A partir da década de 90, a abertura e liberalização de fluxos de capitais, somados à intensificação dos processos de fusões, aquisições e privatizações evidenciaram a necessidade de se saber qual é o valor de uma empresa e quais variáveis o afetam. Ao encontro a esta necessidade, este trabalho foi desenvolvido para investigar as variáveis consideradas como direcionadores de valor e, em específico macrodirecionadores de valor, e sua relação com o preço da ação. As implicações de se saber se um macrodirecionador de valor pode predizer e provocar alterações no preço da ação, são de ampla utilidade e importância tanto para investidores quanto para os gestores da empresa. Este trabalho objetivou saber se o desempenho dos macrodirecionadores de valor pode ser, e de que forma, preditor do desempenho do preço das ações das empresas de capital aberto mais líquidas da Bolsa de Valores de São Paulo, durante o período de 1994 a 2005. Além disso, buscou entender também, se uma variação em um macrodirecionador pode provocar e explicar uma variação no preço da ação. Para a realização da pesquisa utilizou como modelo estatístico a Causalidade de Granger e a Auto-Regressão Vetorial. Os resultados foram poucos significativos ou revelaram relações pouco consistentes para a maioria das empresas da amostra. Contudo, os resultados também revelaram a possibilidade de determinados macrodirecionadores de valor poderem ser preditores do preço da ação, para um número restrito de empresas da amostra. Esta possibilidade de relação foi apontada para os macrodirecionadores de valor: taxa de crescimento em vendas na Embraer e Usiminas; margem de lucro operacional na Embraer, Eletrobrás e Companhia Siderúrgica Nacional; e taxa de investimento adicional na Eletrobrás. / Since the decade of 90, liberalization of capitals markets, added to the processes of merger and acquisitions had evidenced the need to know which is the right value of a company and which drives it. This paper was developed to investigate the considered value drivers of a stock. This paper intended to understand the relation between value drivers and stock prices. The benefits of this knowledge may help managers and investor in they decisions. The paper focus was investigated if a change in value drivers can predict and motive a stock price change. This work objective knowing if the performance of value drivers can be predictor of stock price performance, to Brazilians enterprises, between 94 to 2005. Moreover, it searched to also understand, if a variation in a value drives can motive and explain a variation in the stock price. To accomplishment the research it used two statistical model: Granger Cause and Auto-Regression Vector (VAR). The results had few significant ones or had little disclosed consistent relations for the majority of the sample companies. However, the results had also disclosed the possibility of definitive value drivers being able to predict and motive stock prices. This possibility relation was pointed for some value drivers: revenue growth in Embraer and Usiminas; earnings before interest and tax in the Embraer, Eletrobrás and Companhia Siderúrgica Nacional; and tax of additional investment in the Eletrobrás.

Much has been written during the past decade to explain the relationship between financial and operating leverage and stock-price volatility. However, the relationship between combined leverage and stock-price volatility has yet to be fully explored. Mandelker and Rhee's (MR) recent study uses both operating and financial leverage in a regression (equivalent to the traditional total leverage—DTL) and shows that both types of leverage are positively associated with common stock betas. Huffman recently demonstrated that there are interactions between operating leverage and financial leverage. Therefore, MR's model could be oversimplified. This study examines the relationship between firms' combined leverage and their stock-price volatility. The study also examines industry and industry growth to see if the relationship is influenced by these factors. The question is whether DOCL is a better risk measure than DTL and whether there is an interaction between operating and financial leverage. The inferences that can be drawn from the study's results are as follows: (a) Stock risk is a function of combined leverage; (b) Industry significantly influences the relationship between stock risk and DOCL; (c) High growth increases the relationship between stock risk and DOCL; (d) Combined leverage (DOCL) is a better risk measure than total leverage (DTL). Further, the problem with the traditional total leverage measure is the omission of the interaction between DOL and DFL. This is consistent with Huffman's theory and suggests Mandelker and Rhee's model is oversimplified.

Stock Prices have been modeled using a variety of techniques such as neural networks, simple regression based models and so on with limited accuracy. We attempt to use Random Walk method to model movements of stock prices with modifications to account for market sentiment. A simulator has been developed as part of the work to experiment with actual NASDAQ100 stock data and check how the actual stock values compare with the predictions. In cases of short and medium term prediction (1-3 months), the predicted prices are close to the actual values, while for longer term (1 year), the predictions begin to diverge. The Random Walk method has been compared with linear regression, average and last known value across four periods and has that the Random Walk method is no better that the conventional methods as at 95% confidence there is no significant difference between the conventional methods and Random Walk model. / Prediction of stock markets has been the research interest of many scientists around the world. Speculators who wish to make a “quick buck” as well as economists who wish to predict crashes, anyone in the financial industry has an interest in predicting what stock prices are likely to be. Clearly, there is no model which can accurately predict stock prices; else markets would be absolutely perfect! However, the problem is pertinent and any improvement in the accuracy of prediction improves the state of financial markets today. This forms the broad motivation of our study.

The first paper revisits the link between interest rates and corporate bond credit spreads by applying Rigobon’s (2003) heteroskedasticity identification methodology. The second paper investigates the assumption that financial asset prices including stocks and bonds, reflect intrinsic value. The third paper decomposes the stock price into fundamental permanent, fundamental transitory, and non-fundamental shocks in order to explore the determinants of stock price fluctuations.

This study investigates FTSE/JSE All Share index monthly and daily equity returns for evidence of the January and TY effect. Four different measures of monthly return are analysed for the 1995-2006 period, whilst daily returns are analysed during the 1995-2005 period. In addition to this, analysis is conducted on monthly Fama-MacBeth risk premium estimates tor the FTSE/JSE All Share Index. Descriptive statistics are first analysed, followed by ANOV A or Kruskai-Wallis tests, the paired t-test and finally dummy variable regression analysis in investigating the seasonality of FTSE/JSE All Share Index returns and risk premia. Analysis on monthly returns reveals an absence of the January effect, however a positive slightly statistically significant December effect is found. Thus, investors earn abnormal returns on equity during the month of December. The results from the Fama-MacBeth risk premia estimates reveals highly statistically significant negative risk premia seasonal patterns during March, July and September. Thus, investors are in fact penalised for investing in equities during these months. In addition, the analysis reveals an absence of a December effect in risk premia, which contradicts the risk-return trade-off central to modem finance. The daily return analysis reveals a highly significant Turn-of-the-Year effect (TY), which suggests that investors earn abnormal returns on days at the turn of the year. Therefore, it is concluded that a December effect is apparent in South African equity monthly returns, whilst a March, July and September effect is apparent in South African equity risk premia contradicting the risk-return trade-off central to modem finance. In addition to this, a TY effect is present in South African equity daily returns.

The aim of this thesis is to investigate stock price sensitivity of 50 European companies to oil price changes using panel data analysis. Besides that, this Thesis compares sensitivities of different groups of companies. The first comparison is between Eastern and Western European companies. The second comparison is between different segments of the oil industry. Specifically, Upstream, Midstream, Downstream segment and integrated oil companies. The main finding of this thesis is that there is a positive dependence between oil price and stock prices of European oil companies. Moreover, there is a significant difference between sensitivity of Upstream and integrated companies. However, no significant difference in sensitivity of western and eastern European comapnies was found.

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

The field of stock returns and assessing stock returns utilising financial ratios has attracted substantial interest from various stakeholders. In terms of previous research, the role of financial ratios on stock returns has been based on studies in developed markets, with limited research in emerging markets. This research study provides an understanding of two specific financial ratios, namely the Return on Equity (ROE) and Dividend Payout (DPO) ratios and their impact on annual stock returns (ASR) in emerging stock markets in South Africa and Nigeria. A longitudinal analysis was performed from 2000 to 2013 for companies listed on the JSE Top 40 Index and from 2006 to 2013 for companies listed on the NSE 50 Index. The tests between the mean ROE and the mean ASR for companies listed on the JSE Top 40 Index revealed a significant positive correlation. The conclusions drawn from the relationship between the mean ROE and the mean ASR for companies listed on the NSE 50 Index and both the relationships between the mean DPO and the Mean ASR for both companies listed on the JSE top 40 Index and the NSE 50 Index was inconclusive. / Dissertation (MBA)--University of Pretoria, 2014 / lmgibs2015 / Gordon Institute of Business Science (GIBS) / unrestricted

As empresas são constantemente avaliadas, no que tange a resultados financeiros e econômicos, bem como as suas estratégias. As demonstrações financeiras são relatórios importantes na avaliação do desempenho da evolução patrimonial das organizações, fornecendo uma visão global da organização. Este estudo tem como objetivo identificar a relação entre Grau de Investimento, definido pelo rating do método de Guth, com o das certificadoras Standard & Poor's, Moody's e Fitch Ratings, com base nos dados das empresas listadas no IBRX 50. O grau de investimento de uma empresa concede um selo de “bom pagador”, esta avaliação ocorre de forma quantitativa e qualitativa, permitindo uma visão ampla dos negócios da organização. Para analisar a aderência do método que utiliza apenas indicadores financeiros e o divulgado pelas principais certificadoras será realizada uma pesquisa pelo método quantitativo-descritivo, utilizando as empresas listadas no IBRX50. A pesquisa é classificada como aplicada, com abordagem quantitativa, sendo apurado o grau de investimento pelo método de Guth através das demonstrações contábeis das empresas listadas no IBRX50, no ano de 2016, comparando com o divulgado pelas agencias certificadoras. Quanto ao objetivo é descritiva, utilizando procedimentos documentais, baseada em relatórios contábeis financeiros para calcular o grau de investimentos e os pareceres divulgados pelas certificadoras para comparar o rating divulgado como o apurado a partir dos seguintes indicadores financeiros: liquidez, rentabilidade, lucratividade, , solvência, endividamento e giro do ativo. Como resultados verificou-se a existência de diferenças entre o rating divulgado pelas agências, empresas que não possuem classificação divulgada pelas três certificadoras concomitantemente. A lista do IBRX50 contempla empresas que não foram avaliadas pelas certificadoras. / Companies are constantly evaluated in terms of financial and economic results as well as their strategies. The financial statements are important reports in assessing the performance of the organization's equity evolution, providing a global view of the organization. This study has as general objective to identify the relation between Investment Grade, defined by the rating of Guth’s Method, with the certifiers Standard & Poor's, Moody's and Fitch Ratings, based on the data of the companies listed in the IBRX 50. The degree of investment of a company grants it a "good payer" seal, this evaluation occurs quantitatively and qualitatively, allowing a broad view of the organization's business. In order to analyze the adherence of the method that uses only financial indicators and that disclosed by the main certifiers, a research based on a quantitative-descriptive method will be done, using the companies listed in the IBRX50, in 2016. The objective is descriptive, using documentary procedures, based on financial accounting reports to calculate the degree of investments and the opinions published by the certifiers to compare the rating disclosed as calculated from the financial indicators: Liquidity indebtedness, Immediate liquidity, Profitability of the asset, Profitability, Current liquidity, Dry liquidity, Solvency, Indebtedness of Liquid Equity, Return on Liquid Equity and Asset turnover. As results it was verified that there are differences between the ratings disclosed by the three agencies. The IBRX50 list includes companies that have not been evaluated by the certifiers. / Companies are constantly evaluated in terms of financial and economic results as well as their strategies. The financial statements are important reports in assessing the performance of the organization's equity evolution, providing a global view of the organization. This study has as general objective to identify the relation between Investment Grade, defined by the rating of Guth’s Method, with the certifiers Standard & Poor's, Moody's and Fitch Ratings, based on the data of the companies listed in the IBRX 50. The degree of investment of a company grants it a "good payer" seal, this evaluation occurs quantitatively and qualitatively, allowing a broad view of the organization's business. In order to analyze the adherence of the method that uses only financial indicators and that disclosed by the main certifiers, a research based on a quantitative-descriptive method will be done, using the companies listed in the IBRX50, in 2016. The objective is descriptive, using documentary procedures, based on financial accounting reports to calculate the degree of investments and the opinions published by the certifiers to compare the rating disclosed as calculated from the financial indicators: Liquidity indebtedness, Immediate liquidity, Profitability of the asset, Profitability, Current liquidity, Dry liquidity, Solvency, Indebtedness of Liquid Equity, Return on Liquid Equity and Asset turnover. As results it was verified that there are differences between the ratings disclosed by the three agencies. The IBRX50 list includes companies that have not been evaluated by the certifiers.

京都大学 / 0048 / 新制・課程博士 / 博士(経済学) / 甲第22218号 / 経博第606号 / 新制||経||292(附属図書館) / 京都大学大学院経済学研究科経済学専攻 / (主査)教授 柴田 章久, 准教授 遊喜 一洋, 准教授 高橋 修平 / 学位規則第4条第1項該当 / Doctor of Economics / Kyoto University / DGAM