41 resultados para Stock market technical analysis


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The recent financial crisis has drawn the attention of researchers and regulators to the importance of liquidity for stock market stability and efficiency. The ability of market-makers and investors to provide liquidity is constrained by the willingness of financial institutions to supply funding capital. This paper sheds light on the liquidity linkages between the Central Bank, Monetary Financial Institutions and market-makers as crucial elements to the well-functioning of markets. Results suggest the existence of causality between credit conditions and stock market liquidity for the Eurozone between 2003 and 2015. Similar evidence is found for the UK during the post-crisis period. Keywords: stock

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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

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Dissertação para obtenção do Grau de Doutor em Alterações Climáticas e Políticas de Desenvolvimento Sustentável

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In this paper we investigate what drives the prices of Portuguese contemporary art at auction and explore the potential of art as an asset. Based on a hedonic prices model we construct an Art Price Index as a proxy for the Portuguese contemporary art market over the period of 1994 to 2014. A performance analysis suggests that art underperforms the S&P500 but overperforms the Portuguese stock market and American Government bonds. However, It does it at the cost of higher risk. Results also show that art as low correlation with financial markets, evidencing some potential in risk mitigation when added to traditional equity portfolios.

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In the stock market, information takes on special relevance, due to the market’s permanent updating and the great fluidity of information existent therein. Just as in any other negotiations, the party with the better information has a bargaining advantage, as it is able to make more advantageous business decisions. However, unlike most other markets, the proper functioning of the stock market is greatly dependent on investors’ trust in the market itself. As such, if there are investors who, due to any condition they possess or office they hold, have access to relevant information which is not accessible to the general public, distrust is bred within the market and, consequently, investment is lessened. Thus, there is a need to prevent those who hold privileged information from using it in abusive ways. In Portugal, abuse of privileged information is set out and punished criminally in Article 378. of the Portuguese Securities Code (‘Código dos Valores Mobiliários’). In this dissertation, I have set out, firstly, to analyze the inherent conditions for there to be a crime of abuse of privileged information; secondly, to analyze two well-known cases, which took place and were decided in other jurisdictions, and attempt to understand how these cases would fall under Article 378. of the Portuguese Securities Code. Whereas the first case, Chiarella v. United States, was scrutinize under Article 378 of the Portuguese Securities Code, in the second, Lafonta v. AMF, the conclusion arrived at was that the crime taken place was different. This analysis allowed, on one hand, the application to a particular case of prerequisites and concepts which were explained, at a first approach, from a more theoretical perspective; on the other hand, it also allowed the further development of specific aspects of the regime, namely the difference between an insider and a tipee, as well as to more clearly set out the limits to the precise character of the information at hand.

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This paper presents an application of an Artificial Neural Network (ANN) to the prediction of stock market direction in the US. Using a multilayer perceptron neural network and a backpropagation algorithm for the training process, the model aims at learning the hidden patterns in the daily movement of the S&P500 to correctly identify if the market will be in a Trend Following or Mean Reversion behavior. The ANN is able to produce a successful investment strategy which outperforms the buy and hold strategy, but presents instability in its overall results which compromises its practical application in real life investment decisions.

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This study aims to replicate Apple’s stock market movement by modeling major investment profiles and investors. The present model recreates a live exchange to forecast any predictability in stock price variation, knowing how investors act when it concerns investment decisions. This methodology is particularly relevant if, just by observing historical prices and knowing the tendencies in other players’ behavior, risk-adjusted profits can be made. Empirical research made in the academia shows that abnormal returns are hardly consistent without a clear idea of who is in the market in a given moment and the correspondent market shares. Therefore, even when knowing investors’ individual investment profiles, it is not clear how they affect aggregate markets.

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The present research analyses overnight returns’ outperformance in relation to daytime returns. In a first stage, it will be assessed whether these returns are robust throughout time, markets and across different scopes of analysis (e.g. weekdays, months, states of the economy). In a second stage, several hypothesis will be empirically tested, in an attempt to understand what drives non-trading period returns (e.g. liquidity, market volatility). Even though several authors have analysed overnight returns and suggested several explanatory factors, there seems to be no consensus in the literature regarding its drivers.

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Throughout the last years technologic improvements have enabled internet users to analyze and retrieve data regarding Internet searches. In several fields of study this data has been used. Some authors have been using search engine query data to forecast economic variables, to detect influenza areas or to demonstrate that it is possible to capture some patterns in stock markets indexes. In this paper one investment strategy is presented using Google Trends’ weekly query data from major global stock market indexes’ constituents. The results suggest that it is indeed possible to achieve higher Info Sharpe ratios, especially for the major European stock market indexes in comparison to those provided by a buy-and-hold strategy for the period considered.

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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA – School of Business and Economics