the pro-cyclicality of redemptions is consistent with survey evidence of pro-cyclicality of equity return expectations of investors (view post here).
  • Leverage: Risk-reduction in banks and other financial intermediaries does not only constrain their own asset holdings but, indirectly, those of other market participants, particularly leveraged investors such as hedge funds. This creates both relative price distortions and high directional risk premia. Most obviously, limitations of arbitrage capital give rise to price differentials between contracts with similar risk profiles (view post here). Also, empirical analyses have found that the leverage provided by Broker-Dealers, i.e. their funding of others, is an important explanatory variable for the risk premium paid on equity and credit exposure (view post here). When credit supply is ample, risk premia and future excess returns are low. When credit supply is scarce, risk premia and future excess returns are high.

“Convexity hedging flows tend to exacerbate rate sell-offs especially in the 5-10 year part of the curve, widen swap spreads and increase short dated volatility.”

Bank of America – Merrill Lynch Research, 2013

Price distortions and market liquidity

Liquidity problems and liquidity risk

Market liquidity here refers to the costs of trading in and out of a security. Market liquidity risk denotes the risk that trading costs surge when the need for trading becomes more urgent (view post here). Both liquidity and liquidity risk influence prices. Most institutional and private investors are willing to pay a premium on securities with high and reliable liquidity and require a discount on securities with low and uncertain liquidity (view post here). This means that changes in liquidity or liquidity risk of a contract lead to a change in its price, irrespective of its expected discounted present value. Therefore, uncertain and unstable liquidity conditions lend themselves to price distortions. Small shocks can produce large price moves and apparent dislocations.

Moreover, poor liquidity can give rational price distortions when trades are motivated predominantly by other market participants’ positions and expected actions (view post here). In OTC (over-the-counter or bilateral) markets lack of liquidity means that dealers do not “buffer” flows much and institutional effectively investors transact with each other. In this situation, investors take each other’s bid and offers as signals and may operate under the laws of game theory. In particular, when investors observe each other’s selling pressure they can rationally transact at prices below true value and give rise to so-called “run equilibria”, self-reinforcing price dynamics away from fundamental value.

There is evidence that liquidity as a price factor and source of price distortions has increased since the 2000s:

  • Regulatory tightening after the great financial crisis has apparently discouraged risk warehousing of banks and made global liquidity more precarious (view postshere and here ). For example, in the U.S. the Volcker Rule has banned proprietary trading of banks with access to official backstops.  Market making has become more onerous as restrictions and ambiguities of the rule make it harder for dealers to manage inventory and to absorb large volumes of client orders in times of distress (view post here).
  • By contrast, the role of institutional asset managers as liquidity providers has increased(view post here). Investment funds often buy and sell with the market, chasing return trends (view post here), due to redemptions and reliance on collateralized funding. Also, asset managers often engage in in cash hoarding, which means that they sell more underlying assets in market downturns than is necessary to meet redemptions (view post here). This holds true particularly in markets with more precarious liquidity. On the whole, investment funds seem to make liquidity more pro-cyclical and may aggravate market price swings, thereby giving rise to upside price distortions in bull markets and downside price distortions in downturns.

“A rise in market illiquidity, which means a greater cost of trading, makes forward-looking investors require higher future yields on their investments for any given cash flows generated.”

Yakov, Mendelson, and Pedersen, 2014

Past experiences

Liquidity risk premia and related distortions have featured prominently in many major markets.

  • In developed foreign exchange markets liquidity shocks have been highly cross-correlated. In systemic crises FX liquidity shocks have in the past formed negative feedback loops with funding constraints and volatility, leading to escalatory dynamics and fire sales (view post here). Even a “normal” tightening in dollar funding conditions can trigger one-sided flows in FX swap markets, which serve as a conduit of secured dollar funding. Large one-sided flows can lead to a breakdown in the conventional non-arbitrage condition of the “covered interest parity”, leading to arbitrage or enhanced trading opportunities (view post here). Such opportunities can be measured by the “cross-currency basis” and have become common (view post here). Indeed, a new theory of risk-adjusted covered interest parity suggests that FX swap rates, i.e. the difference between FX spot and forward prices, deviate from risk-free interest rate differentials in accordance with the relative liquidity risk premia for the currency areas (view post here).
  • The 2013 sell-off in the U.S. treasury market has illustrated that dealers or intermediaries may reduce their own inventories and market making after risk shocks, thereby aggravating rather than buffering liquidity shocks (view post here). More generally, empirical research has shown that sudden large drawdowns in government bond markets are aggravated by poor liquidity (view post here). This tendency could increase overtime, as consequence of increased capital charges on market making, extended transparency rules for dealers, elevated assets under management in bond funds and the liquidity transformation functions of bond funds (view post here).
  • Emerging markets appear to be particularly vulnerable to liquidity events. Assets under management of dedicated EM funds have increased markedly since the 1990s. Trading flows are highly correlated due to a the usage of benchmarks, and EM asset prices and final investor flows tend to be pro-cyclical and mutually reinforcing (view post here). The discretionary decisions of fund managers seem to aggravate this pro-cyclicality: they usually increase cash holdings in times of market turmoil due to increased risk of future client redemptions (view post here). Local-currency emerging debt markets in particular have become more vulnerable to global liquidity and other market shocks, with foreign ownership being a key determinant of that vulnerability (view post here). As a consequence, global shocks can trigger sizeable relative price distortions between markets and currencies that feature high foreign participation and those that are more isolated.

” Both retail and institutional investor flows to emerging market assets, and the total returns on these assets in US dollar terms, are generally pro-cyclical.”

BIS Quarterly Review, September 2014

Price distortions and rebalancing rules

Rules overruling fundamentals

Most institutions and portfolio managers must abide by rebalancing rules. This means that they are under legal or reputational obligation to change portfolios in accordance with the conventions of their investment mandate. Such rules-based rebalancing can be quite mechanical. And mechanical rebalancing naturally gives rise to market flows and price formation that are unrelated to the fundamental value of the underlying assets. Hence, it is a source of price distortions.

  • The most common cause of rules-based rebalancing is benchmarking. Many investment managers are formally or informally benchmarked against some market index and often contain deviations of their returns from the benchmark index. “Underweights” in volatile but outperforming assets are the main risk of violating these margins, because the outperforming assets simultaneously gain weight in the benchmark. Hence investment managers often find themselves compelled to buy overpriced and risky assets merely to contain streaks of underperformance (view post here). Empirical research has confirmed a “low risk effect” in financial markets, i.e. the recurrent outperformance of low-risk versus high-risk assets, once both are scaled by volatility (view post here).
  • Benchmarking effects should not be confused with benchmark effects. Benchmark effects arise from changes in global securities indices that are commonly tracked by investment managers. In particular, the surge in passive investment means that a large share of institutional investors is under obligation to buy and sell in accordance with the constituents and weights used by benchmark indices, regardless of assets’ fundamental values (view post here). Moreover, benchmark companies revise indices regularly, causing re-weighting of sectors or countries that is not in proportion to market capitalization. Sovereign credit rating changes, for example, can establish or remove the eligibility of a country’s securities for inclusion in benchmark indices. There is empirical evidence that these changes induce sizeable portfolio re-allocations and international capital flows, entailing an outperformance of ‘upgraded’ assets at time of announcement and actual index adjustment (view post here). Upgrading here does not mean necessarily better asset quality, but rather greater demand due to the assets tracking the relevant indices and the asset’s share in these indices.
  • Strategic rebalancing can sometimes be caused by regulatory changes. This motive is important for tightly regulated institutions such as insurance companies and pension funds (view post here).
  • The rebalancing of exchange-traded funds (ETFs) is completely rules based. It can trigger price distortions particularly in the case of equity ETFs that are leveraged or inverse: Leveraged ETFs are subject to automatic rebalancing rules, requiring them to buy when prices rise and sell when they fall. As leveraged ETFs have become a significant factor in U.S. equity markets they can reinforce or even escalate large directional moves in the stock market, both through their own transactions and other market participants’ front running (view post here).
  • A more subtle rebalancing mechanism is the so-called “uncovered equity parity“. It suggests thatwhen foreign equity holdings outperform domestic U.S. holdings, USD-based investors are exposed to unwanted exchange rate risk. There is empirical evidence that this will trigger hedging flows. On aggregate these flows put downward pressure on the outperforming market’s currency (view post here).
  • High-frequency trading strategiesrespond at high speed to changes in prices using relatively simple strategies. Speed, rather than reflection, is of the essence. While related algorithms may produce satisfactory returns and help market stability most of the time, there is a non-zero probability of “glitches” that can escalate modest price changes toward systemically destabilizing events (view post here). Moreover, trading speeds have increased across market participants, probably increasing the risk of short-term liquidity events (view post here).

” Changes in the weights that a popular benchmark gives to different countries can trigger a similar rebalancing among the funds that track it and result in sizeable movements in international portfolio allocations and capital flows.”

Raddatz, Schmukler and Williams, 2015

Price distortions and government intervention

Political agenda, interventions and regulation

Governments occasionally seek to instrumentalize markets for political-economic purposes, such as affordable housing, currency undervaluations, or financial conditions-driven economic expansions. Resulting legislation, regulations, and interventions can have both intended and unintended consequences.

  • The most common example is government policies that influence interest rates. The dominant influence of central banks over short-term rates is well known, but since the global financial crisis both monetary and regulatory policies also seem to have played an important role in the compression of term premia (view post here), liquidity risk premia and credit risk premia. The pervasive influence of government policies over yields at all maturities arises not only from their direct influence on demand and supply but also from it their repercussions on the functioning of markets. This makes the valuation of many assets highly dependent on the underlying policy agenda. Doubts regarding this agenda could lead to disruptive re-pricing of duration risk (view posts here and here). Note that duration risk has a great bearing on many other financial claims, particularly low-beta high-quality stocks. The concept of equity duration represents stocks’ sensitivity to long-dated discount factors (view post here).
  • Financial laws or rules can severely impair liquidity and the functioning of markets. A drastic case would be the introduction of financial transactions taxes that has been discussed for some time in developed markets (view post here). A more subtle example for secondary unintended consequences are the effects of cheap financing and capital controls in China on demand for physical metals (view post here).
  • Government interventions often seek to “manage” a fundamental trend rather than stop or reverse it. The classical example is currency interventions, which often serve to “control volatility” or to temper the pace of appreciation or depreciation. When central banks “lean against the wind” with sterilized interventions they create a combination of price inertia and carry opportunity (view post here), enhancing the profitability of FX carry trades. Moreover, price distortions arise from the fear of announcement or execution of the intervention. Value generation along the fundamental trend can resume after the intervention has been implemented (view post here).
  • Financial regulation can lead to unintended price distortions. For example, the regulatory reform in the European life insurance industry (“Solvency II”) has enhanced the importance of market-based discount factors of the liabilities of some of the largest global bond investors.  This has accentuated the tendency of declines in (already low) long-term bond yields to escalate in feedback loops, as a consequence of large mechanical hedging flows with little consideration of fundamental asset value (view post here).

“Other things equal, lower interest rates make it easier to sustain or decrease debt, and require a more limited fiscal consolidation.”

Blanchard, Furceri and Pescatori, 2014