## 1 Introduction  
Predicting stock prices on stock exchanges is a complex problem due to the highly nonlinear and non-stationary behavior of financial markets. Accurate predictions require extensive expertise and deep knowledge from professionals [1]. Accurate predictions can yield substantial economic benefits. For example, in 2024, 20% of the world's 200 richest individuals will accumulate their wealth through finance and investments [2]. This financial potential motivates investors to seek methods for estimating future stock prices to maximize profits and reduce losses.  
To date, research on daily stock-exchange price prediction can be grouped into five main lines of inquiry. First, studies that focus on stock indices--indicators of the aggregated value of the whole market or of specific sectors [3]--have sought to anticipate whether an index will rise or fall [4, 5, 6], its closing level [7, 8, 9], and even its intraday peak [10].  
Second, a substantial body of work targets individual stock prices, forecasting their closing quotations [7; 10; 11; 12; 13; 14] as well as their daily minima and maxima [15]. Third, researchers address stock price trends, classifying whether a given share is likely to appreciate or depreciate in the following session [12; 16; 17; 18]. Fourth, a set of decision-support models for investors has emerged: some aggregate return, profit, and risk into a weighted score [19], while others generate buy/hold/sell signals over horizons of one to six days [20]. Finally, attention has turned to cryptocurrencies, with Bitcoin in particular inspiring models that predict its future USD exchange rate and trend direction [21]. While most existing studies focus on predicting closing prices or general market direction, [15] is one of the few that targets both maximum and minimum daily prices, which aligns closely with the objective of this study.  
Traditional ML and deep-learning (DL) approaches often perform inconsistently across data sets. To mitigate this limitation, recent studies employ stacking ensembles instead of a single ML or DL model [22; 23; 24; 25]. In stacking, several base (\"weak\") learners are trained in parallel, and their predictions, together with the true labels, feed a meta-learner that learns how to combine them optimally, thereby improving overall accuracy [26; 27]. Although stacking has been used in financial forecasting, most studies focus on predicting closing prices or binary movement direction. In contrast, this study focuses on predicting both the maximum and minimum daily stock prices using a structured hybrid ensemble. Furthermore, we combine neural networks, SVR, and decision trees in a specific configuration optimized for performance consistency across multiple datasets. This design, combined with the cross-market application to both Brazilian stocks and a U.S. index, represents a novel empirical contribution.  
This research builds upon the results presented in the bachelor's thesis of [PERSON], defended at the Universidad Nacional Mayor de San Marcos (UNMSM) in 2025. The current study extends his preliminary work by incorporating additional financial indicators, refining the hybrid model architecture based on stacking techniques, and evaluating its predictive performance across different stock markets, including both national and international indices.