Understanding consumer choice is fundamental to marketing and management research, as firms increasingly seek to personalize offerings and optimize customer engagement. Traditional choice modeling frameworks, such as multinomial logit (MNL) and mixed logit models, impose rigid parametric assumptions that limit their ability to capture the complexity of consumer decision-making. This study introduces the Mixture of Experts (MoE) framework as a machine learning-driven alternative that dynamically segments consumers based on latent behavioral patterns. By leveraging probabilistic gating functions and specialized expert networks, MoE provides a flexible, nonparametric approach to modeling heterogeneous preferences. Empirical validation using large-scale retail data demonstrates that MoE significantly enhances predictive accuracy over traditional econometric models, capturing nonlinear consumer responses to price variations, brand preferences, and product attributes. The findings underscore MoEs potential to improve demand forecasting, optimize targeted marketing strategies, and refine segmentation practices. By offering a more granular and adaptive framework, this study bridges the gap between data-driven machine learning approaches and marketing theory, advocating for the integration of AI techniques in managerial decision-making and strategic consumer insights.

This study evaluates the effectiveness of a Mixture of Experts (MoE) model for stock price prediction by comparing it to a Recurrent Neural Network (RNN) and a linear regression model. The MoE framework combines an RNN for volatile stocks and a linear model for stable stocks, dynamically adjusting the weight of each model through a gating network. Results indicate that the MoE approach significantly improves predictive accuracy across different volatility profiles. The RNN effectively captures non-linear patterns for volatile companies but tends to overfit stable data, whereas the linear model performs well for predictable trends. The MoE model's adaptability allows it to outperform each individual model, reducing errors such as Mean Squared Error (MSE) and Mean Absolute Error (MAE). Future work should focus on enhancing the gating mechanism and validating the model with real-world datasets to optimize its practical applicability. Keywords: Mixture of Experts, Recurrent Neural Network, Stock Price Prediction, Volatility, Gating Network, Linear Regression, Predictive Modeling, Financial Markets. 1. Introduction The accurate prediction of stock prices is one of the most challenging and essential tasks in financial markets. Investors, financial analysts, and policymakers are all interested in understanding price dynamics to make informed decisions regarding investments, risk management, and market regulation. However, the inherent volatility and complexity of financial data make this a difficult endeavor. Price movements are influenced by numerous factors, ranging from company-specific events to broader macroeconomic indicators, resulting in behavior that can vary significantly between different companies and over time.

This paper introduces a novel approach to optimizing portfolio rebalancing by integrating Graph Neural Networks (GNNs) for predicting transaction costs and Dijkstra's algorithm for identifying cost-efficient rebalancing paths. Using historical stock data from prominent technology firms, the GNN is trained to forecast future transaction costs, which are then applied as edge weights in a financial asset graph. Dijkstra's algorithm is used to find the least costly path for reallocating capital between assets. Empirical results show that this hybrid approach significantly reduces transaction costs, offering a powerful tool for portfolio managers, especially in high-frequency trading environments. This methodology demonstrates the potential of combining advanced machine learning techniques with classical optimization algorithms to improve financial decision-making processes. Future research will explore expanding the asset universe and incorporating reinforcement learning for continuous portfolio optimization.

This paper examines the impact of fiscal incentives on industrial investment in Uruguay from 1974 to 2010. Using a mixed-method approach that combines econometric models with machine learning techniques, the study investigates both the short-term and long-term effects of fiscal benefits on industrial investment. The results confirm the significant role of fiscal incentives in driving long-term industrial growth, while also highlighting the importance of a stable macroeconomic environment, public investment, and access to credit. Machine learning models provide additional insights into nonlinear interactions between fiscal benefits and other macroeconomic factors, such as exchange rates, emphasizing the need for tailored fiscal policies. The findings have important policy implications, suggesting that fiscal incentives, when combined with broader economic reforms, can effectively promote industrial development in emerging economies.

Abstract: By integrating survival analysis, machine learning algorithms, and economic interpretation, this research examines the temporal dynamics associated with attaining a 5 percent rise in purchasing power parity-adjusted GDP per capita over a period of 120 months (2013-2022). A comparative investigation reveals that DeepSurv is proficient at capturing non-linear interactions, although standard models exhibit comparable performance under certain circumstances. The weight matrix evaluates the economic ramifications of vulnerabilities, risks, and capacities. In order to meet the GDPpc objective, the findings emphasize the need of a balanced approach to risk-taking, strategic vulnerability reduction, and investment in governmental capacities and social cohesiveness. Policy guidelines promote individualized approaches that take into account the complex dynamics at play while making decisions. JEL: 04, C8, C5, O1 1. Introduction In contemporary economic research, the exploration of temporal dynamics in a nation's journey to achieve a specific level of GDP per capita gains paramount importance. This empirical investigation, conducted across 33 American countries, adopts a nuanced approach by incorporating a comprehensive dataset that includes countries with right-censored data (9 countries) and those reaching a 5% increase in GDP per capita at purchasing power parity (PIBpcPPP) within 120 months (24 countries). In addressing the central query, this research aims to unravel the intricate relationship of variables and risks influencing the time required for a country to achieve the specified 5% increase in GDP per capita. Leveraging advanced statistical techniques, particularly survival analysis, the study incorporates key variables such as Vul_Inherent, Vul_Fragility_Democracy, and Vul_Human Rights, offering a robust understanding of multifaceted vulnerabilities. This academic pursuit emphasizes rigorous methodologies, empirical analyses, and data-driven insights.

Due to advancements in information technology and the rapid rise of the Internet, the data revolution of the past several decades has caused businesses to create more data than they can utilize or understand (see Erevelles, S.; Fukawa, N.; Swayne, L., 2016; Seng, J.L.; Chen, T., 2010). The expansion in the volume of data, the variety of data kinds, and the scope of analysis has necessitated technological advancements beyond storage, transport, and processing (see Seng, J.L.; Chen, T., 2010) The data must be translated into information and knowledge in order to transfer knowledge into decision-making tools for enterprises. This data is used in marketing research to identify intriguing links between market segmentation in industrial, tourist, and other markets, customer lifetime value, loyalty and client segment, direct market, marketing campaign, and other applications (Tkáˇc, M.; Verner, R., 2016). With the application of Machine Learning (ML) methods (see Bahari, T.F.; Elayidom, M.S., 2015; Jessen, H.C.; Paliouras, G., 2001), it is currently anticipated that enormous volumes of stored data may be explored, and usable information extracted. ML are strategies that equip computers with the capacity to comprehend, using data and experiences similar to the human brain (Çelik, Ö., 2018).