ESG investing has been a hot topic during several years and there have been numerousstudies examining the relationship between sustainable assets and non-sustainable assetsincluding green bonds, social bonds, environmental bonds, ESG-bonds and ESG indices;conventional bonds, S&P 500, common stocks and non-ESG indices. During negative marketshocks several ESG stocks and indices have been shown to outperform common stocks andindices. Green bonds demonstrated an asymmetric relationship to other assets providinginvestors with an opportunity for diversification. We’ve looked at the relationship andperformance of sustainable assets and non-sustainable assets by using Markowitz portfoliometrics and Engle Rs’ DCC-GARCH. Our findings propose green bonds and treasuries toprovide hedging and diversification opportunities during crises but demonstrate sustainablefixed income assets to underperform non-sustainable fixed income assets during the COVID19 market shock as opposed to previous studies.

本文以摩根台指與台灣50指數成分股調整的股價效應做比較，兩者對於成分股調整的宣告及生效是否存在異常報酬，而異常報酬的不同是否與其指數編制方式有關。實證結果發現摩根台指新增股具有 顯著正報酬、剔除股具有顯著負報酬，且在宣告日二十天後價格呈現反轉，符合價格壓力假說。而台灣50指數新增股異常報酬則不顯著異於零，兩種指數的新增股在宣告成份股調整後皆有超額成交量、流動性持續增加。再者摩根台指的宣告效果比台灣50指數強，且透過加權的方式較能表現出指數成份股調整所帶來的現貨價格影響。 / The study examines both the price and volume effect of stock additions or deletions on both the MSCI Taiwan index and Taiwan50 index. We document significant abnormal returns of stock additions and deletions for the MSCI Taiwan Index both on the announced period or on the effective period. In addition, we also find a significant abnormal return of stock deletions for Taiwan 50 Index either announced period or the effective period. While we do not find any significant abnormal return of stock additions. Further more, both the announced date effect and the effective date effect for MSCI Taiwan Index are stronger than those for Taiwan 50 Index. Our results support the price pressure hypothesis.

Factor models attribute stock returns to a linear combination of factors. A model with great explanatory power (R2) can be used to estimate the systematic risk of an investment. One of the most important factors is the industry which the company of the stock operates in. In commercial risk models this factor is often determined with a manually constructed stock classification scheme such as GICS. We present Natural Language Industry Scheme (NLIS), an automatic and multivalued classification scheme based on topic modeling. The topic modeling is performed on transcripts of company earnings calls and identifies a number of topics analogous to industries. We use non-negative matrix factorization (NMF) on a term-document matrix of the transcripts to perform the topic modeling. When set to explain returns of the MSCI USA index we find that NLIS consistently outperforms GICS, often by several hundred basis points. We attribute this to NLIS’ ability to assign a stock to multiple industries. We also suggest that the proportions of industry assignments for a given stock could correspond to expected future revenue sources rather than current revenue sources. This property could explain some of NLIS’ success since it closely relates to theoretical stock pricing. / Faktormodeller förklarar aktieprisrörelser med en linjär kombination av faktorer. En modell med hög förklaringsgrad (R2) kan användas föratt skatta en investerings systematiska risk. En av de viktigaste faktorerna är aktiebolagets industritillhörighet. I kommersiella risksystem bestäms industri oftast med ett aktieklassifikationsschema som GICS, publicerat av ett finansiellt institut. Vi presenterar Natural Language Industry Scheme (NLIS), ett automatiskt klassifikationsschema baserat på topic modeling. Vi utför topic modeling på transkript av aktiebolags investerarsamtal. Detta identifierar ämnen, eller topics, som är jämförbara med industrier. Topic modeling sker genom icke-negativmatrisfaktorisering (NMF) på en ord-dokumentmatris av transkripten. När NLIS används för att förklara prisrörelser hos MSCI USA-indexet finner vi att NLIS överträffar GICS, ofta med 2-3 procent. Detta tillskriver vi NLIS förmåga att ge flera industritillhörigheter åt samma aktie. Vi föreslår också att proportionerna hos industritillhörigheterna för en aktie kan motsvara förväntade inkomstkällor snarare än nuvarande inkomstkällor. Denna egenskap kan också vara en anledning till NLIS framgång då den nära relaterar till teoretisk aktieprissättning.

本研究針對臺灣摩根指數的成分股進行分析，研究樣本期間從2008年至2010年，合計三個年度，正好歷經景氣的一個多空循環週期。本研究利用技術指標作為判讀多空的工具，技術指標包含價與量的技術分析工具，價格的技術指標有趨勢指標MA、擺盪指標KD與MACD，量的技術指標則是OBV。並利用優化的方式挑選出合適的參數值。本研究的風險控管則是控管個股的偏離程度，當允許的偏離程度愈大，模型便愈能區別出強勢股與弱勢股，風險的衡量指標則是採用年化追蹤誤差值來衡量，本研究設定的限制條件為最大累積年化追蹤誤差值不得超越6%。 實證結果發現，當模組的模型年化追蹤誤差值設定愈大，個股的偏離程度就愈大，模組的報酬表現就愈佳，但同樣的風險也愈大，即年化追蹤誤差值愈大。當模型年化追蹤誤差值設定在24%，並搭配MA、MACD與OBV三個技術指標得到的績效最佳，同時亦能夠將風險控制在設定的6%水準之下。 / This study analyzed the component stocks in MSCI Taiwan Index. The analyzed data from 2008 to 2010 was exactly an economic cycle. The study was based on technical analysis, including price and volume to judge that the price was bullish or bearish. The price technical analysis included Moving Average (MA), Stochastic Line (KD) and Moving Average Convergence and Divergence (MACD). The volume technical analysis was On Balance Volume (OBV). The study used the method of optimization to choose the best parameter of each technical analysis. The risk control was to limit the bias of each stock. When the bias of each stock was larger, the model could easily distinguish the stock was bullish or bearish. The risk indicator was annual tracking error limited to 6% in the study. The empirical results showed that the larger the model annual tracking error set, the large bias the stock show, and the outperformance of the return. But with the performance of the return larger, the risk of tracking error was also getting larger. When the model annual tracking error set to 24%, and utilized MA, MACD and OBV would get the best performance and the risk of annual tracking error was under 6%.