Can Fundamental Analysis Provide Relevant Information for Understanding the Underlying Value of a Company?

This chapter investigates the relevance of fundamental analysis (FA) for companies listed on the Euronext 100 index. Can FA provide relevant information that increases understanding of the underlying value of a company? This study leverages an FA strategy to select shares in a portfolio that can systematically yield significant, positive excess market buy-and-hold returns, 1 and 2 years after the portfolio formation. Using annual financial data available from 2000 to 2016, this analysis calculates three scores applied to construct the portfolios: the L-score, F-score, and PEIS. These insights inform investors’ potential uses of fundamental signals (scores) to obtain abnormal returns. The results show that portfolios formed with high versus low scores earn 1- and 2-year abnormal returns between 2000 and 2016. This chapter contributes to scarce accounting research in European capital markets by furthering understanding of the possibility of mispriced securities

Optimized Portfolios: All Seasons Strategy

Our study explores the efficient frontier of optimal investment, taking behind the Markowitz’s theory, while advocating a diversified portfolio to reduce risk. To perform it, six portfolio models are proposed, and its formation are made by a solver, where the selected solving method is the GRG Nonlinear engine for linear solver problems. Our main goal is to design portfolios that resists to financial crisis but at the same time persists in a wealthy period. We analyze the decade where we assisted to two crashes (2000–2010) and a semi-decade where we assist to a wealthy period (2011–2018). The assets used are varied, such as Equities indexes form various countries, sector equities, bonds, commodities, EURUSD exchange and VIX. Results show that the GRG Nonlinear engine is powerful, providing excess returns in all six models

Long Memory and Volatility Clustering: is the empirical evidence consistent across stock markets?

Long memory and volatility clustering are two stylized facts frequently related to financial markets. Traditionally, these phenomena have been studied based on conditionally heteroscedastic models like ARCH, GARCH, IGARCH and FIGARCH, inter alia. One advantage of these models is their ability to capture nonlinear dynamics. Another interesting manner to study the volatility phenomena is by using measures based on the concept of entropy. In this paper we investigate the long memory and volatility clustering for the SP 500, NASDAQ 100 and Stoxx 50 indexes in order to compare the US and European Markets. Additionally, we compare the results from conditionally heteroscedastic models with those from the entropy measures. In the latter, we examine Shannon entropy, Renyi entropy and Tsallis entropy. The results corroborate the previous evidence of nonlinear dynamics in the time series considered.Comment: 8 pages; 2 figures; paper presented in APFA 6 conferenc