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etely opposite theories and even though such strategies have been practiced to a great extent it is only in the last 10 years that academics have become involved in both quantifying and accounting for their success.

Most academics in their studies have tried to identify the possible reasons for momentum but still have not reached a common explanation for the momentum premium. “In an efficient market trading strategies, based on past prices or publicly available information, are unlikely to succeed as the information that investors seek to exploit is already reflected in prices. Ceteris paribus, a profitable momentum strategy is an anomaly because it is inconsistent with market efficiency”. (Agyei-Ampomah, Sam, 2005)

The momentum premium has been described as a compensation for bearing higher risk (Conrad and Kaul; 1998), a result of data mining (Black, 1993; MacKinlay, 1995), or deceptive and economically insignificant in an attempt to rationalise the anomaly (Lesmond et al, 2004; Hanna and Ready, 2003). However, there are studies that show that the risk-based and data mining claims do not sufficiently explain the momentum premium.

The most likely explanation for the momentum effect seems to be investors’ underreaction/overreaction to information, which is gradually integrated into prices during the next period. Supporters of this view believe that momentum strategies produce higher returns by taking advantage of irrational market behaviour, such as investor underreaction and/or overreaction to information. Daniel et al (1998) suggest that the momentum effect is a product of a delayed market overreaction.

The work of Hong and Stein (1999) and Barberis and Shleifer (2003) also indicate early underreaction and a succeeding overreaction which is consistent with short term price continuations. Doukas and McKnight (2005) find evidence in support of this view, as in Hong and Stein (1999, 2000). Du (2002) argued that investors can be characterised by high or low levels of confidence and thus underreaction arises when investors with low confidence are slow to make decisions.

According to Du (2002) “Delays in acting upon information cause the effects of new information to persist inducing a continuation pattern in returns”. Firm trading level characteristics have also been found to influence momentum profits. Lee and Swaminathan (2000) reported that firms with high trading volume have higher momentum than firms with low trading volume.

4. Transaction Costs

Except for the sources of momentum profits, another significant but still puzzling subject has to do with the cost-effective significance of the momentum returns. Although transaction costs are essential in evaluating an investment strategy, past momentum studies fail to appreciate the trading costs associated with the strategy. Jegadeesh and Titman (1993) and Liu et al (1999) both assume a one-off cost of 0.5%.

This is an ambiguous estimation based on average trading costs and ignores the frequency of trades. Lesmond et al (2004) conclude that momentum returns cannot be realised due to considerable transaction costs. In contrast, Korajczyk and Sadka (2004) find that for certain momentum strategies it would require about $6bn for profits generated by momentum strategies to “vanish” due to transaction costs. Although transaction costs could be substantial本论文由英语论文网提供整理，提供论文代写，英语论文代写，代写论文，代写英语论文，代写留学生论文，代写英文论文，留学生论文代写相关核心关键词搜索。