ACONTECIMIENTO INFORMACIÓN

Because perceived risk varies inversely with time horizon, required returns vary directly with perceived risk. Furthermore, with market liquidity varying directly with distribution of investor horizons, changes in distribution of investor horizons might affect the level and volatility of market returns, i.e, if average horizons shrink, perceptions of risk should increase, therefore, higher returns (Olsen and Khaki, 1998). In addition, because horizon length is bounded by zero, a reduction of dispersion of horizons created by shortening of horizons (say from monthly to weekly data) should lead to poorer liquidity and greater volatility in market prices (Greezy, 1997). Finally, but not least, Peter (1994) supports that prices exhibit a pattern of volatility consistent with time horizons.

Moreover, Holmes (2006) reports that the rise in commodity prices is partly due to the more diversified types of financial investors and investment strategies, particularly, passively managed investments. In that line of thought, Beenen (2005) supports that such fund investors (large speculators) often pursue a fully collateralized long-only futures strategy with a longer term investment horizon. Investing in such longer term horizon (e.g monthly) include benefits such as diversification at a relatively low cost. Historically, commodity prices have had a relatively low correlation with prices in other asset classes and a high correlation with inflation (Gorton and Rouwenhorst, 2004). These academics also showed that historically, the return on a diversified basket of long commodities futures has been comparable with return on other asset classes with similar risk characteristics such as equity.

BIS (2007) also supports that non-commercials were dominated by managed money traders (MMT). In a seminal paper, Haigh et al. (2005) suggest that MMT

42 participants do not change their positions as frequently as other participants. Wang (2004) further examines the relation between trading activity by trader type and futures returns over different horizons and found results were consistent on average. Similarly, he found negative conditional betas using weekly returns which are consistent with Bessembinder (1992) who used monthly data. These add support to the use of monthly CFTC data. Lastly, but not least, Wang (2003) supports it is less likely for traders’ perception of risk to be changed over a short interval. The choice of monthly data interval not only makes the results comparable to the previous studies on backwardation or hedging pressure theories, but allow for consistency with monthly macroeconomic variables included in regression models.

2.13 Contrarians

2.13.1 Contrarians v/s naïve strategies

For decades, scholars and investment professionals have argued that value strategies outperform the market Dreman (1977). These value strategies generally looks upon buying stocks that have low prices relative to earnings, dividends, historical prices, book assets, or other measures of value. De Bondt and Thaler (1987) argue that extreme losers outperform the market over the subsequent several years. While it is argued that value strategies have produced superior returns, the interpretation of why they have done so is more of a debate. Value strategies might produce higher returns because they are contrarians to “naïve”25 _{strategies followed by other investors. These naïve strategies}

might vary from forecasting using old earnings data, to overreaction to information, or equating a good bargain with a well-run company’s year irrespective of price. Anyhow, some investors tend to get overly excited26 about stocks that have done very well in the past and buy them up, so that these “glamour” stocks become overpriced. Likewise, they overreact to stocks that have done very badly, oversell them, and these out-of-favour “value” stocks become under priced. In brief, contrarians bet against these naïve

25 _{“naïve” strategies are also sometimes referred as “popular” models (Shiller, 1984) and “noise” (Black,}

1986)

43 strategies (Lakonishok et al., 1994). Because contrarian strategies invest disproportionately in stock that are under priced and under invest in stocks that are overpriced, they outperform the market (DeDondt and Thaler, 1985; and Haugen, 1994). In that line of thought, while Levis and Liodakis (2001) find biases in analysts’ earnings forecasts, Dechow and Sloan (1997) find that naïve reliance on analysts’ forecasts of future earnings growth can explain over half of the higher returns to contrarian strategies.

2.13.2 Over reaction hypothesis: short-run and long-run perspectives 2.13.2.1 Long-run perspective

The long-run perspective, which suggests that stock prices momentarily digress from their fundamental values due to swings of optimism and pessimism, has been examined using monthly returns by researchers including De Bondt and Thaler (1985) and Chopra, Lakonishok and Ritter (1992). Evidence from the long run perspective is generally not consistent with the hypothesis. As Bowman and Iverson (1998) argue, the overreaction event comes from basic human biasedness in processing information, so if it is authenticated, it should manifest itself in other markets. Kryzanowski and Zhang’s (1992) long-term over-reaction findings in Canadian markets seem to contradict those of De Bondt and Thaler (1985) in US markets and those of Alonso and Rubio (1990) in the Spanish market27. Notwithstanding, Jegadeesh (1990) reports that a contrarian strategy, based on information from the previous month, yields statistically significant abnormal returns of 1.99% per month over 1934-1987 period in US markets, and 1.75% outside January. This result is quite outstanding, as these abnormal returns are nearly double those resulting from De Bondt and Thaler’s long term contrarian strategies. There is no out-of-sample evidence to support Jegadeesh’s (1990) one-month contrarian findings28.

27 _{Kryzanowski and Zhang’s (1992) find significant continuation behaviour for winners and losers in the}

subsequent one and two years, and insignificant reversal over long test periods. Cleary and Inglis (1998) also find performance continuation over the medium term in Canadian markets.

28 _{Lehmann (1990) finds that one-week winners and losers experience significant return reversals the next}

week, thereby reflecting arbitrage profits that persist after corrections for bid-ask spreads. However, Conrad, Gultekin, and Kaul (1997) argue that Lehmann’s (1990) results are largely attributable to the bid- ask bounce of transaction prices.

44

2.13.2.2 Short-run perspective

The short-run perspective, which focuses on biases in the stock market reaction to the arrival of unexpected information, has also been analysed using daily returns by researchers such as Akhigbe, Gosnell and Harikumar (1998). Bowman and Iverson (1998) perform a similar analysis using weekly returns. Evidence on the short-run perspective favors the overreaction hypothesis. Alternatively stated if investor over- reaction/under-reaction is real, then the price correction process should primarily occur over a very short-term period since it is difficult to justify that any arbitrage opportunity arising from these deviations persists over a long period (Daniel, Hirshleifer, and Subrahmanyam, 1998).

2.13.3 Anatomy of a contrarian strategy

T here is much support from behavioural finance that individuals form their predictions of the future without a full understanding of mean reversion. In other words, individuals tend to base their expectations on past data for the individual case they are considering without appropriately weighting data on what psychologists call the “base rate” or the class average. Kahneman and Tversky (1982, p.417) explain:

“…One of the basic principles of statistical prediction, which is also one of the least intuitive, is that the extremeness of predictions must be moderated by considerations of predictability… Predictions are allowed to match impressions only in the case of perfect predictability. In intermediate situations, which are of course the most common, the prediction should be regressive, i.e, it should fall between the class average and the value that best represents one’s impression of the case at hand. The lower the predictability the closer the prediction should be to the class average. Intuitive predictions are typically non-regressive: people often make extreme predictions on the basis of information whose reliability and predictive ability are known to be low…”

45 To make the most of this defect of intuitive forecasts, contrarian investors should sell stocks with high past growth as well as high expected future growth and buy stocks with low past growth as well as low expected future growth. Prices of these stocks are most likely to reflect the failure of investors to impose mean reversion on growth forecasts (La Porta, 1995).

2.13.4 Are contrarian strategies riskier?

Two alternative theories explain why value strategies have produced higher returns in the past. The first one saying that they have done so because they exploit the mistakes of naïve investors, is backed by the fact that investors appear to extrapolating the past too far into the future, even though the future does not warrant such extrapolation. As to value stocks being fundamentally riskier than glamour stocks, these would be so if they underperform glamour stocks in some states of the world, and second, those are on average “bad states” (in which the marginal utility of wealth is high, making value stocks unappealing to the risk-averse investor). Interestingly, the reward for bearing fundamental risk does not seem to explain higher average returns on value stocks than on appealing stocks (Lakonishok et al., 1994).

2.13.5 Performance of contrarians

Contrarians buy stocks that performed poorly over the past two to five years (prior losers) and sell short stocks that performed well over the same period (prior winners). This approach earns subsequent excess returns of about 8 per cent per year (DeBondt and Thaler, 1985). However, the profits may be partly misleading, a product of methodological and measurement problems (Ball, Kothari, and Shanken, 1995). It may also be that the excess returns are “real” but rational reward for time-varying risk (Fama, 1991). Nonetheless, other academics like (Schiereck et al., 1999) find contrarian strategies to beat a passive approach invested in the market index. Odean (1998) supports that the investors at a US discount brokerage house are reluctant to realize losses, and presents evidence which are consistent with contrarian behaviour. Moreover,

46 using Finland’s data, domestic investors, particularly households, tend to be contrarians (Grinblatt and Keloharju, 2000). Evidence from the US, Japan, U.K., and other European countries suggests that over long time intervals, contrarian strategies generate significant abnormal returns (Arshanapali, Coggin, and Doukas, 1998; Fama and French, 199829). Finally but not least, Jegadeesh (1990) observes a seasonality pattern in contrarian profits and document a significantly different return pattern in January.

Importantly too, many researchers attribute the performance of contrarian strategies to investor behaviour. De Bondt and Thaler (1985) also mention that past performance can provide a proxy for investor sentiment, and since prices are initially biased either by unnecessary optimism or pessimism; prior losers would make more attractive investments than prior winners over the long-term. Their argument is consistent with the hypothesis of long-term over-reaction by investors to information – a hypothesis documented in several other markets (e.g, Gunaratne and Yonesaaw (1997) in Japan, Schiereck, De Bondt, and Weber (1999) in Germany).

2.13.6 Robustness in contrarian investing

We must be vigilant in drawing conclusions about the relative importance of each horizon from a cross-horizon comparison of the share of positive buy ratio differences. While it is fair to conclude that uneven large magnitudes for the more recent horizons imply that the more recent horizons are more important, the converse need not apply. Larger magnitudes for the more distant horizons, can be simply be due to larger return inconsistencies between winning and losing stocks for the more distant horizons than for the more recent horizons. This is because the more distant horizons identify winners and losers over a larger number of days (Grinblatt and Keloharju, 2000). Also, it’s worth encompassing the fact that the contrarian strategy almost inevitably leads to initial losses as an undervalued stock continues to go down. People are very averse to losses

47 (Kahneman and Tversky, 1979). Likewise, Chopra, Lakonishok, and Ritter (1992) find that the overreaction phenomenon is considerably stronger for smaller firms than for larger firms. Similarly, Odean (1998) finds that small investors are reluctant to realize their losses, and they sell winners “early”. That’s critical for small investors’ decisions (Bange, 2000).