Read the text and explain all concept discussed in the text below: (This subject is Behavioral Finance)

The Limits to Arbitrage

A key argument in behavioral finance is that the existence of behavioral biases among investors (noise traders) will affect asset prices and returns on a sustained basis only if limits to arbitrage also exist that prevent rational investors from exploiting short-term mispricings and, by doing so, returning prices to equilibrium values. Evidence suggests that limits to arbitrage exist, for example, in the failure to eliminate obvious and straightforward mispricing situations. Mitchell, Pulvino, and Stafford (2002) are able to document 82 cases in which the market value of a company is less than the market value of the companys stake in its subsidiary. These situations imply arbitrage opportunities leading to swift correction of the pricing anomaly, but the authors find a degree of persistence that indicates barriers to arbitrage.

Barberis and Thaler (2003) outline the various issues that create limits to arbitrage. When the mispriced asset lacks a fairly priced close substitute, arbitrageurs are faced with fundamental risk in that they are unable to effectively hedge their position in the mispriced asset from adverse changes in fundamentals. Even if a close substitute is available, arbitrageurs face noise trader risk. Because trading by uninformed investors may cause the mispricing to increase before it corrects, the arbitrageur may be unable to maintain the position in the face of margin calls, especially when trading with other peoples capital, as in institutional investment management. Finally, other issues include high implementation costs for any arbitrage trade. At the extreme, taking a short position in an overpriced security may be impossible if, for example, stock lending is prohibited or no shares are available to borrow.

On the latter point, Lamont and Thaler (2003) review examples in which the market value of spun-out subsidiaries of tech companies exceeded that of the parent company that retained a majority stake in the spinout. In these cases, short-selling of the spinout was difficult, expensive, or impossible, reducing or eliminating the arbitrage opportunity.

Behavioral Asset Pricing

Whereas academics talk about asset pricing and about explaining the cross-section of stock returns, for practitioners, the same issues fall under the simpler heading of stock picking. If behavioral biases among investors cause mispricing of stocks in a predictable fashion, then active managers may have the scope to beat the market by using strategies based on these sources of mispricing.

Investor Sentiment. One important issue is whether investor sentiment has the potential to affect stock returns, which is considered self-evident by most practitioners. But traditional finance theory has little role for sentiment in asset pricing.

Recent behavioral literature (Baker and Wurgler 2006; Kumar and Lee 2006; Tetlock 2007) suggests evidence of investor sentiment affecting stock returns. The effect is most pronounced for stocks that are difficult to value and/or hard to arbitrage. This category includes small stocks, young stocks, unprofitable stocks, and extreme- growth stocks. When investor sentiment is high, subsequent returns for these types of stocks tend to be relatively low, and vice versa.

Causes of swings in investor sentiment vary and, in some cases, can be quite trivial. Hirshleifer and Shumway (2003) present evidence that daily returns across the worlds markets are affected by the weather in the city of the countrys leading stock exchange. Unfortunately, a strategy to exploit this predictability in returns involves quite frequent trading, and trading costs may well eliminate any available gains for most investors. Kamstra, Kramer, and Levi (2003) provide similar evidence, showing that returns in various countries through the year are related to hours of daylighta result possibly driven by the occurrence of seasonal affective disorder.

The effect of sentiment is evident in various arenas. For example, Gemmill and Thomas (2002) show that noise trader sentiment, as proxied by retail investor fund flows, leads to fluctuations in the discount of closed-end funds. Of note, one measure of sentiment that does not predict returns is the current sentimentbullish or bearishof investment newsletter writers. Rather, recent past returns predict the sentiment of the writers, which, in turn, has no correlation with future returns (Clarke and Statman 1998).

Under- and Overreaction. Another key area of behavioral research relates to the extent to which investors under- or overreact to information in pricing securities. The available empirical evidence appears to suggest short-term (up to 12 months) return continuations, or momentum (e.g., Jegadeesh and Titman 1993), but longer term (three- to five-year) reversals (e.g., De Bondt and Thaler 1985; Lakonishok, Shleifer, and Vishny 1994). This evidence poses something of a challenge for behavioral researchers to come up with a theory that explains initial underreaction but longer term overreaction and rebuts Famas (1998) contention that a market that overreacts about as much as it underreacts can be regarded as broadly efficient.

Various behavioral models have been developed to explain the empirical findings. In Barberis, Shleifer, and Vishny (1998), investors suffer conservatism bias and use the representativeness heuristic. Conservatism means that individuals are slow to change their beliefs in the face of new evidence and can explain why investors would fail to take full account of the implications of an earnings surprise. The representativeness heuristic means that individuals assess the probability of an event or situation based on superficial characteristics and similar experiences they have had rather than on the underlying probabilities. This approach can mean that investors, seeing patterns in random data, could extrapolate a companys recent positive earnings announcements further into the future than is warranted, creating overreaction.

Daniel, Hirshleifer, and Subrahmanyam (1998) present a related model based on overconfidence and biased self-attribution. Overconfidence leads investors to overweight their private information in assessing the value of securities, causing the stock price to overreact. When public information arrives, mispricing is only partially corrected, giving rise to underreaction. Furthermore, biased self-attribution means that when public information confirms the initial private signal, investor confidence in the private signal rises, leading to the potential for overreaction.

Finally, Hong and Stein (1999) present a model populated by news watchers, those who base their trades on private information but not past prices, and momentum traders, those who base their trades on past price trends. News spreads slowly among the news watchers, causing initial underreaction, but it is followed by momentum buying that can create an eventual overreaction.

Related empirical work includes Dreman and Berrys (1995) study that finds an asymmetry of response to earnings surprise between low and high P/E stocks. Low P/E (i.e., value) stocks respond most favorably to a positive earnings surprise, suggesting the low P/E status may be the result of prior overreaction to negative news. Lee and Swaminathan (2000) show that turnover levels provide a link between value and momentum effects. Winners with high past volume experience reversals at five-year horizons, consistent with initial underreaction and eventual overreaction. They argue also that as stocks decline in popularity, trading volume drops off and the stocks become neglected value stocks. Taffler, Lu, and Kausar (2004) document market underreaction to the bad news contained in going-concern-modified audit reports. The underreaction may be the result of the limits to arbitrage in the sample companies, predominantly small loser stocks, but the authors cannot rule out the behavioral explanation of investors (professional and individual) being in denial of the implications of the going-concern opinion.

Other articles attempt to explain short-term momentum in returns, arguably the most difficult empirical finding to reconcile with traditional rational finance theory. Grinblatt and Han (2005) argue that prospect theory, and the resulting tendency of investors to hold losing positions and sell winners, explains the momentum effect. This trading behavior of investors means prices underreact to news and momentum occurs as the mispricing slowly corrects. For example, when good news emerges about a stock, selling by investors who, subject to the disposition effect, are inclined to sell winners will slow the pace at which the good news can be reflected in a higher stock price. The authors find that a proxy for unrealized gains, which will determine investors disposition to sell or hold, can explain the level of momentum profits.