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Researching 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 mandated to trade for the purpose of risk-return optimization. In short, they measure gaps between mark-to-market price and economic value. Distortions imply that market prices can be evidently misaligned and send misleading signals to the economy at large.

In practice, traders observe many market flows and transactions that obstruct the alignment of price and value. Their causes include (a) rigid institutional and risk management rules, (b) liquidity shocks, i.e. a sudden deterioration of the tradability of assets, (c) mechanical allocation rules of exchange traded funds, indexed fund, and related structured products, (d) government intervention and regulation, and (e) investment styles that are governed by statistical rules without regard of fundamental value.

There are three ways to detect price distortions:

  • The first is to understand and identify the causes of distortions, as summarized above and further explained in the subsequent sections A.-D. If a market is being heavily influenced by any of these causes it is probable that prices are distorted and that there will be payback subsequently.
  • The second way is to detect apparent misalignments of prices and fundamental value. Diagnosing price distortions this way is not the same as estimating price-value gaps, which is based on information efficiency (view summary here). A price-value misalignment is is only a valid indicator of distortions if it is large, conspicuous, and clearly explained by obstacles to aribitrage and or high trading costs.
  • And 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). Generally, a self-reinforcing price dynamics that is not a reflection or cause of underlying value changes is indicative of distortions.

Identifying price distortions by their causes

A. Price distortions from risk management rules

Portfolio managers can often be prevented from exploiting price distortions as a consequence of rigidities in their own institutional framework. Indeed the risk management of banks and asset managers can itself be a cause of distortions and even set in motion self-reinforcing feedback loops. As shareholders and investors cannot trust traders or portfolio managers with prudent risk management, they set rules, which tend to converge on widely accepted standards. If these common standards coerce one-sided flows in markets with limited liquidity, market prices can deviate from fundamental by wide margins.

The most prominent concept for risk management today is value-at-risk (VaR), i.e. a statistical measure of expected losses at a specific horizon within a specific confidence interval (a range marked by a high probability such as 99% or 95%). Alas, such statistical assessment of risk relies on (short-term) historical experiences, and can be subject to sudden major revisions. The half-time of many VaR measures is no more than 11 days. To make things even more tricky, 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 gives rise to so-called ‘VaR shocks’, where VaR-sensitive institutions have to reduce their holdings subsequent to an initial shock (view post here), because of a mechanical re-assessment of the riskiness of positions. Analogously, many trading desks or asset managers set “drawdown limits”, i.e. loss thresholds for a portfolio’s net asset value beyond which traders have to liquidate part of all of their positions, regardless of their assessment of value and return prospects.

Distortions arising from risk management can conspire with other “non-linearities” (disproportionate changes in prices in response to a fundamental shock) and negative feedback loops

  • Predominant “short volatility” or “short gamma” positions on institutions’ books may require dynamic hedging that reinforces initial market moves. This does not only concern outright options. For example, U.S. financial institutions have long 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 become a large holder of MBS that hedges little and infrequently (view post here).
  • Risk management and market frictions can also create feedback loops through 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.
  • The most general cause of non-linearity is the emergence of fear in the broader public. Fear of disasters, such as economic depressions or war, is more frequent than the actual occurrence of these events. In normal or good times, people tend to completely ignore these risk. As economic or political conditions deteriorate, people begin to consider and the subsequently raise their concerns of disaster. If public fear is rising, asset managers or even banks experience outflows and must on aggregate sell assets, whatever their price and value. This pro-cyclicality can explain large changes and some predictability in equity prices (view post here). It is consistent with survey evidence of pro-cyclicality of equity return expectations of investors (view post here).

The risk management of 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 clients that hold leveraged positions. Empirical analyses have found that the leverage of 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.

B. Price distortions from liquidity shocks

The costs of trading in and out of a security 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). There is evidence that regulatory tightening after the great financial crisis has discouraged risk warehousing of banks and made global liquidity more precarious (view post here ). The role of investment funds as liquidity provider has increased (view post here). And since 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, this makes liquidity more procyclical.

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. As systemic events FX liquidity shocks have occasionally formed a negative feedback loop with funding constraints and volatility, leading to escalatory dynamics and fire sales (view post here).
  • The 2013 sell-off in the U.S. treasury market has illustrated that dealers or intermediaries can on occasion 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), a tendency that 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, as are often hoarding cash in times of market turmoil, when there is 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.

C. Price distortions from mechanical rebalancing

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

  • The most common cause of rules-based rebalancing is benchmark effects. These are changes in important global indices that are tracked by investment managers and exchange traded funds. Benchmark companies revise indices regularly, causing frequent re-weighting of sectors or countries that is not in synch with market capitalization. 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.
  • Another common rebalancing mechanism is the so-called “uncovered equity parity“. The principle is 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).
  • 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).
  • Similar trading and escalation mechanics can occur in high-frequency trading. High-frequency 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).

D. Price distortions from government intervention 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 simplest example is government policies to reduce short-term interest rate or – increasingly in recent times – long-term yields. The compression of interest rates makes the valuation of many assets dependent on the underlying policy agenda. Doubts regarding this agenda could lead to disruptive re-pricing of duration risk (view posts here and here). And 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 functioning of markets. A drastic case would be the introduction of financial transactions taxes 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 more to slow a fundamental trend than to 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 or announcement of the intervention and 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).

Identifying distortions by price-value gaps

A discrepancy between a market price and estimated fundamental value can sometimes be indicative of price distortions. For this to be the case the estimation should be based on a broad consensus of method. Unlike in the case of information-based strategies the point is not to be beat the markets in the fields of research, but to diagnose an apparent large price value gap. Moreover, the gap alone is typically not sufficient evidence, as we may simply miss an important value factor. However, a wide price-value gap in the presence of high market information costs (a state of confusion over key value factors that are difficult to ascertain) and high trading costs (that deter market participants from taking advantage of the gap) makes it probable that a distortion is present.

The above sections (A.-D.) give examples how large value-price gaps can come about. It is also important to understand why they are not corrected immediately through “unrestricted” investors. The critical point to is that only a minority of investors require a deep understanding of the value of the contract they are trading. Indeed, many strategies quite explicitly disregard it. Simple trend following has been a common and successful algorithmic investment strategy (view post here) that does not even have to distinguish between a commodity, equity or fixed income contract. 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).

However, even if market participants have a good grasp at fundamental value they may find it dangerous or impractical to trade on it, because they must fear to be overwhelmed by a tide of non-fundamental flows.  An important point in case are commodity futures. These markets have broadened beyond the trading between suppliers and consumers. Instead, their pricing and flows now critically depend on hedge funds and commodity index funds, a development called “financialization” (view post here). It means that inflows, redemptions, and funding conditions of these funds have a strong bearing, even if they have no direct impact on demand and supply of commodities. Pure value-based trading is difficult in the context of risk and VaR limits.

The academic view

Academic literature has long realized that the serene frictionless rational-expectations model world fails to explain essential features of financial markets, such as excess volatility and the profitability of certain investment styles. This has led to an array of concepts classifying deviations from efficient pricing (references at the end of the page):

  • Financial frictions can prevent rational agents from exploiting trades. Examples of these restrictions have been provided for example by Bernanke and Gertler (2001) and Carlstom and Fuerst (1997).
  • Agents may be liquidity and credit constrained. A list of papers, by no means comprehensive, that uses related ideas to explain financial volatility and its effects on economic activity would include the work of Brunnermeir (2012); Brunnermeir and Sannikov (2012); Farmer (2013);Geanakoplos (2010); Gu and Wright (2010) and Rochetau and Wright (2010).
  • Agents may not always act rationally in a pure economic sense. Examples of non-rational agents include Barsky and DeLong (1993), who introduce noise traders, as well Bullard, Evans, and Honkapohja (2010) who study models of learning where agents do not have rational expectations.
  • Agents may have preferences that differ from classical utility maximizing assumptions. For example there may be habit persistence in preferences as in Campbell and Cochrane (1999), or non time-separable preferences as in Epstein and Zin (1989, 1991) or more complex aspects of behavioral finance, as surveyed by Barberis and Thaler (2003).

References without link:

Bernanke, B. S., and M. Gertler, (2001): “Should Central Banks Respond to Movements in Asset Prices?,” American Economic Review, 91(2), 253–257.

Barberis, N.,and R. Thaler (2003): “A survey of behavioral finance,” in Handbook of the Economics of Finance, ed. by R. Stulz, and M. Harris. Noth Holland, Amsterdam.

Barsky, R. B.,and J. B. DeLong (1993): “Why Does the Stock Market Fluctuate,” Quarterly Journal of Economics, 107, 291–311.Brunnermeir, M. K. (2012): “Macroeconomics with Financial Frictions,” in Advances in Economics and Econometrics. Cambridge University Press.

Brunnermeir, M. K., and Y. Sannikov (2012): “Redistributive Monetary Policy,” Paper presented at the 2012 Jackson Hole Conference, August 31st – September 2nd 2012.

Bullard, J., G. Evans, and S. Honkapohja (2010): “A model of near-rational exuberance,” Macroeconomic Dynamics, 14, 106–88.

Carlstom, C., and T. S. Fuerst (1997): “Agency costs, net worth and business fluctuations: A computable general equilbrium analysis,” American Economic Review, 87(5), 893–910.

Campbell, J. Y.,and J. H. Cochrane (1999): “By force of habit: A consumption-based explanation of of aggregate stock market behavior,” Journal of Political Economy, 107, 205–251.

Epstein, L., and S. Zin (1989): “Substitution, Risk Aversion and the Temporal Behavior of Consumption and Asset Returns: An Empirical Analysis.,” Journal of Political Economy, 99, 263–286.

Farmer, R. E. A., (2013): “Animal Spirits, Financial Crises and Persistent Unemployment,”Economic Journal, 568.

Geanakoplos, J.(2010): “The Leverage Cycle,” in NBER Macroeconomics Annual 2009, ed. by D. Acemoglu, K. Rogoff, and M. Woodford. University of Chicago Press.

Gu, C.,and R. Wright (2010): “Endogenous Credit Cycles,” University of Wisconsin, mimeo.

Rochetau, G.,and R. Wright (2010): “Liquidity and Asset Market Dynamics,” University of Wisconsin mimeo.


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