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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.  

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 investigates the role of large language models (LLMs) in retail investors (RIs) decision-making processes from the perspective of the Theory of Planned Behaviour (TPB). It explores whether LLMs can replace or change the role of financial experts and whether introducing LLMs may lead to more infromed RIs’ decisions. Qualitative interviews were conducted with experienced RIs (n = 8). Secondary data were gathered from YouTube recordings (n = 44). Thematic analysis and Retrieval-Augmented Generation (RAG) methodology was used for data extraction and analysis of the scripts. The findings indicate that while LLMs have the potential to enhance accessibility to expert opinions and provide more informed investment decisions, they are unlikely to replace human experts. RIs show a preference for combining LLM insights with human expertise, recognising the limitations of LLMs in managing complex and nuanced investment information. The study highlights the usefulness of the TPB as a framework for the exploration of the topic. It also introduces a novel research method - advanced data extraction techniques on a vast unstructured dataset.?Results contribute to the understanding of LLMs potential in supporting RIs and confirms the usefulness on the AESTIMA tool for data extraction and analysis. 

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 investigates the role of large language models (LLMs) in retail investors (RIs) decision-making processes from the perspective of the Theory of Planned Behaviour (TPB). It explores whether LLMs can replace or change the role of financial experts and whether introducing LLMs may lead to more infromed RIs’ decisions. Qualitative interviews were conducted with experienced RIs (n = 8). Secondary data were gathered from YouTube recordings (n = 44). Thematic analysis and Retrieval-Augmented Generation (RAG) methodology was used for data extraction and analysis of the scripts. The findings indicate that while LLMs have the potential to enhance accessibility to expert opinions and provide more informed investment decisions, they are unlikely to replace human experts. RIs show a preference for combining LLM insights with human expertise, recognising the limitations of LLMs in managing complex and nuanced investment information. The study highlights the usefulness of the TPB as a framework for the exploration of the topic. It also introduces a novel research method - advanced data extraction techniques on a vast unstructured dataset.?Results contribute to the understanding of LLMs potential in supporting RIs and confirms the usefulness on the AESTIMA tool for data extraction and analysis. 

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.