Publication: A stress-awareness approach to safer decision making in the trading process integrating biometric sensor technology
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Publication date
2011-09
Defense date
2012-06-15
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Abstract
La economía mundial ha llegado a tener una importancia fundamental y un claro impacto en nuestro día a día. Los traders, trabajadores de los mercados financieros, trabajan bajo estadísticas, análisis de compañías, noticias y muchos otros factores que influyen en la economía global en tiempo real. Además de tomar continuamente decisiones de riesgo, los traders también son influidos por sus propias emociones, llegando a atravesar momentos realmente estresantes. El trading es una de las profesiones más estresantes reconocidas mundialmente. Esta tesis aúna conocimientos sobre los efectos del estrés y sobre sensores como componente tecnológico, revisando, comparando y resaltando estudios relevantes y productos disponibles en el ámbito comercial. Este trabajo es utilizado para desarrollar un sistema que, usando la tecnología de sensores biométricos, puede ayudar a los traders a evitar que la toma de decisiones sea condicionada por el estrés durante el proceso de trading. Múltiples disciplinas, desde programas basados en inteligencia artificial hasta complejas funciones matemáticas, son usadas para ayudar a los traders en su esfuerzo por maximizar los beneficios. El problema es que hay un componente esencial que aún no es considerado como es la peligrosa influencia del estrés en la toma de decisiones de los traders, en este rápido entorno evolutivo que es el mercado financiero. Esta tesis toma en consideración la negativa influencia del estrés sobre los individuos y propone un sistema diseñado bajo una nueva arquitectura (Self-Aware Architecture) con base en la definición de unos principios biométricos para trading, proveyendo a los traders de la información necesaria para que sean conscientes en tiempo real de sus propios niveles de estrés, evitando de esta manera una toma de decisiones arriesgada por el propio estado del trader. El sistema ha sido diseñado considerando aspectos tecnológicos y psicológicos para mostrar esta información de la manera adecuada. Sensores biométricos son usados para reunir los datos necesarios para mostrar la información al trader. El sistema resultante es capaz de funcionar en traders individuales y en equipos de traders, ofreciendo en este último caso el nivel predominante de estrés colectivo. El sistema ha sido probado dentro de un entorno real y los resultados obtenidos son mostrados en esta tesis mostrando la evidencia de que un trader consciente de sus propios niveles de estrés puede mejorar su promedio de beneficios reduciendo el riesgo en su continua toma de decisiones. ------------------------------------------------------------------------------------------------------------------------------------------------------------------------
The role of the global economy is fundamentally important to our daily lives. The stock markets reflect the state of the economy on a daily basis. Traders are the workers within the stock markets who deal with numbers, statistics, company analysis, news and many other factors that influence the economy in real time. However, whilst making significant decisions within their workplace, traders must also deal with their own emotions. In fact, traders have one of the most stressful professional occupations. This work studies the current knowledge about stress effects and sensor technology by reviewing, comparing, and highlighting relevant existing research and commercial products that are available on the market. This study is made in order to design a system using sensor technology that supports traders to avoid the poor decision making during the trading process. Multiple disciplines, from programs with artificial intelligence to complex mathematical functions, are used to help traders in their effort to maximize profits. However, an essential problem yet not considered in this rapidly evolving environment is that traders are not supported to adequately manage how stress influences their decisions. This work takes into consideration the negative influences of stress on individuals and proposes a system designed to support traders by providing them with information that can reduce the likelihood of poor decision making. Traders are not aware of how their stress levels jeopardize safe decision making. This work, taking into consideration the known influences of stress on biometric changes, proposes a system, based in biometric principles for trading context, designed to cover this information gap and minimize the likelihood of poor decision making. The system has been designed bearing in mind both technical and physiological aspects to show the information in a suitable way. Biometric sensors are used to collect data associated with stress and a software platform based on a new architecture (Self-Aware architecture) has been developed to collect, analyse and display this information. This architecture is derived from a general model where the trading context will be a specific context fitting in the more general model to take advantage of the architecture in other stressful areas. The resulting system is capable of efficiently providing self-aware information for individual traders and self-aware collective information for teams of traders in trading companies. The system is tested in a real environment and the results provide evidence that self-aware traders could positively improve their daily final balance and diminish risky decision making.
The role of the global economy is fundamentally important to our daily lives. The stock markets reflect the state of the economy on a daily basis. Traders are the workers within the stock markets who deal with numbers, statistics, company analysis, news and many other factors that influence the economy in real time. However, whilst making significant decisions within their workplace, traders must also deal with their own emotions. In fact, traders have one of the most stressful professional occupations. This work studies the current knowledge about stress effects and sensor technology by reviewing, comparing, and highlighting relevant existing research and commercial products that are available on the market. This study is made in order to design a system using sensor technology that supports traders to avoid the poor decision making during the trading process. Multiple disciplines, from programs with artificial intelligence to complex mathematical functions, are used to help traders in their effort to maximize profits. However, an essential problem yet not considered in this rapidly evolving environment is that traders are not supported to adequately manage how stress influences their decisions. This work takes into consideration the negative influences of stress on individuals and proposes a system designed to support traders by providing them with information that can reduce the likelihood of poor decision making. Traders are not aware of how their stress levels jeopardize safe decision making. This work, taking into consideration the known influences of stress on biometric changes, proposes a system, based in biometric principles for trading context, designed to cover this information gap and minimize the likelihood of poor decision making. The system has been designed bearing in mind both technical and physiological aspects to show the information in a suitable way. Biometric sensors are used to collect data associated with stress and a software platform based on a new architecture (Self-Aware architecture) has been developed to collect, analyse and display this information. This architecture is derived from a general model where the trading context will be a specific context fitting in the more general model to take advantage of the architecture in other stressful areas. The resulting system is capable of efficiently providing self-aware information for individual traders and self-aware collective information for teams of traders in trading companies. The system is tested in a real environment and the results provide evidence that self-aware traders could positively improve their daily final balance and diminish risky decision making.
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Keywords
Biometric sensors, Stress-awareness, Traders, Self-aware architecture, Decision making