Tactile interaction plays an essential role in human-to-human interaction. People gain comfort and support from tactile interactions with others and touch is an important predictor for trust. While touch has been explored as a communicative modality in HCI and HRI, we here report on two studies in which touching a social robot is used to regulate people's stress levels and consequently their actions. In the first study, we look at whether different intensities of tactile interaction result in a physiological response related to stress, and whether the interaction impacts risk-taking behaviour and trust. We let 38 participants complete a Balloon Analogue Risk Task (BART), a computer-based game that serves as a proxy for risk-taking behaviour. In our study, participants are supported by a robot during the BART task. The robot builds trust and encourages participants to take more risk. The results show that affective tactile interaction with the robot increases participants' risk-taking behaviour, but gentle affective tactile interaction increases comfort and lowers stress whereas high-intensity touch does not. We also find that male participants exhibit more risk-taking behaviour than females while being less stressed. Based on this experiment, a second study is used to ascertain whether these effects are caused by the social nature of tactile interaction or by the physical interaction alone. For this, instead of a social robot, participants now have a tactile interaction with a non-social device. The non-social interaction does not result in any effect, leading us to conclude that tactile interaction with humanoid robots is a social phenomenon rather than a mere physical phenomenon.

Recent advancements in Natural Language Processing (NLP) with Large Language Models (LLMs) have made the sentiment analysis of financial news by machines a practical achievement and no longer just a dream. More precisely, Large Language Models (LLMs) have marked a major step forward in processing large contexts, exhibiting human-level performance on various professional and academic benchmarks, although they still have limitations such as reliability issues and limited context windows [OpenAI, 2023]. Their ability to process more context has shown particularly interesting applications in many business areas [George and George, 2023]. Hence exploring the potential to extract either weak or strong signals from financial news to enhance a risk-on risk-off investment strategy becomes highly pertinent. Indeed, extracting sentiment from financial news is not new [Tetlock, 2007, Schumaker and Chen, 2009], and finance has a longstanding tradition of exploiting textual data [Kearney and Liu, 2014].