A Study of Stock Market Fluctuations and their Relations to Business Conditions

Most research on stock prices is based on the present value model or the more general consumption-based model. When applied to real economic data, both of them are found unable to account for both the stock price level and its volatility. Three essays here attempt to both build a more realistic model, and to check whether there is still room for bubbles in explaining fluctuations in stock prices. In the second chapter, several innovations are simultaneously incorporated into the traditional present value model in order to produce more accurate model-based fundamental prices. These innovations comprise replacing with broad dividends the more narrow traditional dividends that are more commonly used, a nonlinear artificial neural network (ANN) forecasting procedure for these broad dividends instead of the more common linear forecasting models for narrow traditional dividends, and a stochastic discount rate in place of the constant discount rate. Empirical results show that the model described above predicts fundamental prices better, compared with alternative models using linear forecasting process, narrow dividends, or a constant discount factor. Nonetheless, actual prices are still largely detached from fundamental prices. The bubble-like deviations are found to coincide with business cycles. The third chapter examines possible cointegration of stock prices with fundamentals and non-fundamentals. The output gap is introduced to form the non-fundamental part of stock prices. I use a trivariate Vector Autoregression (TVAR) model and a single equation model to run cointegration tests between these three variables. Neither of the cointegration tests shows strong evidence of explosive behavior in the DJIA and S&P 500 data. Then, I applied a sup augmented Dickey-Fuller test to check for the existence of periodically collapsing bubbles in stock prices. Such bubbles are found in S&P data during the late 1990s. Employing econometric tests from the third chapter, I continue in the fourth chapter to examine whether bubbles exist in stock prices of conventional economic sectors on the New York Stock Exchange. The ‘old economy’ as a whole is not found to have bubbles. But, periodically collapsing bubbles are found in Material and Telecommunication Services sectors, and the Real Estate industry group.

A estrutura conceptual da informação financeira e o governo das sociedades

A informação contabilística constitui um dos pilares fundamentais para a formação de juízos e tomada de decisões empresariais e, para isso, deve cumprir os requisitos de qualidade, objectividade, transparência, rigor, responsabilidade e independência. Para o efeito, a informação é elaborada com base em políticas e critérios contabilísticos, conceitos e pressupostos subjacentes à “Estrutura Conceptual” dos normativos emitidos por organismos nacionais e internacionais, e em princípios sustentados no “Governo das Sociedades” e respectivas recomendações, considerando as necessidades dos seus utilizadores. O presente estudo tem por objectivo explorar a relação entre a Informação Financeira Divulgada e o Modelo de Governação das Sociedades e fornecer uma melhor e mais profunda compreensão das características das sociedades que influenciam a divulgação. Neste estudo, foi utilizada uma amostra de empresas portuguesas não financeiras, emitentes de acções, que se encontram admitidas a negociação no mercado de cotações oficiais da New York Stock Exchange, adiante designada por NYSE Euronext Lisbon, no período de 2010 a 2012. Os elementos foram recolhidos através da leitura e análise dos Relatórios e Contas Anuais publicados e por consulta da base de dados DataStream. Os dados obtidos foram analisados através de modelos de regressão linear múltipla, considerando o modelo de dados em painel, tendo sido utilizado o programa de estatística Gnu Regression, Econometrics and Time- Series Library (GRETL), versão 1.9.92 (Sep 20, 2014), embora, para a análise das correlações, tenha sido utilizado, também, o programa estatístico Statistical Package for the Social Sciences, adiante denominado IBM SPSS Statistics, versão 19. A análise dos resultados indica que as principais determinantes subjacentes à divulgação relacionada com o índice de cumprimento sobre o Governo das Sociedades são as variáveis explicativas relacionadas com: (i) dimensão da empresa; (ii) endividamento; (iii) concentração accionista; (iv) negociabilidade; (v) percentagem de acções detidas pelos administradores executivos; (vi) rendibilidade do investimento total; e (vii) remuneração dos administradores não executivos. Os testes efectuados mostram que os resultados alcançados são robustos para modelar especificações e problemas de existência de multicolinearidade. O software utiliza o método de Arellano (2004) com a finalidade de corrigir o problema de possibilidade de existência de heteroscedasticidade e de autocorrelação. Nesta base, verificaram-se todas as situações que poderiam criar obstáculos e causar enviesamento nos estimadores e definições das variáveis. Relativamente ao índice global de divulgação dos riscos e incertezas, concluiu-se que é provavelmente determinado por diferentes variáveis. Esta investigação enriquece a discussão sobre as relações entre a divulgação da informação e a estrutura do Governo das Sociedades. Assim, o estudo pode ser útil para accionistas, administradores, credores e outros investidores, quanto à reflexão da adequação da informação divulgada nos relatórios e contas anuais, com a finalidade da tomada de decisões idóneas e fundamentadas. Porém, é essencial que os reguladores incentivem as empresas a adequarem melhor as práticas de governo e a criarem mais mecanismos de forma a alcançar um maior nível de independência e uma adopção mais ajustada das recomendações sobre o Governo das Sociedades.

Study and implementation of some quantitative trading models

Quantitative or algorithmic trading is the automatization of investments decisions obeying a fixed or dynamic sets of rules to determine trading orders. It has increasingly made its way up to 70% of the trading volume of one of the biggest financial markets such as the New York Stock Exchange (NYSE). However, there is not a signi cant amount of academic literature devoted to it due to the private nature of investment banks and hedge funds. This projects aims to review the literature and discuss the models available in a subject that publications are scarce and infrequently. We review the basic and fundamental mathematical concepts needed for modeling financial markets such as: stochastic processes, stochastic integration and basic models for prices and spreads dynamics necessary for building quantitative strategies. We also contrast these models with real market data with minutely sampling frequency from the Dow Jones Industrial Average (DJIA). Quantitative strategies try to exploit two types of behavior: trend following or mean reversion. The former is grouped in the so-called technical models and the later in the so-called pairs trading. Technical models have been discarded by financial theoreticians but we show that they can be properly cast into a well defined scientific predictor if the signal generated by them pass the test of being a Markov time. That is, we can tell if the signal has occurred or not by examining the information up to the current time; or more technically, if the event is F_t-measurable. On the other hand the concept of pairs trading or market neutral strategy is fairly simple. However it can be cast in a variety of mathematical models ranging from a method based on a simple euclidean distance, in a co-integration framework or involving stochastic differential equations such as the well-known Ornstein-Uhlenbeck mean reversal ODE and its variations. A model for forecasting any economic or financial magnitude could be properly defined with scientific rigor but it could also lack of any economical value and be considered useless from a practical point of view. This is why this project could not be complete without a backtesting of the mentioned strategies. Conducting a useful and realistic backtesting is by no means a trivial exercise since the \laws" that govern financial markets are constantly evolving in time. This is the reason because we make emphasis in the calibration process of the strategies' parameters to adapt the given market conditions. We find out that the parameters from technical models are more volatile than their counterpart form market neutral strategies and calibration must be done in a high-frequency sampling manner to constantly track the currently market situation. As a whole, the goal of this project is to provide an overview of a quantitative approach to investment reviewing basic strategies and illustrating them by means of a back-testing with real financial market data. The sources of the data used in this project are Bloomberg for intraday time series and Yahoo! for daily prices. All numeric computations and graphics used and shown in this project were implemented in MATLAB^R scratch from scratch as a part of this thesis. No other mathematical or statistical software was used.

Finite-Sample Diagnostics for Multivariate Regressions with Applications to Linear Asset Pricing Models

In this paper, we propose several finite-sample specification tests for multivariate linear regressions (MLR) with applications to asset pricing models. We focus on departures from the assumption of i.i.d. errors assumption, at univariate and multivariate levels, with Gaussian and non-Gaussian (including Student t) errors. The univariate tests studied extend existing exact procedures by allowing for unspecified parameters in the error distributions (e.g., the degrees of freedom in the case of the Student t distribution). The multivariate tests are based on properly standardized multivariate residuals to ensure invariance to MLR coefficients and error covariances. We consider tests for serial correlation, tests for multivariate GARCH and sign-type tests against general dependencies and asymmetries. The procedures proposed provide exact versions of those applied in Shanken (1990) which consist in combining univariate specification tests. Specifically, we combine tests across equations using the MC test procedure to avoid Bonferroni-type bounds. Since non-Gaussian based tests are not pivotal, we apply the “maximized MC” (MMC) test method [Dufour (2002)], where the MC p-value for the tested hypothesis (which depends on nuisance parameters) is maximized (with respect to these nuisance parameters) to control the test’s significance level. The tests proposed are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995. Our empirical results reveal the following. Whereas univariate exact tests indicate significant serial correlation, asymmetries and GARCH in some equations, such effects are much less prevalent once error cross-equation covariances are accounted for. In addition, significant departures from the i.i.d. hypothesis are less evident once we allow for non-Gaussian errors.

Exact Skewness-Kurtosis Tests for Multivariate Normality and Goodness-of-fit in Multivariate Regressions with Application to Asset Pricing Models

We study the problem of testing the error distribution in a multivariate linear regression (MLR) model. The tests are functions of appropriately standardized multivariate least squares residuals whose distribution is invariant to the unknown cross-equation error covariance matrix. Empirical multivariate skewness and kurtosis criteria are then compared to simulation-based estimate of their expected value under the hypothesized distribution. Special cases considered include testing multivariate normal, Student t; normal mixtures and stable error models. In the Gaussian case, finite-sample versions of the standard multivariate skewness and kurtosis tests are derived. To do this, we exploit simple, double and multi-stage Monte Carlo test methods. For non-Gaussian distribution families involving nuisance parameters, confidence sets are derived for the the nuisance parameters and the error distribution. The procedures considered are evaluated in a small simulation experi-ment. Finally, the tests are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995.

Exact Multivariate Tests of Asset Pricing Models with Stable Asymmetric Distributions

In this paper, we propose exact inference procedures for asset pricing models that can be formulated in the framework of a multivariate linear regression (CAPM), allowing for stable error distributions. The normality assumption on the distribution of stock returns is usually rejected in empirical studies, due to excess kurtosis and asymmetry. To model such data, we propose a comprehensive statistical approach which allows for alternative - possibly asymmetric - heavy tailed distributions without the use of large-sample approximations. The methods suggested are based on Monte Carlo test techniques. Goodness-of-fit tests are formally incorporated to ensure that the error distributions considered are empirically sustainable, from which exact confidence sets for the unknown tail area and asymmetry parameters of the stable error distribution are derived. Tests for the efficiency of the market portfolio (zero intercepts) which explicitly allow for the presence of (unknown) nuisance parameter in the stable error distribution are derived. The methods proposed are applied to monthly returns on 12 portfolios of the New York Stock Exchange over the period 1926-1995 (5 year subperiods). We find that stable possibly skewed distributions provide statistically significant improvement in goodness-of-fit and lead to fewer rejections of the efficiency hypothesis.

Aproximación al proceso de internacionalización del sector de hidrocarburos: ¿Cómo Ecopetrol logra hacer parte del ranking “Fortune Global 500”?

Este documento analiza algunas estrategias de internacionalización aplicadas a casos de estudio de la empresa Colombiana de Petróleos, Ecopetrol con el fin de encontrar las razones que llevan a dicha compañía a ser un referente de crecimiento. Para ello, en primer lugar se explica el contexto en el cual la empresa se desarrolla; y pasa de ser una empresa estatal, para ocupar el puesto 280 dentro del Ranking Global Fortune 500. En segundo lugar el trabajo se centra en las diferentes estrategias y teorías de internacionalización en las cuales Ecopetrol se basa para lograr su éxito. Finalmente se realiza un análisis financiero con base en datos presentados por la herramienta bloomberg y entrevistas realizadas a especialistas en temas bursátiles y de internacionalización.

Testing the Markov property with ultra high frequency financial data

This paper develops a framework to test whether discrete-valued irregularly-spaced financial transactions data follow a subordinated Markov process. For that purpose, we consider a specific optional sampling in which a continuous-time Markov process is observed only when it crosses some discrete level. This framework is convenient for it accommodates not only the irregular spacing of transactions data, but also price discreteness. Further, it turns out that, under such an observation rule, the current price duration is independent of previous price durations given the current price realization. A simple nonparametric test then follows by examining whether this conditional independence property holds. Finally, we investigate whether or not bid-ask spreads follow Markov processes using transactions data from the New York Stock Exchange. The motivation lies on the fact that asymmetric information models of market microstructures predict that the Markov property does not hold for the bid-ask spread. The results are mixed in the sense that the Markov assumption is rejected for three out of the five stocks we have analyzed.

Atraso no ajuste de preços das ações de baixa liquidez no mercado brasileiro: autocorrelações cruzadas e previsibilidade dos retornos das ações

Esta dissertação investigou, no mercado brasileiro, se o atraso no ajuste de preços das ações de baixa liquidez gera previsibilidade do retorno dessas ações quando comparadas às mais líquidas. O interesse estava em confrontar os resultados com os existentes na literatura internacional que apresentavam esse efeito. Para tanto, utilizamos a metodologia proposta no artigo “Trading volume and cross-autocorrelations in stock returns”, de Chordia e Swaminathan (2000), onde foi analisada a Bolsa de Valores de Nova York (NYSE). Verificamos que, na Bolsa de Valores de São Paulo (BOVESPA), uma vez controlados pelo tamanho das empresas, os retornos, sejam diários ou semanais, de portfólios com maior liquidez antecipam os retornos dos portfólios de menor liquidez, mais explicitamente nos quartis com pequenas e médias empresas. Os efeitos de não sincronia nas negociações e as autocorrelações próprias não são suficientes para explicar os padrões de antecipação-defasagem observados nos retornos das ações, já que esses são mais significativamente influenciados pelo volume negociado. As diferenças na velocidade da incorporação de novas informações aos preços ocorrem porque as ações menos líquidas parecem responder mais lentamente a informações de mercado, pelo menos nos portfólios de empresas de menor tamanho. Dessa maneira, podemos afirmar que no Brasil, assim como nos Estados Unidos, a baixa liquidez induz um atraso no ajuste de preços das ações de pequenas e médias empresas capaz de gerar previsibilidade dos retornos dessas ações, sugerindo alguma ineficiência do mercado. Os resultados são interessantes, já que indicam que, tanto no mercado nacional quanto nos de países desenvolvidos, os volumes negociados possuem um papel significativo na velocidade em que os preços se ajustam, jogando uma luz sobre como eles podem se tornar mais eficientes.

Non- parametric specification tests for conditional duration models

This paper deals with the estimation and testing of conditional duration models by looking at the density and baseline hazard rate functions. More precisely, we foeus on the distance between the parametric density (or hazard rate) function implied by the duration process and its non-parametric estimate. Asymptotic justification is derived using the functional delta method for fixed and gamma kernels, whereas finite sample properties are investigated through Monte Carlo simulations. Finally, we show the practical usefulness of such testing procedures by carrying out an empirical assessment of whether autoregressive conditional duration models are appropriate to oIs for modelling price durations of stocks traded at the New York Stock Exchange.

The Amplifier - v. 7, no. 4

In this issue...Maimstrom Air Force Base, Newman Club, Coach Ed Simonich, Harvest Ball, glaciers, New York Stock Exchange, Montana Power, Christmas, Bill Tiddy

Valoración de recursos propios en empresas mineras: evidencia y comparación con otros sectores

El capital financiero es muy volátil y si el inversor no obtiene una remuneración adecuada al riesgo que asume puede plantearse el retirar su capital del patrimonio de la empresa y, en consecuencia, producir un cambio estructural en cualquier sector de la economía. El objetivo principal es el estudio de los coeficientes de regresión (coeficiente beta) de los modelos de valoración de activos empleados en Economía Financiera, esto es, el estudio de la variación de la rentabilidad de los activos en función de los cambios que suceden en los mercados. La elección de los modelos utilizados se justifica por la amplia utilización teórica y empírica de los mismos a lo largo de la historia de la Economía Financiera. Se han aplicado el modelo de valoración de activos de mercado (capital asset pricing model, CAPM), el modelo basado en la teoría de precios de arbitraje (arbitrage pricing theory, APT) y el modelo de tres factores de Fama y French (FF). Estos modelos se han aplicado a los rendimientos mensuales de 27 empresas del sector minero que cotizan en la bolsa de Nueva York (New York Stock Exchange, NYSE) o en la de Londres (London Stock Exchange, LSE), con datos del período que comprende desde Enero de 2006 a Diciembre de 2010. Los resultados de series de tiempo y sección cruzada tanto para CAPM, como para APT y FF producen varios errores, lo que sugiere que muchas empresas del sector no han podido obtener el coste de capital. También los resultados muestran que las empresas de mayor riesgo tienden a tener una menor rentabilidad. Estas conclusiones hacen poco probable que se mantenga en el largo plazo el equilibrio actual y puede que sea uno de los principales factores que impulsen un cambio estructural en el sector minero en forma de concentraciones de empresas. ABSTRACT Financial capital is highly volatile and if the investor does not get adequate compensation for the risk faced he may consider withdrawing his capital assets from the company and consequently produce a structural change in any sector of the economy. The main purpose is the study of the regression coefficients (beta) of asset pricing models used in financial economics, that is, the study of variation in profitability of assets in terms of the changes that occur in the markets. The choice of models used is justified by the extensive theoretical and empirical use of them throughout the history of financial economics. Have been used the capital asset pricing model, CAPM, the model XII based on the arbitrage pricing theory (APT) and the three-factor model of Fama and French (FF). These models have been applied to the monthly returns of 27 mining companies listed on the NYSE (New York Stock Exchange) or LSE(London Stock Exchange), using data from the period covered from January 2006 to December 2010. The results of time series and cross sectional regressions for CAPM, APT and FF produce some errors, suggesting that many companies have failed to obtain the cost of capital. Also the results show that higher risk firms tend to have lower profitability. These findings make it unlikely to be mainteined over the long term the current status and could drive structural change in the mining sector in the form of mergers.

Equity pricing in the mining sector: evidence from NYSE and LSE

The study examines the Capital Asset Pricing Model (CAPM) for the mining sector using weekly stock returns from 27 companies traded on the New York Stock Exchange (NYSE) or on the London Stock Exchange (LSE) for the period of December 2008 to December 2010. The results support the use of the CAPM for the allocation of risk to companies. Most companies involved in precious metals (particularly gold), which have a beta value less than unity (Table 1), have been actuated as shelter values during the financial crisis. Values of R2 do not shown very explanatory power of fitted models (R2 < 70 %). Estimated coefficients beta are not sufficient to determine the expected returns on securities but the results of the tests conducted on sample data for the period analysed do not appear to clearly reject the CAPM

Money trust investigation. : Investigation of financial and monetary conditions in the United States under House resolutions nos. 429 and 504, before a subcommittee of the Committee on banking and currency. (In three volumes).

Arsene P. Pujo, chairman.

Money trust investigation. Investigation of financial and monetary conditions in the United States under House resolutions nos. 429 and 504, before a subcommittee of the Committee on banking and currency. (In four volumes) ....

The hearings, paged continuously, as in this issue, were published also in current numbers. The "Report of the committee appointed pursuant to House resolutions 429 and 504 to investigate the concentration of control of money and credit", together with "Views of the minority" by Everis A. Hayes, Frank E. Guernsey and William H. Heald, and "Views of Mr. McMorran", was published as House rept. 1593, 62d Cong., 3d sess. The "Report" without minority views, and the "Minority report of Henry McMorran" were also published separately without document series notes.