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.

The literature emphasizes that interactions between biotic (the individual) and abiotic entities (the institutional environment) are central to entrepreneurial ecosystems. However, despite the importance of digital entrepreneurial ecosystem (DEE) elements, it might be questioned if all elements are equally necessary. Furthermore, different outputs might require different conditions. The same can happen with different levels of a given output. The answer to these questions is of particular concern from a policy perspective. By using necessary condition analysis (NCA) alongside with fuzzy-set qualitative comparative analysis (fs/QCA), this study advances understanding of the entrepreneurial ecosystems. While fs/QCA identifies only one necessary condition to produce digitally-enabled unicorns – market conditions – and none to unicorns and new business creation, NCA shows that all elements of DEE are necessary to produce digitally-enabled unicorns, and most of them are also necessary for producing unicorns. NCA also identifies formal institutions, regulations, and taxation and finance as necessary conditions for new business creation. Moreover, NCA shows that necessary conditions do not have the same degree of importance, and the necessity of a given condition does not automatically imply its highest level is required. For researchers, these results emphasize the importance of using NCA as a complement of fs/QCA. For practitioners, these findings can be used to optimize the allocation of policy resources, particularly targeting the elements that constitute bottlenecks. Plain English Summary: Policymakers should target different levels of entrepreneurial ecosystem pillars performance to produce unicorns. Ambitious entrepreneurship is important for country competitiveness in the digital age. Digital entrepreneurial ecosystems can facilitate the rise of digitally-enabled unicorns, an extreme case of ambitious entrepreneurship. This study shows which conditions are necessary to produce this output; these conditions can be required at different levels. The comparison of country’s performance on each condition shows which of them constitute bottlenecks. Although all elements of digital entrepreneurial ecosystems are relevant for digitally-enabled unicorns, policymakers should target the ones that constrain the emergence of this output. This study identifies the levels that should be reached in each condition, not only for digitally-enabled unicorns, but also for unicorns in general. The results show relevant differences between the levels needed for these outputs. For example, knowledge creation and dissemination seem to be more important to boost digitally-enabled unicorns rather than unicorns in general. Thus, policymakers should consider specific levels of the conditions to optimize resource allocation.

Recent work has been devoted to study the use of multiobjective evolutionary algorithms (MOEAs) in stock portfolio optimization, within a common mean-variance framework. This article proposes the use of a more appropriate framework, mean-semivariance framework, which takes into account only adverse return variations instead of overall variations. It also proposes the use and comparison of established technical analysis (TA) indicators in pursuing better outcomes within the risk-return relation. Results show there is some difference in the performance of the two selected MOEAs – non-dominated sorting genetic algorithm II (NSGA II) and strength pareto evolutionary algorithm 2 (SPEA 2) – within portfolio optimization. In addition, when used with four TA based strategies – relative strength index (RSI), moving average convergence/divergence (MACD), contrarian bollinger bands (CBB) and bollinger bands (BB), the two selected MOEAs achieve solutions with interesting in-sample and out-of-sample outcomes for the BB strategy

Measuring the impacts of collaborative projects between industry and academia raises significant challenges. It involves stakeholders with different outlooks and impact expectations. Moreover, the multidimensional nature of the impacts themselves means they are tangible and intangible, short- and long-term, direct and indirect, positive and negative, making their measurement process very complex. To gain a deeper understanding of how university-industry R&D collaborations (UICs) impact society, this study conducts a systematic review, using thematic analysis of 92 selected articles published between 2000 and 2022. The paper identifies and categorizes the impacts resulting from UICs, examines the challenges associated with measuring these impacts, and explores the strategies that can be employed to overcome such challenges. Finally, the paper integrates all such findings into a comprehensive framework. This study contributes to the theoretical advancement of impact measurement within the field of UICs, providing a foundation for the development of methodologies aimed at assessing impacts. Furthermore, it highlights important avenues for future research.