In this study, we delve into the financial market to compare the performance of prominent AI and robotics-related stocks against traditional IT indices, such as the Nasdaq, and specialized AI and robotics ETFs. We evaluate the role of these stocks in diversifying portfolios, analyzing their return potential and risk profiles. Our analysis includes various investment scenarios, focusing on common AI-related stocks in the United States. We explore the influence of risk management strategies, ranging from “buy and hold” to daily rebalancing, on AI stock portfolios. This involves investigating long-term strategies like buy and hold, as well as short-term approaches, such as daily rebalancing. Our findings, covering the period from 30 April 2021, to 15 September 2023, show that AI-related stocks have not only outperformed in recent years but also highlight the growing “AI bubble” and the increasing significance of AI in investment decisions. The study reveals that these stocks have delivered superior performance, as indicated by metrics like Sharpe and Treynor ratios, providing insights into market trends and financial returns in the technology and robotics sectors. The results are particularly relevant for investors and traders in the AI sector, offering a balanced view of potential returns against the risks in this rapidly evolving market. This paper adds to the financial market literature by demonstrating that investing in emerging trends, such as AI, can be more advantageous in the short term compared to traditional markets like the Nasdaq.

Bitcoin maximalists, individuals who invest exclusively in Bitcoin and reject diversification across other cryptocurrencies, theoretically constitute a significant segment within the broader crypto investor population. This paper investigates whether these investors really differ systematically from other cryptocurrency investors in terms of financial attitudes, and socio-demographic characteristics. Using microdata from the Federal Reserve's Survey of Consumer Payment Choice (SCPC), the analysis shows that maximalists are more likely to be older-each additional year of age increases the probability of being a maximalist by about 0.3-0.4 percentage points, male, and have lower household income-being in a higher income category reduces this likelihood by roughly 1 percentage point, a profile consistent with early adoption and a stronger attachment to Bitcoin. In addition, those motivated by speculation or who have sold cryptocurrency in the past year are 11-16 percentage points less likely to be maximalists, indicating a preference for long-term holding over opportunistic trading.

Following the rise of new blockchain-based assets like NFTs and DeFi tokens, alongside the high demand for cryptocurrencies, investors are altering the construction of their portfolios by not just relying on classical strategies in FOREX, stocks, and commodities markets. To highlight the overall risk transmission between the different segment of the markets, we study the connectedness using VAR and TVP-VAR models at various frequencies. We compare the t-variant components across different scales. This approach allows for the investigation of time-frequency dynamics and the identification of patterns and relationships between these assets. The study will utilize historical data and apply wavelet coherence techniques to analyze the co-movements and dependencies at various time scales. The empirical results indicate a strong correlation between DeFi, other cryptocurrencies, and Bitcoin, while NFTs exhibit independence from nearly all segments of the network. The transmission of shocks between markets depends on time and frequency, with most transmission occurring at short-term frequencies. Dynamic transmission is influenced by crises such as the COVID-19 outbreak. Our findings contribute to the literature on blockchain-based assets by examining the transmission of shocks with established markets. These results are significant for investors in managing their portfolios and making informed decisions by considering their holdings in the classical market. JEL Classification: G11; G14 ; C5 ; C58 ; L86 ; G19