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.

The first essay examines the relation between fund performance and stock selection process. I classify mutual funds into two groups according to their distinctive stock selection approaches: tire kickers who rely on fund managers' personal judgment and fundamental analysis to pick stocks, and quant jocks who use computer-based models to select stocks. I examine how the stock selection approach affects mutual fund performance and economies of scale. I document an increasing trend of quantitative techniques used by mutual funds, in addition to some unique characteristics of quant jocks. Quant jocks and tire kickers have similar factor-adjusted alphas, but quant jocks have higher Sharpe ratios. Quant jocks tend to be much smaller than tire kickers. I explore possible explanations for the size difference. I find that although quant jocks can cheaply screen a large universe of stocks, the stocks that quant jocks invest in are smaller and less liquid, which results in higher transaction costs and limited scalability of quantitative investment strategies. The second essay investigates mutual fund managers' private information about future stock returns as revealed in their portfolio holdings. Specifically, we develop three different stock alpha estimators to predict stock returns based on portfolio compositions and past performance of mutual funds. We find that investment strategies based on our stock alpha estimators perform well, when using information on recent fund holdings and fund purchases. This evidence suggests that fund managers' stock selection skills are quite persistent, and vary widely in the cross-section. We also compare our strategies with 12 quantitative investment signals based on market anomalies, and find that our strategies are not subsumed by these quantitative signals. Thus, our stock alpha estimators reflect private skills of active fund managers that are unrelated to known anomalies. Finally, we develop a conditional stock alpha estimator using information on stock characteristics and fund characteristics. Investment strategies based on the conditional stock alphas deliver further improved performance.

This thesis investigates the application of principal component analysis to the Australian stock market using ASX200 index and its constituents from April 2000 to February 2014. The first ten principal components were retained to present the major risk sources in the stock market. We constructed portfolio based on each of the ten principal components and named these “principal portfolios

Syftet med denna studie var att förklara olika konjunkturlägens påverkan på totalavkastningen samt den riskjusterade avkastningen för tre olika investeringsstrategier. Dessa var Stock Selection for the Defensive Investor samt indexen OMX Stockholm 30 och OMX Stockholm Small Cap Price Index. Den förstnämnda strategin utgår ifrån det 14:e kapitlet i Benjamin Grahams bok, The Intelligent Investor. Genom att ställa höga krav på faktorer som lönsamhet, kontinuitet av utdelningar och låg värdering m.m. filtrerar denna aktiva investeringsstrategi bort många bolag och lämnar kvar stabilare bolag med lägre risk. OMX Stockholm Small Cap Price Index valdes eftersom det innehåller helt andra sorters bolag än Stock Selection for the Defensive Investor, vilket är småbolag. OMX Stockholm 30 valdes i sin tur för att bolagen i detta index, likt de Stock Selection for the Defensive Investor väjer ut, är stora bolag som ofta associeras med lägre risk. Detta genomfördes med syftet att hitta större kontraster mellan strategierna. Dessa strategier undersöktes under lågkonjunkturen 2007-2011, högkonjunkturen 2016-2019 samt under 15-årsperioden 2007-2021. Avkastningarna mättes i totalavkastning och CAGR medan deriskjusterade avkastningarna mättes med hjälp av Sharpekvot, Treynorkvot samt Jensen’s Alpha. Denna studie kom fram till att totalavkastningen för de olika strategierna skiljer sig åt mellan de olika perioderna. OMXS30 genererade högst totalavkastning under lågkonjunkturen medan OMXSSCPI genererade högst avkastning under både högkonjunkturen och under 15-årsperioden. Resultaten för de riskjusterade måtten visade på att det inte fanns någon statistisk signifikant skillnad mellan strategierna, vilket indikerar att skillnaderna i totalavkastningen beror på den risk som tas. / This study aimed to explain the impact of different economic conditions on the total return and riskadjusted return of three investment strategies: Stock Selection for the Defensive Investor, OMXStockholm 30, and OMX Stockholm Small Cap Price Index. The first strategy is based on the 14th chapter of Benjamin Graham's book, "The Intelligent Investor." By demanding high profitability, dividend continuity, low valuation, and other criteria, this active investment strategy filters out manycompanies and focuses on more stable companies with lower risk. OMX Stockholm Small Cap Price Index was chosen because it includes a different set of companies compared to Stock Selection for the Defensive Investor, specifically small-cap companies. On the other hand, OMX Stockholm 30 was selected because the companies in this index, similar to those preferred by Stock Selection for the Defensive Investor, are large companies often associated with lower risk. This was done in orderto identify more significant contrasts between the strategies. These strategies were examined during the recession 2007-2011, the economic boom 2016-2019, and a 15-year period 2007-2021. Returns were measured in terms of total return and compound annual growth rate (CAGR), while risk-adjusted returns were assessed using the Sharpe ratio, Treynor ratio, and Jensen's Alpha. This study found that the total returns of the different strategies varied across the different periods. OMX Stockholm 30 generated the highest return during the low economic cycle, while OMX Stockholm Small Cap Price Index produced the highest return during both the high economic cycle and the 15-year period. The results for the risk-adjusted measures indicated no significant differences between the strategies, suggesting that the variations in total returns are attributable to the level of risk undertaken.

本文改良金融投資技術分析操作方法中, 傳統的「單一移動平均」選股法為「多重移動平均」選股法, 其係以道氏理論上, 所謂的市場同時存在三種趨勢 (主要趨勢, 次級趨勢, 小型趨勢) 為基礎, 建立多重時間架構, 輔以移動平均線為股價趨勢判斷, 以及葛蘭碧八大法則之股價突破 (或跌破) 判斷原則作為操作訊號, 所彚整而提出。實證上, 採用2014年12月31日台灣證券交易所公告之台灣50、中型100, 以及富櫃50成分股為樣本, 並以2001年1月1日至2014年12月31日為回溯期間。在進行策略交易的模擬分析與績效差異檢定後, 實證結果發現, 多重移動平均選股法投資策略績效, 在統計分析上並無法較單一周期投資策略績效為優, 但卻能有效過濾沒必要的交易行為, 使突破買進之假訊號降低, 間接的降低交易次數及減少交易成本。 / This study enhanced from the traditional single period moving average for stock selection into multiple-period moving average counterpart. The theoretical foundation comes from the Dow Theory, which states that there exist three trends simultaneously, that is, major trend, secondary trend, and minor trend. Also, the Granville Rules suggest stock price breaking out may serve as entry and exit signal for trading. Our sample are grouped into three subsamples, Taiwan 50, Mid-Cap 100, Gretai 50. The sample period ranges from 2001/1/1 to 2014/12/31. Our empirical backtesting and performance test suggests that, contrary to our expectations, the multiple period method does not outperform its single period counterpart. However, the multiple period stock selection method may filter out false signals, and thereby reduce not only possible price risk associated with noisy trades but the accompanying transaction costs. / 摘要 I Abstract II 致謝詞 III 目錄 V 圖次 VII 表次 VIII 第一章　緒論 1 第一節　研究背景及動機 1 第二節　研究目的 2 第三節　研究對象與範圍 2 第四節　研究流程 4 第二章　文獻回顧 6 第一節　技術分析理論 6 一、技術分析基本邏輯 6 二、技術分析主要的型態類型 7 第二節　移動平均線的原理 9 一、簡單移動平均線的計算 9 二、移動平均線的常見應用 9 第三節　多重移動平均理論及選股法 11 一、多重移動平均的原理 11 二、多重移動平均的選股模式 11 第四節　相關研究文獻回顧與評析 11 一、過去研究文獻 11 二、文獻評析 16 三、本文假說推論 16 第三章　研究方法 17 第一節　傳統移動平均線選股模式 17 第二節　YC指標選股模式 17 第三節　選股模式績效差異檢定 19 第四節　資料來源與變數選取 19 第四章　實證分析 20 第一節　操作策略績效估計 20 第二節　操作策略績效比較 28 第三節　多重策略模型之適性歸納–由規模的角度 36 第五章　結論與建議 43 參考文獻 44 中文部份 44 英文部份 46 參考網址 46 圖次 圖1-4-1 研究流程圖 5 圖2-1-1 型態類技術理論的基本分類 6 圖2-1-2 市場同時存在三種趨勢 7 圖2-1-3 K線的基本構造 8 圖2-2-1 葛蘭碧(Granville)八大法則概念圖 10 表次 表1-3-1 台股之台灣50成分股 2 表1-3-2 台股之中型100成分股 3 表1-3-3 台股之富櫃50成分股 3 表2-4-1 過去研究文獻的整理 14 表4-1-1 台灣50成份股總交易次數及成本 20 表4-1-2 中型100成份股總交易次數及成本 22 表4-1-3 富櫃50成份股總交易次數及成本 26 表4-1-4 單一與多重模式下交易次數與進出場交易成本彚整 28 表4-2-1 台灣50成份股總報酬及總報酬率 28 表4-2-2 中型100成份股總報酬及總報酬率 30 表4-2-3 富櫃50成份股總報酬及總報酬率 34 表4-2-4 單一與多重策略下的平均總報酬與平均總報酬率彚整 36 表4-3-1 多重策略下總報酬率與市值之迴歸分析 36 表4-3-2 多重策略下總報酬率與股本之迴歸分析 37 表4-3-3 台灣50股本前20%成份股之策略績效及差異比較 37 表4-3-4 台灣50股本後20%成份股之策略績效及差異比較 38 表4-3-5 中型100股本前20%成份股之策略績效及差異比較 39 表4-3-6 中型100股本後20%成份股之策略績效及差異比較 40 表4-3-7 富櫃50股本前20%成份股之策略績效及差異比較 41 表4-3-8 富櫃50股本後20%成份股之策略績效及差異比較 42

The purpose of this study was to develop a new investment strategy called NC-Score. The strategy is based on Chapter 14 of The Intelligent Investor written by Benjamin Graham, but with other key figures and criteria. The key figures were chosen on the basis of creating a varied and comprehensive picture of the companies as possible. They describe the companies' valuation, profitability, growth, cash flows, and capital structure. The strategy was tested between 2011 and 2020 at OMX Stockholm Large Cap. An index and Graham's original strategy have been used to compare NC-Score's performance during the period. The purpose of the index was to emulate the market and act as a minimum requirement for returns. Graham's portfolio gives a picture of how a similar strategy performs under the same conditions. The result for the strategy was a high return at a lower risk than our benchmark index and the original Graham strategy. Between 2011 and 2020, NC-Score generated a return of 191.67% with a Sharpe Ratio of 2.70. During the same period, OMX Stockholm 30 GI generated a return of 110.48% with a Sharpe Ratio of 0.91. NC-Score's results cannot be considered to be significantly positively risk-adjusted since it cannot be ruled out that the higher return in relation to the risk was due to a coincidence. / Syftet med denna studie var att utveckla en ny investeringsstrategi som benämns NC-Score. Strategin utgår ifrån kapitel 14 av The Intelligent Investor skriven av Benjamin Graham, fast med andra nyckeltal och kriterier. Nyckeltalen valdes med utgångspunkt att skapa en varierande och så omfattande bild av bolaget som möjligt. De beskriver bolagens värdering, lönsamhet, tillväxt, kassaflöden samt kapitalstruktur.  Strategin testades mellan 2011 och 2020 på OMX Stockholm Large Cap. Ett index samt Grahams ursprungliga strategi har använts för att jämföra prestationen av NC-Score under tidsperioden. Indexets syfte var att efterlikna marknaden och agera som ett minimikrav för avkastningen. Grahams portfölj ger en bild av hur en liknande strategi presterar under samma förutsättningar. Resultatet för strategin var en hög avkastning till en lägre risk än vårt jämförelseindex samt den ursprungliga Graham strategin. Mellan 2011 till 2020 genererade NC-Score en avkastning på 191,67% med en Sharpe Ratio på 2,70. Under samma period genererade OMX Stockholm 30 GI en avkastning på 110,48% med en Sharpe Ratio på 0,91. NC-Scores resultat kan inte anses vara signifikant positivt riskjusterad, då det inte kan uteslutas att den högre avkastningen i förhållande till risk berott på slumpen.