Financial crises and stock market crashes have clearly demonstrated the impact of investors sentiment on asset pricing and stock markets efficiency. Herd behavior, which is behavioral similarity based on individuals interaction that leads to convergence of action and correlated trading (), is one of the most important behavioral biases that is more likely to occur during periods of market stress when individual investors prefer to follow the market consensus, being reluctant to follow their own knowledge or beliefs (). Herding has been widely studied in financial markets (including the stock market, bond market, foreign exchange market, exchange-traded funds market, etc.), and it is evident in both retail and institutional investors behavior.

Herding can be rational when it relates to payoff externalities, informational learning, principalagent, and reputation-based problems, or it can derive from behavioral factors (Devenow and Welch, 1996). In any case, this behavior has important implications for market efficiency and portfolio diversification. However, there is also spurious herding (ie, correlated decision making basedon the same set of fundamental information rather than imitation), which does not cause market inefficiency (Bikhchandani and Sharma, 2000).

Even though empirical evidence of herding in developed and emerging markets is mixed based on the period and the market under examination, herding is expected to be more pronounced in emerging markets since their special characteristics (thin trading, incomplete regulatory framework and corporate information disclosure, low transparency, information asymmetries, etc.) may facilitate herding behavior (Kallinterakis and Kratunova, 2007).

In the same spirit, frontier markets are also expected to display herding behavior. Frontier markets are less developed and less liquid markets that are too small to be considered as emerging (Balcilar et al., 2015; De Groot et al., 2012) and are characterized by low trading volume, high concentration, difficult access, inexperienced market participants, incomplete institutional framework, and limited information disclosure (Economou et al., 2015b; Speidell and Krohne, 2007).Quisenberry (2010)employs the definition of Merrill Lynch, according to which a frontier market is characterized as an emerging emerging market (ie, a market that is expected to become emerging). There is growing interest in these markets since their low correlations both among them and with developed markets offer market diversification benefits for international portfolios (Berger et al., 2011; Cheng et al., 2009; Jayasuriya and Shambora, 2009; Speidell and Krohne, 2007). As a result, their stock market behavior should be further analyzed in order to enhance the understanding of frontier markets.

While there is a growing strand of literature about herd behavior in developed and emerging stock markets, the existing literature dealing with frontier markets is limited.Balcilar et al. (2014)examine the cash- and oil-rich Gulf Cooperation Council (GCC) markets, indicating strong and persistent evidence of herding in Dubai, Kuwait, Qatar, and Saudi Arabia, while there is less frequent herding in Abu Dhabi. The authors also establish a direct link between herding and market volatility, and document the impact of shocks due to global factors on herding. A previous study of the GCC frontier markets indicates the presence of three market regimes regarding volatility as well as crossmarket herding effects driven by common factors in the GCC, especially during periods of extreme volatility (Balcilar et al., 2013). The retail investorsdominated Saudi Arabia stock market has also been studied byRahman et al. (2015). The authors find evidence of herding irrespective of market conditions, which is more pronounced during periods of positive market returns and higher trading activity. Apart from retail investors, herding is also evident in institutional investors in frontier markets.Economou et al. (2015b)indicate that fund managers in Bulgaria and Montenegro herd significantly and intentionally, with herding being stronger during periods of positive market returns and high volume as well as during low-volatility periods for Montenegro. Moreover, Bulgarian and Montenegrin fund managers herded significantly both before and after the outbreak of the global financial crisis.

In this chapter we extend the limited herding literature in frontier markets by examining herd behavior in two African frontier markets, namely Nigeria andMorocco from 2004 to 2014 employing the cross-sectional dispersion of returns approach ofChang et al. (2000). Moreover, we test for possible asymmetries in herding estimations under different market states (up/down market returns, market volatility, and volume) as well as for the impact of additional explanatory variables, such as the oil price return, the US stock market return, and the US sentiment captured by the Chicago Board Options Exchange (CBOE) VIX index. The impact of the global financial crisis on the two markets under examination also provides interesting insight into the international stock markets dynamics.

The rest of the chapter is structured as follows: Section2presents the methodology and the data set employed in order to examine herding in frontier markets, Section3reports the empirical results and finally Section4offers conclusions.

RISTOKARJALAINEN, inAdvanced Trading Rules (Second Edition), 2002

If the price patterns observed earlier reflect overreaction by the markets, assessment ofinvestor sentimentbecomes important. Option data are particularly interesting in this respect, since option markets offer a more detailed picture of market behaviour that can be achieved by looking at the underlying market alone. In this section, we study volume and open interest data for options to see where there are any regularities corresponding to the observed price patterns and the timing of the trading rule signals.

In the past, researchers have usually looked at a so-called put-call ratio, i.e. the ratio of put option volume to call volume, sometimes smoothed by taking a moving average. The reasoning behind the ratio is that when trading is concentrated on the put options (i.e. the put-call ratio is high),investor sentimentis bearish. From a contrarian viewpoint, this is considered to be a buy signal. Similarly, a low put-call ratio is thought to be an indicator or bullish sentiment, which is taken as a signal to sell.Billingsley and Chance (1988)tested two such indicators using data from the S&P 100 options, but found no evidence of abnormal stock market returns after transaction costs.Chance (1990)showed that the correlation between the S&P 500 return and the put-call ratio was largely contemporaneous. However, he also found that some of the trading strategies he tested were at least marginally profitable, especially in futures where transaction costs are low.

There are several active markets for options related to S&P 500 futures, including options for the S&P 100 index (OEX), options for the S&P 500 index (SPX), as well as options for the futures themselves. Of these three markets, the S&P 100 options are the most liquid, being in fact the most actively traded option market in the world. There is also evidence that OEX options are actually priced off the S&P 500 futures (Figlewski, 1988). Becausetrading volume and open interest data for all the tree markets are highly correlated with one another, OEX data was used in the following tests.

Using a methodology similar to looking at cash flows in event time, we analysed the option data relative to the trading rule signals during the test period from December 1988 to August 1993. Put-call ratios were formed for both the trading volume (as in the previous research) and for open interest. In the trading volume put-call ratio, there was indeed a pattern where put-call ratio declines to its trough on the precise day that the rules switch over from a long position to a short one, and bounces back during the next few days. Similarly, the put-call ratio achieved its peak on the day when the rules send a buy signal.

While the volume data were suggestive, the open interest data turned out to be even more revealing.Figure 12.9shows the open interest put-call ratio (PCO) in event time. For transitions from long to short positions, the put-call ratio steadily increases before the trading rule signal, and keeps growing for another three to four days afterwards.Figure 12.10shows that the pattern for buy signals is essentially a mirror image from the sell signals. Were the two figures pasted together, we would see a pattern very much resembling a sinewave, with the put-call ratio fluctuating from peak to trough, with thecurve punctuated by the trading rule signals at the midpoint between the extremes.

Figure 12.9.The open interest put-call ratio (PCO) in event time for transitions from long to short positions for the 100 trading rules for SP 500 futures during the test period from December 1988 to August 1993. The horizontal axis corresponds to the event time, relative to the signal on day 1. The vertical axis corresponds to the average put-call ratio for each event day. The dashed horizontal line indicates the average put-call ratio for the 100 rules

Figure 12.10.The open interest put-call ratio (PCO) in event time for transitions from short to long positions for the 100 trading rules for SP 500 futures during the test period from December 1988 to August 1993. The horizontal axis corresponds to the event time, relative to the signal on day 1. The vertical axis corresponds to the average put-call ratio for each event day. The dashed horizontal line indicates the average put-call ratio for the 100 rules

Figures 12.11and12.12show the open interest put-call ratio during the combined training and selection period. The option data exhibit patterns very similar to the test period. This suggests that the trading rules worked out-of-sample because the market behaved similarly across the different periods. There is some evidence, however, of a fastened pace in the markets. The peaks and troughs tend to follow each other with a higher frequency in the test period, compared with the more tranquil training years. The trading volume data are also largely similar to the test period, except that the put-call ratio achieves its extrema one to three days after the signal.

Figure 12.11.The open interest put-call ratio (PCO) in event time for transitions from long to short positions for the 100 trading rules for SP 500 futures during the training and selection periods from June 1983 to November 1988. The horizontal axis corresponds to the event time, relative to the signal on day 1. The vertical axis corresponds to the average put-call ratio for each event day. The dashed horizontal line indicates the average put-call ratio for the 100 rules

Figure 12.12.The open interest put-call ratio (PCO) in event time for transitions from short to long positions for the 100 trading rules for SP 500 futures during the training and selection periods from June 1983 to November 1988. The horizontal axis corresponds to the event time, relative to the signal on day 1. The vertical axis corresponds to the average put-call ratio for each event day. The dashed horizontal line indicates the average put-call ratio for the 100 rules

To summarize the analysis of the trading rule signals, it was found that the rules made bets against the market, stepping in and buying when prices fell and selling after large price increases. In the time period studied here, the strategy was profitable because large returns were partially reversed after a few days. A study of option data on a related market showed that the rules held long positions when the open interest and volume was concentrated on call options, and were short when the option trading activity was focused on puts. The results are consistent with a contrarian view of the markets wherebullish sentiment precedes price declines and bearish sentiment peaks before price increases. While the observed patterns may be due to rational hedging activity, the fact remains that the patterns are there, even though the option data were not used as inputs to the genetic algorithm. The patterns were stable throughout the years, allowing the algorithm to find rules in the training period that generalized to an out-of-sample test period.

AlexanderLjungqvist, inHandbook of Empirical Corporate Finance, 2007

Behavioral finance is interested in the effect on stock prices of irrational or sentiment investors. The potential for such an effect would seem particularly large in the case of IPOs, since IPO firms are young, immature, and relatively informationally opaque and hence hard to value. The first paper to model an IPO companys optimal response to the presence of sentiment investors isLjungqvist, Nanda, and Singh (2004). They assume some sentiment investors hold optimistic beliefs about the future prospects for the IPO company. The issuers objective is to capture as much of the surplus under the sentiment investors downward-sloping demand curve as possible, that is, to maximize the excess valuation over the fundamental value of the stock. Flooding the market with stock will depress the price, so the optimal strategy involves holding back stock in inventory to keep the price from falling. Eventually, nature reveals the true value of the stock and the price reverts to fundamental value. That is, in the long-run IPO returns are negative, consistent with the empirical evidence inRitter (1991)and others. This assumes the existence of short sale constraints, or else arbitrageurs would trade in such a way that prices reflected fundamental value even in the short term.

Regulatory constraints on price discrimination and inventory holding prevent the issuer from implementing such a strategy directly. Instead, the optimal mechanism involves the issuer allocating stock to regular institutional investors for subsequent resale to sentiment investors, at prices the regulars maintain by restricting supply. Because the hot market can end prematurely, carrying IPO stock in inventory is risky, so to break even in expectation regulars require the stock to be underpricedeven in the absence of asymmetric information. However, the offer price still exceeds fundamental value, as it capitalizes the regulars expected gain from trading with the sentiment investors, and so the issuer benefits from this mechanism.

The model generates a number of new and refutable empirical predictions. Most obviously, the model predicts that companies going public in a hot market subsequently underperform, both relative to the first-day price and to the offer price. Underperformance relative to the first-day price is not surprising; it follows from the twin assumptions ofsentiment investors and short-sale constraints (seeMiller, 1977). Underperformance relative to the offer price is a stronger prediction. It follows because the offer price exceeds fundamental value by an amount equal to the issuers share in the surplus extracted from the sentiment investors.Purnanandam and Swaminathan (2004)lend support to the prediction that the offer price can exceed fundamental value. They show that compared to its industry peers multiples, the median IPO firm in 19801997 was overpriced at the offer by 50%. Interestingly, it is the firms that are most overpriced in this sense which subsequently underperform.Cook, Jarrell, and Kieschnick (2003)refine this analysis by conditioning on hot and cold markets. They find that IPO firms trade at higher valuations only in hot markets, consistent with the spirit of theLjungqvist, Nanda, and Singh (2004)model.Cornelli, Goldreich, and Ljungqvist (2006)use data from the grey market (the when-issued market that precedes European IPOs and that involves mostly retail traders) to show that long-run underperformance is concentrated among those IPOs whose grey market prices were particularly high. They also report evidence suggesting that grey market investors do not update their prior beliefs about the value of an IPO in an unbiased fashion.

Ofek and Richardson (2003)show that high initial returns occur when institutions sell IPO shares to retail investors on the first day, and that such high initial returns are followed by sizeable reversals to the end of 2000, when the dot-com bubble eventually burst. This is precisely the patternLjungqvist, Nanda, and Singh (2004)predict.

At the heart ofLjungqvist, Nanda, and Singhs (2004)story is the idea that banks market IPOs and that it matters whom they target in their marketing.Cook, Kieschnick, and Van Ness (2006)find a significant positive relation between promotional activities (proxied by the number of newspaper articles mentioning the IPO firm in the prior six months) and the valuations at which IPOs are sold, which they interpret as evidence that investment bankers manage to sell overvalued IPO stock to retail investors to the benefit of the issuer and the investment banks regular clients.

Using German data on IPO trading by 5,000 retail customers of an online broker,Dorn (2002)documents that retail investors overpay for IPOs following periods of high underpricing in recent IPOs, and for IPOs that are in the news. Consistent with theLjungqvist, Nanda, and Singh (2004)model, he also shows that hot IPOs pass from institutional into retail hands. Over time, high initial returns are reversed as net purchases by retail investors subside, eventually resulting in underperformance over the first six to 12 months after the IPO.

The model may also be able to reconcile the conflicting empirical evidence regarding the relation between underpricing and long-run performance.Ritter (1991)documents that underpricing and long-run performance are negatively related, whileKrigman, Shaw, and Womack (1999)find a positive relation. In theLjungqvist, Nanda, and Singh (2004)model, the relation is not necessarily monotonic. In particular, the relation is negative only if the probability of the hot market ending is small. If the hot market is highly likely to end, the issuer optimally reduces the offer size, implying regular investors hold smaller inventories and so require less underpricing to break even. At thesame time, the reduction in offer size aggravates long-run underperformance, given the negative slope of the sentiment demand curve.

Recall fromSection 3.1that the empirical evidence on the relation between underwriter reputation and underpricing is mixed. Consistent with evidence from the 1990s (Beatty and Welch, 1996),Ljungqvist, Nanda, and Singh (2004)predict that underpricingincreases in underwriter reputation. Underwriters enjoying a large IPO deal flow can more easily punish regular investors who attempt to free-ride on the inventory-holding strategy by dumping their shares prematurely, before the price falls. This in turn implies that the more active banks can underwrite larger IPOs, as more inventory can be held over time. Since underpricing is compensation for the expected inventory losses in the face of a non-zero probability that the hot market will end before all inventory has been unloaded, the more active underwriters will be associated with greater underpricing.

Donald M.DePamphilisPh.D., inMergers, Acquisitions, and Other Restructuring Activities (Ninth Edition), 2018

Abnormal returns to acquirer shareholders are largely situational, varying according to the size of the acquirer, the type and size of the target, the form of payment, firm-specific characteristics, andinvestor sentiment(Table 1.4).

Table 1.4.Acquirer Returns Differ by Characteristics of the Acquirer, Target, and Deal as Well as Investor Sentiment

On US buyouts are often positive when the targets are privately owned (or are subsidiaries of public companies) and slightly negative when the targets are large publicly traded firms (i.e., so-called listing effect), regardless of the country

On cross-border deals generally positive except for those involving large public acquirers that are often zero to negative

On equity-financed acquisitions of large public firms often negative andless thancash-financed deals in the United States

On equity-financed acquisitions of public or private firms frequentlymore thanall-cash-financed deals in EU countries

On equity-financed deals involving private firms (or subsidiaries of public firms) often exceed significantly cash deals

On cross-border deals financed with equity often negative

Smaller acquirers often realize higher returns than larger acquirers

Relatively small deals often generate higher acquirer returns than larger ones

Acquirer returns may be lower when the size of the acquisition is large relative to the buyer (i.e., more than 30% of the buyers market value)

Managers at large firms tend to overpay more than those at smaller firms, since large-firm executives may have been involved in more deals and be overconfident. This is sometimes referred to as the size effect. Incentive systems at larger firms also may skew compensation to reflect more the overall size of the firm than its ongoing performance. Finally, managers of large firms may pursue larger, more risky investments (such as unrelated acquisitions) in an attempt to support the firms overvalued share price. In contrast, CEOs of small firms tend to own a larger percentage of the firms outstanding shares than those of larger firms. On average CEOs of small firms own 7.4% of their firms stock, while those of larger firms own on average 4.5%. Consequently, small-company CEOs may be more risk averse in negotiating M&As. Regardless of the reason, research shows that large public acquirers tend to destroy shareholder wealth, while small acquirers create wealth.94

While the size effect has been widely documented in developed countries, large size can have significant benefits for M&As in countries characterized by weak corporate governance, (i.e., countries in which the laws and courts fail to protect shareholders rights). Realizing positive abnormal financial returns averaging 1.3% around the deal announcement date, large acquirers in such countries often are better able to insulate themselves from corrupt governments due to their sheer size, prestige, influence, and political connections. Reflecting their excellent political connections in weak governance environments, large acquirers often take less time to complete deals than in countries with more rigorous governance practices enabling a more rapid realization of anticipated synergies.95

US acquirers of private firms or subsidiaries of publicly traded firms often realize positive abnormal returns of 1.5%2.6% around the announcement date.96Acquirers pay less for private firms or subsidiaries of public companies due to the limited availability of information and the limited number of bidders for such firms. That is, the market for private firms is relatively illiquid. Since these targets may be acquired at a discount from their true value, acquirers are able to realize a larger share of the combined value of the acquirer and target firms.

Acquirer announcement date financial returns tend to be three times larger for acquisitions involving small targets than for those involving large targets.97High-tech firms realize attractive returns by acquiring small, but related, target firms to fill gaps in their productofferings.98Larger deals tend to be riskier for acquirers99and experience consistently lower postmerger performance, possibly reflecting the challenges of integrating large target firms and realizing projected synergies. There are exceptions: firms making large acquisitions show less negative or more positive returns in slower-growing than in faster-growing sectors.100In slow-growth industries, integration may be less disruptive than in faster-growing industries, which may experience a slower pace of new product introductions and upgrade efforts. Furthermore, small firms are less likely than larger firms to receive overpriced stock offers since they often are less attractive to larger firms, which are more prone to use overvalued stock.101Why? Because small deals often do not provide the incremental revenue and profit to jumpstart growth for the larger firms. Therefore, purchases of smaller firms at reasonable prices provide a greater likelihood of realizing attractive financial returns.

Announcement date returns to acquirer shareholders often are negative when the acquirer and the target are publicly traded and the form of payment consists mostly of stock. For publicly traded firms, acquirers tend to issue stock when they believe it is overvalued, because they can issue fewer new shares resulting in less earnings dilution. Investors treat such decisions as signals that the stock is overvalued and sell their shares when the new equity issue is announced, causing the firms share price to decline. However, the majority of the decline in acquirer shares on the announcement date may be related more to merger arbitrage activity than to investors believing the shares are overvalued. About 60% of the sharp decline in acquirer shares on the announcement date may reflect short selling as arbs buy the targets shares and short the acquirers.102

Other factors contributing to negative acquirer returns in stock for stock deals may reflect a tendency of acquirers using overvalued stock to overpay for target firms and the extent of the overpayment prevents such firms from recovering the premium paid through synergy.103Acquirers that use cash to purchase the target firm are less likely to overpay and exhibit better long-term performance than do those using stock.104However, equity-financed transactions in the European Union often display higher acquirer returns than those using cash, due to the existence of large block shareholders, whose active monitoring tends to improve the acquired firms performance. Such shareholders are less common in the United States.105

Why would target shareholders accept overvalued acquirer shares? Target firms may accept such payment terms because in share exchanges the requirement to pay capital gains taxes may be deferred until the target shareholders sell their shares,106the takeover may betoo big for the acquirer to finance with cash, overvaluation may not be obvious, or the use of acquirer shares may reduce the leverage of the combined firms. The latter factor recognizes that the postmerger risk associated with acquirer shares reflects the combined leverage of both the target and acquiring firms. Overvalued acquirer shares may be justified if their issuance will result in a reduction in the leverage (i.e., a lower debt to total capital ratio) of the combined firms.107The resulting risk reduction may reduce or eliminate the overvaluation of the acquirer shares.

Announcement-period gains to acquirer shareholders tend to dissipate within 35 years, even when the acquisition was successful, when stock is used to acquire a large public firm.108These findings imply that shareholders, selling around the announcement dates, may realize the largest gains from either tender offers or mergers.

A tendency for target firms to manage premerger earnings also may explain their underperformance in the years immediately following a takeover, even when acquirers use cash rather than equity. Targets may deliberately overaccrue revenue and underaccrue expenses during the year prior to takeovers in order to boost reported earnings used by the acquirer to value the target firm.109In cash transactions, the full cost of such earnings management is borne by acquirer shareholders who may see their shares decline in value as GAAP is accurately applied in future years, while target shareholders having received cash at closing are unhurt.110

Acquirer characteristics may explain to a greater extent than deal-specific factors (e.g., form and timing of payment) the variation in financial returns to acquiring firms.111While a growing literature using increasingly larger and more diverse samples over the past three decades has identified a number of determinants of acquirer performance, the overall variation in acquirer returns remains largely unexplained. Most studies explain a small percentage of such variation as measured by the adjustedR2of the regression models used in these studies. An example of a firm-specific factor is the firms organizational deal making skill and knowledge possibly residing in an in-house corporate development team charged with screening deals, performing due diligence, and undertaking most of the analysis underlying acquisition decisions. Other factors could include a firms time-tested postmerger integration process, specific industry or proprietary knowledge, or accumulated experience. Successful acquirers tend to internalize the M&A process such that continuing success is not dependent on the leadership at the top and the deal structure employed.

Abnormal returns to acquirer shareholders tend to be higher when investors are optimistic around the announcement date of the deal resulting in an overestimation of potential synergies and an underestimation of risk.112The impact ofinvestor sentimentvaries by sophistication of bidder shareholders, whether the bidder is public or private, and the size of the target firm compared to the bidder. This optimism tends to be greatest for firms with few large, sophisticated institutional investors (or blockholders) and mostly smaller, less knowledgeable shareholders.Investor sentimentalso is impacted significantly when deals receive extensive media atte