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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.

This paper represents a new approach in the exchange rate determination by using microstructural and macroeconomic variables. We test a combination of fundamentals and microstructure variables in cointegrated relationship of the USD/JPY and USD/GBP currencies' pairs. The ‘twofold' model includes interest rate, money supply and net foreign assets as fundamentals, and spread and high-low spread as a microstructure variable. Then we compare the different models of macroeconomic and twofold model with the random walk using an error-correction method. We find that the twofold model outperforms the random structural model in out-of-sample and in-sample forecast test for both exchange rates. Twofold model outperforms in out-of-sample forecast the random walk test for the USD/JPY. Keywords: exchange rate, spreads, interest rate, money supply, net foreign assets, twofold model, cointegration JEL Classifications: G15, G17, G18, F31, F62 DOI: https://doi.org/10.32479/ijefi.11305