Price distortion-based strategies are trading rules to apparent price-value gaps. They require consistent monitoring, flexibility, privileged market access, superior financial product knowledge and – most of all – rational attitude, rather than a great information advantage. 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. Their root causes can be risk management conventions, liquidity concerns, mechanical trading rules and government interventions.
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 “fundamentals” and evidently so. In this case prices would send misleading signals to the economy at large, for example about the default risk of a company or a country.
Price distortions are broadly neglected by financial market theory and may appear in conflict with market efficiency. Yet to understand their existence one simply needs to recognize that not every trade in financial markets in motivated by return optimization. In practice one can observe many market flows and transactions that obstruct the alignment of price and value. Common causes of such flows include:
- formal and rigid risk management rules across that apply to many insititutions,
- 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.
Moreover, experimental research has produced robust evidence for mispricing of assets relative to their fundamental values even with unrestricted active trading and sufficient information. Academic studies support a wide range of causes for such mispricing, including asset supply, peer performance pressure, overconfidence in private information, speculative overpricing, risk aversion, confusion about macroeconomic signals and – more generally – inexperience and cognitive limitations of market participants (forthcoming post).
Detecting price distortions
Unlike information-based trading, price distortion-based strategies do not require a great information advantage. They do not focus on in-depth research of the value of an asset or contract. Instead, these strategies ascertain some apparent price-value gap and 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.
Typically price distortions 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 deter 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 are distorted and that there will be payback subsequently.
- The second angle are metrics of misalignment between prices and fundamental value. 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 prices or exchange rates in conjunction with some judgment on underlying fundamentals can help detecting short-term distortions (view post here). Generally, a self-reinforcing price dynamics that is not a reflection or cause of underlying value changes is indicative of distortions.
Price distortions prevail because investors are either unwilling or unable to exploit them. Indeed, many investment strategies quite explicitly disregard them. 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 investor behavior pattern (view post here) that can be efficient from the individual portfolio manager’s perspective, because it saves research costs (view post here). Herding can 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).
Price distortions and risk management rules
Risk (management) shocks
Institutional investors abide by risk management rules that converge on widely accepted standards. These common risk management standards can coerce common flows. And one-sided flows in markets with limited liquidity can push prices far from fundamental values. In this way standard risk management rules can be a cause of distortions and even set in motion self-reinforcing feedback loops.
Prominent concepts for risk management today 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. The statistical assessment of risk relies 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’, where VaR-sensitive institutions have to reduce holdings of assets subsequent to an initial shock (view post here), because of a mechanical re-assessment of the riskiness of positions. 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”, which loss thresholds for a portfolio’s net asset value beyond which traders have to liquidate part of all of their positions. Managers are under obligation to do so 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.
Initial risk shocks to financial markets can “team up” self-reinforcing reinforcing dynamics within the financial system:
- Dynamic hedging: Many institutions run explicit or implicit “short volatility” positions. They pay regular risk premiums in normal times, but can incur occasional outsized losses. 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 has informally much wider application. For example, U.S. financial institutions have historically been “short volatility” in respect to long-term interest rates, having given home owners the choice of mortgage prepayments (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. While CDS are assumed to represent a measure of default risk, in practice this (less liquid) market can move in large installments, 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 into an 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 the dwindling of their wealth 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. In particular, this is an issue for leveraged investors. 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.
Price distortions and market liquidity
Liquidity problems and liquidity risk
When market liquidity is poor or uncertain small shocks can produce large price moves and hence dislocations. Liquidity here refers to the costs of trading in and out of a security, which have an important bearing on its price. The vast majority of institutional and private investors are willing to pay a premium for high and reliable liquidity and charge a premium for low and uncertain liquidity. Hence, both liquidity and liquidity risk (of trading costs rising when the need for trading increases) are important price factors (view post here).
Moreover, in OTC (over-the-counter or bilateral) markets lack of liquidity effectively means that dealers do not “buffer” flows and institutional 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 (view post here).
There is evidence that regulatory tightening after the great financial crisis has discouraged risk warehousing of banks and made global liquidity more precarious (view posts here 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 provider has increased (view post here). However, investment funds often buy and sell with the market, i.e. chase return trends (view post here), due to redemptions and their reliance on collateralized funding. There is also evidence and reason for asset managers engaging in cash hoarding, which means they are selling 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 price distortions.
Hence it is no surprise that liquidity premia and related distortions feature prominently in many major markets.
- For example, in developed foreign exchange markets liquidity shocks have been highly correlated. In systemic crises FX liquidity shocks have in the pastformed 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 forwards, which serve as a source of secured dollar funding and can respond more strongly than unsecured money markets. This can lead to a breakdown in the conventional non-arbitrage condition of the “covered interest parity”. Such events 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 can 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 susceptible 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.
Price distortions and rebalancing
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 conventions related to their investment mandate or investment philosophy. Such rules-based rebalancing can be quite mechanical. And mechanical rebalancing often 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 must 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 in order to remain in business (view post here).
- Benchmarking effects should not be confused with benchmark effects. The latter can also lead to rules-based distortions. They are changes in global securities indices that are commonly tracked by investment managers and exchange traded funds. 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 enforced 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 that when 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 strategies respond at high speed to changes in prices using most 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).
Price distortions and government intervention
Political agenda, interventions and regulation
Governments regularly 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 has long been accepted, 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 (forthcoming post). The pervasive influence of government policies over yields at all maturities 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).
- In some cases new 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 serve to “manage” a fundamental trend rather than stop or reverse it. The classical example is currency interventions. Outside regimes of explicit pegs and rigid target corridors, fx interventions more often serve to “control volatility” or to temper the pace of appreciation or depreciation. In these cases the price distortions arises 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).
- Regulation can also indirectly create causes for 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).