Evidence of the heterogeneous market hypothesis using wavelet multi -resolution analysis

In finance literature many economic theories and models have been proposed to explain and estimate the relationship between risk and return. Assuming risk averseness and rational behavior on part of the investor, the models are developed which are supposed to help in forming efficient portfolios that either maximize (minimize) the expected rate of return (risk) for a given level of risk (rates of return). One of the most used models to form these efficient portfolios is the Sharpe's Capital Asset Pricing Model (CAPM). In the development of this model it is assumed that the investors have homogeneous expectations about the future probability distribution of the rates of return. That is, every investor assumes the same values of the parameters of the probability distribution. Likewise financial volatility homogeneity is commonly assumed, where volatility is taken as investment risk which is usually measured by the variance of the rates of return. Typically the square root of the variance is used to define financial volatility, furthermore it is also often assumed that the data generating process is made of independent and identically distributed random variables. This again implies that financial volatility is measured from homogeneous time series with stationary parameters. In this dissertation, we investigate the assumptions of homogeneity of market agents and provide evidence for the case of heterogeneity in market participants' information, objectives, and expectations about the parameters of the probability distribution of prices as given by the differences in the empirical distributions corresponding to different time scales, which in this study are associated with different classes of investors, as well as demonstrate that statistical properties of the underlying data generating processes including the volatility in the rates of return are quite heterogeneous. In other words, we provide empirical evidence against the traditional views about homogeneity using non-parametric wavelet analysis on trading data, The results show heterogeneity of financial volatility at different time scales, and time-scale is one of the most important aspects in which trading behavior differs. In fact we conclude that heterogeneity as posited by the Heterogeneous Markets Hypothesis is the norm and not the exception.

The impact of negative social environmental events on the market value of supply chain partners

The present work analyzes the impact of negative social / environmental events on the market value of supply chain partners. The study offers a contextualized discussion around important concepts which are largely employed on the Operations Management and Management literature in general. Among them, the developments of the literature around supply chains, supply chain management, corporate social responsibility, sustainable development and sustainable supply chain management are particularly addressed, beyond the links they share with competitive advantage. As for the theoretical bases, the study rests on the Stakeholder Theory, on the discussion of the efficient-market hypothesis and on the discussion of the adjustment of stock prices to new information. In face of such literature review negative social / environmental events are then hypothesized as causing negative impact in the market value of supply chain partners. Through the documental analysis of publicly available information around 15 different cases (i.e. 15 events), 82 supply chain partners were identified. Event studies for seven different event windows were conducted on the variation of the stock price of each supply chain partner, valuing the market reaction to the stock price of a firm due to triggering events occurred in another. The results show that, in general, the market value of supply chain partners was not penalized in response to such announcements. In that sense, the hypothesis derived from the literature review is not confirmed. Beyond that, the study also provides a critical description of the 15 cases, identifying the companies that have originated such events and their supply chain partners involved.

The Myth of the Rational Market: Nudging Each Other Away from Fool’s Gold

A flurry of media commentary and several new books are focused on the recent financial crisis and near economic collapse. A Newsweek article by Zakaria (2009), “Greed is Good (To a Point),” suggests reconsidering the role of greed in capitalism. This is also the theme in Fools Gold (Tett, 2009), a story about the way derivatives markets have evolved: showing greed at its worst. In many ways this is the core source of the current set of problems. In some sense, these perspectives are integrated in The Myth of the Rational Market by Fox (2009), who traces the thinking on the efficient market hypothesis, now understood for what it is: a myth. Both books are based in large part on interviews with major players in the crisis. There are also books drawing mainly on science, but still quite accessible to general readers, as represented in Nudge by Thaler and Sunstein (2008). Both have done extensive research on human foibles in economic choice. There is also Animal Spirits (Akerlof and Schiller, 2009), a book about what Keynesian economics is really about, a look at human forces at work. Akerlof is a Nobel prize winner in economics, who before this has pointed to the problems with presuming rationality in real markets. Schiller is one of the few economists who predicted these events.

An empirical analysis on the efficiency of the microfinance investment market

This paper empirically analyzes the market efficiency of microfinance investment funds. For the empirical analysis, we use an index of the microfinance investment funds and apply two kinds of variance ratio tests to examine whether or not this index follows a random walk. We use the entire sample period from December 2003 to June 2010 as well as two sub-samples which divide the entire period before and after January 2007. The empirical evidence demonstrates that the index does not follow a random walk, suggesting that the market of the microfinance investment funds is not efficient. This result is not affected by changes in either empirical techniques or sample periods.

How long is the memory of the US stock market?

The Efficient Market Hypothesis (EMH), one of the most important hypothesis in financial economics, argues that return rates have no memory (correlation) which implies that agents cannot make abnormal profits in financial markets, due to the possibility of arbitrage operations. With return rates for the US stock market, we corroborate the fact that with a linear approach, return rates do not show evidence of correlation. However, linear approaches might not be complete or global, since return rates could suffer from nonlinearities. Using detrended cross-correlation analysis and its correlation coefficient, a methodology which analyzes long-range behavior between series, we show that the long-range correlation of return rates only ends in the 149th lag, which corresponds to about seven months. Does this result undermine the EMH?

Stochastic modelling of financial time series with memory and multifractal scaling

Financial processes may possess long memory and their probability densities may display heavy tails. Many models have been developed to deal with this tail behaviour, which reflects the jumps in the sample paths. On the other hand, the presence of long memory, which contradicts the efficient market hypothesis, is still an issue for further debates. These difficulties present challenges with the problems of memory detection and modelling the co-presence of long memory and heavy tails. This PhD project aims to respond to these challenges. The first part aims to detect memory in a large number of financial time series on stock prices and exchange rates using their scaling properties. Since financial time series often exhibit stochastic trends, a common form of nonstationarity, strong trends in the data can lead to false detection of memory. We will take advantage of a technique known as multifractal detrended fluctuation analysis (MF-DFA) that can systematically eliminate trends of different orders. This method is based on the identification of scaling of the q-th-order moments and is a generalisation of the standard detrended fluctuation analysis (DFA) which uses only the second moment; that is, q = 2. We also consider the rescaled range R/S analysis and the periodogram method to detect memory in financial time series and compare their results with the MF-DFA. An interesting finding is that short memory is detected for stock prices of the American Stock Exchange (AMEX) and long memory is found present in the time series of two exchange rates, namely the French franc and the Deutsche mark. Electricity price series of the five states of Australia are also found to possess long memory. For these electricity price series, heavy tails are also pronounced in their probability densities. The second part of the thesis develops models to represent short-memory and longmemory financial processes as detected in Part I. These models take the form of continuous-time AR(∞) -type equations whose kernel is the Laplace transform of a finite Borel measure. By imposing appropriate conditions on this measure, short memory or long memory in the dynamics of the solution will result. A specific form of the models, which has a good MA(∞) -type representation, is presented for the short memory case. Parameter estimation of this type of models is performed via least squares, and the models are applied to the stock prices in the AMEX, which have been established in Part I to possess short memory. By selecting the kernel in the continuous-time AR(∞) -type equations to have the form of Riemann-Liouville fractional derivative, we obtain a fractional stochastic differential equation driven by Brownian motion. This type of equations is used to represent financial processes with long memory, whose dynamics is described by the fractional derivative in the equation. These models are estimated via quasi-likelihood, namely via a continuoustime version of the Gauss-Whittle method. The models are applied to the exchange rates and the electricity prices of Part I with the aim of confirming their possible long-range dependence established by MF-DFA. The third part of the thesis provides an application of the results established in Parts I and II to characterise and classify financial markets. We will pay attention to the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), the NASDAQ Stock Exchange (NASDAQ) and the Toronto Stock Exchange (TSX). The parameters from MF-DFA and those of the short-memory AR(∞) -type models will be employed in this classification. We propose the Fisher discriminant algorithm to find a classifier in the two and three-dimensional spaces of data sets and then provide cross-validation to verify discriminant accuracies. This classification is useful for understanding and predicting the behaviour of different processes within the same market. The fourth part of the thesis investigates the heavy-tailed behaviour of financial processes which may also possess long memory. We consider fractional stochastic differential equations driven by stable noise to model financial processes such as electricity prices. The long memory of electricity prices is represented by a fractional derivative, while the stable noise input models their non-Gaussianity via the tails of their probability density. A method using the empirical densities and MF-DFA will be provided to estimate all the parameters of the model and simulate sample paths of the equation. The method is then applied to analyse daily spot prices for five states of Australia. Comparison with the results obtained from the R/S analysis, periodogram method and MF-DFA are provided. The results from fractional SDEs agree with those from MF-DFA, which are based on multifractal scaling, while those from the periodograms, which are based on the second order, seem to underestimate the long memory dynamics of the process. This highlights the need and usefulness of fractal methods in modelling non-Gaussian financial processes with long memory.

A note on trader Sharpe Ratios

Traders in the financial world are assessed by the amount of money they make and, increasingly, by the amount of money they make per unit of risk taken, a measure known as the Sharpe Ratio. Little is known about the average Sharpe Ratio among traders, but the Efficient Market Hypothesis suggests that traders, like asset managers, should not outperform the broad market. Here we report the findings of a study conducted in the City of London which shows that a population of experienced traders attain Sharpe Ratios significantly higher than the broad market. To explain this anomaly we examine a surrogate marker of prenatal androgen exposure, the second-to-fourth finger length ratio (2D:4D), which has previously been identified as predicting a trader's long term profitability. We find that it predicts the amount of risk taken by traders but not their Sharpe Ratios. We do, however, find that the traders' Sharpe Ratios increase markedly with the number of years they have traded, a result suggesting that learning plays a role in increasing the returns of traders. Our findings present anomalous data for the Efficient Markets Hypothesis.

Principles of investments

"Emphasises asset allocation while presenting the practical applications of investment theory. The authors concentrate on the intuition and insights that will be useful to students throughout their careers as new ideas and challenges emerge from the financial marketplace. It provides a good foundation to understand the basic types of securities and financial markets as well as how trading in those markets is conducted. The Portfolio Management section is discussed towards the end of the course and supported by a web-based portfolio simulation with a hypothetical $100,000 brokerage account to buy and sell stocks and mutual funds. Students get a chance to use real data found in the Wall Street Survivor simulation in conjunction with the chapters on investments. This site is powered by StockTrak, the leading provider of investment simulation services to the academic community. Principles of Investments includes increased attention to changes in market structure and trading technology. The theory is supported by a wide range of exercises, worksheets and problems."--publisher website Contents: Investments: background and issues -- Asset classes and financial markets -- Securities markets -- Managed funds and investment management -- Risk and return: past and prologue -- Efficient diversification -- Capital asset pricing and arbitrage pricing theory -- The efficient market hypothesis -- Bond prices and yields -- Managing bond portfolios -- Equity valuation -- Macroeconomic and industry analysis -- Financial statement analysis -- Investors and the investment process -- Hedge funds -- Portfolio performance evaluation.

Portfolio selection using artificial intelligence

The application of artificial intelligence in finance is relatively new area of research. This project employed artificial neural networks (ANNs) that use both fundamental and technical inputs to predict future prices of widely held Australian stocks and use these predicted prices for stock portfolio selection over a long investment horizon. The research involved the creation and testing of a large number of possible network configurations and draws conclusions about ANN architectures and their overall suitability for the purpose of stock portfolio selection.

Essays on momentum investing strategies

The momentum investment strategy, which buys recent winner stocks and sells recent loser stocks, earns returns that are simply too good to be explained by traditional finance theories. This thesis extends our understanding of the sources of momentum profits. The research shows that part of the seemingly anomalous returns can be explained by the market's reaction to public news, is affected by how delisting returns are calculated, and is biased by ignoring the time-varying risk of the trading strategy.

Análise comparativa dos modelos de cálculo dos retornos anormais utilizando o evento recompra de ações na Bovespa

Este trabalho tem por objetivo verificar se há diferença quanto ao nível de significância estatística no cálculo do retorno anormal realizado através de quatro modelos estatísticos utilizados em estudos de eventos, tendo como objeto de estudo empresas no mercado de ações no Brasil durante o período de março de 2003 até julho de 2010 na Bovespa. Considerando a importância do cálculo do retorno anormal nos estudos de eventos, bem como possíveis variações de resultados decorrentes da escolha do modelo de cálculo, este estudo utiliza um tema bastante conhecido, qual seja, o anúncio de recompra de ações feito pela própria companhia emissora dos títulos. A metodologia utilizada nesta pesquisa foi quantitativa, utilizando o estudo de corte transversal. Os resultados apontam que há diferença entre os níveis de significância encontrados. Ao analisar o gráfico dos modelos calculados no período da janela do evento, verificou-se que as empresas que recompraram ações a fizeram quando os papéis estavam com retorno anormal acumulado negativo e, após a recompra, os papéis tiveram retornos anormais acumulados positivos. Recalculou-se os dois modelos que utilizam o Ibovespa em sua fórmula de cálculo, através de um Ibovespa sem ponderação e conclui-se que os resultados apontam na direção de se evitar o uso de índices ponderados de mercado, preferindo a utilização de carteiras compostas apenas com uma ação para cada empresa componente da carteira de controle. Após este recálculo, verificou-se que o modelo que era menos próximo dos demais graficamente era o modelo de retorno ajustado ao mercado ponderado. Por fim, as evidências empíricas indicam que o mercado de capitais brasileiro ajusta tempestivamente os papéis das empresas que realizaram recompra de ações, em linha com o que prescreve a hipótese do mercado eficiente na sua forma semiforte.

Mudança no Critério de Contabilização de Instrumentos Financeiros pelo IASB: estudo de evento sobre os títulos soberanos da Grécia,

A alteração feita pelo IASB em 2008 na classificação dos instrumentos financeiros para reduzir as perdas bancárias com a crise do subprime e de títulos soberanos dos países-membros da União Europeia, após um pedido protocolado pela Comissão da União Europeia, motivou esta pesquisa. A referida alteração ensejou a mudança do critério de avaliação, que passou de valor justo para valor amortizado, para os instrumentos reclassificados, muito embora alguns bancos não tenham aderido à reclassificação, mantendo a orientação original que determinava a avaliação pelo valor justo. Através de Estudo de Evento testou-se a Hipótese de Eficiência de Mercado - HEM, analisando 33 instituições bancárias detentoras de títulos soberanos gregos. Embora a alteração tenha colaborado para que essas instituições bancárias protelassem essas perdas no resultado, não afetou os fluxos de caixa futuros. E como evidenciam os resultados da pesquisa, o mercado foi equitativo com essas instituições, penalizando-as com base no grau de exposição aos títulos gregos, independentemente do critério utilizado, corroborando a HEM: o valor de um ativo é o valor presente dos fluxos de caixa futuros e não dos lucros. Uma consequência importante foi que os governos, através da terceira revisão do Acordo de Capital de Basileia, adotaram medidas para regulamentar com mais rigor as instituições financeiras, no intuito que essas instituições, futuramente, possam suportar melhor os efeitos de uma crise financeira.

Analyses on Gold and US Dollar in Vietnam's Transitional Economy

This paper looks into economic insights offerred by considerations of two important financial markets in Vietnam, gold and USD. In general, the paper focuses on time series properties, mainly returns at different frequencies, and test the weak-form efficient market hypothesis. All the test rejects the efficiency of both gold and foreign exchange markets. All time series exhibit strong serial correlations. ARMA-GARCH specifications appear to have performed well with different time series. In all cases the changing volatility phenomenon is strongly supported through empirical data. An additional test is performed on the daily USD return to try to capture the impacts of Asian financial crisis and daily price limits applicable. No substantial impacts of the Asian crisis and the central bank-devised limits are found to influence the risk level of daily USD return.