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代写留学生作业,Efficient Trading Market
发表日期:2013-09-09 09:08:03 | 来源:assignment.cc | 当前的位置:首页 > 代写留学生作业 > 正文
Efficient Trading Market

One of the most renowned concepts of modern portfolio theory is the Efficient Market Hypothesis. According to the EMH,“capital markets are efficient in an informational sense. A capital market is considered to be efficient if, through trading activities, investors set the price of any particular security in a manner that impounds new information about that security in a direct way”(Financial Advisor Magazine, 2004).

This implies that a market is informationally efficient when current prices reflect all past and currently available information. Hence, the market price of a security can only be altered if new information, which by definition is not fully foreseeable, appears. Most importantly, the EMH presupposes that investors are rational and therefore value securities rationally. When new information comes out, investors evaluate its effect on current and future cash flows, and adjust the price of the stock to reflect the new information.

However, there are some challenges to the EMH which have been extensively documented. The two most broadly known and extensively studied are contrarian and momentum trading strategies, which presume that future returns are predictable on the basis of past returns. Momentum strategies buy stocks which have performed well in the past (winners) and sell stocks which have performed poorly in the past (losers), profiting from return continuation.

Contrarian trading strategies support the opposite method of buying past winners and selling past losers, thus generating profits from return reversals. Both these strategies, when properly carried out have been shown to generate exceptional returns. Contrarian strategies are found to be profitable at very short time horizons up to approximately one month and at long horizons of several years, whereas momentum strategies are found to be profitable at intermediate horizons of up to one year.

2. Literature Review

2.1 Momentum strategies

A momentum strategy is to buy past 1 to 12 months’ winners and to sell losers in the next 1 to 12 months. It is a straightforward trading strategy, according to which the portfolios are constructed based on cumulative return criterion over certain horizons. A “contrarian” strategy is accomplished in the same way, but winners are sold and losers are bought.

The amount of academic studies documenting the momentum strategy has been remarkable. The momentum strategy gained great importance in 1993 when a study carried out by Sheridan Titman at the University of Texas and Narasimha Jegadeesh, who was then at the University of Illinois was published.

According to Titman and Jegadeesh (Journal of Finance, 1993) “maintaining a long position in past strong performers and shorting past weak performers could earn investors abnormally large returns over a six-to 12-month horizon”. The momentum effect indicates that stocks with high returns over the past 3 to 12 months tend to outperform in the future. Historically, momentum strategies earned profits of about 1% per month over the following 12 months in the stock market (Titman and Jegadeesh, 1993) and in the foreign currency spot market (Okunev and White, 2003).

Several studies in the field of momentum strategies like the ones conducted by Jegadeesh (1990), Jegadeesh and Titman (1993, 2001, 2002), Chan, Jegadeesh and Lakonishok (1996), Rouwenhorst (1998), Grundy and Martin (2001) and Lewellen (2002) have provide evidence that by taking a long position in stocks with the strongest past performance and taking a short position in stocks with the poorest past performance generates excess returns over intermediate investment horizons (3-12 months).

These studies indicate that prices underreact to information and are mainly based on intermediate horizons. In their study, Jegadeesh and Titman show that stocks with higher returns over a particular intermediate investment horizon continue to perform better than firms with lower past returns in the same period. Using data from US stock exchanges, Conrad and Kaul (1998), and Hong et al. (1999, 2000) provide evidence supporting momentum strategies. Furthermore, Liu et al. (1999) have found comparable results in the UK.

Hon and Tonks (2003) expand the sample of Liu et al. to a total of 6600 stocks over the period January 1955 to December 1996. The results of Hon and Tonks are consistent with those of Liu et al. finding significantly positive returns, with the most profitable strategy being the one that holds the momentum portfolio for 6 months after a 12 month ranking period, yielding an annualised return of 16.2%. There is also evidence of abnormal excess return generated by momentum strategies across industries (Moskowitz and Grinblatt 1999).

Rouwenhorst (1998), Chui, Titman and Wei (2000), Griffin, Ji and Martin (2003) state that even after eliminating the effect of individual stock momentum, industry momentum strategies yield significant abnormal returns, across countries and Chen and DeBondt (2004) have found evidence of excess returns by momentum strategies across equity styles. Okunev and White (2003) find that it also exists in foreign currency spot market and Miffre and Rallis (2005) show the evidence of it in commodity futures market. Rouwenhorst (1998) investigates the possibility that momentum is limited to the US, examining momentum strategies in 12 European countries. Rouwenhorst’s results are similar to those of Jegadeesh and Titman, adding extra significance to the importance of the US results.

Except for the US and the UK, the span of evidence on the international presence of stock momentum is examined by Chui et al. (2000) who document momentum profits in Asian markets with the exceptions of Japan and Korea. Doukas and McKnight (2005) find significant momentum returns in 8 out of 13 European markets, whilst Forner and Marhuenda (2003) find significant momentum returns in the Spanish market.

2.2 Contrarian strategies

The studies carried out by DeBondt and Thaler (1985, 1987), Chan (1988), Chopra, Lakonishok, and Ritter (1992), and Richards (1997) show that the contrarian strategy of ranking portfolios according to past returns, buying the stocks with the worst performance and selling those with the best performance, generates positive excess returns over a long holding period (usually three to five years).

The overreaction hypothesis stands in contrast to the underreaction hypothesis supported by proponents of the momentum strategy. The success of the contrarian strategy has been credited to the overreaction hypothesis of DeBondt and Thaler (1985, 1987), DeLong et al. (1990, 1991), and Hong and Stein (1999). In 1996, Chan, Jagadeesh and Lakonishok argued that “Spelling out the links between momentum strategies and contrarian strategies remains an important area of research.”

Market overreaction, first observed by DeBondt and Thaler as mentioned earlier, has introduced a new area of research in finance and their ideas have greatly influenced securities selection for practitioners. Chopra et al. (1992) find that loser portfolios, formed on the basis of prior five-year returns, outperform winners by 5 to 10% per year during the subsequent five years.

Using national stock market indexes, Richards, stresses that during the first six months after portfolio formation, winners continue to outperform losers (momentum), but over the 3 and 4 years horizon, losers start to outperform winners (contrarian). DeBondt and Thaler (1985, 1987) in their study found that the stocks that performed poorly over the previous 3 to 5 years will generate higher returns over the next 3 to 5 years than the stocks that performed well during the same period. They also found that a contrarian strategy of selling past winners and holding past losers generates approximately 8 percent per year from years 3 to 5.

This study shows that contrarian strategies are more profitable over long holding period of 3 to 5 years. Trading strategies of taking long positions on losers and short positions on winners have been employed by Lehman (1990) and Park (1997). Jegadeesh (1990), Martell and Trevino (1990), Lehman (1990), Jegadeesh and Titman (1995), Antoniou, Galariotis, Spyrou (2003), Wang, Yu (2004), and have proved that reversals also exist over shorter horizons (of one week to three months).

For instance, Lehman finds that portfolios of securities that had positive returns in one week typically had negative returns in the following week, while portfolios with negative returns in one week typically had positive returns in the next week. Jegadeesh (1990) finds the same result using the monthly data from 1934 to 1987. Both of them show contrarian strategy yield abnormal returns in the very short forming period. Chopra et al. (1992) find some support for the conclusions of DeBondt and Thaler, but report that only 2.5% of the 14% return on their contrarian strategy is attributable to overreaction.

Nevertheless, evidence of overreaction has continued to emerge from different markets such as Japan (Bremer et al. (1999)) and China (Kang et al. (2002)). UK evidence on overreaction has been largely consistent with the early US study of DeBondt and Thaler (1985). Power et al. (1991) and Macdonald and Power (1991) find evidence of long-term overreaction, although only for a small sample. Another study by Clare and Thomas (1995) provides support for this evidence, but is found to be largely attributable to the size effect.

Dissanaike (1997) finds more than 50 supporting evidence for overreaction in the UK over the period 1993-2000 using FTSE 500 data. Along with these studies of long-term overreaction, very short-term overreaction has been documented by Jegadeesh (1990) and Lehmann (1990), but is now generally considered to be the product of short-term microstructure biases as argued by Kaul and Nimalendran (1990) and Lo and MacKinlay (1990).

3. Sources of Momentum

Undoubtedly, the body of research on momentum strategies has grown significantly over the past years. Momentum and contrarian trading strategies rely on completely 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, they do not completely clarify the existence of the momentum premium, hence further analysis on the economic significance of the momentum strategy is necessary.

5. Data

For the purposes of this paper, we would like to examine whether there is evidence of abnormal returns in the Greek stock market (Athens Stock Exchange--ASE from now on) and especially in the FTSE 20 index of large capitalization stocks, over a period of 21 years from January 1987 to January 2008 following either a momentum strategy or a contrarian strategy. The motivation of this project is the fact that momentum profitability in emerging stock markets such as the ASE has not been examined to a great extent.

A few years before, Greek stock market was a relatively small and under-examined emerging market. However, innovation and other major reforms that have taken place in the last 20 years caused the market to gain more power. In the last decade an increasing number of new companies were admitted in the ASE in order to raise capital, and a growing number of investors entered the market by investing in corporate stocks. These developments enhanced the local and international investment interest for the ASE, which is now expected to gain the characterization of a more developed market.

The database we will use in this paper consists of the daily spot stock prices of all the stocks that form the FTSE 20 index, which can be sourced from either DataStream or Bloomberg. FTSE 20 index is a large capitalization index which includes the 20 largest companies (blue chips) listed in ASE. We have chosen this period because it covers some interesting periods of stock market behavior and the Greek economy as a whole as indicated by 1) four national elections, 2) the entry of Greece to the “Euro-Zone” (2001) 3) the characterization of the Greek stock market as a developed market since 2001, 4)the “Black Monday” crash in 1987 -- a world-wide phenomenon where all major world markets decreased substantially 5) the crash of the Athens Stock Exchange in 1999 and 6) The ‘free-fall’ of the Greek Stock Market Index from 6500 points (September 1999) to 1800 points (September 2002). All these events make the study of the existence of momentum extra-returns in the ASE to be a rather challenging task and we expect to discover some rather interesting results.

6. Methodology

This paper aims to extent previous studies for the ASE by employing the same methodology that was used in one of the most significant studies about momentum strategy by Jegadeesh and Titman (1993, 2001, and 2002). In their study, they tested whether momentum strategy yields abnormal return using the sample of all the stocks traded on the New York Stock Exchange and American Stock Exchange for the testing period from 1965 to 1989. Koutmos et al (1993), Antoniou et al (2005), Spyrou and Mandalis (2003), in their studies have provided evidence of return predictability in ASE using the methodology employed by DeBondt and Thaler (1985).

From the dataset described in section 5, we will compute the returns of each stock that comprise the FTSE 20 index using the formula below:

where is the stock return for period t, is the spot stock price at period t and is the spot stock price at period t-1.

We select the stocks based on their performance over the last 3, 6, 9, 12 months, the formation period. When the returns are calculated, the stocks will be ranked from highest to lowest based on the historical return. The next step will be to engage in a series of strategies to create long positions in the stocks with the highest return and create short positions in the stocks with the lowest return.

The return on the momentum portfolio is then measured as the difference between the returns of the winner and the loser stock. The procedure is moved forward to create new winner, loser and momentum portfolios. For example, in the 3-1 strategy, the winner/loser portfolio is formed at the end of December, based on past 3 months return, holding this portfolio for 1 month, and forming a new portfolio at the end of January.

Once a stock exposure is initiated it is held for a period, at which time the foreign exchange position is re-evaluated. The return of momentum is simply defined as the difference in the returns of the winner and loser stock. We can form 16 distinct strategies; (1-1, 1-3, 1-6, 1-12, 3-1, 3-3, 3-6, 3-12, 6-1, 6-3, 6-6, 6-12, 12-1, 12-3, 12-6, 12-12). A brief description of the 1-1 strategy follows:

1-1 strategy:

Both the ranking and the holding period for this strategy is 1 month. We calculate the returns for all stocks beginning from January 1987 to November 2007 and rank them from the most attractive stock to the least attractive for each month. Once the strongest and weakest stocks are identified, a long/short position is taken by buying the most profitable stock and selling the least profitable one.

For each month, we calculate the profits/losses of our “sub-portfolios” and at the end of the year we calculate the total profits/losses of our portfolio. For example, in this strategy the winner/loser portfolios were formed at the end December , based on the previous month’s returns, holding this portfolio for 1 month, and forming a new portfolio at the end of January. We will follow the same procedure to form the other strategies, by changing the ranking and holding period for each one and we will also carry out the contrarian strategy for the same ranking and holding periods under which, the weakest momentum stock is bought and the strongest momentum stock is sold.

7. Conclusions

After carrying out the study we expect that the momentum profitability effect will be present in the Greek stock market and these results would be consistent with the results in previous studies about the existence of momentum profits in the European and emerging markets and the studies for the existence of momentum profitability in the ASE.

A study published in the Financial Times on February 18th by Dimson, Marsh and Staunton states that “momentum investing in equity markets delivers striking and remarkably persistent excess returns” They conclude: “Though costly to implement on a stand-alone basis, all investors need to be acutely aware of momentum. Even if they do not set out to exploit it, momentum is likely to be an important determinant of their performance”.

The study of both momentum and contrarian investment strategies is a very interesting and challenging discipline in finance, not only because the effectiveness of such strategies suggest a rejection of the EMH, but because research into the sources of these profits lies at the centre of comprehending the behaviour of market participants and how markets price securities. From the perspective of investors the significance is equally great, indicating the methods of safe and profitable investment, and giving answers to the questions of corporate managers who wish to know how the market will react to announcements about the wellbeing of their companies and their plans for the future.

 

 

高效的交易市场
其中最知名的现代投资组合理论的概念是有效市场假说。根据有效市场假说, “资本市场是有效的信息意义。资本市场被认为是有效率的,如果通过交易活动,投资者在直接的办法“(理财顾问”杂志,2004年)的新信息,安全的方式,扣留任何特定安全设置价格。
这意味着一个市场信息有效,当目前的价格反映了所有过去和目前可用的信息。因此,安全的市场价格只能被改变,如果有新的信息,它的定义是不完全可预见的,出现。最重要的是,有效市场假说的前提是,投资者是理性的,因此合理价值证券。当新的信息出来,投资者评估其对当前及未来现金流量,股票的价格调整,以反映新信息。
但是,也有一些已被广泛记录的有效市场假说的挑战。两个最广为人知和广泛的研究是逆势而上的动量交易策略,推测,未来的回报的基础上,过去的回报是可以预见的。动量策略,买股票都表现不错(优胜者)在过去和出售表现不佳的股票,在过去(失败者) ,从中获利回报延续。
逆势交易策略买过去的获奖者和销售过去的失败者支持相反的方法,从而产生利润回报逆转。已经示出了这两种策略,在正确进行生成呈回报。反转策略是有利可图的,在很短的时间视野时约一个月,在长几年视野,而动量策略是有利可图的中间视野长达一年。
2。文献综述
2.1动量策略
势头策略是,买过去1个月至12个月的获奖者,并出售失败者在接下来的1个月至12个月。这是一个简单的交易策略,根据组合构造的基础上累计回报超过一定的视野标准。 A“逆势”策略,以同样的方式来完成的,但销往赢家和输家买。
记录动量策略的学术研究十分显着。动量策略谢里登蒂特曼在得克萨斯州和纳拉辛哈Jegadeesh ,谁当时在伊利诺伊大学的大学开展的一项研究发表在1993年获得了极大的重视。
据Titman和Jegadeesh (财经杂志,1993年) , “在过去的表现强劲,并维持长仓过去的表现弱短路可以赚取投资者异常大的回报超过6到12个月的地平线” 。动量效应表明,在过去的3个月至12个月高回报的股票往往在未来跑赢大市。从历史上看,动量策略赢得了超过12个月的每月1%左右的利润,在股市( Titman和Jegadeesh ,1993) ,并在外汇现货市场( Okunev和White ,2003) 。
Jegadeesh (1990) , Jegadeesh和蒂特曼(1993年, 2001年, 2002年) ,陈Jegadeesh和Lakonishok ( 1996年) , Rouwenhorst ( 1998年) ,格兰迪和马丁进行的一些研究等领域的动量策略(2001)卢埃林(2002)提供的证据表明,通过采取好仓淡仓股票的过往表现最差的在过往表现最强的股票,并采取中级以上的投资期限( 3-12个月)产生的超额收益。
这些研究表明,价格信息的反应不足,主要是基于中间视野。在他们的研究中, Jegadeesh和蒂特曼表明,在一个特定的中间投资期限的回报率较高的个股继续有更好的表现比过去的回报率较低,在同一时期的企业。使用从美国证券交易所,康拉德和考尔(1998) ,以及香港等数据。 ( 1999年,2000年)提供了证据支持动量策略。此外, Liu等人。 (1999)发现在英国比较的结果。
汉和唐克斯Liu等人(2003)扩大样本。共有6600只股票在1955年1月至1996年12月期间。汉和唐克斯的结果是一致的,与刘等人。发现显着的正回报,最有利可图的策略,作为一个拥有6个月的动量组合排名后12个月期间,产生的年回报率为16.2% 。也有证据表明整个行业( 1999年莫斯科维茨和矫正Grinblatt )所产生的动量策略的超额回报。
Rouwenhorst (1998) ,翠, Titman和伟(2000) ,格里芬,姬和马丁(2003) ,即使在消除个股势头的效果,行业动量策略产生显着的异常报酬,跨越多个国家和陈和DeBondt ( 2004 )发现动量策略风格涵盖股票超额收益的证据。 Okunev和白色(2003)发现,它也存在于外汇现货市场和Miffre拉利斯(2005)的商品期货市场的证据。 Rouwenhorst (1998)调查这一势头的可能性是有限的美国,研究动量策略在12个欧洲国家。 Jegadeesh和蒂特曼rouwenhorst的结果是相似的,美国结果的重要性增加额外的意义。
除美国和英国对国际存在的股票势头的证据,跨度翠等检查。 (2000)文件势头利润在亚洲市场上,日本和韩国例外。 DOUKAS和麦克奈特(2005)发现重大的势头进8出13个欧洲市场的回报,而福尔和Marhuenda的(2003)发现在西班牙市场的势头显著回报。
2.2反转策略
开展的研究逆势排名组合策略DeBondt和Thaler ( 1985年, 1987年) ,陈(1988) ,乔普拉, Lakonishok和Ritter (1992) ,理查兹( 1997年)显示,根据过去的回报,买股票表现最差和销售具有最佳性能,产生正的超额收益​​,在一个较长的持有期(通常为三至五年) 。
过度反应假说站在相反的势头策略的支持者支持的反应不足假说。逆势策略的成功已计入过度反应假说的DeBondt和Thaler ( 1985年, 1987年) ,德隆等。 ( 1990年, 1991年) ,香港和Stein (1999) 。在1996年,陈Jagadeesh Lakonishok认为,“拼写出来的动量策略和反向策略之间的联系仍然是一个重要的研究领域。 ”
市场过度反应,首次观察到由DeBondt和Thaler提到较早,已推出一个新的研究领域,在金融,极大地影响了他们的想法证券从业选择。 Chopra等人。 (1992)发现,的失败者组合的基础上,形成了前五年回报在随后的5年,每年的5~10% ,跑赢大盘的赢家。
使用国家股市指数,理查兹,强调组合形成后首六个月期间,获奖者继续跑赢失败者(势头) ,但在3年和4年的地平线上,的失败者开始超越获奖者(逆势) 。 DeBondt和Thaler (1985 ,1987)在他们的研究中发现,比上年的3至5年的股票表现不佳,在未来3至5年将产生更高的回报比股票,以及在同一时期进行。他们还发现,逆势策略卖过去的获奖者和过去的失败者每年产生约8 % ,从3至5年。
这项研究表明,反向策略更有利可图超过3至5年的持有期长。输家和赢家上的淡仓多头头寸的交易策略已聘请雷曼兄弟(1990)和公园( 1997年) 。 Jegadeesh (1990) ,马爹利和特维诺(1990) ,雷曼兄弟(1990) , Jegadeesh和蒂特曼(1995年) ,安东尼奥, Galariotis SPYROU (2003) ,王宇(2004) ,并已证明在较短的视野也存在逆转( 1个星期到3个月) 。
例如,雷曼兄弟认为,在一个星期内,有正回报的证券投资组合通常有负回报,在接下来的一周,而在一个星期的负回报的投资组合通常在下周有正面回报。 Jegadeesh (1990)发现, 1934年至1987年的月度数据使用相同的结果。他们都显示逆势策略收益率在很短的形成期异常报酬。 Chopra等人。 (1992)发现的的结论DeBondt和Thaler一些支持,但报告说,他们的逆势策略的14 %的回报率只有2.5%是由于过度反应。
然而,过度反应的证据不断涌现,从不同的市场如日本(布雷默等人(1999) )和中国( Kang等人(2002)) 。英国过度反应的证据已经与美国早期的DeBondt和Thaler (1985)的研究基本一致。电源等。 (1991)和麦克唐纳和电力(1991 )发现长期过度反应的证据,虽然只是一个小样本。克莱尔和托马斯的另一项研究(1995)提供了这方面的证据支持,但被发现主要是由于规模效应。
Dissanaike (1997)发现50多的证据支持使用富时500数据在1993-2000年期间在英国的过度反应。非常短期的过度反应,随着这些长期过度反应的研究,已记录Jegadeesh (1990)和莱曼( 1990 ) ,但现在一般认为是由Kaul和Nimalendran的辩称短期微观偏见的产品(1990)和Lo和麦金利( 1990年) 。
3。动量来源
毫无疑问,身体的动量策略的研究在过去几年大幅增长。依靠动量和逆势交易策略完全相反的理论,尽管已经实行了这种战略在很大程度上,它只是在过去的10年,已经成为学术界参与量化和会计为他们的成功。
在他们的研究中,大多数学者试图找出势头的可能原因,但还没有达成一个共同的解释势头溢价。 “在一个有效的市场交易策略,基于对过去的价格或公开资料,是不可能成功的,为投资者寻求利用已经反映在价格的信息。在其他条件不变的情况下,一个有利可图的动量策略是一种反常现象,因为它是与市场效率不一致“ 。 ( Agyei Ampomah ,山姆, 2005年)
动量溢价已被描述为用于补偿轴承较高的风险(康拉德·考尔1998) ,数据挖掘(黑, 1993年麦金利,1995年)的结果,或欺骗性和经济意义的,企图合理化异常( Lesmond等,2004年, :汉娜和准备,2003年) 。不过,也有研究表明,基于风险和数据挖掘的要求并没有充分解释的势头溢价。
动量效应最可能的解释似乎是投资者在今后一个时期,它正逐渐融入价格信息的反应不足/过度反应。这种观点的支持者认为,动量策略利用市场非理性行为,如投资者的过度反应和/或信息的过度反应,产生更高的回报。丹尼尔等人(1998)建议的势头效应是一个产品的市场过度反应延迟。
香港和Stein (1999)和巴伯里和Shleifer (2003)的工作也表明早期的过度反应和随后的过激反应,这是,符合短期价格延续。 DOUKAS和麦克奈特(2005)发现支持这一观点的证据,如在香港和Stein ( 1999年, 2000年) 。杜(2002)认为,投资者可以特点是高或低的置信水平,从而出现过度反应,当投资者信心低慢作出决定。
据杜(2002)“在作用于信息的延误造成的影响的新信息坚持诱导延续回报模式” 。商行贸易水平的特点也被发现影响势头利润。李斯瓦米纳坦(2000)报道,比公司低成交量,成交量高的公司具有较高的势头。
4 。交易成本
除为势头利润的来源,另一个显着,但仍然令人费解的题目做的气势回报的成本效益的意义。虽然交易成本是必不可少的,在评估投资策略,未能认识到过去的动量研究与战略相关的交易成本。 Jegadeesh和蒂特曼(1993)和Liu等人(1999)都承担0.5%的一次性成本。
这是一个模糊的估计的基础上平均交易成本,而忽略了交易频率。 Lesmond等人(2004)的结论势头回报无法实现,由于可观的交易成本。在的相比之下, Korajczyk Sadka (2004)发现,一定的动量策略将需要大约60亿美元的利润所产生的动量策略“消失” ,由于交易成本。虽然交易成本可能是巨大的,他们并没有完全澄清的势头溢价的存在,从而进一步分析动量策略的经济意义上是必要的。
5 。数据
对于本文的目的,我们想检查是否有证据表明希腊股市异常报酬(雅典证券交易所 - ASE从现在起) ,尤其是在富时20指数的大市值股票,过一段21年,从1987年1月至2008年1月,一个的动量策略或逆势策略。这个项目的动机势头,盈利能力在新兴股票市场,如日月光尚未审查在很大程度上是事实。
几年前,希腊股市是一个相对较小的,并根据检查的新兴市场。然而,创新和其他重要改革,在过去的20年里已经发生引起了市场获得更多的权力。在过去十年中,越来越多的新公司在ASE承认,为了筹集资金,越来越多的投资者进入市场,通过投资于公司股票。这些发展提高了本地和国际的投资权益,日月光,现在有望获得一个较为发达的市场表征。
我们将在本文中使用的数据库包括所有的股票,形成了富时20指数,可以来源于数据流或彭博每日现货股票价格。富时20指数是一个大市值指数,其中包括20家最大的上市公司(蓝筹股) ASE 。我们选择了这个时期,因为它涵盖了一些有趣的股票作为一个整体的市场行为,希腊经济时期为1表示)四个全国大选, 2 )希腊进入“欧元区” (2001年)3 )自2001年以来,希腊股市作为一个发达的市场特性,4)于1987年的“黑色星期一”撞车 - 一个全球性的现象,在全球各大市场大幅下跌5 )坠毁雅典证券交易所在1999年6 ) “自由下落”的希腊股市指数从6500点( 1999年9月) , 1800点( 2002年9月) 。所有这些事件的研究中存在的额外的势头在ASE回报是一个相当具有挑战性的任务,我们希望发现一些相当有趣的结果。
6 。方法论
本文旨在Jegadeesh和蒂特曼( 1993年,2001年和2002年)关于动量策略的最重要的研究之一,它被应用于采用相同的方法为ASE以前的研究程度。在他们的研究中,他们测试,动量策略是否产生异常返回使用的所有交易的股票在纽约证券交易所和美国证券交易所的测试期间, 1965年至1989年的样本。等Koutmos (1993) ,安东尼奥等人(2005 ) , SPYROU Mandalis (2003年) ,在他们的研究提供证据返回日月光使用由DeBondt和Thaler (1985)所采用的方法的可预见性。
从第5节中描述的数据集,我们将每只股票的回报,包括富时20指数使用下列公式计算:
哪里是t期的股票报酬,是在t时期的价格和现货库存是现货股票在t-1期的价格。
我们选择的股票根据他们的表现,在过去的3 , 6, 9 , 12个月,形成期。计算回报时,将股票从最高到最低的基础上的历史回报排名。下一步将要进行一系列的策略,回报率最高的股票建立多头头寸并建立淡仓的股票回报率最低。
返回的势头组合,然后测量的赢家和输家股票的回报之间的差额。向前移动的过程,创造新的赢家,输家和动力组合。赢家/输家组合在3-1的战略,例如,在12年底形成的,过去3个月回报的基础上,对该组合进行1个月,并在月底形成一个新的投资组合。
一旦股票开始曝光,它是一段时间,持有的外汇头寸被重新评估。返回的势头被简单地定义为赢家和输家股票的回报的差异。我们可以形成16种不同的策略(1-1 , 1-3 , 1-6, 1-12 , 3-1 , 3-3 , 3-6 , 3-12 , 6-1 ,6-3 ,6 - 6, 6-12 , 12-1 , 12-3 , 12-6 , 12-12) 。 1-1策略的简要说明如下:
1-1战略:
这一战略排名和持有期为1个月。我们计算从1987年1月至2007年11月开始对所有股票的回报,他们最有吸引力的股票,每个月至少有吸引力的排名。一旦最强和最弱的个股都已经确定,长/短仓买最赚钱的股票,卖的最有利可图的。
每个月,我们计算的利润/亏损,我们的“组合” ,并在今年年底,我们计算我们的投资组合的总利润/亏损。例如,在这一战略的赢家/输家组合,形成在12月底,根据前一个月的回报,这个组合为1个月,并在月底形成一个新的投资组合。我们将遵循相同的步骤,形成其他的策略,通过改变排名和持有期为每一个和我们还将开展相同的排名和持有期,势头最弱买股票逆势策略最强势头股票卖出。
7。结论
进行研究后,我们预期的势头,盈利能力的效果会出现在希腊股市,这些结果与以往的研究势头利润的存在,在欧洲市场和新兴市场的存在和研究的结果是一致的在ASE的势头,盈利能力。
2月18日在英国“金融时报”上发表的一项研究迪姆松,沼泽及士丹顿说:“在股市投资势头提供了醒目和显着持久的超额收益”他们总结说: “虽然昂贵,单机的基础上实现,所有的投资者需要敏锐地意识到势头。即使他们不利用它,势头很可能是一个重要的决定因素,他们的表现“ 。
既有气势,逆势投资策略的研究是一个非常有趣和具有挑战性的财政纪律,这不仅是因为这类战略的有效性,建议拒绝EMH ,但因为研究这些利润的来源在于在理解的中心市场参与者和市场价格的证券的行为。从投资者的角度来看,意义同样很大,说明安全和有利可图的投资方法,并给予答案的问题,企业管理者想知道市场将如何反应,他们的公司和他们的计划的福祉公告未来。