Forecasting the stock market with precision remains a task because of the intricate and ever-changing nature of market data dynamics. This research seeks to enhance precision by harnessing ensemble machine-learning models that capitalize on the unique advantages of each model while addressing their drawbacks. This study addresses this gap by testing models across six major global markets (S&P 500, NASDAQ, DAX, FTSE, Nikkei 225, and Hang Seng), providing insights into the ensemble model's performance in diverse economic contexts. We employed gradient boosting, the decision trees method, the neural networks approach, and Bayesian ridge models to foresee stock prices. We merged them through voting and stacking strategies. The models were assessed using mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE) and the a20 index. Analysis involving analysis of variance (ANOVA) and subsequent Tukey's honest significant difference (HSD) tests was performed to validate performance variations among the tested models. In the global market, the stacking ensemble showed the lowest MAE of 0.0332, followed closely by the voting ensemble, which achieved 0.0347 MAE. These results hint that ensemble models are more accurate in making predictions than standalone and hybrid models in stock price prediction tasks.

We examined the influence of digital financial inclusion (DFI) on economic growth and environmental sustainability in ASEAN nations from 2005 to 2022. Employing advanced econometric techniques such as Pooled Ordinary Least Squares (POLS), Two-Stage Least Squares (2SLS), and Generalized Method of Moments (GMM), we uncovered compelling findings: DFI boosts the economy but at an environmental cost. This surprising outcome poses a significant challenge for policymakers striving to balance economic benefits with ecological health. Our study calls for strategic government action to reconcile these critical areas, ensuring sustainable regional development.

<p>We analyze how the sentiment of financial news can be used to predict stock returns and build profitable trading strategies. Combining the textual analysis of financial news headlines and statistical methods, we build multi-class classification models to predict the stock return. The main contribution of this paper is twofold. Firstly, we develop a performance evaluation metric to compare multi-class classification methods, taking into account the precision and accuracy of the models and methods. By maximizing the metric, we find optimal combinations of models and methods and select the best approach for prediction and decision-making. Secondly, this metric enables us to construct profitable option trading strategies, which can also be used as an assessment tool to analyze models' prediction power. We apply our methodology to historical data from Apple stock and financial news headlines from Reuters from January 1, 2012 to May 31, 2019. During validation (May 31, 2018, to May 31, 2019), our models consistently outperformed the market, with two-class one-stage models yielding returns between 30% and 45%, compared to the S & P500 index's 1.73% return over the same period.</p>

<abstract> <p>Around 333BC, Diogenes Laertius cited (Greek quote): "οὐκ ἐν τῷ μεγάλῳ τὸ εὖ…, ἀλλ᾿ ἐν τῷ εὖ τὸ μέγα", which means that volume (big data: tera, peta, velocity and variety) doesn't guarantee quality. Furthermore, Protagoras's doctrine about homo mensura and the Socratic Maieutic method evidences demonstrated the way that queries (SQL) on big data can play on specific needs when crafted, such as turning big data into knowledge and a competitive advantage. In this research, an alternative to the data-driven approach will be propounded with the development of the strategic alignment model (SAM) based on the principles of Greek philosophy. The SAM model will support decisions of big data analytics structure with strategies to fit and align in a way to create a competitive advantage. Mathematical formulation of the model will help to optimize a competitive advantage through the economic value added (EVA) to guide the proper strategy and big data analytics structure in a holistic framework. Conclusions will be drawn on how strategy and the structure of big data analytics can be aligned.</p> </abstract>

<abstract> <p>For ten simple horizontal merger tests, I analytically derive the probability of blocking a merger. The assumptions are the symmetry of diversion ratios, price-cost margins, and efficiency gains, and the uniform joint distribution of these variables. The robustness analyses suggest that the limitations arising from the simplifying assumptions are only moderate. Based on these results, I provide an easy-to-use Excel calculator (without VBA macro), with which the probabilities associated with any product market and price increase threshold can be quickly calculated. Additionally, I propose a paired testing protocol for unknown demand systems by linking linear- and isoelastic-demand indicators through the threshold or the alarm probability.</p> </abstract>

<abstract><p>Trade is one of the essential features of human intelligence. The securities market is the ultimate expression of it. The fundamental indicators of stocks include information about the effects of noise and bias on stock prices; however, distinguishing between them is generally hard. In this article, I present the fundamentals hypothesis based on rational expectations and detect the global bias components from the actual fundamental indicators using a log-normal distribution model based on the fundamentals hypothesis. The analysis results show that biases generally exhibit the same characteristics, strongly supporting our theory. Notably, the positive price-to-cash flows from the investing activities ratio are proxies for the fundamentals. The answer is simple: "Cash is a fact, and profit is an opinion." Namely, opinions of management and accounting added noise to fundamentals. As a result, we obtain the Kesten process and the Pareto distribution. This result means the market knows this noise and shows a stable global bias in the stock market.</p></abstract>

<abstract><p>In the realm of machine learning, where data-driven insights guide decision-making, addressing the challenges posed by class imbalance in datasets has emerged as a crucial concern. The effectiveness of classification algorithms hinges not only on their intrinsic capabilities but also on their adaptability to uneven class distributions, a common issue encountered across diverse domains. This study delves into the intricate interplay between varying class imbalance levels and the performance of ten distinct classification models, unravelling the critical impact of this imbalance on the landscape of predictive analytics. Results showed that random forest (RF) and decision tree (DT) models outperformed others, exhibiting robustness to class imbalance. Logistic regression (LR), stochastic gradient descent classifier (SGDC) and naïve Bayes (NB) models struggled with imbalanced datasets. Adaptive boosting (ADA), gradient boosting (GB), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), and k-nearest neighbour (kNN) models improved with balanced data. Adaptive synthetic sampling (ADASYN) yielded more reliable predictions than the under-sampling (UNDER) technique. This study provides insights for practitioners and researchers dealing with imbalanced datasets, guiding model selection and data balancing techniques. RF and DT models demonstrate superior performance, while LR, SGDC and NB models have limitations. By leveraging the strengths of RF and DT models and addressing class imbalance, classification performance in imbalanced datasets can be enhanced. This study enriches credit risk modelling literature by revealing how class imbalance impacts default probability estimation. The research deepens our understanding of class imbalance's critical role in predictive analytics. Serving as a roadmap for practitioners and researchers dealing with imbalanced data, the findings guide model selection and data balancing strategies, enhancing classification performance despite class imbalance.</p></abstract>

<abstract><p>Given that inflation is one of the most important problems facing the Egyptian economy, determining the factors affecting it is very important. Thus, we aim to use machine learning algorithms like Support vector machine (SVM), K-nearest neighbor (KNN), Random Forest (R.F.), Neural Network (ANN), Gradient boosting (G.B.) and decision tree (D.T.) to determine the accurate algorithm and analyze the factors affecting on Egypt inflation. The study found that the G.B. algorithm is the most accurate among the used algorithms and showed that The major significant variables determining inflation in Egypt are the exchange rate (30.5%), gross fixed formation (24.5%) and government expenditure (12.3%). We also found a positive relationship between the inflation rate and government expenditure, money supply, gross domestic product (GDP) growth, gross fixed formation, foreign direct investment, GDP per capita and exchange rate. Furthermore, there is a negative relationship between the inflation rate, household expenditure and the external trade balance.</p></abstract>

<abstract><p>On account of the enormous role of various government policies, it is very important and valuable to find remarkable independent variables from many practical factors of influencing American economy via choosing suitable methods and effective "optimal" models or equations. Based on the American economic data in practice, in order to creatively analyze and predict American economy under two different government policies, we analyze the relationship between each factor and the economy and obtain some greater impact factors to American gross domestic product (in short, GDP) by using a class of stepwise regression, and then we give optimal analysis and predict the influence to American GDP via employing support vector machine models. Further, we establish data comparison to provide beneficial reference for choosing from two different government policies. Finally, we propose some work for future research.</p></abstract>