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This study examines whether Large Language Models (LLMs) can support momentum-based breakout strategies for retail investors by analysing technical charts and validating statistical patterns. We analysed 3,621 price charts (2023–2025) to assess LLMs’ ability to identify trading setups and deviations from random walk behaviour. 13,624 tests were run on 1,204 trades using various statistical tests. While 70% of patterns conformed to random walk behaviour in the short term (7–15 days), momentum signals rose to 26.42% over longer periods (50–250 days). Complex prompting produced both higher random walk classification (72.41% vs. 69.29%) and better returns than single-shot prompting. Incorporating Gamma Exposure (GEX) improved caution but reduced return rates. Discrepancies of up to 20% between JavaScript and Python implementations underscored LLMs’ current limitations in statistical precision. Overall, LLMs may democratise technical analysis but still require human oversight, structured prompting, and validation through traditional methods.

This study examines the impact of new digital-only competitors on the Swiss banking business models and value chain. Despite various studies and articles available in the literature about the impact of new digital competitors on the banking industry, there is little research focusing on the Swiss market. The comprehensive research conducted in this study and the data collected through the survey provides a foundation to gauge the impact of the new digital competitors’ pressure on business models and value chain in the Swiss banking industry. The design of the research instrument employed for collecting the primary data has been achieved through a survey shared with 75 managers and experts working in the Swiss banking industry including Swiss banks, FinTechs, BigTechs, and other financial services and consultancy firms. One sample z-test and descriptive statistics have been applied to the survey results to gain a deeper understanding. The outcome of the analysis suggests that the competitive pressure of BigTechs and FinTechs is expected to have a significant impact on the Swiss banking industry, and mainly BigTechs are anticipated to be significantly dominant with disruptive impact.  The obtained results also strongly indicate that the cross-industry ecosystems and close partnerships with the new digital competitors are the potential key strategies to be pursued as the future Swiss banking business models. Besides, the disruptive new market entrants are anticipated to be highly likely to gain significant market share in certain market segments of the banking industry and also to create an “ecosystem” accordingly. The area of Personal and Corporate Banking is found out to be more vulnerable to digital disruption in comparison with the other banking areas.  

The importance given by the governments to building a sound intellectual property infrastructure is increasing in developing countries and especially in Central Asian countries. This infrastructure is continuously improved to live up to a common standard in collaboration with government agencies, educational institutions and international agencies. In this paper, the infrastructure developments that took place in the Central Asian countries is going to be elaborated and furthermore some statistical analyses will be used in order to compare the differences and similarities between the Central Asian republics within themselves and the rest of the world. Patent based statistical data reveal a broad range of information concerning the innovative capability of countries, regions and firms. The number of patents that a country obtains in different technological fields and the change in this number over the years may provide useful information regarding the growth potential of the country and the ability to follow technological advances. For this purpose, patent statistics collected by institutions like World Intellectual Property Organization (WIPO) have been analyzed using statistical techniques. In addition to basic statistics, multidimensional scaling analysis (MDS) has been applied to the data sets.