Momentum Trading in New York Stock Exchange (NYSE) Energy Stocks

This paper investigates whether the momentum effect exists in the NYSE energy sector. Momentum is defined as the strategy that buys (sells) these stocks that are best (worst) performers, over a pre-specified past period of time (the 'look-back' period), by constructing equally weighted portfolios. Different momentum strategies are obtained by changing the number of stocks included in these portfolios, as well as the look-back period. Next, their performance is compared against two benchmarks: the equally weighted portfolio consisting of most stocks in the NYSE energy index and the market portfolio, and the S&P500 index. The results indicate that the momentum effect is strongly present in the energy sector, and leads to highly profitable portfolios, improving the risk-reward measures and easily outperforming both benchmarks.

Understanding Economic Cycle Effects on Top Executive Compensation: An Analysis Applied to American NYSE Listed Companies.

Rapport de recherche présenté à la Faculté des arts et des sciences en vue de l'obtention du grade de Maîtrise en sciences économiques.

Avaliação do impacto da lei Sarbanes-Oxley no Value-at-Risk das empresas que operam na NYSE: uma abordagem de quebra estrutural em regressão quantílica

O objetivo do presente estudo é avaliar a existência de quebra estrutural no Value-at-Risk (VaR) das empresas que negociam suas ações na bolsa de valores de Nova Iorque (NYSE). O evento que justi ca a suspeita de mudança estrutural é a lei de governança corporativa conhecida como Sarbanes-Oxley Act (ou simplesmente SOX), a mais profunda reforma implementada no sistema de legislação nanceira dos Estados Unidos desde 1934. A metodologia empregada é baseada em um teste de quebra estrutural endógeno para modelos de regressão quantílica. A amostra foi composta de 176 companhias com registro ativo na NYSE e foi analisado o VaR de 1%, 5% e 10% de cada uma delas. Os resultados obtidos apontam uma ligação da SOX com o ponto de quebra estrutural mais notável nos VaRs de 10% e 5%, tomando-se como base a concentração das quebras no período de um ano após a implementação da SOX, a partir do teste de Qu(2007). Utilizando o mesmo critério para o VaR de 1%, a relação encontrada não foi tão forte quanto nos outros dois casos, possivelmente pelo fato de que para uma exposição ao risco tão extrema, fatores mais especí cos relacionados à companhia devem ter maior importância do que as informações gerais sobre o mercado e a economia, incluídas na especi cação do VaR. Encontrou-se ainda uma forte relação entre certas características como tamanho, liquidez e representação no grupo industrial e o impacto da SOX no VaR.

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

Comment Letter: The NYSE and the End of the Structural Separation between Regulatory and Commercial Interests of the Exchange

The NYSE transformed into a for profit entity in 2006. As part of the approval process, the NYSE agreed to structurally separate the regulatory function from the business function. In doing so, the NYSE created NYSE Regulation, a non-profit with an independent board, to handle most regulatory matters. During the comment period, a spirited debate arose over the ability of a for profit company to carry out a regulatory mission. Some suggested that the regulatory function was incompatible with a "for profit" motive and that NYSE Regulation should be spun off. Others accepted the proposed structure but called for additional changes designed to reduce the possible influence of the public holding company over the regulatory function. In the end, the SEC approved the structure but with a number of prophylactic safeguards including the requirement that NYSE Regulation have a board consisting of all independent directors (save the CEO) and that directors from the for profit holding company could not make up a majority of the board. More recently, however, the NYSE has proposed to end the structural separation of the two functions and instead put in place a functional separation. The proposal would result in the termination of the delegation agreement between the Exchange and NYSE Regulation and the creation of both a Regulatory Oversight Committee of the Board of Directors of the Exchange and the creation of a Chief Regulatory Officer. This letter examines the history of the separation of the two functions and critiques the NYSE's proposal.

Nero fáklyái; tanulmányok a kereszténység első századaihoz ...

Mode of access: Internet.

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.

Rebuilding the limit order book: Sequential Bayesian inference on hidden states

The limit order book of an exchange represents an information store of market participants' future aims and for many traders the information held in this store is of interest. However, information loss occurs between orders being entered into the exchange and limit order book data being sent out. We present an online algorithm which carries out Bayesian inference to replace information lost at the level of the exchange server and apply our proof of concept algorithm to real historical data from some of the world's most liquid futures contracts as traded on CME GLOBEX, EUREX and NYSE Liffe exchanges. © 2013 © 2013 Taylor & Francis.

Volume, volatility, and leverage: A dynamic analysis

This paper uses dynamic impulse response analysis to investigate the interrelationships among stock price volatility, trading volume, and the leverage effect. Dynamic impulse response analysis is a technique for analyzing the multi-step-ahead characteristics of a nonparametric estimate of the one-step conditional density of a strictly stationary process. The technique is the generalization to a nonlinear process of Sims-style impulse response analysis for linear models. In this paper, we refine the technique and apply it to a long panel of daily observations on the price and trading volume of four stocks actively traded on the NYSE: Boeing, Coca-Cola, IBM, and MMM.

Identifying economic periods and crisis with the multidimensional scaling

This paper applied MDS and Fourier transform to analyze different periods of the business cycle. With such purpose, four important stock market indexes (Dow Jones, Nasdaq, NYSE, S&P500) were studied over time. The analysis under the lens of the Fourier transform showed that the indexes have characteristics similar to those of fractional noise. By the other side, the analysis under the MDS lens identified patterns in the stock markets specific to each economic expansion period. Although the identification of patterns characteristic to each expansion period is interesting to practitioners (even if only in a posteriori fashion), further research should explore the meaning of such regularities and target to find a method to estimate future crisis.