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This paper aims to comprehensively investigate the multifaceted realm of Initial Coin Offerings (ICOs), delving into their unique characteristics, analyzing their far-reaching influence, and uncovering broader implications within the ever-evolving financial landscape. By addressing the research gap concerning the impact of team diversity on ICO success, we contribute nuanced insights to the existing discourse. Through meticulous data collection and econometric modeling, our purpose is to unravel the intricate dynamics at play, offering valuable perspectives on the transformative role of ICOs and the potential significance of team diversity in shaping their outcomes.

To explore the impact of team diversity on the success of Initial Coin Offerings (ICOs), we compiled a comprehensive database comprising 3,082 profiles and 309 projects from LinkedIn, ICOBench, and Coindesk. This dataset facilitated the creation of diverse variables for our econometric model, enabling a nuanced analysis of interactions and dynamics in the context of our research question. Through this methodical approach, we aim to contribute valuable insights into the role of team diversity in shaping the outcomes of ICO campaigns.

Our analysis of 3,082 profiles and 309 projects sheds light on the intricate dynamics of Initial Coin Offerings (ICOs). Team diversity emerges as a pivotal factor, significantly impacting the success of ICO campaigns. The econometric model, enriched with variables derived from our extensive dataset, reveals nuanced interactions. Teams characterized by diverse profiles exhibit a tangible influence on campaign outcomes, underscoring the importance of inclusivity in shaping the transformative potential of ICOs. These findings contribute valuable insights into the evolving landscape of financial innovation, emphasizing the role of diverse teams in navigating the complexities of decentralized, inclusive investment paradigms.

This study contributes to the evolving discourse on Initial Coin Offerings (ICOs) by pioneering an exploration into the uncharted territory of team diversity and its impact on campaign success. While previous research has delved into ICO performance and success variables, our focus on team diversity as a critical determinant presents a novel perspective. By methodically assembling a substantial dataset and applying an intricate econometric model, we offer a unique lens through which to understand the nuanced interplay of diverse teams in shaping the outcomes of ICOs. This fills a significant research gap and provides valuable insights into the multifaceted dynamics of contemporary financial innovation.

Public debt forecasts represent a key policy issue. Many methodologies have been employed to predict debt sustainability, including dynamic stochastic general equilibrium models, the stock flow consistent method, the structural vector autoregressive model and, more recently, the neuro-fuzzy method. Despite their widespread application in the empirical literature, all of these approaches exhibit shortcomings that limit their utility. The present research adopts a different approach to public debt forecasts, that is, the random forest, an ensemble of machine learning.

Using quarterly observations over the period 2000–2021, the present research tests the reliability of the random forest technique for forecasting the Italian public debt.

The results show the large predictive power of this method to forecast debt-to-GDP fluctuations, with no need to model the underlying structure of the economy.

Compared to other methodologies, the random forest method has a predictive capacity that is granted by the algorithm itself. The use of repeated learning, training and validation stages provides well-defined parameters that are not conditional to strong theoretical restrictions This allows to overcome the shortcomings arising from the traditional techniques which are generally adopted in the empirical literature to forecast public debt.