Helder Sebastião

Purpose: This study aims to profile Portuguese investors in cryptoassets and examine the sociodemographic and behavioral determinants of ownership. Data were collected from a 2023 survey conducted by the CMVM (Portuguese Securities Market Commission) in collaboration with a consortium of Portuguese universities. Design/methodology/approach: Logistic regression models with sequential variable elimination were applied to the full sample. The sample was also divided by gender and financial confidence to examine differences in the determinants of cryptocurrency ownership across these subgroups. Findings: Results show that younger, male, employed individuals who trade more frequently and have higher subjective digital literacy are more likely to invest. Objective financial literacy is positively correlated with ownership, while subjective financial literacy is negatively correlated. Partitioning by gender indicates broadly similar determinants, but with some differences. For men, securities ownership and regular social media use increase the likelihood of investing, whereas for women, higher education and objective literacy are most relevant. Confidence also matters. Overconfident male employees and underconfident individuals with strong digital literacy and social media use are more likely to hold cryptoassets. Originality/value: This study is among the first to analyze the determinants of cryptoasset ownership in Portugal by integrating demographic, behavioral and literacy factors with financial confidence. In particular, it offers a unique examination of how overconfidence and underconfidence affect the influence of information sources, digital engagement and trading behaviors on investment decisions.

Using daily data from November 9, 2017 to December 31, 2022, this paper uses Granger causality in the mean and the distribution to investigate the transmission of information between return, volume, volatility, and illiquidity for Bitcoin and the nine most important altcoins in terms of market capitalization. Additionally, the forecastability of Bitcoin returns is examined using linear models with different predictor spaces estimated using LASSO and the performance of several trading strategies devised upon those forecasts is assessed. The causal relationships between returns, volumes and volatilities of Bitcoin and each altcoin are more evident in the left tail of the distribution, where Bitcoin acts mostly as a transmitter of information, and in the right tail for causality regarding illiquidity. In bullish markets, Bitcoin acts mostly as a receiver of information. The best Bitcoin trading strategy is based on the model which incorporates the information on all cryptocurrencies, exhibiting a cumulative return of 331% and an annualized Sharpe ratio of 94.59%, considering an enter/exit threshold of 0.25% and after 0.5% round-trip transaction costs. These results are statistically significant when compared with the buy-and-hold strategy, which renders a cumulative return of 121% and a Sharpe ratio of 64.74%. These results point out the importance of considering information from other cryptocurrencies to forecast and trade on Bitcoin.

This study examines the predictability of three major cryptocurrencies—bitcoin, ethereum, and litecoin—and the profitability of trading strategies devised upon machine learning techniques (e.g., linear models, random forests, and support vector machines). The models are validated in a period characterized by unprecedented turmoil and tested in a period of bear markets, allowing the assessment of whether the predictions are good even when the market direction changes between the validation and test periods. The classification and regression methods use attributes from trading and network activity for the period from August 15, 2015 to March 03, 2019, with the test sample beginning on April 13, 2018. For the test period, five out of 18 individual models have success rates of less than 50%. The trading strategies are built on model assembling. The ensemble assuming that five models produce identical signals (Ensemble 5) achieves the best performance for ethereum and litecoin, with annualized Sharpe ratios of 80.17% and 91.35% and annualized returns (after proportional round-trip trading costs of 0.5%) of 9.62% and 5.73%, respectively. These positive results support the claim that machine learning provides robust techniques for exploring the predictability of cryptocurrencies and for devising profitable trading strategies in these markets, even under adverse market conditions.

This study employs dynamic model averaging and selection of Vector Autoregressive and Time-Varying Parameters Vector Autoregressive models to forecast out-of-sample monthly returns of US stocks, bonds, and Real Estate Investment Trusts (REITs) indexes from October 2006 to December 2021. The models were recursively estimated using 17 additional predictors chosen by a genetic algorithm applied to an initial list of 155 predictors. These forecasts were then used to dynamically choose portfolios formed by these assets and the riskless asset proxied by the 3-month US treasury bills. Although we did not find any predictability in the stock market, positive results were obtained for REITs and especially for bonds. The Bayesian-based approaches applied to just the returns of the three risky assets resulted in portfolios that remarkably outperform the portfolios based on the historical means and covariances and the equally weighted portfolio in terms of certainty equivalent return, Sharpe ratio, Sortino ratio and even Conditional Value-at-Risk at 5%. This study points out that Constant Relative Risk Averse investors should use Bayesian-based approaches to forecast and choose the investment portfolios, focusing their attention on different types of assets.

The cryptocurrency market has been growing frantically in number of cryptocurrencies, online exchanges, and market capitalization, which has amplified the need for comprehensive and robust pricing models. Using a database of all eligible cryptocurrencies listed on the CoinMarketCap website, we study the relationship between returns and several potential pricing factors, such as size (market capitalization), momentum, liquidity, and maturity. The analysis was conducted from December 27, 2013, to December 29, 2020, using weekly data for 3'667 cryptocurrencies. Results point out that portfolios of cryptocurrencies with smaller market capitalization, higher reversal, lower liquidity, and lower maturity tend to offer higher returns. The 5-factor model that additionally includes illiquidity and maturity performs better than the 3-factor model previously proposed in the literature, meaning that illiquidity and maturity significantly help capture the cross-sectional cryptocurrency risk premia. The 5-factor model presented seems robust to different procedures to construct portfolios and factors.

Purpose: This work uses survey data from the Portuguese Securities Market Commission (Comissão de Mercado de Valores Mobiliários – CMVM) to examine financial literacy and literacy bias. The main objective of this study is to shed light on this issue by identifying the individual characteristics that are associated with financial literacy, namely overconfidence and underconfidence, which in turn might help explain individuals' financial decisions. The study distinguishes two groups, i.e. students and nonstudents, and considers several characteristics that are usually employed in this stream of research.Design/methodology/approach: The data are based on a survey conducted by a partnership between the CMVM and a consortium of Portuguese universities. This paper has a three-fold aim. First, it studies the main individual features associated with objective financial literacy. Second, it analyzes the relationship between those variables and the bias between self-perceived and objective literacy, distinguishing overconfidence and underconfidence. Third, and most originally, this framework was also used to examine the differences between students and nonstudents. Those aims are pursued using cross-sectional ordinary least squares (OLS) regressions, except for the study of the literacy bias, for which the authors use an ordered probit.Findings: Literacy is higher in individuals of the male gender who are older, have higher incomes, live in metropolitan areas, are highly educated, have a field of study related to finance and have high self-perceived literacy. Younger people are more overconfident. Unconditionally, women are less overconfident than men, but conditionally, they overestimate their knowledge. People holding securities and with a field of study related to finance are more overconfident. The gender effect is mainly driven by students, and the impact of a field of study and of holding securities on overconfidence decreases and increases, respectively, for students. The results highlight the importance of financial education.Research limitations/implications: Due to the way that the questionnaire was made available, there is no guarantee that the sample is representative of the Portuguese general population, or, for that matter, representative of the typical Portuguese retail investors or households. Also, there is no guarantee that the same individual did not answer the questionnaire more than once, although this is highly improbable. The link to the online questionnaire was only transmitted within e-mail databases owned by the CMVM and Portuguese universities, so the authors cannot guarantee its unbiasedness.Practical implications: The authors' results may help the National Plan for Financial Education (the acronym in Portuguese is PNFF) fine-tune the required actions towards different target groups and, most importantly, highlight that different groups may require different approaches aiming to narrow the gap between objective and perceived literacy. The first step should be creating procedures to provide feedback on the objective and perceived literacy of those who enroll in financial formation programs.Social implications: The study distinguishes two groups, students and nonstudents, providing additional insights that might guide policymakers on how to structure financial education to enhance individual financial behavior. This is especially important in a country such as Portugal which has the lowest objective financial literacy in the Eurozone.Originality/value: This study contributes to the financial literacy literature, in particular to the stream of research that focuses on psychological biases, by shedding light on the factors associated with both individual overconfidence and underconfidence. Differentiating between students and nonstudents provides additional insights, which might guide policymakers on how to structure financial education to enhance individual financial behavior.

In this study, we analyze the lead-lag relationships between the US industry index and those of six other major countries from January 1973 to May 2021. We identify the leading role played by the US internationally by showing that the weekly returns of US industries, especially the US basic materials and energy industries, significantly Granger cause the returns of most other countries’ industries, suggesting that non-US industries react with some delay to new information. This delayed reaction is even more noticeable during periods of recession in the US when cross-country correlations are higher. This implies that the ability of the lagged returns of US industries to predict industries’ returns from other countries is even more pronounced when the US experienced an economic recession. A similar asymmetric relationship is found between the volatility of US industries and that of industries in other markets. The analysis of causality in the distribution of returns and volatility shows that causality runs mainly from the US to other countries, particularly in the presence of extreme negative shocks. Finally, we demonstrate that our predictions are valuable to real-world investors. Long-short strategies generate sizable and statistically significant alphas, and a constant relative risk-averse investor obtains certainty equivalent returns well above the risk-free rate.

Since its launch in 2009, bitcoin has thrived, attracting the attention of investors, regulators, academia, and the public in general. Its price dynamics, characterized by extreme volatility, severe jumps, and impressive long-term appreciation, suggest that bitcoin is a new digital asset. This study presents a comprehensive overview of the fractality of bitcoin in a high-frequency framework, namely by applying Multifractal Detrended Fluctuation Analysis (MF-DFA) and a Multifractal Regime Detecting Method (MRDM) to Bitstamp 1 min bitcoin returns from January 2013 to July 2020. The results suggest that bitcoin is multifractal, with smaller and larger fluctuations being persistent and anti-persistent, respectively. Multifractality comes from significant long-range correlations, which cast some doubts on the informational efficiency at this frequency, but mainly comes from fat-tails, which highlights the significant risks undertaken by investors in this market. Our most important result is that the degree and richness of multifractality is time-varying and increased after 2017, when volumes and prices experienced an explosive behaviour. This complexity puts into perspective the duality of bitcoin: while it is characterized by long-run attractiveness and increasing valuation, it also has a high short-run instability. Hence, this study provides some empirical evidence supporting the relationship between these two observable features.

We analyze the path from cryptocurrencies to official Central Bank Digital Currencies (CBDCs), to shed some light on the ultimate dematerialization of money. To that end, we made an extensive search that resulted in a review of more than 100 academic and grey literature references, including official positions from central banks. We present and discuss the characteristics of the different CBDC variants being considered—namely, wholesale, retail, and, for the latter, the account-based, and token-based—as well as ongoing pilots, scenarios of interoperability, and open issues. Our contribution enables decision-makers and society at large to understand the potential advantages and risks of introducing CBDCs, and how these vary according to many technical and economic design choices. The practical implication is that a debate becomes possible about the trade-offs that the stakeholders are willing to accept.