Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Explainable AI for trading 50 consumer discretionary stocks in the S&P 500(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12) Sanromán Iñiguez, Paulina Monserrat; Mendoza Montoya Omar; emipsanchez; Antelis Ortiz, Javier Mauricio; Guizar Mateos, Isaí; School of Engineering and Sciences; Sede EGADE Monterrey; Bernal Ponce, Luis ArturoThis document presents a study that merges computer science techniques with finance, focusing on the development of an Explainable Supervised Machine Learning (SML) model aimed at achieving a balance between predictive accuracy and interpretability in price forecasting for Algorithmic Trading (AT). Utilizing SHAP (SHapley Additive exPlanations), both global explanations are provided to facilitate feature selection and determine the importance of various macroeconomic and technical indicators derived from historical data of 50 companies within the Consumer Discretionary sector of the S&P 500 Index. The study also employs hyperparameter tuning on lagged values to assess whether the price movements from one day can effectively predict subsequent market prices. Algorithmic Trading (AT) currently constitutes approximately 60% to 75% of total trading activity in U.S. equity markets, European financial markets, and major Asian capital markets (Groette, 2024). Projections indicate a significant growth trajectory for this sector. The driving force behind this expansion is the advancement of Artificial Intelligence (AI). As AI models incorporate more data, they tend to become increasingly intricate and opaque, evolving into what are commonly referred to as black box models. This complexity raises critical concerns surrounding explainability, interpretability, and transparency, as well as adherence to regulatory standards. Neglecting these issues can lead to severe market disruptions, including panic selling, liquidity evaporation, increased asset correlations, and a lack of clarity regarding the decision-making processes of AI models. Such challenges underscore the imperative for developing transparent and interpretable AI solutions in AT to mitigate risks and enhance market stability.
- Analyzing factors that impact alumni income with a machine learning approach(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-05) Gómez Cravioto, Daniela Alejandra; Hernández Gress, Neil; puelquio; Ceballos Cancino, Héctor; López Guajardo, Rafael; Ceballos Cancino, Héctor Gibrán; School of Engineering and Sciences; Campus Monterrey; Preciado Arreola, José LuisThis thesis presents an exploration of different machine-learning algorithms and different approaches for predicting alumni income. The aim is to obtain insights regarding the strongest predictors for income and a ``high" earners class. The study examines the alumni sample data obtained from a survey from Tec de Monterrey, a multi-campus Mexican private university. Survey results encompass 17,898 observations before cleaning and preprocessing and 12,275 observations after this. The dataset includes values for income and a large set of independent variables, including demographic and occupational attributes of the former students and academic attributes from the institution's history. For the problem of income prediction, there have been several attempts in both social science and econometric studies. However, this study investigates whether the accuracy of conventional algorithms in econometric research to predict income can be improved with a data science approach. Furthermore, we present insights obtained with explainable AI techniques. The results show that the Gradient Boosting Model outperformed the parametric models, Linear Regression and Logistic Regression, in predicting the current income of alumni with statistically significant results (p<0.05) in three different approaches: OLS regression, Multi-class Classification, and Binary Classification. The study also identified that for predicting the alum's first income after graduation, the Linear and Logistic Regression models were the most accurate methods, as the non-parametric models did not show a significant improvement. Succinctly, we identified that age, gender, working hours per week, their first income after graduation, and those factors related to their job position and their firm contributed to explaining their income. Simultaneously, post-graduation education and family background had an insignificant contribution to the model. In addition, the results, which showed a gender wage gap indicate that further work is required to enable equality in Mexico.
