Thesis (MScEng (Industrial Engineering))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: Several studies have shown that abnormal returns can be generated simply by buying past winning stocks and selling past losing stocks. Being able to predict future price behaviour by past price movements represents a direct challenge to the Efficient Market Hypothesis, a centrepiece of contemporary finance. Fund managers have attempted to exploit this effect, but reliable footage of the performance of such funds is very limited. Several academic studies have documented the presence of the momentum effect across different markets and between different periods. These studies employ trading rules that might be helpful to establish whether the momentum effect is present in a market or not, but have limited practical value as they ignore several practical constraints. The number of shares in the portfolios formed by academic studies is often impractical. Some studies (e.g. Conrad & Kaul, 1998) require holding a certain percentage of every share in the selection universe, resulting in an extremely large number of shares in the portfolios. Others create portfolios with as little as three shares (e.g. Rey & Schmid, 2005) resulting in portfolios that are insufficiently diversified. All academic studies implicitly require extremely high portfolio turnover rates that could cause transaction costs to dissipate momentum profits and lead the returns of such strategies to be taxed at an investor’s income tax rate rather than her capital gains tax rate. Depending on the tax jurisdiction within which the investor resides these tax ramifications could represent a tax difference of more than 10 percent, an amount that is unlikely to be recovered by any investment strategy. Critics of studies documenting positive alpha argue that momentum returns may be due to statistical biases such as data mining or due to risk factors not effectively captured by the standard CAPM. The empirical tests conducted in this study were therefore carefully designed to avoid every factor that could compromise the results and hinder a meaningful interpretation of the results. For example, small-caps were excluded to avoid the small firm effect from influencing the results and the tests were conducted on two different samples to avoid data mining from being a possible driver. Previous momentum studies generally used long/short strategies. It was found, however, that momentum strategies generally picked short positions in volatile and illiquid stocks, making it difficult to effectively estimate the transaction costs involved with holding such positions. For this reason it was chosen to test a long-only strategy. Three different strategies were tested on a sample of JSE mid-and large-caps on a replicated S&P500 index between January 2000 and September 2009. All strategies yielded positive abnormal returns and the null hypothesis that feasible momentum strategies cannot generate statistically significant abnormal returns could be rejected at the 5 percent level of significance for all three strategies on the JSE sample. However, further analysis showed that the momentum profits were far more pronounced in “up” markets than in “down” markets, leaving macroeconomic risk as a possible explanation for the vast returns generated by the strategy. There was ample evidence for the January anomaly being a possible driver behind the momentum returns derived from the S&P500 sample. / AFRIKAANSE OPSOMMING: Verskillende studies het gewys dat abnormale winste geskep kan word deur eenvoudig voormalige wenner aandele te koop en voormalige verloorder aandele te verkoop. Die moontlikheid om toekomstige prysgedrag te voorspel deur na prysbewegings uit die verlede te kyk is ‘n direkte uitdaging teen die “Efficient Market Hypothesis”, wat ’n kernstuk van hedendaagse finansies is. Fondsbestuurders het gepoog om hierdie effek te benut, maar akademiese ondersteuning vir die gedrag van sulke fondse is uiters beperk. Verskeie akademiese studies het die teenwoordigheid van die momentum effek in verskillende markte en oor verskillende periodes uitgewys. Hierdie akademiese studies benut handelsreëls wat gebruik kan word om te bepaal of die momentum effek wel in die mark teenwordig is al dan nie, maar is van beperkte waarde aangesien hulle verskeie praktiese beperkings ignoreer. Sommige studies (Conrad & Kaul, 1998) vereis dat 'n sekere persentasie van elke aandeel in die seleksie-universum gehou moet word, wat in oormatige groot aantal aandele in die portefeulle tot gevolg het. Ander skep portefeuljes met so min as drie aandele (Rey & Schmid, 2005), wat resulteer in onvoldoende gediversifiseerde portefeuljes. Die hooftekortkoming van alle akademiese studies is dat hulle portefeulleomsetverhoudings van hoër as 100% vereis wat daartoe sal lei dat winste uit sulke strategieë teen die belegger se inkomstebelastingskoers belas sal word in plaas van haar kapitaalaanwinskoers. Afhangende van die belastingsjurisdiksie waaronder die belegger val, kan hierdie belastingseffek meer as 10% beloop, wat nie maklik deur enige belegginsstrategie herwin kan word nie. Kritici van studies wat abnormale winste dokumenteer beweer dat sulke winste ‘n gevolg kan wees van statistiese bevooroordeling soos die myn van data, of as gevolg van risikofaktore wat nie effektief deur die standaard CAPM bepaal word nie. Die empiriese toetse is dus sorgvuldig ontwerp om enige faktor uit te skakel wat die resultate van hierdie studie sal kan bevraagteken en ‘n betekenisvolle interpretasie van die resultate kan verhinder. Die toetse sluit byvoorbeeld sogenaamde “small-caps” uit om die klein firma effek uit te skakel, en die toetse is verder op twee verskillende monsters uitgevoer om myn van data as ‘n moontlke dryfveer vir die resultate uit te skakel. Normaalweg toets akademiese studies lang/ kort nulkostestrategieë. Dit is gevind dat momentum strategieë oor die algemeen kort posisies kies in vlugtige en nie-likiede aandele, wat dit moeilik maak om die geassosieerde transaksiekoste effektief te bepaal. Daar is dus besluit om ’n “lang-alleenlik” strategie te toets. Drie verskillende strategieë is getoets op ‘n steekproef van JSE “mid-caps” en “large-caps” en op ‘n gerepliseerde S&P500 index tussen Januarie 2000 en September 2009. Alle strategieë het positiewe abnormale winste opgelewer, en die nul hipotese dat momentum strategieë geen statisties beduidende abnormale winste kan oplewer kon op die 5% vlak van beduidendheid vir al drie strategieë van die JSE monster verwerp word. Verdere analiese het wel getoon dat momentumwinste baie meer opvallend vertoon het in opwaartse markte as in afwaartse markte, wat tot die gevolgtrekking kan lei dat makro-ekonomiese risiko ‘n moontlike verklaring kan wees. Daar was genoegsaam bewyse vir die Januarie effek as ‘n moontlike dryfveer agter die momentum-winste in die S&P500 monster.

In this thesis we will evaluate the effectiveness of using daily return percentiles and power means as momentum indicators for quantitative portfolio selection. The statistical significance of momentum strategies has been well-established, but in this thesis we will select the portfolio size and holding period based on current (2012) trading costs and capital gains tax laws for an individual in the United States to ensure the viability of using these strategies. We conclude that the harmonic mean of daily returns is a superior momentum indicator for portfolio construction over the 1970-2011 backtest period.

Thesis (MBA)--Stellenbosch University, 2012. / The success of momentum investing has puzzled the investment society for quite some time. Numerous academics have released studies that proved the success of different momentum investing strategies, even after compensating for trading costs. According to the efficient market hypothesis investors can only realise additional returns by taking additional risks. But no real risk factors can be ascribed to momentum investing. This study investigated the success of momentum investing strategies on the Johannesburg Stock Exchange (JSE) during the period January 1997 to March 2012. Three strategies were tested, namely: return momentum, price relative to high price and the crossover ratio. These strategies were tested using different combinations of testing and holding periods and only the more liquid stocks trading on the JSE were used in the study. The study showed that the momentum investing strategies generated statically significant outperformance over the period. The momentum investing strategies were then dissected according to the three risk factors identified by the Fama and French (1992) three-factor model. None of the risk factors were able to explain the outperformance of the momentum strategies. The outperformance of the momentum strategies also showed remarkable resilience after being subjected to trading costs. The success of the three momentum investing strategies is in clear contravention of the efficient market hypothesis and adds to the growing body of evidence against the hypothesis.

Researchers recommended investing according to the long only momentum (MOM) strategy to generate excess returns for private investors. The general problem of this study was that it was unclear when to enter and when to exit declining financial markets to avoid larger losses and to improve the overall performance with the MOM strategy. Therefore, it was important to understand the influence of a timing indicator on the MOM strategy. The purpose of this study was to examine the relationship between different moving average (MA) settings, the MOM strategy, and the performance of the returns from the construction of small U.S. stock portfolios. The research question was what MA setting as a strategy adjustment could improve the MOM strategy performance for small portfolios of U.S. stocks. A quasi-experimental research design was chosen to answer this research question. For the methods and analysis, simple- and exponential- MA, 2 econometric models, and abnormal Sharpe ratios were computed on the sample basis of 30 Dow Jones Industrial Average (DJIA) stocks. The computations allowed me to determine the optimal trading frequencies for the MA MOM strategy. The key result was that the MA MOM strategy could improve the MOM strategy on average by 0.16% per month. The optimal trading frequency for the MA MOM strategy with $5,000 was tri yearly through which (0.90 - 1.85 %) net monthly return could be achieved. The MOM strategy can be adjusted by a simple moving average (SMA) indicator on a 6 versus 36-month basis as a recommendation. This study might contribute to positive social change by adjusting the MOM strategy, which specifically impacts private investors in declining stock markets to improve the overall performance when trading the MA MOM strategy.

Background: Buying past winners and selling past losers has historically generated both profits and losses. The momentum strategy has been researched with risk measures and portfolio creation as fundamental components. While no definitive framework exists, prior research has explored industry segmentation within portfolio construction but has yet to reach a clear conclusion. Purpose: The purpose is to determine if there is a significant momentum effect in industry-portfolios, and if some industries are more prone to momentum strategy than others. Method: The research followed a positivistic paradigm with deductive reasoning using a quantitative approach. Secondary data of industry returns for 30 industries from the American stock market is collected from Kenneth R French database. The portfolios are analyzed from a statistical perspective to draw conclusions of the market anomaly. Findings: Three hypotheses were formed to address the research question and purpose. The winner-portfolio yielded significant raw returns in 14 of 16 tests for various periods, while loser and winner-loser portfolios showed negative raw returns. Accounting for systematic risk generated significant profits for all the winner portfolios. Further, industry-specific momentum was examined, revealing no momentum in some industries and momentum in others.   Conclusion: We find evidence that the industry portfolio can generate significant excess return over the market for 3–12-month periods, that can't be explained by the assets systematic risks. The study concludes that while industry-specific momentum is a viable strategy for diversification and capturing winners, its effectiveness varies across industries and has shown diminishing excess returns over the past two decades.

本研究以台灣證券交易所1995年2月至2008年所有上市公司的資料為樣本，比較Jegadeesh and Titman (1993)提出的價格動能策略、Moskowitz and Grinblatt (1999)提出的產業動能策略以及George and Hwang (2004)的52週高價動能策略之間的獲利能力。研究分別進行了月平均報酬比較、元月效果檢視、配對比較、迴歸分析以及加入定錨效果的強韌性檢視。 / 結果發現，在持有期為6個月下，只有52週高價動能策略的獲利能力為顯著且報酬率最佳，月平均報酬率達1.12%，且其對報酬率的解釋能力無法被價格動能策略或產業動能策略給替代，然而52週高價動能策略卻能部分替代價格及產業動能策略的解釋能力，顯示52週高價動能策略相較於價格及產業動能策略而言有優勢性。本研究也發現動能策略投資組合的報酬率存在元月效應，無論是哪一種動能策略的贏家或輸家，在一月份的報酬皆大幅顯著的高於其他11個月份，顯示元月效應的確存在且會影響分析的結果。 / 而最後在迴歸分析裡，結果顯示在控制了公司市值、前一期報酬率、各動能投資策略的影響後，無論是全樣本或一月份除外，依然只有52週高價動能策略的獲利能力是顯著的。然而在經過F-F三因子模型風險調整後，各動能策略投資組合的報酬率皆下降，其中價格動能策略投資組合有顯著的負報酬率，而產業動能策略與52週動能策略投資組合則有不顯著的負報酬率，顯示動能投資策略可能暴露在市場風險下，投資人在採用動能投資策略進行投資決策時應謹慎對待。而強韌性的結果顯示加入定錨效果指標後，其對本研究之結果無顯著的改變。