Estimation and hypothesis testing in mixed linear models

Dissertação apresentada para obtenção do Grau de Doutor em Matemática, Estatística, pela Universidade Nova de Lisboa, faculdade de Ciências e Tecnologia

The effects of OF ETF creation on the price efficiency of underlying stocks

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

The effects Ishares IBovespa index fund creation on the liquidity of underlying stocks

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

Liquidity, business cycles and monetary policy: A simulation

A Masters Thesis, presented as part of the requirements for the award of a Research Masters Degree in Economics from NOVA – School of Business and Economics

The impact of credit conditions on market liquidity – a case for European stock markets

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

Money never sleeps – overnight returns in equity markets

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.

A historical approach to the new secular stagnation hypothesis

In this paper, we analyze the behavior of real interest rates over the long-run using historical data for nine developed economies, to assess the extent to which the recent decline observed in most advanced countries is at odds with the past data, as suggested by the Secular Stagnation hypothesis. By using data from 1703 and performing stationarity and structural breaks tests, we find that the recent decline in interest rates is not explained by a structural break in the time series. Our results also show that considering long-run data leads to different conclusions than using short-run data.

Monitoring liquidity risk at BPI GA

Field lab: Business project

The relationship between liquidity and profitability in the food & beverage industry evidence from Europe and United States

This Work Project clarifies the relationship between liquidity and profitability based on a sample in the Food & Beverage (F&B) industry, and comparing the largest European and United States companies. The research concludes that liquidity, proxied by current ratio or quick ratio, correlates with return on assets taken as the measure of profitability, and so does the cash conversion cycle and its components. Moreover, company size correlates with liquidity, and indirectly affects ROA. This research contributes and addresses to managers in the F&B industry and recommends how they should act in order to improve profitability in the industry.

Proving a market for a startup befor scaling using the hypothesis-driven entrepreneurship process

The year is 2015 and the startup and tech business ecosphere has never seen more activity. In New York City alone, the tech startup industry is on track to amass $8 billion dollars in total funding – the highest in 7 years (CB Insights, 2015). According to the Kauffman Index of Entrepreneurship (2015), this figure represents just 20% of the total funding in the United States. Thanks to platforms that link entrepreneurs with investors, there are simply more funding opportunities than ever, and funding can be initiated in a variety of ways (angel investors, venture capital firms, crowdfunding). And yet, in spite of all this, according to Forbes Magazine (2015), nine of ten startups will fail. Because of the unpredictable nature of the modern tech industry, it is difficult to pinpoint exactly why 90% of startups fail – but the general consensus amongst top tech executives is that “startups make products that no one wants” (Fortune, 2014). In 2011, author Eric Ries wrote a book called The Lean Startup in attempts to solve this all-too-familiar problem. It was in this book where he developed the framework for The Hypothesis-Driven Entrepreneurship Process, an iterative process that aims at proving a market before actually launching a product. Ries discusses concepts such as the Minimum Variable Product, the smallest set of activities necessary to disprove a hypothesis (or business model characteristic). Ries encourages acting briefly and often: if you are to fail, then fail fast. In today’s fast-moving economy, an entrepreneur cannot afford to waste his own time, nor his customer’s time. The purpose of this thesis is to conduct an in-depth of analysis of Hypothesis-Driven Entrepreneurship Process, in order to test market viability of a reallife startup idea, ShowMeAround. This analysis will follow the scientific Lean Startup approach; for the purpose of developing a functional business model and business plan. The objective is to conclude with an investment-ready startup idea, backed by rigorous entrepreneurial study.