270 Int. J. Behavioural Accounting and Finance, Vol. 3, Nos. 3/4, 2012

Copyright © 2012 Inderscience Enterprises Ltd.

Asymmetry in market efficiency across economic states: explanation and implication

Yacine Hammami Department of Finance, University of Tunis, ISG Tunis, 41, Rue de la Liberté, Cité Bouchoucha 2000, Le Bardo, Tunisia E-mail: hammami_yacine@yahoo.fr

Abstract: The empirical financial literature has recently suggested that the US stock market might be efficient in bad times and inefficient in good times. This article explains why some psychological phenomena such as wishful thinking, overconfidence and the house money effect might cause deviations from full rationality principally in good times and not in bad times. Furthermore, this article gives several reasons why the arbitrage process might be effective in bad times and limited in good times. These results are important both for policymakers and for portfolio management.

Keywords: efficiency; overreaction; investor psychology; limits of arbitrage.

Reference to this paper should be made as follows: Hammami, Y. (2012) ‘Asymmetry in market efficiency across economic states: explanation and implication’, Int. J. Behavioural Accounting and Finance, Vol. 3, Nos. 3/4, pp.270–279.

Biographical notes: Yacine Hammami is an Associate Professor of Finance at the University of Tunis, ISG Tunis. His research interests fall broadly into the area of asset pricing theory and financial econometrics.

1 Introduction

Market efficiency is one of the most critical issues in economics and finance. Fama (1970) underlines that there are three versions of market efficiency: weak-form, semi-strong-form and strong-form. As pointed out by Grossman and Stiglitz (1980), strong-form market efficiency, which is based on the premise that stock prices reflect public and private information, is unrealistic given its restrictive hypotheses. Also, semi-strong-form market efficiency is very complicated to deal with in empirical work given many methodological concerns (see, e.g., Fama, 1998). That is why this paper focuses on weak-form market efficiency, which is based on the simple statement that stock prices reflect all information that can be extracted from past returns.

Fama (1991) stresses that the empirical tests of weak-form market efficiency are conducted by investigating the time-series and the cross-sectional predictability of security returns. Many empirical papers have recently challenged the presence of time-series predictability in stock returns (Bossaerts and Hillion, 1999; Goyal and Welch, 2003; Boudoukh et al., 2008; Ang and Bekaert, 2007). In addition, once we relax the

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hypothesis of constant expected return in the random-walk model, time-series predictability can imply either market efficiency or market inefficiency (Fama and French, 1988).

Alternatively, as underscored by Barberis and Thaler (2003, p.1223), “empirical studies of the behavior of individual stocks have unearthed a set of facts which is altogether more frustrating for the rational paradigm. Many of these facts are about the cross-section of average returns: they document that one group of stocks earns higher average returns than another. These facts have come to be known anomalies because they cannot be explained by the simplest and most intuitive model of risk and return…the capital asset pricing model.”

On the one hand, there is a body of work that proposes a risk-based explanation of the anomalies. The rationale is that the market beta is insufficient to account for the cross-sectional differences across expected stock returns. Many papers show that implementing other asset pricing models that summarise better systematic risk than the capital asset pricing model (CAPM) do a good job in explaining many anomalies such as size, value and momentum (see, e.g., Pástor and Stambaugh, 2003; Vassalou, 2003; Petkova, 2006; Liu and Zhang, 2008).

On the other hand, there is a strand of literature that proposes a behavioural-based explanation of the anomalies. Lakonishok et al. (1994) argue that the past performance of glamour stocks (those characterised by low book-to-market, large market equity or high six-month prior returns) is extrapolated too far into the future, which makes contrarian strategies or momentum investing profitable regardless of risk (see, e.g., Daniel et al., 1998, 2001; Hong et al., 2000; Barberis et al., 1998; Lemmon and Portniaguina, 2006).

Hammami (2011) emphasises that addressing this issue separately across economic states might bring about more unambiguous results. Indeed, he documents an intriguing finding for the US stock market: some anomalies such as the book-to-market and size effects are explained by risk in bad times and by investor sentiment in good times. This outcome hints at the possibility that the stock market might be efficient in bad times and inefficient in good times. This article is an attempt to reconcile this empirical finding with the extensive literature on behavioural and rational theories.

This article is organised as follows. Section 2 explains why irrational traders might cause persistent deviations from fundamental value mostly in good times. Section 3 explains why rational traders are not able (or not willing) to remove mispricing in good times. Section 4 outlines the practical implications of asymmetry in market efficiency. Section 5 concludes.

2 Psychology

2.1 Moods and the real economy

Exuberance essentially means enthusiasm and optimism, which naturally appear in good times when the economy’s prospects are good. For this reason, it would not be surprising if the ‘irrational exuberance’ of Shiller (2000) or other theories based on overreaction comes out specifically in economic booms instead of in financial crises, as the former are scenarios in which investors become more cautious about future investment opportunities.

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The foundation of this theory might be moods and feelings: investors tend to be in bad moods during recessions and in good moods during periods of economic expansions. According to Wright and Bower (1992), and Hirshleifer and Shumway (2003), individuals tend to be more overconfident when they are in good moods than when they are in bad moods. Hirshleifer and Shumway (2003, p.1022) claim that “Individuals in bad moods, who process information more carefully, should react more strongly to truly relevant news…individuals in good moods should be more prone to reacting to irrelevant announcements.” In the same vein, based on the psychological evidence presented by Petty et al. (1991), Hirshleifer (2001, p.1551) asserts that “bad moods are associated with more detailed and critical strategies of evaluating information.”

This connection between economic recessions, bad moods and investors’ ability to process information more reasonably explains why the stock market might be efficient in bad times. On the other hand, the connection between economic booms, good moods and overconfidence explains why the stock market might be inefficient in good times.

Indeed, in good times, a piece of good news about some corporate earnings makes people overexcited about the corresponding stocks. As new public information shows another round of earning or price increase, investors get more overconfident about their private signals, thereby triggering ‘positive feedback trading’ (Daniel et al., 1998, 2001). Their exuberance or the interaction between ‘biased self-attribution’ and overconfidence pushes prices too high relative to their fundamental such as earnings. Representativeness can also be the reason why investors overreact to some assets such as ‘glamour’ stocks, as many rounds of good earnings or successive high returns make positive feedback traders extrapolate past performance too far into the future (Delong et al., 1990b; Barberis and Thaler, 2003). Furthermore, the positive feedback reinforces the firm’s stance and reputation with regard to its stakeholders which can affect positively its cash flow, thereby exacerbating and maintaining positive bubbles for a long time (Subrahmanyam and Titman, 2001; Hirshleifer et al., 2006; Ozdenoren and Yuan, 2008). When investors are faced up with the evidence that ‘glamour’ stocks are overvalued, they overreact by buying the ‘out of favour’ stocks (Lakonishok et al., 1994). ‘Contrarian’ investors who bet against irrational investors make the ‘out of favour’ stocks profitable in good times, even though there is no concomitant increase in their relative systematic risk.

In bad times, such a positive feedback trading is inconsistent since investors are likely to be more careful and more rational. On the contrary, there is a possibility that confronted to a deep recession, investors become too pessimistic and too averse to losses, which might generate negative feedback trading as stressed by Shiller (2003), and Ozdenoren and Yuan (2008). Hence, the alternative to the efficient market hypothesis in bad times would rather be the underreaction theory or the loss aversion theory, more than the overreaction theory. Some papers suggest this possibility. Theoretically, Veronesi (1999) demonstrates that investors may underreact to good news in bad times due to the high uncertainty about the state of the economy. Kogan et al., (2006) show that the price impact of pessimistic investors in bad times can persist even if they do not survive in the long run. Baker and Wurgler (2006) highlight empirically the tendency of the market to undervalue stocks in bad states. Nevertheless, the empirical results in Hammami (2011) substantiate ‘efficiency’ over behavioural theories in bad times. The reason could lie in the fact that the initial fall in stock prices cannot leave arbitrageurs and smart money insensitive to these huge investment opportunities, which prevents the occurrence of negative bubbles. After all, this initial tumbling in stock prices is just the translation of the premium expected by risk-averse investors to hold risky stocks in these tough times.

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Since investors are likely to implement accounting and financial measures to assess systematic risk (Campbell et al., 2010), they consider stocks with high fundamental to price ratios as risky [which is confirmed in Petkova and Zhang (2005) and Hammami (2011)]. As noted by Cochrane (2001), investors who buy value stocks in a bottom of a recession actually bear a big risk because “these are companies with years of poor past returns, years of poor sales”. That is why, value stocks are on average profitable in bad times, because they entail a higher risk than the safer growth stocks.

2.2 A natural implication of time-varying risk-aversion

The fact that risk-aversion is very high in bad times (Campbell and Cochrane, 1999; Duffee, 2005) generates high discount rates that tend to be driven primarily by systematic risk. In contrast, low discount rates in economic booms, due to low risk-aversion, produce high stock prices that tend to trigger positive feedback trading and speculative bubbles. Why would this be a natural consequence? The model developed by Barberis et al. (2001) based on the ‘house money effect’ put forth by Thaler and Johnson (1990) could answer this question. Barberis et al. (2001) argue that after many dividend increases (which are likely to occur in good times) investors tend to push stock prices too high due to the ‘house money effect’, while after successive bad dividends (which are likely to occur in bad times) investors act in a more risk-averse manner.

2.3 Overreaction to bad news in good times

According to Veronesi (1999) and Ozoguz (2009), the price of an asset is more sensitive to news in good times than in bad times because “the equilibrium price of the asset is an increasing and convex function of investors’ posterior probability of the high (good) state” [Veronesi, (1999), p.977]. Specifically, Veronesi (1999) argues that when investors in good times start off to be uncertain about the state of the economy, they have a tendency to overreact strongly to bad news, as investors seek to hedge against the higher risk they take on. The results in Hammami (2012) give support to this argument.

2.4 Liquidity, overconfidence, and credit cycle variations

Ruckes (2004) shows that banks involved in intense competition during economic expansion have the tendency to lend to lower-quality borrowers. Paradoxically, they tend to reject good credit in recessions, as competition lessens. These credit cycle variations could be a factor generating overreaction in good times and market efficiency in bad times. Indeed, the liquidity overflow in good times could fuel investor overconfidence, thereby generating overreaction in good times. In contrast, the lack of liquidity in bad times is likely to make investors more ‘buying beware’ when they invest in risky stocks.

The empirical results in Hammami (2012) bear out this intuition. In fact, he finds that during periods of monetary tightening, in which the US Federal Reserve increases interest rates due to liquidity overflow, average stock returns seem to be driven primarily by investor sentiment. On the contrary, in periods of monetary easing, in which the US Federal Reserve decreases interest rates due to credit crunch, average stock returns seem to be driven primarily by risk.

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3 The arbitrage is limited essentially in good times

The premise of efficiency-based theories is that as soon as a stock price deviates from its fundamental value due to the behaviour of irrational traders, smart money grabs the arbitrage opportunity and brings the price back to its fair value. In contrast, behavioural theories bring to light many cases in which the arbitrage process is limited, making the price impact of irrational traders long-lasting. Below are some suggestions explaining why the arbitrage process might be limited mostly in good times.

3.1 Long horizons in bad times and short horizons in good times

An important factor could lie in the tendency of investors, when they buy stocks, to have long horizons in bad times and short horizons in good times. In the midst of an economic recession, as stocks get cheaper, investors who buy stocks are likely to be motivated by large returns in the long run, when the stock market bounces back as the economy recovers. In contrast, it seems that in good times, investors who buy stocks are attracted by the short-term success of positive bubbles. This can explains why the ‘noise trader risk’ cited by Delong et al. (1990a) and the ‘synchronisation risk’ documented by Abreu and Brunnermeier (2002) could limit the arbitrage principally in good times.

More specifically, when a stock is undervalued in bad times, investors run a ‘noise trader risk’ because the too-pessimistic investors might drive stock prices down and give rise to a risk that they themselves create. Given the long-horizon profile of investors in bad times, smart money is likely to be willing to run such a risk in the short-term (i.e., to tolerate short-term decline), because they intend to hold this stock for the long-term. For the same reason, an arbitrageur might accept investing in an underpriced stock before its peer and hence neglect the ‘synchronisation risk’. In contrast, in good times, Delong et al. (1990b) argue that smart money, aware of the existence of a bubble, contributes to the purchase of stocks expecting further price increases, instead of short-selling them to wipe out the mispricing. Here, ‘the noise trader risk’ as well as the ‘synchronisation risk’ might matter, since arbitrageurs short-selling overpriced stocks bear the risk of further price increases, which might force them to liquidate their position at a loss given short-sale constraints (margin requirements, legal restrictions, etc.).

Besides, the agency problem raised by many articles, such as Shleifer and Vishny (1997), may have less consequence for stock prices in bad times. The point is that given the lack of liquidity in bad times, people who agree to not withdraw their money from institutional funds in these tough periods are likely to look for returns in the long run; therefore, they can be convinced by managers that investing in risky stocks is profitable, even if the arbitrage position taken by the fund is under pressure in the short-term. On the contrary, as institutional investors’ clients tend to have a short horizon in good times, they will be very upset if their fund experiences successive months of negative returns, while the market enjoys the ephemeral positive returns inherent in the bubble (Shleifer and Vishny, 1997).

3.2 Short-sale constraints and heterogeneous beliefs: a typical theory for good times

Miller’s (1977) theory about short-sale constraints and heterogeneous beliefs is based on the premise that when prices reflect only overoptimistic investors’ opinions due to

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short-sales constraints, stock prices tend to be overvalued relative to their fundamental value. This theory is explicitly designed to identify the causes of positive bubbles and the persistence of the overvaluation of stocks, which is likely to occur in good times.

Because stocks tend to be undervalued in bad times given pessimistic investors and overvalued in good times due to the presence of optimistic investors, the arbitrage process consists of buying undervalued stocks in recessions and short-selling overpriced stocks in booms. While it is possible to take a long position in bad times, arbitrageurs may find many constraints if they short-sell a stock in good times (Ofek et al., 2004; Nagel, 2005; Berkman et al., 2009). Indeed, in the midst of a positive bubble, when a risk-averse arbitrageur shorts an overvalued stock at a given price, anticipating price depreciation, the risk that positive feedback traders will increase the price makes the potential loss huge [unlimited, as stressed by Shiller (2003)].

In addition, as underscored by Miller (1977), when some bullish investors get overexcited by some stocks, they may end up holding all the outstanding stocks. Therefore, smart money aware of the arbitrage opportunity may find it difficult to short-sell the stocks, which ultimately may make them unable to eradicate the mispricing. The high lending costs and legal restrictions also can prevent arbitrageurs from seizing arbitrage opportunities (Liu and Longstaff, 2004; Bhojraj et al., 2009). The fact that many institutional investors are not allowed to short-sell stocks (Nagel, 2005) also inhibits the arbitrage process and prevents bad news from being reflected in stock prices (Hong and Stein, 2003).

Conversely, because in deep recessions some investors tend to be too pessimistic and inclined to oversell some stocks, the simple way to grab the opportunity for smart money is to buy these undervalued stocks and hold them until the economy recovers, which is plausible if we assume that in bad times arbitrageurs have long horizons. Bear in mind that contrary to short-selling, only the original investment is exposed when investors buy stocks. Moreover, there is no problem of margin calls when investors take long positions. Finally, although some stocks might be overpriced in bad times, it is implausible that:

1 prices will go up further triggering a positive bubble in bad times, making the potential loss unlimited for short-sellers

2 one investor holding all the stocks at a persistent too high price (refusing to sell these stocks to provide itself with liquidity; a precious scarce good in bad times).

3.3 A large fraction of investors must be overconfident: a realistic hypothesis in good times

Daniel et al. (2001) argue that one condition for equilibrium prices to be affected by smart money as well as noise traders is that a large fraction of investors must be overconfident. This argument may fail in bad times, since as noted earlier individuals seem to be more cautious in those times. On the other hand, the condition of a ‘non-negligible’ fraction of overconfident investors is very plausible in good times, where in the midst of a general euphoria individuals get caught up in the excitement, and through social interaction and media contagion, this can result in a very large number of irrational investors (Shiller, 2000; Hong et al., 2004).

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4 Implications of asymmetry in market efficiency across economic states

4.1 Portfolio management: benchmarking in bad times and stock picking in good times

In an efficient market, money fund managers cannot beat the market: this is one of the most important practical implications of market efficiency. Because according to the CAPM, there is no better risk-return profile than the market portfolio, the latter must generate in the long-run the best performance in terms of the risk-return relationship. However, in the last two decades there has been considerable evidence throughout the literature that multifactor models describe expected returns better than the CAPM, which means that it is a combination of many portfolios that produces the best risk-return profile. As a result, a passive management that replicates some strategies such as ‘value’ beats active management in the long-run (active in the sense of individual stock selection).

Because prices are unlikely to reflect the fundamental value of stocks in good times, active managers can create value over benchmarks in these periods by picking some stocks in which they believe they possess private information. Furthermore, as shown by Hammami (2011), rational managers who adopt a passive management in good times can lose a substantial amount of money in the long-run. Likewise, the ‘late irrational traders’ of Hirshleifer et al. (2006) or the ‘strongly optimistic traders’ of Kogan et al. (2006) also lose a huge amount of money in the long-run during these periods by investing too much in ‘glamour’ stocks. By contrast, ‘contrarian’ strategies undertaken by ‘early irrational traders’ lead to substantial profits without exposing investors to an additional risk.

4.2 The normative implications for regulatory policy

In an efficient market, resources are allocated optimally by rational investors. As pointed out by Shiller (2003, p.102), “the recent stock market boom, and then crash after 2000, had its origins in human foibles and arbitrary feedback relations and must have generated a real and substantial misallocation of resources”. Although Shiller (2003, p.102) recognises himself the harmful consequences of inefficiency on the economy, he concluded that “the challenge for economists is to make this reality (inefficiency) a better part of their models”. Even though this is important to explain irrationality, taking into account the irrationality of investors in our models might exacerbate the inefficiency of the market, and hence might lead to more misallocation of resources in the economy. One critical issue in future researches is to figure out how it is possible to make the market efficient, when investors turn out to be irrational.

Because market inefficiency seems to arise especially in good times, regulators might intervene during these periods. For example, the US Federal Reserve might supervise bank competition more closely in good times to avoid the bad effects of credit cycle variation. In addition, when the economy has just recovered from a deep recession, the Federal Reserve should act more aggressively and more swiftly in the outset of positive bubbles through its various tools to restrict liquidity. Also, regulators might step in to prevent the irrational valuation of some stocks by lifting short-sell constraints in good times. As short selling may entail price decrease regardless of fundamental value, as discovered by Haruvy and Noussair (2006), it would be interesting to maintain short-sell constraints in bad times to avoid panic and crashes.

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5 Conclusions

Recently, the empirical literature has suggested the presence of asymmetry in market efficiency across economic states. Specifically, the market tends to be inefficient in good times and efficient in bad times. This paper intends to bridge the gap between this empirical evidence and the extensive behavioural and rational literatures.

This paper argues that the connection between moods and the state of the economy might explain why bounded rationality of investors, such as overconfidence, appears essentially in good times. Credit cycle variations could be a factor exacerbating this asymmetry in market efficiency. In efficiency-based theories, the impact of irrational investors on stock prices should be neutralised by arbitrageurs. This paper highlights two mains reasons why the arbitrage process might be limited essentially in good times. The first reason is the tendency of investors, when they buy stocks, to have long horizons in bad times and short horizons in good times. The second reason is the tendency of short-sale constraints to prevent the irrational valuation of some stocks only in good times.

These findings and interpretations have important implications both for policymakers and for portfolio management. For example, they imply that active portfolio managers could outperform passive portfolio management only in good times. It would be interesting in future empirical work to study the performance of portfolio managers across economic states. Investigating the different explanations proposed in this paper are other possible directions for future researches, such as examining Miller’s (1977) theory across economic states, studying the connection between moods and the state of the economy, and investigating the impact of investor horizons on prices across economic states.

References Abreu, D. and Brunnermeier, M.K. (2002) ‘Synchronization risk and delayed arbitrage’, Journal of

Financial Economics, Vol. 66, Nos. 2–3, pp.341–360. Ang, A. and Bekaert, G. (2007) ‘Stock return predictability: is it there?’, Review of Financial

Studies, Vol. 20, No. 3, pp.651–707. Baker, M. and Wurgler, J. (2006) ‘Investor sentiment and the cross-section of stock returns’,

Journal of Finance, Vol. 61, No. 4, pp.1640–1680. Barberis, N. and Thaler, R. (2003) ‘A survey of behavioral finance’, in Constantinides, G.,

Harris, M. and Stulz, R. (Eds.): Handbook of the Economics of Finance, Chapter 18, pp.1053–1123.

Barberis, N., Huang, M. and Santos, T. (2001) ‘Prospect theory and asset prices’, Quarterly Journal of Economics, Vol. 116, No. 1, pp.1–53.

Barberis, N., Shleifer, A. and Vishny, R. (1998) ‘A model of investor sentiment’, Journal of Financial Economics, Vol. 49, No. 3, pp.307–343.

Berkman, H., Dimitrov, V., Jain, P., Tice, S. and Koch, P.D. (2009) ‘Sell on the news: differences of opinion, short-sales constraints, and returns around earnings announcements’, Journal of Financial Economics, Vol. 92, No. 3, pp.376–399.

Bhojraj, S., Bloomfield, R.J. and Tayler, W.B. (2009) ‘Margin trading, overpricing, and synchronization risk’, Review of Financial Studies, Vol. 22, No. 5, pp.2059–2085.

Bossaerts, P. and Hillion, P. (1999) ‘Implementing statistical criteria to select return forecasting models: what do we learn?’, Review of Financial Studies, Vol. 12, No. 2, pp.405–428.

278 Y. Hammami

Boudoukh, J., Richardson, M. and Whitelaw, R.F. (2008) ‘The myth of long-horizon predictability’, Review of Financial Studies, Vol. 24, No. 4, pp.1577–1605.

Campbell, J.Y. and Cochrane, J.H. (1999) ‘By force of habit: a consumption based explanation of aggregate stock market behaviour’, Journal of Political Economy, Vol. 107, No. 2, pp.205–251.

Campbell, J.Y., Polk, C. and Vuolteenaho, T. (2010) ‘Growth or glamour? Fundamentals and systematic risk in stock returns’, Review of Financial Studies, Vol. 23, No. 1, pp.305–344.

Cochrane, J.H. (2001) Asset Pricing, Princeton University Press, New Jersey. Daniel, K., Hirshleifer, D. and Subrahmanyam, A. (1998) ‘Investor psychology and security market

under- and overreactions’, Journal of Finance, Vol. 53, No. 5, pp.1839–1886. Daniel, K., Hirshleifer, D. and Subrahmanyam, A. (2001) ‘Mispricing, covariance risk, and the

cross section of security returns’, Journal of Finance, Vol. 56, No. 3, pp.921–965. DeLong, J.B., Shleifer, A., Summers, L.H. and Waldmann, R. (1990a) ‘Noise trader risk in

financial markets’, Journal of Political Economy, Vol. 98, No. 4, pp.703–738. DeLong, J.B., Shleifer, A., Summers, L.H. and Waldmann, R. (1990b) ‘Positive feedback

investment strategies and destabilizing rational speculation’, Journal of Finance, Vol. 45, No. 2, pp.375–395.

Duffee, G.R. (2005) ‘Time variation in the covariance between stock returns and consumption growth’, Journal of Finance, Vol. 60, No. 4, pp.1673–1712.

Fama, E.F. (1970) ‘Efficient capital markets: a review of theory and empirical work’, Journal of Finance, Vol. 25, No. 2, pp.383–417.

Fama, E.F. (1991) ‘Efficient capital markets II’, Journal of Finance, Vol. 46, No. 2, pp.1575–1643. Fama, E.F. (1998) ‘Market efficiency, long-term returns and behavioural finance’, Journal of

Financial Economics, Vol. 49, No. 3, pp.283–306. Fama, E.F. and French, K.R. (1988) ‘Permanent and temporary components of stock prices’,

Journal of Political Economy, Vol. 96, No. 2, pp.246–273. Goyal, A. and Welch, I. (2003) ‘Predicting the equity premium with dividend ratios’, Management

Science, Vol. 49, No. 5, pp.639–654. Grossman, S.J. and Stiglitz, J.E. (1980) ‘On the impossibility of informationally efficient markets’,

American Economic Review, Vol. 70, No. 3, pp.393–408. Hammami, Y. (2011) ‘Is the stock market efficient in bad times and inefficient in good times?’,

Applied Financial Economics, Vol. 21, No. 12, pp.905–915. Hammami, Y. (2012) ‘Monetary environment and market inefficiency’, International Journal of

Monetary Economics and Finance, Vol. 5, No. 1, pp.393–408. Haruvy, E. and Noussair, C.N. (2006) ‘The effect of short selling on bubbles and crashes in

experimental spot asset markets’, Journal of Finance, Vol. 61, No. 3, pp.1119–1157. Hirshleifer, D. (2001) ‘Investor psychology and asset pricing’, Journal of Finance, Vol. 64, No. 4,

pp.1533–1597. Hirshleifer, D. and Shumway, T. (2003) ‘Good day sunshine: stock returns and the weather’,

Journal of Finance, Vol. 58, No. 3, pp.1009–1032. Hirshleifer, D., Subrahmanyam, A. and Titman, S. (2006) ‘Feedback and the success of irrational

investors’, Journal of Financial Economics, Vol. 81, No. 2, pp.311–338. Hong, H. and Stein, J. (2003) ‘Differences of opinion, short-sales constraints, and market crashes’,

Review of Financial Studies, Vol. 16, No. 2, pp.487–525. Hong, H., Kubik, J. and Stein, J. (2004) ‘Social interaction and stock-market participation’, Journal

of Finance, Vol. 59, No. 1, pp.137–163. Hong, H., Lim, T. and Stein, J. (2000) ‘Bad news travels slowly: size, analyst coverage and the

profitability of momentum strategies’, Journal of Finance, Vol. 55, No. 1, pp.265–295.

Asymmetry in market efficiency across economic states 279

Kogan, L., Ross, S., Wang, J. and Westerfield, M. (2006) ‘The survival and price impact of irrational traders’, Journal of Finance, Vol. 61, No. 1, pp.195–229.

Lakonishok, J., Shleifer, A. and Vishny, R.W. (1994) ‘Contrarian investment, extrapolation, and risk’, Journal of Finance, Vol. 49, No. 5, pp.1541–1578.

Lemmon, M. and Portniaguina, E. (2006) ‘Consumer confidence and asset prices: some empirical evidence’, Review of Financial Studies, Vol. 19, No. 4, pp.1499–1529.

Liu, J. and Longstaff, F.A. (2004) ‘Losing money on arbitrage: optimal dynamic portfolio choice in markets with arbitrage opportunities’, Review of Financial Studies, Vol. 17, No. 3, pp.611–641.

Liu, L.X. and Zhang, L. (2008) ‘Momentum profits, factor pricing, and macroeconomic risk’, Review of Financial Studies, Vol. 21, No. 6, pp.2417–2448.

Miller, E. (1977) ‘Risk, uncertainty, and divergence of opinion’, Journal of Finance, Vol. 32, No. 4, pp.1151–1168.

Nagel, S. (2005) ‘Short sales, institutional investors and the cross-section of stock returns’, Journal of Financial Economics, Vol. 78, No. 2, pp.277–309.

Ofek, E., Richardson, M.P. and Whitelaw, R. (2004) ‘Limited arbitrage and short sale constraints: evidence from the options market’, Journal of Financial Economics, Vol. 74, No. 2, pp.305–342.

Ozdenoren, E. and Yuan, K. (2008) ‘Feedback effects and asset prices’, Journal of Finance, Vol. 63, No. 4, pp.1939–1975.

Ozoguz, A. (2009) ‘Good times or bad times? Investors’ uncertainty and stock returns’, Review of Financial Studies, Vol. 22, No. 11, pp.4377–4422.

Pástor, L. and Stambaugh, R.F. (2003) ‘Liquidity risk and expected stock returns’, Journal of Political Economy, Vol. 111, No. 3, pp.642–685.

Petkova, R. (2006) ‘Do the Fama-French factors proxy for innovations in predictive variables?’, Journal of Finance, Vol. 61, No. 2, pp.581–612.

Petkova, R. and Zhang, L. (2005) ‘Is value riskier than growth?’, Journal of Financial Economics, Vol. 78, No. 1, pp.187–202.

Petty, R., Gleicher, F. and Baker, S.M. (1991) ‘Multiple roles for affect in persuasion’, in Forgas, J. (Ed.): Emotion and Social Judgments, Pergamon, Oxford.

Ruckes, M. (2004) ‘Bank competition and credit standards’, Review of Financial Studies, Vol. 17, No. 4, pp.1073–1102.

Shiller, R.J. (2000) Irrational Exuberance, Princeton University Press, New Jersey. Shiller, R.J. (2003) ‘From efficient markets theory to behavioural finance’, Journal of Economic

Perspectives, Vol. 17, No. 1, pp.83–104. Shleifer, A. and Vishny, R.W. (1997) ‘The limits of arbitrage’, Journal of Finance, Vol. 52, No. 1,

pp.35–55. Subrahmanyam, A. and Titman, S. (2001) ‘Feedback from stock prices to cash flows’, Journal of

Finance, Vol. 56, No. 6, pp.2389–2413. Thaler, R. and Johnson, E.J. (1990) ‘Gambling with the house money and trying to break even: the

effects of prior outcomes on risky choice’, Management Science, Vol. 36, No. 6, pp.643–660. Vassalou, M. (2003) ‘News related to future GDP growth as a risk factor in equity returns’, Journal

of Financial Economics, Vol. 68, No. 1, pp.47–73. Veronesi, P. (1999) ‘Stock market overreaction to bad news in good times: a rational expectations

equilibrium model’, Review of Financial Studies, Vol. 12, No. 5, pp.975–1007. Wright, W.F. and Bower, G.H. (1992) ‘Mood effects on subjective probability assessment’,

Organizational Behavior and Human Decision Processes, Vol. 52, No. 5, pp.276–291.