This thesis examines the theoretical underpinnings and practical construction of select economic and financial indexes. Such indexes are used for a variety of purposes, including the measurement of inflation, portfolio return performance, and firm productivity. Chapter 1 motivates interest in economic and financial indexes and introduces the principal ideas in the thesis. Chapter 2 focuses on one potential source of bias in the Canadian consumer price index (CPI) that arises from the emergence of large discount/warehouse stores—the so-called outlet substitution bias. Such outlets have gained market share in Canada in recent years, but current CPI procedures fail to capture the declines in average prices that consumers enjoy when they switch to such outlets. Unrepresentative sampling, and the fact that discount stores often deliver lower rates of price increase can further bias the CPI. Bias estimates for some elementary indexes are computed using data from Statistics Canada's CPI production files for the province of Ontario. It is shown that the effect on the Canadian CPI of inappropriately accounting for such discount outlets can be substantial. Another area in which indexes are frequently used is the stock market. Several stock market indexes exist, including those produced by Dow Jones and Company, Standard and Poor's Corporation, Frank Russell and Company, among others. These indexes differ in two fundamental respects: their composition and their method of computation—with important implications for their usage and interpretation. Chapter 3 introduces the concept of a stock index by asking what, in fact a stock market index is—this is tantamount to considering the purpose for which the index is intended, since stock indexes should be constructed according to their usage. Because stock indexes are most commonly used as measures of returns on portfolios, the main considerations in constructing such return indexes are examined. Chapter 4 uses the Dow Jones Industrial Average (DJIA) as a case study to examine its properties as a return index. It is shown that the DJIA is not the return on a market portfolio consisting of its thirty component stocks: in fact the DJIA measures the return performance on a very particular (and unusual) investment strategy, a fact that is not well understood by institutional investors. An examination of some other popular stock indexes shows that they all differ in their computational formula and that each is consistent with a particular investment strategy. Numerical calculations reveal that the return performance of the DJIA can vary considerably with the choice of basic index number formula, particularly over shorter time horizons. Given the numerous ways of constructing stock market return indexes, the user is left to determine which is 'best' in some sense. The choice of an appropriate (or 'best') formula for a stock market index is formally addressed in chapter 5. The test or axiomatic approach to standard bilateral index number theory as in Eichhorn & Voeller (1983), Diewert (1993a), and Balk (1995) is adapted here. A number of a priori desirable properties (or axioms) are proposed for a stock index whose purpose is to measure the gross return on a portfolio of stocks. It is shown that satisfaction of a certain subset of axioms implies a definite functional form for a stock market return index. Chapter 6 evaluates the various stock indexes is use today in terms of their usefulness as measures of gross returns on portfolios. To this end the axioms developed in chapter 5 are used to provide a common evaluative framework, in the sense that some of the indexes satisfy certain axioms while others do not. It is shown that the shortcomings of the DJIA as a measure of return arise from its failure to satisfy a number of the basic axioms proposed. Notwithstanding this, each index corresponds to a different investment strategy. Thus, when choosing an index for benchmarking purposes an investor should select one which closely matches his/her investment strategy—a choice that cannot be made by appealing to axioms alone. / Arts, Faculty of / Vancouver School of Economics / Graduate

ABSTRACT MAJID MEMARI, for the Masters of Science degree in Computer Science, presented on November 3rd, 2017 at Southern Illinois University, Carbondale, IL. Title: PREDICTING THE STOCK MARKET USING NEWS SENTIMENT ANALYSIS Major Professor: Dr. Norman Carver Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. GDELT is the largest, most comprehensive, and highest resolution open database ever created. It is a platform that monitors the world's news media from nearly every corner of every country in print, broadcast, and web formats, in over 100 languages, every moment of every day that stretches all the way back to January 1st, 1979, and updates daily [1]. Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on an exchange. The successful prediction of a stock's future price could yield significant profit. The efficient-market hypothesis suggests that stock prices reflect all currently available information and any price changes that are not based on newly revealed information thus are inherently unpredictable [2]. On the other hand, other studies show that it is predictable. The stock market prediction has been a long-time attractive topic and is extensively studied by researchers in different fields with numerous studies of the correlation between stock market fluctuations and different data sources derived from the historical data of world major stock indices or external information from social media and news [6]. The main objective of this research is to investigate the accuracy of predicting the unseen prices of the Dow Jones Industrial Average using information derived from GDELT database. Dow Jones Industrial Average (DJIA) is a stock market index, and one of several indices created by Wall Street Journal editor and Dow Jones & Company co-founder Charles Dow. This research is based on data sets of events from GDELT database and daily prices of the DJI from Yahoo Finance, all from March 2015 to October 2017. First, multiple different classification machine learning models are applied to the generated datasets and then also applied to multiple different Ensemble methods. In statistics and machine learning, Ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Afterwards, performances are evaluated for each model using the optimized parameters. Finally, experimental results show that using Ensemble methods has a significant (positive) impact on improving the prediction accuracy. Keywords: Big Data, GDELT, Stock Market, Prediction, Dow Jones Index, Machine Learning, Ensemble Methods

Background: The increase in development and welfare that has been achieved in recent decades has led to a deterioration of the planet. Increased awareness on this matter has led to a concern to become more sustainable, both on an individual level and firm level. The concept of sustainable development integrates consideration to economic growth, protection of the environment, and social justice. Firms have sustainability regulations they must follow to be allowed to operate, but when they go beyond their economic interests it is referred to as corporate social responsibility (CSR). Certain indexes select which firms to include based on their CSR performance. In this way, firms can use CSR to build a reputation. Objectives: The purpose of this study is to investigate if inclusion in or exclusion from Dow Jones Sustainability Europe (DJSEUR) index has a significant effect on a firm’s stock price. The sub-purpose of this study is to investigate if different sector characteristics significantly impact the investors’ response to inclusion and exclusion. The study is conducted on firms in the European region. Methods: The event study methodology was used to examine abnormal returns associated with inclusion in and exclusion from a sustainability stock index. Further, a linear regression was developed to distinguish if sector affiliation affects the reactions of investors when firms are included or excluded from the index. This study uses stock data for firms over a period of time from 2014-2019. Results: The results suggest no significant increase in CAAR from being included in the index. However, exclusion from the index significantly affected CAAR negatively on the day of the announcement. Five sectors experienced a significantly different CAAR from the event of exclusion from the index. Only the result from one sector indicated to be significantly affected by the event of inclusion in the index. Conclusions: Our results provide evidence that being removed from the DJSEUR results in a decrease in a firm’s share price, suggesting that the European stock market penalizes firms for not obtaining a sufficiently high level of CSR. But investors do not financially reward firms for inclusion in the DJSEUR index. Implying that investors in the European stock market expect firms to implement CSR but do not reward them financially. Therefore, managers need to satisfy the pressure from various stakeholders, including shareholders, while trying to maximize stock value. / Bakgrund: Ökningen av utveckling och välfärd som uppnåtts under det senaste decenniet har lett till nedbrytning av planeten. Medvetenhet om problemet har lett till ökad angelägenhet för att agera mer hållbart, både på individnivå och företagsnivå. Konceptet av hållbar utveckling integrerar ekonomisk tillväxt, skydd av planeten och sociala rättigheter. Företag måste uppfylla vissa krav inom hållbarhet för att vara verksamma. Om de gör mer än vad som krävs benämns det som företags sociala ansvar, eller corporate social responsibility (CSR) i engelska termer. Det finns speciella index som selekterar företag baserat på deras prestationer inom CSR. På det sättet kan företag använda sig av CSR för att påverka sitt rykte.  Syfte: Syftet med denna studie är att undersöka huruvida inkludering i eller exkludering från Dow Jones Sustainability Europe (DJSEUR) index har en significant påverkan på företags aktiepriser. Det sekundära syftet är att undersöka om olika sektorer, samt karaktärsdrag, har en signifikant påverkan på investerares respons till inkludering och exkludering. Studien inkluderar företag inom Europaregionen. Metod: Metodiken för en eventstudie användes för att beräkna abnormala avkastningar associerade med inkludering i eller exkludering från ett hållbarhetsindex. En linjär regression tillämpades för att skilja på om sektortillhörighet påverkade investerarnas reaktioner på när företag inkluderades i eller exkluderades från indexet. Studien använder aktiedata för företag från tidsperioden 2014-2019.  Resultat: Resultaten tyder på att inkluderingar från indexet inte signifikant ökar CAAR. Exkluderingar från indexet påverkade dock CAAR negativt på dagen för tillkännagivandet. För exkluderingar redovisade resultatet att fem sektorer har en signifikant skillnad i CAAR, men enbart en sektor för inkluderingar.  Slutsatser: Våra resultat visar att exkludering från DJSEUR minskar företagets aktiekurs, vilket tyder på att den europeiska aktiemarknaden straffar företag för att de inte bibehåller en tillräckligt hög nivå av CSR. Men investerare belönar inte företag för att inkluderas i DJSEUR indexet. Vilket innebär att investerare på den europeiska aktiemarknaden förväntar sig att företag implementerar CSR men belönar dem inte finansiellt. Därför tvingas managers tillfredsställa trycket från olika intressenter, inklusive aktieägare, samtidigt som de försöker maximera aktiens värde.

This thesis evaluates the mean-variance analysis framework by comparing the performance of an optimized portfolio consisting of stocks from the Dow Jones Industrial Average to the performance of the Dow Jones Industrial Average index itself. The results show that the optimized portfolio performs better than the corresponding index when evaluated on the period between 2015 and 2019. However, the variance of the returns are high and therefore it is difficult to determine if mean-variance analysis performs better than its corresponding index in the general case. Furthermore, it is shown that individual stocks can still influence the movement of an optimized portfolio significantly, even though the model is supposed to diversify firm-specific risk. Thus, the authors recommend modifying the model by restricting the amount that is allowed to be invested in a single stock, if one wishes to apply mean-variance analysis in reality. To be able to draw further conclusions, more practical research within the subject needs to be done. / Denna uppsats utvärderar ramverket ”mean-variance analysis” genom att jämföra prestandan av en optimerad portfölj bestående av aktier från Dow Jones Industrial Average med prestandan av indexet Dow Jones Industrial Average självt. Resultaten visar att att den optimerade portföljen presterar bättre än motsvarande index när de utvärderas på perioden 2015 till 2019. Dock är variansen av avkastningen hög och det är därför svårt att bedöma om mean-variance analysis generellt sett presterar bättre än sitt motsvarande index. Vidare visas det att individuella aktier fortfarande kan påverka den optimerade portföljens rörelser, fastän modellen antas diversifiera företagsspecifik risk. På grund av detta rekommenderar författarna att modifiera modellen genom att begränsa mängden som kan investeras i en individuell aktie, om man önskar att tillämpa mean-variance analysis i verkligheten. För att kunna dra vidare slutsatser så krävs mer praktisk forskning inom området.

企業社會責任的實踐，已然成為當前企業經營的「顯學」，企業以永續經營為思考主軸，是企業經營時不能漠視的動力。同時，評量企業社會責任實踐績效的機制也應運而生。雖然還沒有一套評量方式可以放諸四海皆準，但是企業社會績效與企業財務績效的正向關聯，日益獲得國際社會的認同，追求企業永續經營的永續性指數量化指標，則成為檢視企業社會責任執行效益的評分卡。 本文以道瓊永續性指數(DJSI)成份股的評選機制，做為檢核企業社會責任實踐成果的工具，並以宏碁公司為探討個案，以價值鏈模式，分析宏碁從事企業社會責任的驅動原因，並進一步以道瓊永續性指數成份股的入選評量準則，探究宏碁企業社會責任的實踐現狀與該準則間之落差，繼而提出改善建議。期望有助於宏碁及台灣其他有志於入列道瓊永續性指數之企業，強化其企業社會責任的實踐績效。 / The notion of ``Corporate Social Responsibility`` (CSR) has come to fall into place as a driver to sustain any companies' growth. While there have been lacking well recognized criteria to assess performance of companies that put CSR into actions, it has been found that their associated financial performance frequently signals the efforts. As a result, whether to be able to be listed in publicly accessible financial sustainability indexes provides a convenient yardstick for the companies enforcing the notion. In this study, given a goal to be a component in the Dow Jones Sustainability Index (DJSI), we evaluate in depth why Acer, an international brand personal computer company, has been motivated to assume the CSR as well as pursue the goal. The value-chain model is applied to gauge the strength and weakness in managements presently facing Acer. Serving other Taiwanese companies equally well, our analyses contribute to identify various dimensions in the value chain on which the company could make good improvements.

A stochastic differential equationdriven by a Brownian motion where the dispersion is determined by a parameter is considered. The parameter undergoes a change at a certain time point. Estimates of the time change point and the parameter, before and after that time, is considered.The estimates were presented in Lacus 2008. Two cases are considered: (1) the drift is known, (2) the drift is unknown and the dispersion space-independent. Applications to Dow-Jones index 1971-1974  and Goldmann-Sachs closings 2005-- May 2009 are given.

<p>A stochastic differential equationdriven by a Brownian motion where the dispersion is determined by a parameter is considered. The parameter undergoes a change at a certain time point. Estimates of the time change point and the parameter, before and after that time, is considered.The estimates were presented in Lacus 2008. Two cases are considered: (1) the drift is known, (2) the drift is unknown and the dispersion space-independent. Applications to Dow-Jones index 1971-1974  and Goldmann-Sachs closings 2005-- May 2009 are given.</p>

A volatilidade é uma medida de incerteza quanto às variações dos preços de ativos. Este trabalho tem como objetivo analisar a volatilidade, através dos diversos modelos da família GARCH, de três índices de mercados financeiros: Dow Jones, IBovespa e S&P 500. Com este intuito, foram aqui utilizadas técnicas univariadas e multivariadas, bem como análises de Causalidade de Granger. Através das duas primeiras ferramentas, escolhemos o melhor modelo para cada um destes casos. Usando a terceira ferramenta, concluímos que o IBovespa é significativamente influenciado pela abertura do Dow Jones e do S&P500. Por outro lado, mostramos que a abertura do IBovespa não impacta, nem à 10% de significância, os índices Dow Jones e S&P 500. Também concluímos que a incorporação de um dos índices americanos ao modelo do IBovespa torna-o mais significativo, uma vez que o mercado acionário brasileiro é impactado pelos dois índices citados anteriormente. Desta forma, este trabalho mostra que os modelos GARCH multivariados aparentam ser mais eficazes na estimação da volatilidade de ativos financeiros do que os modelos GARCH univariados. / The volatility is a measure of the uncertainty of variations of asset prices. The main goal of this work is to analyze the volatility, by the use of several models of the GARCH family, of three financial market indexes: Dow Jones, IBovespa and S&P 500. With this purpose, we use univariate and multivariate techniques, as well as Granger Causality. Using these first two tools, we choose the best model for each one of these cases. Using the third tool, we conclude that the IBovespa is significatively influenced by the opening of the Dow Jones and the S&P 500 indexes. On the other hand, we show that the opening of the IBovespa does not impact, not even at 10% of significance, the Dow Jones and S&P 500 indexes. We also conclude that incorporation of one of these American indexes to the model involving IBovespa makes it more significant, once the Brazilian Stock Market is impacted by the two American indexes we mention before. This work shows that multivariate GARCH models seem to be more efficient in the volatility estimation of financial assets than univariate GARCH models.

A volatilidade é uma medida de incerteza quanto às variações dos preços de ativos. Este trabalho tem como objetivo analisar a volatilidade, através dos diversos modelos da família GARCH, de três índices de mercados financeiros: Dow Jones, IBovespa e S&P 500. Com este intuito, foram aqui utilizadas técnicas univariadas e multivariadas, bem como análises de Causalidade de Granger. Através das duas primeiras ferramentas, escolhemos o melhor modelo para cada um destes casos. Usando a terceira ferramenta, concluímos que o IBovespa é significativamente influenciado pela abertura do Dow Jones e do S&P500. Por outro lado, mostramos que a abertura do IBovespa não impacta, nem à 10% de significância, os índices Dow Jones e S&P 500. Também concluímos que a incorporação de um dos índices americanos ao modelo do IBovespa torna-o mais significativo, uma vez que o mercado acionário brasileiro é impactado pelos dois índices citados anteriormente. Desta forma, este trabalho mostra que os modelos GARCH multivariados aparentam ser mais eficazes na estimação da volatilidade de ativos financeiros do que os modelos GARCH univariados. / The volatility is a measure of the uncertainty of variations of asset prices. The main goal of this work is to analyze the volatility, by the use of several models of the GARCH family, of three financial market indexes: Dow Jones, IBovespa and S&P 500. With this purpose, we use univariate and multivariate techniques, as well as Granger Causality. Using these first two tools, we choose the best model for each one of these cases. Using the third tool, we conclude that the IBovespa is significatively influenced by the opening of the Dow Jones and the S&P 500 indexes. On the other hand, we show that the opening of the IBovespa does not impact, not even at 10% of significance, the Dow Jones and S&P 500 indexes. We also conclude that incorporation of one of these American indexes to the model involving IBovespa makes it more significant, once the Brazilian Stock Market is impacted by the two American indexes we mention before. This work shows that multivariate GARCH models seem to be more efficient in the volatility estimation of financial assets than univariate GARCH models.

A volatilidade é uma medida de incerteza quanto às variações dos preços de ativos. Este trabalho tem como objetivo analisar a volatilidade, através dos diversos modelos da família GARCH, de três índices de mercados financeiros: Dow Jones, IBovespa e S&P 500. Com este intuito, foram aqui utilizadas técnicas univariadas e multivariadas, bem como análises de Causalidade de Granger. Através das duas primeiras ferramentas, escolhemos o melhor modelo para cada um destes casos. Usando a terceira ferramenta, concluímos que o IBovespa é significativamente influenciado pela abertura do Dow Jones e do S&P500. Por outro lado, mostramos que a abertura do IBovespa não impacta, nem à 10% de significância, os índices Dow Jones e S&P 500. Também concluímos que a incorporação de um dos índices americanos ao modelo do IBovespa torna-o mais significativo, uma vez que o mercado acionário brasileiro é impactado pelos dois índices citados anteriormente. Desta forma, este trabalho mostra que os modelos GARCH multivariados aparentam ser mais eficazes na estimação da volatilidade de ativos financeiros do que os modelos GARCH univariados. / The volatility is a measure of the uncertainty of variations of asset prices. The main goal of this work is to analyze the volatility, by the use of several models of the GARCH family, of three financial market indexes: Dow Jones, IBovespa and S&P 500. With this purpose, we use univariate and multivariate techniques, as well as Granger Causality. Using these first two tools, we choose the best model for each one of these cases. Using the third tool, we conclude that the IBovespa is significatively influenced by the opening of the Dow Jones and the S&P 500 indexes. On the other hand, we show that the opening of the IBovespa does not impact, not even at 10% of significance, the Dow Jones and S&P 500 indexes. We also conclude that incorporation of one of these American indexes to the model involving IBovespa makes it more significant, once the Brazilian Stock Market is impacted by the two American indexes we mention before. This work shows that multivariate GARCH models seem to be more efficient in the volatility estimation of financial assets than univariate GARCH models.