盈餘是公司過去經營績效良窳之最終表現，而盈餘數值高低與公司股價報酬有密不可分的關係。然則，盈餘是企業營運的一連串會計處理結果，不同的會計原則及假設會影響會計處理的結果，使得當期及未來盈餘數值均會受到影響，因此在評估或預測企業的盈餘時，應對盈餘本身之品質加以探討，亦即，如何確認財務報表中那些是攸關盈餘品質優劣的資訊，透視盈餘本身的真正內涵以輔助投資人形成最佳投資策略，十分值得我們進一步研究。 近年來由於人工智慧之類神經網路快速地發展，加上類神經網路具備了平行分散式處理、關聯式記憶、自範例中學習等類似人類非線性思考的能力，在財務系統的應用上，學者所建構的類神經網路都比統計方法獲得了更好的結果。 有鑑於此，本研究即依據上述概念，以民國七十九年至八十四年共計五個年度財務報表資訊，以第一、二類上市公司一共十三個產業為研究樣本，建構了盈餘品質類神經網路預測模式，找出盈餘品質資訊內涵與盈餘成長率之關聯性。並以模式預測結果形成投資組合並據以作為投資策略操作。 在網路模式建構階段，本研究採取了過去學者所採用的盈餘品質指標作為網路之輸入結點；以每股盈餘成長率作為網路之輸出結點；以整體市場為學習範例，進行隱藏層結點個數之操弄，以找出學習效果較佳之網路模式，並以此網路模式作為後續研究採用之依據。以整體市場為樣本所進行的網路測試過程中，本研究所找出之較佳網路模式為：9-9-1。 本研究根據前述方法所進行的研究中，獲得了以下結論： 一、以整體市場為樣本所進行的測試中發現，模式之區別能力大致介於六成至七成之間。而預測能力大約是在五成至六成之間。 二、在整體市場、紡織類股以及電子類股之測試結果方面，以電子業之模式區別能力及預測能力最好，其次為紡織業。顯示以單一產業為樣本之模式學習效果優於整體市場。 三、在網路穩定性方面，則以紡織業組之穩定性較高，但與其它兩組之差異性並不明顯。 其次本研究以事件研究法進行投資策略分析，以模式之預測結果，輔以益本比評價法形成投資組合並進行投資決策，獲得了以下結論： 一、以整體市場、紡織類、電子類為投資對象均獲了超額報酬，在觀察期間內分別獲得了38.51%、34.62%以及56.89%的超額報酬率。其中以電子類股之表現最為突出。顯示本研究對於如電子業較重視研究發、資本密集之產業盈餘品質萃取能力較佳。 二、在觀察期間內，投資組合與類股報酬率表現均呈現正向相關，在類股指數上漲月份中，投資組合之超額報酬率較小，然而在類股指數下跌月份投資組合會出現較大幅的超額報酬。推論其原因在於本研究是以盈餘品質為基礎，而此類盈餘品具成長性且一致性、穩定性較高之公司較具抗跌性及長期持有之價值。 三、本研究驗證了盈餘品質網路模式能有效擷取財務報表盈餘資訊內涵，以之形成投資策略能獲取超額報酬。 關鍵字：盈餘品質、類神經網路、盈餘品質預測模式、投資組合、投資策略、累計超額報酬

本研究以修改傳統 模型為出發點，探討基金經理人所具備的動態行為特質。傳統模型重點主要放在經理人對於市場報酬率走勢的預期，並未考慮到其對市場未來波動性走勢的預期能力。因此本文認為有失偏頗。故研究方法即加入波動擇時能力特質進入模型，希望能強化傳統模型的擇時能力表現，以更完整地建立有關經理人擇時能力的資訊。 本研究採用三十支國內股票型共同基金為研究樣本。研究期間為2001//7/1~2005/6/30四年。利用日資料方法來補捉基金經理人每日動態特質，並且建立了隨機投資組合作為比較基準點，期望能更客觀的分析經理人是否具備優良從事交易策略的能力。 研究結果發現，研究樣本的基金經理人，以三因子或單因子模型分析，多數經理人具備波動擇時能力，但是報酬擇時能力並不顯著。這說明國內共同基金經理人在面對股市的未來報酬高度不確定性，會重視高波動所帶來的高風險。故會在未來走勢高波動時，適時的減少市場風險曝露及投資部位。 另外，研究結果也發現，當模型中異常報酬考慮到市場上波動時，基金經理人波動選股能力係數並不顯著，故無法說明其會隨著市場波動性改變，而運用選股能力強化績效，創造基金異常報酬。

隨著時間的發展，基金的種類與數量成倍數增長，導致投資人在挑選基金時，亦面臨了選擇股票時的窘境：投資標的數目過多、複雜度高，身陷其中，而不知如何挑選理想的投資組合。目前由於人們對於退休金的相關規劃愈益重視，遂有基金商品針對此概念來設計。 生命週期基金基本上符合這樣的概念，生命週期基金基本上是屬於一種組合型基金，但是並不一定要以組合型基金的型態來顯現，美國80只生命週期基金中將近半數為基金的基金。生命週期基金是為了滿足某個年份左右退休投資者的退休投資目標的基金，如FidelityFreedom系列、FrankRussell Life Points系列、T.Rowe PriceRetirement系列、Vanguard LifeStrategy系列等。例如FidelityFreedom2020是針對2020年左右退休的投資者設計的，為實現投資者退休的投資目標的基金，主要投資在Fidelity旗下股票型基金、債券型基金和貨幣市場基金等各類基金。我們便想要了解此種商品的投資型態下具有何種特色。 我們首先要探討基金在不同投資策略其表現如何，而我們衡量的方式---簡單的說是以是否能夠達到投資人的要求報酬率為基準，以投資報酬率來建構出年金終值，最後以各種投資策略所得到的最終價值之差距做為成本的衡量，之後我們則根據生命週期基金的樣態，自行設計出兩種投資模式同樣來探討不足要求資本的相關概念。 再來以投資聚集效果(pooling effect)為主題，因為在基金存在著不同風險容忍程度的投資人，所以我們希望探討在不同投資策略下所建構的效率前緣對於不同風險忍受程度的投資人是否具有超額報酬。 首先我們就兩種投資標的(股票、債劵)之投資報酬率變化以下列方式作設定---利用隨機模型(Stochastic Model)：並利用蒙地卡羅模擬的方式來建構投資標的之報酬率。 我們觀察不同的起始投資比重(股票資產權重考慮由0%~100%，間隔為1%，共101組；債券資產的權重則為1-股票資產權重，也就是100%~0%)，並以投資組合保險中三種常見的投資策略：買入持有(Buy & Hold；BH)、固定比例混合法(Constant Mixture；CM)及時間不變性投資組合保護(Time-invariant Portfolio Protection；TIPP)，作為投資策略。在完成對投資標的之報酬率變化及投資策略的設定後，就可以在三種投資策略及每個投資策略有101個起始權重下，得到303組不同的投資結果，如此我們就可以得到帳戶的最終價值，就可以針對是否符合投資者要求的報酬率做相關的研究。 同樣的我們可以就個別的投資策略建立個別的效率前緣。之後我們就不同風險容忍程度的投資大眾，以Harry M.Markowitz等人所提出的optimal frontier的概念加以設定風險點(risk point) ，各種不同風險程度的投資人即代表不同的風險點，如此我們便可以就不同的投資模型來探討基金的投資聚集效果(pooling effect) 。 最後我們想探討的部分則是希望讓投資大眾知道如果其處於何種經濟體之下，應該採用何種投資方式，或者是在投資人的不同要求之下，可以知道採取何種投資策略，以求學術上的操作可以應用到實務上，並求取更佳的效果。 / With the development of time, the kind and quantity of the fund become multiples to increase, cause investors to face the awkward situation while choosing the stock when they select funds: There is too much figure of the investment object marking investment complexity more diffcult , and does not know how to select ideal investment combination. Nowdays, people put emphasize on retirement plan more and more. so some mutual funds are designed for this concept. Lifecycle fund is identical to this concept .Lifecycle fund is a kind of Fund of Funds basically, but might not appear like the Fund of Funds , 80 Fund of Funds in U.S.A. nearly half appear like Fund of Funds . Lifecycle fund is for the fund of retired investors' retired investment needs which is different from age-changed , such as Fidelity Freedom series, Frank Russell Life Points series , T.Rowe Price Retirement series , Vanguard Life Strategy series ,etc.. For example Fidelity Freedom2020 is designed for pensioner's investor to retire about 2020 year, the fund that in order to realize the goal of investors when they retired, make an investment in many objectives, such as stock fund ,bond fund and money market fund ,etc. under command of Fidelity mainly. I want to know the characteristic of lifecycle fund and based on this concept to design mutual fund. I will discuss behavior of fund in different investment strategies, and the way which we measure ---It is to set the rate of returns by meeting investor's requirement as the datum, build and pay the end value of the annuity to construct by investing in the rate of returns, for the measurement of " bankrupt " with the disparity of the final value got of various kinds of investment strategies , later I designed two kinds of investment ways according to the concept of lifecycle fund and also discuss the concept of " bankrupt ". This research will also make emphasize on pooling effect , There are a lot of investors of different risk tolerance in the fund ,so I hope to discuss investor of different risk tolerance will have abcdrmal return under different efficiency frontier which are derived by different invest model and strategies. First, two kind investment target (stock, bond) Investment rate of returns by way of the following to settle ---Utilize Stochastic Mode: Wilkie investment model, Taiwan investment model and the rate of returns of the one that make use of simulation that build and construct investment terms. In each method, we will consider 101 different initial ratio of stock value and three different investment strategies: Buy & Hold(BH)、Constant Mixture(CM) and Time-invariant Portfolio Protection(TIPP).According to theses investment combination, I can construct different efficiency frontier under different investment models and strategies. Such final value of the account that we can receive so I can do relevant research to the rate of returns according with investor's request . Later, according to investor of different risk tolerance , set some risk point with the concept of optimal frontier published by Harry M.Markowitz, the investors of different risk degrees represent risk point, I can discuss pooling effect in fund under different investment model and strategies. Finally, the topic I want to discuss is let the investor know at which kind of economy , should adopt the investment strategies , or under investors' different requests, can know which kind of investment tactics are adopted , so that the operation on academy can be applied to the practice , and ask for better result.

投資組合保險的概念發源自1980年代，對於較保守或是對於股市未來走勢不清楚的投資人來說，是一種不錯的投資策略，既可以保障原本所投資的本金，亦可參與上方的獲利。投資組合保險策略所運用的範疇很廣，尤其適用於大筆資金之持有者，且只願意承受一定範圍的損失風險，如：退撫基金、保險基金或各類信託基金之基金經理人。 本研究以台灣50ETF(指數股票型基金)為研究對象，探討複製性賣權及固定比例投資組合保險等兩種資產配置策略，在不同市況下(2006年至2011年)之績效，並與買入持有策略做比較。其中，本文以GARCH波動度模型估計複製性賣權策略中之波動度；在CPPI策略中，由於考量到不同市場狀況下，投資人之風險偏好程度應會有所不同，風險乘數亦會有所改變，因此本文將風險乘數最適化，以改善傳統之固定風險乘數CPPI策略。 由本研究之實證結果可以得到以下結論： 1. 複製性賣權策略在空頭市場之績效會比買入持有策略及台灣50ETF好。然而，在大空頭時，由於股價急速下滑，導致資產配置來不及調整，而產生保險誤差。另外，複製性賣權在多頭市況下，較低的保本比例，會帶來較高之報酬。 2. CPPI策略在各種市況下，其績效大致都會優於買入持有策略，且完全沒有出現保險誤差，但只有在空頭走勢下，CPPI會打敗市場，原因在於CPPI發揮了保護下檔風險的功能，且說明了投資組合保險策略之目的並非超越市場報酬。 3. 將複製性賣權策略與CPPI策略相比時，從報酬率來看，空頭市場下CPPI的保護功能較複製性賣權強，而多頭或盤整市況下，並無一致的結果。從Sharpe ratio、長期相對平均成本、上方獲取率損失等績效指標，CPPI大致上都比複製性賣權好得多。

本文以全球第一個演算法交易雲端平台－Quantopian進行研究，藉由平台社群討論區內公開之演算法交易策略，透過交易策略篩選和初步優化，以演算法交易策略為投資標的，搭配不同權重策略建構投資組合。權重策略部分，本文提出適用於組合式交易策略的績效指標加權 (Performance Index Weighted) 法，應用因子投資的觀念，融合排序相關性較低、不同面向之績效指標作為報酬率驅動因子，並參考Asness et al. (2013) 以因子排序作為權重計算依據，提供了簡單直覺、非最適化求解而且穩健的加權方式，更直接地將交易策略各面向績效的優劣反應在權重上。 根據數值分析，發現組合式交易策略長期而言，整體績效表現平均優於個別演算法交易策略，最小變異、績效指標加權和均等權重投資組合的風險亦明顯低於個別交易策略，且最小變異、績效指標加權和均等權重投資組合在降低投資組合風險的同時，並未犧牲過多報酬，風險調整後績效表現優於個別交易策略。而績效指標加權投資組合之年化報酬率、風險衡量和風險調整後績效表現皆優於最小變異、平均數－變異數、均等權重的加權投資組合，此種權重策略可使投資組合之夏普比率 (Sharpe ratio) 顯著提升，且投資組合的風險大幅降低，最大跌幅 (Max drawdown) 在四年半的實驗區間內降至10%以下的水準，風險調整後績效優異。 透過Quantopian社群演算法交易平台，個人投資者也能站在巨人的肩膀上學習，集合眾人的力量，憑藉量化交易創造出和機構法人一樣具有競爭力的投資組合。如Chan (2009) 所言，個人投資者也能憑藉量化交易，設計一套演算法交易策略。 / Quantopian is a crowd-sourced hedge fund which allows members on the platform to develop their own algorithmic strategies and even get capital allocations from Quantopian. In this paper, we constructed portfolios by Quantopian trading platform and proposed Performance Index Weighted method which generate consistently profit in our study. First, we filtered algorithmic trading strategies shared on the Quantopian community and improved the performance slightly. Second, we combined multiple algorithmic strategies with varied portfolio weight method, such as minimize-variance, performance index weighted, mean-variance, and equal weighed method to construct a portfolio. To elaborate, Performance Index Weighted portfolio is actually an application of factor investing, in which the portfolio weight depends on the ranking of performance index (factors), and these index measure returns, risk, and also risk-adjusted returns, which truly reflects how well the algorithmic strategy is. As a result, we used the performance index as a return driver and invested more in well-ranked strategies directly. Performance index weighted is a simple, robust, and fully intuitively way to construct a portfolio. In numerical analysis, we found that using multiple strategies to construct a portfolio could generate better performance than a single algorithm strategy on average. Moreover, the annual returns, risk measure, and risk-adjusted returns of Performance Index Weighted portfolio turn out to be better than minimize-variance portfolio, mean-variance portfolio, and equal weighted portfolio. As a result, Performance Index Weighted portfolio has significantly higher Sharpe ratio and lower Max Drawdown (lower than 10% in our out-of-sample test) than other portfolios, which shows excellent risk-adjusted performance. Most important of all, retail traders could learn more precisely by standing on the shoulders of giants through Quantopian trading platform. Also, by collecting wisdom from the crowd, we create an opportunity for retail traders to construct competitive portfolios just as institutional investors do.

截至2016年的統計資料，我國產險與壽險業的保險公司家數來到54家，保險業資產總額佔了全台灣所有金融機構總資產的31.78%，資產規模來到新台幣22.6兆元，在如此龐大的資產規模下，保險公司的投資組合管理變成相當的重要，重點漸漸的從投資在什麼樣的商品可以讓資金獲取最大效益轉移到了投資後的管理與部位的調整，以避免不必要的非系統性風險，有鑑於此，台灣在2003年實施了RBC制度，讓保險公司的投資組合的分配有所依據，不過仍然免不了過度集中在某些資產的問題，所以本研究的目的在於能否運用風險集中度的概念來判斷投資組合是否過度集中，而不僅僅只有投資金額的比例來做判斷。 本論文的研究方法會根據各家保險公司的實際投資組合以每半年或每年的型式分別計算Marginal Risk Contribution(MRC)的値，並且進行分析後再以Herfindahl-Hirschman Index(HHI)與 Gini Index 來檢視長期資產組合集中度的趨勢，最後的研究結果可以發現若是從邊際風險貢獻的比例來看，各保險公司的風險分布主要是集中在國內上市普通股與ETF、海內外不動產投資、國外已開發國家或新興市場上市普通股與ETF以及A評等的國外固定收益債券，而利用HHI與Gini Index兩個指標來看，各保險公司的資產集中度是逐年上升的。 / According to the statistical data in 2016, there are 54 insurance companies which includes property and casualty insurance company and life insurance company. And the scale of insurance asset is NTD 2,260 billion, accounting for 31.78% of whole asset of financial institution in Taiwan. Under huge amount of asset, the portfolio management for insurance company become more and more important. The key points of this issue are transferring to the ratio of portfolio management from choosing asset class to get maximum profit in order to avoid the nonsystematic risk gradually. Therefore, the Risk-based Capital policy has established in 2003 in Taiwan. The ratio of the insurance companies’ portfolio had the reference to allocate. However, there were some issues about the excessive concentration of some asset classes. So, the target of this study is using the concept of the risk concentration to judge the portfolio too concentrated or not. Not just judge it by its amount invested. The research process of this thesis is to calculate the marginal risk contribution value of the insurance companies’ portfolio every half a year or every year. Moreover, using the Herfindahl-Hirschman Index (HHI) & Gini Index to observe the trend of long term portfolio concentration. From the marginal risk contribution ratio. We can found the result of this study is the risk concentrated on the domestic listed common stock & ETF, domestic or foreign Real Estate, foreign developed market or emerging market listed common stock & ETF and fixed income bond (A rating). Besides, using the Herfindahl – Hirschman index and Gini index. The concentrated ratio of insurance companies’ portfolio were raising recent years.

衡量投資組合的稀有事件時，即使稀有事件違約的機率極低，但是卻隱含著高額資產違約時所帶來的重大損失，所以我們必須要精準地評估稀有事件的信用風險。本研究係在估計信用損失分配的尾端機率，模擬的模型包含同質模型與異質模型；然而蒙地卡羅法雖然在風險管理的計算上相當實用，但是估計機率極小的尾端機率時模擬不夠穩定，因此為增進模擬的效率，我們利用Glasserman and Li (Management Science, 51(11),2005)提出的重點取樣法，以及根據Chiang et al. (Joural of Derivatives, 15(2),2007)重點取樣法為基礎做延伸的改良式重點取樣法，兩種方法來對不同的投資組合做模擬，更是將改良式重點取樣法推廣至異質模型做討論，本文亦透過變異數縮減效果來衡量兩種方法的模擬效率。數值結果顯示，比起傳統的蒙地卡羅法，此兩種方法皆能達到變異數縮減，其中在同質模型下的改良式重點取樣法有很好的表現，模擬時間相當省時，而異質模型下的重點取樣法也具有良好的估計效率及模擬的穩定性。 / When measuring portfolio credit risk of rare-event, even though its default probabilities are low, it causes significant losses resulting from a large number of default. Therefore, we have to measure portfolio credit risk of rare-event accurately. In particular, our goal is estimating the tail of loss distribution. Models we simulate are including homogeneous models and heterogeneous models. However, Monte Carlo simulation is useful and widely used computational tool in risk management, but it is unstable especially estimating small tail probabilities. Hence, in order to improve the efficiency of simulation, we use importance sampling proposed by Glasserman and Li (Management Science, 51(11),2005) and modified importance sampling based on importance sampling which proposed by Chiang et al. (2007 Joural of Derivatives, 15(2),). Simulate different portfolios by these two of simulations. On top of that, we extend and discuss the modified importance sampling simulation to heterogeneous model. In this article, we measure efficiency of two simulations by variance reduction. Numerical results show that proposed methods are better than Monte Carlo and achieve variance reduction. In homogeneous model, modified importance sampling has excellent efficiency of estimating and saves time. In heterogeneous model, importance sampling also has great efficiency of estimating and stability.

【第一篇論文中文摘要】 集中度風險於結構式商品的量化與分析：以房屋抵押貸款證券為例 "Martin and Wilde (2002)與Gordy (2003)" 針對巴塞爾協定(Basel Accords)中金融機構之投資組合所內藴之集中度風險提出了相對應的微粒化調整(Granularity Adjustment)風險量化準則，然而該模型僅止於單因子架構下探究單一信用標的集中度風險之量化。本文將其架構延用至結構式商品中，允許債權群組內之信用標的具不同區域別，我們採用Hull and White(2010)之跨池違約相關性描述，並結合Pykhtin (2004)中延拓單因子聯繫模型至多因子之方式，進而求取債權群組之單一資產集中度(Name Concentration)與區域類別集中度(Sector Concentration)風險的量化。本文以房屋抵押貸款證券(Mortgage Backed Securities, MBSs)為例，於集中度風險的考量下，藉由檢視不同風險情境下分券之損失起賠點，重新評估房屋抵押貸款證券AAA投資級分券信用評級之合理性。研究結果顯示，AAA評等之分券高度曝險於系統性風險，且於高風險情境下，標的房貸之區域集中現象擴大了違約相關性對債權群組損失分配的影響，致使AAA分券之損失起賠點得以超過其實際擔保額度(subordination)範圍。 【第二篇論文中文摘要】 美國銀行放款多角化對其報酬與風險之影響：相關性與傳染的觀點 本文目的在於分析銀行放款的多角化行為對其報酬與風險之影響。研究發現納入銀行放款投資組合相關性之考量，亦即標的資產之相關性結構以及資產間因契約關係所隱含跨投資組合之傳染途徑，將降低多角化之成效。文中透過因子模型(factor model)建構資產之報酬，同時決定其相關性結構，其中將資產間殘差項相關性作為傳染指標，進一步分析投資組合內標的資產間的平均相關係數、傳染與多角化程度間的關聯性。我們以美國銀行作為研究樣本，分別以赫芬達-赫希曼指數估算投資組合權重分配之集中度、使用組合內標的產業股票報酬資訊來計算投資組合內相關程度，接著利用標的產業與投資組合外產業間的殘差相關性來捕捉產業傳染效果，將此三項指標作為衡量多角化指標，分析其在1987年至2014年間聯貸投資組合多角化情形並試圖分析放款多角化對銀行績效之影響。透過契約關係的界定進而探討顧客傳染如何影響銀行績效。 研究發現於市場處於平穩期間(tranquil period)，所有多角化指標銀行放款均呈現放款多角化程度越高越有助於提高銀行的報酬並降低其風險。然而於危機期間(turmoil period)，銀行應將放款權重集中於部分產業、建構相關性較低之組合或選擇較低之傳染效果之產業作為放款的對象，用以提高銀行績效。隱含在危機期間銀行應該選擇適度之多角化策略，若僅以赫芬達-赫希曼指數作為多角化之衡量將顯示危機期間越集中越有助於銀行的表現，此舉將造成解釋上的偏誤。說明於投資組合多角化的衡量上，不該忽略由相關性結構所引發之集中度風險。 / 【Essay I】 Quantification and Analysis of Concentration Risk in Structured Products: the Case of Mortgage Backed Securities Granularity adjustments, introduced by Martin and While (2002) and Gordy (2003), allow one to quantify the concentration exposures of credit portfolios due to imperfect diversification. However, they focus solely on name concentrations under an Asymptotic Single Risk Factor (ASRF) framework. In this study, by adapting the multi-pool correlation structure of Hull and White (2010) under the multi-factor setting of Pykhtin (2004), we derive quantitative measures of name and sector concentration that facilitate subsequent analysis of the risk profiles embedded in Mortgage Backed Securities (MBSs). Under different stress scenarios, we examine the impacts of concentration exposures on the internal credit enhancements, in particular, the AAA tranche attachment points. We show that, under severe market conditions, the presence of sector concentrations in the underlying mortgage pools can further amplify the effects of default correlation on the portfolio loss distributions. As a direct consequence, the predetermined subordination level determined by the assignment of tranche attachment points can be exceeded. 【Essay II】 How Loan Portfolio Diversification Affects U.S. Banks’ Return and Risk: Correlation and Contagion Perspectives. In this paper we investigate how loan portfolio diversification affects the banks’ return and risk. We argue that, the dependence structure of bank loan portfolios, namely, the correlation structure among loan assets and the presence of contagion channels due to contractual relationships across the border of portfolio, contributes to the costs of diversification. Under the factor model framework, we derive a theoretical model to depict the asset returns and their dependence structure. Based on data of US bank loans collected from 1987-2014, our empirical study employs HHI, intra-portfolio correlation, and contagion as proxies for diversification to examine how loan portfolio diversification affects the banks’ profitability and riskiness. In addition, contractual relationships are identified and we investigate how customer contagion affects the bank’s performance. We find that all diversification measures exhibit a positive effect on the performance of U.S. banks during tranquil periods. However, for turmoil periods, banks with loan portfolios of more concentrated weight distributions, lower intra-portfolio correlation, or lower consumer contagion effects would have improved returns and reduced risk. In other words, during crisis, banks should choose an appropriate concentration strategy rather than focus on selected industries as determined solely by the HHI.

Sloan（1996）研究指出，投資人無法完全地分辨出應計項目與現金流量間盈餘持續性的差別，導致對應計項目資訊反應過度，而對現金流量資訊則反應不足，因此公司擁有相對較高（低）的應計項目使用金額，預期會有負（正）的未來股票異常報酬率，此種存在於應計項目與未來來股票異常報酬率間之負向關係，即本文所稱之「應計項目異常現象」。 投資人方面，本研究依資訊取得優勢，區分為內部關係人、機構投資人（外資、投信、自營商）與自然人；投資人持股行為則分別以持股比例與持股比例變動代表。此外，並將應計項目分別以總應計項目與總應計項目組成要素下之個別營運資金應計項目（應收帳款變動數、存貨變動數與應付帳款變動數）作衡量。首先測試應計項目異常現象是否存在於我國，再利用應計項目異常現象建構之套利投資組合，買進最低應計項目金額的投資組合而賣出最高應計項目金額的投資組合，探討應計項目異常現象與投資人持股行為之關聯性。 實證結果顯示，應計項目異常現象存在於我國，亦存在於個別營運資金應計項目。持股比例方面，外資與內部關係人似乎能利用應計項目異常現象形成之套利投資組合；當總應計項目的金額愈低，持股比例會愈高，但在不同應計項目的衡量方法下會有不同的結果。持股比例變動方面，除內部關係人與自然人稍佳之外，本研究設計之迴歸模型並無對應計項目與投資人持股比例變動間之關聯性有足夠的解釋能力。此外，第二年度的內部關係人持股比例變動雖與總應計項目、存貨變動數有負向的關聯性，惟統計結果並不顯著。 關鍵字：應計項目異常現象、投資人、持股行為、應計項目、機構投資人、內 部關係人、自然人、套利投資組合 / Sloan（1996）results indicate investors failing to distinguish fully between the different properties of the accrual and cash flow components of earnings. This leads to overreaction of the information contained in the accrual components of earnings and underreaction of the cash flow components of earnings.Consequently,firms with relatively high (low) levels of accruals experience negative (positive) future abnormal stock returns. The negative relationship between accounting accruals and subsequent stock returns calls the “Accruals anomaly” in this paper. With repect to the investors, I distinguish them from the advantage of obtaining the information into insiders, institution investors (QFII, mutual funds, security dealers), and individual investors; then use the percentage of the investors’ holding and the percentage of the investors’ holding change to represent the investors’ holding behavior. Besides, I use the total accruals and individual working capital accruals（change in accounts receive, change in inventory, and change in minus accounts payable）to measure accruals. Firstly, I test whether the accruals anomaly exists in our country or not, then exploit the hedge portfolio formed by accruals anomaly,by taking a long position in the stock of firms reporting relatively low levels of accruals and a short position in the stock of firms reporting relatively high levels of accruals generates positive abnormal stock returns to probe into the association between accruals anomaly and investors’ holding behavior. The results suggested that accruals anomaly indeed exists in our country and the individual working capital accruals. With regard to the percentage of the investors holding, QFII and insiders seems to capable of exploiting the hedge portfolio formed by accruals anomaly; when firms with relatively low levels of total accruals experience the percentage of the high investors holding,but there have different results of using dissimilar measurement of accruals. For the percentage of the investors holding change, this paper’s regression model doesn’t have enough capability of explaining the association between accruals and percentage of the investors holding change except insiders and individual investors. Furthermore, although the percentage of the insiders’ holding change in the second year is negatively correlated with total accruals and change in inventory, the empirical results are not significant. Key words: accrual anomaly, investors, holding behavior, accruals, institution investors, insiders, individual investors, hedge portfolio

本研究之宗旨在於探究中國大陸對亞洲區域內國家的金融市場影響程度之變化。由過去的各國股市日報酬率資料間相關程度與政策改變間的影響結果，來觀察是否未來在兩岸政策更開放下會使中國大陸對台灣的影響程度上升，進而使國際間投資組合的風險分散效果下降。本研究自DataStream選取台灣、香港、中國大陸、泰國、印尼、新加坡、馬來西亞、菲律賓、日本以及美國等十國的股價指數日資料，以對數轉換為日報酬率後年化加以分析。選取時間自1991年7月15日(中國大陸上海證券交易所股價指數公開後)至2008年12月31日。本研究選用的方法為使用風險值(VaR; Value at Risk)的概念來取代傳統的標準差，衡量以該十國所分別組成的各投資組合風險值變動情形；以及由風險值所衍生出的Diversification Benefit與Incremental VaR的結果。發現到僅由亞洲區域國家內組成的投資組合風險分散效果逐漸下降；且效果並不如有納入區域外國家(如美國)的投資組合。接著本研究將Gaussian Copula模型放入VaR中以增加對極端值的捕捉能力，結果發現本研究所選用的指數加權移動平均法所求得之相關係數已可有效反應出各國之間的相依程度，即加入Copula的效果有限。另外藉由Copula所求得之相關係數顯示，台灣、香港對中國大陸之間的相依程度已逐漸上升，並開始出現超越美國之現象，其中又以2005年為上升趨勢的起點。最後本研究以向量自我迴歸模型(VARs)來驗證2005年前後中國大陸股市對其他亞洲區域國家的影響力是否存在結構性的改變；並再佐以變異數拆解之方法來觀察2005年前後各國家之間自發性衝擊對彼此之間的影響程度變化。研究結果發現，透過VARs可證明中國大陸對亞洲區域各國的影響力在2005年後轉變為顯著；僅對美國不存在此一現象。另外變異數拆解的結果也顯示各國之間的相依程度在2005年後有明顯的上升，中國大陸對各國的影響程度亦然。透過本研究之結論，在未來兩岸將簽訂金融監理備忘錄使整合關係提升的環境下，需提醒投資人整合關係的上升將使得以之為標的之投資組合風險分散效果下降，需作為投資策略之考量。 / The object of this research is to find out the trend of dependence and correlation between China and other Asian countries. Based on past information about the relationship between equity markets’ correlation and changes in policies, this research can make suggestions to the foreseeable future of Taiwan and China whose relationship will be more solid due to new policy. The data of this research are gathered from DataStream, which includes Taiwan, Hong Kong, China, Thailand, Indonesia, Singapore, Malaysia, Philippines, Japan and United States. Selected from 1991/07/15 (when the Shanghai SE Composite went public) to 2008/12/31, this research calculates the annualized daily return using natural logarithms of two consecutive daily index prices. This research uses Value at Risk (VaR) to measure the risk exposure of portfolios formed by ten countries, and extends to the use of Diversification Benefit and Incremental VaR. The results found out that the diversification effects of portfolio which includes only Asian countries are decreasing and inferior to the effects when cross region countries are included. The second study of this research is to combine Gaussian Copula Model with VaR to capture the effects of extreme values. Empirical results found out that the VaR using Exponentially Weighted Moving Average method is good enough for analyzing Asian stock markets. The correlation in Copula model suggests that the dependence between Taiwan and China had increased since 2005 and has the increasing trend which might overwhelm the dependence between Taiwan and United States. Final research is about using Vector Autoregressions Model (VARs) to testify is there exist any structural change of dependence before and after 2005, and using Variance Decomposition to observe the relationships between these ten countries. The results found out that there exist structural change in 2005, the post-2005 periods shows that for Asian countries the effect from China are significant and greater than pre-2005 periods.