The article examines the current trends in the development of the stock market in the context of the active introduction of artificial intelligence technologies. The relevance of the research is due to the rapid development of digital financial technologies and the growing volume of financial data, which requires the use of new methods for analysing and forecasting the dynamics of financial markets. The purpose of the study is to analyse the main areas of application of artificial intelligence in the stock market and assess its impact on the transformation of investment processes and the functioning of financial markets. The methodological basis of the research consists of methods of analysis and synthesis of scientific literature, statistical analysis, comparative analysis and the method of scientific generalization. The paper uses statistical data from international financial organizations that characterize the structure and dynamics of the global stock market. The results of the study show that artificial intelligence is becoming an important factor in the transformation of the stock market. The most significant areas of its application are related to the development of algorithmic trading, the use of machine learning methods to predict the dynamics of financial assets, the analysis of investor market sentiment, the improvement of financial risk management mechanisms and support for investment decision-making. The introduction of intelligent technologies contributes to improving the efficiency of financial information processing, improving the quality of investment analysis, and developing new models of financial market functioning. At the same time, the widespread use of artificial intelligence in the stock market is accompanied by a number of new challenges related to the growth of algorithmic trading, the increasing technological dependence of financial institutions and the need to improve the mechanisms of regulation of financial markets. The given results can be used for further research in the field of financial technologies and the development of modern stock market analysis tools.

The article examines the current trends in the development of the stock market in the context of the active introduction of artificial intelligence technologies. The relevance of the research is due to the rapid development of digital financial technologies and the growing volume of financial data, which requires the use of new methods for analysing and forecasting the dynamics of financial markets. The purpose of the study is to analyse the main areas of application of artificial intelligence in the stock market and assess its impact on the transformation of investment processes and the functioning of financial markets. The methodological basis of the research consists of methods of analysis and synthesis of scientific literature, statistical analysis, comparative analysis and the method of scientific generalization. The paper uses statistical data from international financial organizations that characterize the structure and dynamics of the global stock market. The results of the study show that artificial intelligence is becoming an important factor in the transformation of the stock market. The most significant areas of its application are related to the development of algorithmic trading, the use of machine learning methods to predict the dynamics of financial assets, the analysis of investor market sentiment, the improvement of financial risk management mechanisms and support for investment decision-making. The introduction of intelligent technologies contributes to improving the efficiency of financial information processing, improving the quality of investment analysis, and developing new models of financial market functioning. At the same time, the widespread use of artificial intelligence in the stock market is accompanied by a number of new challenges related to the growth of algorithmic trading, the increasing technological dependence of financial institutions and the need to improve the mechanisms of regulation of financial markets. The given results can be used for further research in the field of financial technologies and the development of modern stock market analysis tools.

The selection of research topics remains highly flexible and is not rigidly confined to a narrow scope, encompassing a broad spectrum of key financial and economic areas. These include, but are not limited to: observing global economic trends and their drivers, tracking the evolution and development of financial systems across regions, understanding the complexities of capital market structures and their regulatory frameworks, assessing stock market dynamics through quantitative and qualitative lenses, mapping industrial evolution and disruptive innovations, identifying emerging economic development trends such as digital transformation or sustainable growth, collecting and interpreting corporate financial reporting data for transparency and insight, analyzing and forecasting corporate development strategies in competitive environments, conducting comprehensive capital risk assessments to mitigate uncertainties, evaluating wealth management practices for optimal asset allocation, and performing in-depth investment strategy analysis to maximize returns.

This study investigates the impact of foreign portfolio investment (FPI) on Nigerian stock market dynamics through the lens of three core microstructural dimensions, volatility, liquidity, and price discovery, using monthly data from January 2005 to December 2024, a period encompassing major structural reforms, exchange rate regime shifts, and global financial shocks. Employing an integrated multivariate time-series framework that combines ARDL, GARCH-in-mean, VECM, and Hasbrouck’s (1995) information share methodology, the analysis accounts for non-stationarity, structural breaks, and endogeneity to rigorously assess both short- and long-run effects. The findings reveal a dual, context-dependent role of FPI: under stable conditions, FPI inflows reduce volatility, boost liquidity via higher turnover, and enhance price discovery by importing global information; however, during episodes of stress, such as the 2015–2017 foreign exchange crisis, the pandemic, and the 2022–2023 global monetary tightening—FPI amplifies volatility through herding and rapid outflows. Foreign ownership concentration consistently improves liquidity and price efficiency, whereas FPI flow volatility erodes both. Robustness checks confirm the reliability of these results across model specifications and subsamples. As the first study to comprehensively model FPI’s multidimensional impact in Nigeria, this paper demonstrates that the benefits of foreign participation are conditional on macro-financial stability and domestic market depth, highlighting the need for policies that harness FPI’s efficiency gains while mitigating its fragility through stronger domestic investor buffers, enhanced transparency, and prudent macroprudential tools.

The declining explanatory power of interest rates for stock market and business cycle dynamics

This study examines the impact of key macroeconomic variables—namely inflation, interest rates, and exchange rates—on the performance of the NIFTY 50 index in India. The research aims to analyze how changes in these economic indicators influence stock market returns and investor behavior. Using secondary data collected from reliable sources such as the National Stock Exchange (NSE) and the Reserve Bank of India (RBI), the study applies statistical tools including correlation and regression analysis to evaluate relationships between variables. The findings highlight the extent to which macroeconomic conditions affect market performance, providing valuable insights for investors, policymakers, and financial analysts in understanding stock market dynamics. Keywords: NIFTY 50, Inflation, Interest Rates, Exchange Rates, Stock Market Returns, Macroeconomic Factors, Indian Equity Market.

This study examines how aggregate financial risk tolerance (FRT), measured from repeated survey responses, co-evolves with stock-market dynamics over time. The observed FRT index is treated as a noisy preference signal containing both gradual drift and episodic deviations, and its market relevance is evaluated under time variation, frequency components, and stress regimes. Using monthly data that align the survey-based FRT index with market returns and risk measures, a three-part econometric design is implemented. First, a time-varying parameter VAR (TVP-VAR) characterizes bidirectional, non-constant linkages between FRT and market outcomes. Second, signal-extraction methods decompose FRT into a smooth “normal” component and a high-frequency “abnormal” component (with robustness to alternative filters) to test whether short-run deviations contain distinct information for volatility and downside risk. Third, a Markov-switching specification assesses state dependence by testing whether the FRT–market relationship differs between low-stress and high-stress regimes. Across specifications, the FRT–market linkage is strongly state dependent: the sign and magnitude of FRT effects drift over time and differ across regimes, with high-frequency FRT deviations aligning more closely with risk dynamics than the smooth component. Predictive validation is provided via out-of-sample forecasting of next-month market risk using elastic net and gradient boosting relative to an AR(1) benchmark; explainability analysis (SHAP) indicates that abnormal FRT contributes incremental predictive content beyond standard market-state variables. Overall, the framework offers a mathematically transparent approach to modeling survey-based preference signals in markets and supports regime-aware forecasting and risk-management applications.

The article presents a systemic investigation into the behavioral determinants driving international financial relations, specifically within the context of escalating global instability and structural uncertainty. The relevance of the research is predicated on the conceptual insufficiency of classical economic paradigms, notably the Efficient Market Hypothesis (EMH), to adequately interpret the genesis of modern financial crises, the formation of speculative bubbles, and the excessive volatility of cross-border capital flows. The primary objective of this work is to identify, systematize, and critically analyze the key cognitive biases that distort the investment decision-making process, thereby deforming the global market architecture and amplifying systemic risks.The methodological framework of the study is constructed upon a synthesis of behavioral finance theory and international macroeconomics, employing a qualitative analysis of market anomalies. The article provides a detailed examination of specific psychological phenomena, including the “Home Bias Puzzle,” “Herding behavior,” and “Overconfidence.” It is substantiated that the Home Bias effect - manifested in the disproportionate concentration of domestic assets in portfolios - is not merely a consequence of transaction costs or information asymmetry, but is primarily driven by the investor’s psychological aversion to ambiguity and the illusion of competence. This cognitive distortion leads to inefficient global asset allocation and a failure to capitalize on international diversification benefits.Furthermore, the mechanism of “herding” is analyzed as a critical catalyst for the contagion of financial stress across borders. The study demonstrates how information cascades and collective irrationality trigger the formation of asset bubbles and subsequent panic selling. Special attention is paid to the impact of these biases on emerging markets, where shifts in global risk appetite can cause sudden capital stops or reversals unrelated to fundamental economic indicators. Based on a retrospective analysis of stock market dynamics, the author proves that the psychological component of market cycles is a dominant factor in precipitating financial collapses.The practical significance of the results lies in the proposal to integrate behavioral markers into the system of macroprudential regulation. The author concludes that enhancing the financial security of the state requires the development of adaptive mechanisms capable of mitigating the negative effects of investors' irrational exuberance. The article suggests that regulators should utilize behavioral insights to predict market anomalies and correct imbalances in international capital movements before they evolve into full-scale crises.

This study investigates the differential benefits accruing to firms that issue global bonds relative to those issuing domestic bonds. Employing a comprehensive international dataset comprising 11,852 public corporate fixed-rate global bonds and 107,877 domestic bonds denominated in global currencies issued by publicly listed firms over the period 2000–2023, we document that global bond issuance is associated with significantly lower financing costs, enhanced stock market liquidity, increased participation by foreign and long-term institutional investors, and short-term valuation gains. The empirical findings lend support to the investor recognition hypothesis, demonstrating that global bond issuance confers benefits beyond immediate capital-raising objectives by influencing ownership composition and stock market dynamics.

ABSTRACT In this study, we explore the impact of investor sentiment on stock market dynamics through stock closing price predictions. We propose a Multi-Head Attention Long Short-Term Memory Network (MA-LSTM) designed to predict stock closing prices by integrating both stock features and investor sentiment. By merging the capabilities of Long Short-Term Memory (LSTM) and Multi-Head Attention mechanisms, MA-LSTM adeptly captures the temporal dependencies concealed within stock market data and investor sentiment features. Investor sentiment is estimated using a Bidirectional Encoder Representations from Transformers (BERT) model, based on investor messages collected from social media platforms. To enhance sentiment estimation accuracy, we conduct further pre-training of the BERT model in the stock market domain. We combine investor sentiment with stock price data and feed it into the MA-LSTM model for predicting the closing prices of prominent stocks such as Apple and the SPDR S&P 500 ETF. The experimental results demonstrate the superiority of the proposed method over the traditional LSTM model, regardless of the inclusion of sentiment features. Particularly the MA-LSTM model with sentiment features has good effectiveness. It’s evident that incorporating sentiment features enhances the forecasting performance of stock closing prices.

Purpose The purpose of this study is to investigate the influence of the ROBO Global Robotics and Automation Index ETF on stock market in emerging economies. This study examines how advancements in robotics and automation technologies, as tracked by the ROBO ETF, affect stock market behavior in Brazil, China, India, Mexico and South Africa. Design/methodology/approach This study uses Quantile Regression, Quantile-on-Quantile Regression and Rolling Window Wavelet Correlation analysis. Data from October 2014 to September 2024 are used, focusing on indices of Bovespa (Brazil), SSEC (China), NIFTY50 (India), S&P/BMV IPC (Mexico) and FTSE/JSE All Share Index (South Africa). Findings The impact of the ROBO ETF on stock markets varies across emerging economies. Brazil and South Africa show stronger sensitivity to robotics trends, particularly under moderate market conditions. China and Mexico exhibit a weaker relationship, likely because of their more robust economic foundations. India demonstrates a moderate impact, highlighting opportunities in sectors adopting robotics, such as manufacturing and services. Research limitations/implications This research extends Technological Innovation System theory to financial markets, offering insights into automation’s impact on stock behavior. Practical implications This study highlights opportunities in emerging markets for robotics adoption. This paper encourages managers to promote an inclusive approach to automation, ensuring economic growth benefits are widely shared across sectors. Originality/value This study aims to fill the gap in literature by linking technological advancements with financial market outcomes in emerging markets. This study offers a unique empirical examination of the ROBO ETF’s influence on emerging markets.

In the context of modern economic challenges, the effective functioning of the stock market becomes crucial for the sustainable development of the Russian economy. Attracting investments to the real sector requires creating effective tools for market information analysis, which involves processing large volumes of heterogeneous data under conditions of high volatility and geopolitical instability. The aim of the research is to develop an algorithm for building a sparse neural network. This model automatically eliminates insignificant connections between neurons for predicting stock market dynamics. The proposed approachis based on the method of solving a single-point inverse problem with a minimization of the sum of absolute parameter values, which allows reducing the model’s dimensionality. The scientific novelty of the research comprises two aspects. First, the work explores the possibility of using new factors generated by a large language model (an artificial intelligence system for text processing) for predicting stock market dynamics. Second, an original algorithm for constructing a sparse neural network has been developed. The research tested two main hypotheses. The first hypothesis aimed to verify the advantages of sparse neural networks over fully connected architectures in prediction accuracy. The second hypothesis investigated the effectiveness of using features extracted by large language models from unstructured text sources for financial forecasting.Experimental verification on three tasks of stock price and dividend forecasting confirmed both hypotheses. The sparse architecture demonstrated an advantage over fully connected models in prediction accuracy and computational efficiency. Automatic feature selection revealed the relevance of macroeconomic characteristics extracted by the large language model, confirming the promise of integrating modern natural language processing technologies into financial forecasting. The obtained results can be used to form effective strategies of stock market behavior and create intelligent decision support systems. In addition, sparse models can be used in solving other economic problems, including portfolio optimization andfinancial performance management.

The purpose of this study is to examine how profitability ratios such as Gross Profit Margin (GPM), Net Profit Margin (NPM), Operating Profit (OP), Return on Equity (ROE), Return on Investment (ROI), Return on Assets (ROA), Earning Power (EP), and Earning per Share (EPS) have developed during the period between 2020 and 2024 in the mining sub-sector. This study analyzes five issuers on the Indonesia Stock Exchange: BRMS, ESSA, ANTM, INCO, and MDKA. The secondary data used are annual financial reports and closing stock prices as of December 31, which are analyzed using quantitative descriptive methods. The results show that most profitability ratios experienced a significant increase during the 2021–2022 period as a result of post-pandemic economic recovery and rising commodity prices worldwide. However, in the 2023–2024 period, profitability performance tended to decline as a result of increased operational cost pressures and falling commodity prices. Although not always applicable to every issuer, the movement of mining companies' stock prices shows a fluctuating pattern that is usually correlated with changes in profitability. These results provide an overview of the financial conditions and stock market dynamics in the mining industry, which investors can consider when making investment decisions.

Nasdaq Inc. (NDAQ) is one of the leading stock exchanges in the United States, ranking second globally after the New York Stock Exchange (NYSE) based on market capitalization. As a highly dynamic and information-sensitive market, Nasdaq Inc. stock prices respond quickly to internal corporate conditions and external macroeconomic or policy changes. One notable event affecting market stability was President Donald Trump's import tariff policy, aimed at protecting U.S. industries from foreign competition, particularly Chinese imports. The implementation of this policy triggered significant volatility, including a sharp decline in Nasdaq Inc. stock prices on March 2, 2025. This study examines the impact of this policy on Nasdaq Inc. stock movements using the ARIMA(0,2,1) model with an intervention of order b=0, r=1, and s=0. The results show that all model parameters are statistically significant and produce accurate forecasts, with a MAPE of 2.19%, an RMSE of 5.98766, and an MAE of 2.05232. These findings indicate that intervention analysis effectively captures the impact of import tariff policies on stock market dynamics and provides valuable insights for investors and policymakers in anticipating market fluctuations driven by global economic policy changes.

This article explores the role of social networks as a catalyst for instability in financial markets. Its purpose is to present strategies for mitigating the media’s influence on adverse outcomes, primarily by analyzing the information cascades and related transactions in stock markets. The innovative aspect of this research lies in the development of methods for analyzing specific types of information, particularly media reports, to identify indicators of fake that distort public perception. The focus of this study is on fake news and the analysis of its potential linguistic features for identification purposes. A significant outcome of this research is the establishment of a comprehensive method for conducting preliminary linguistic analysis of text content from economic and political news portals, enabling a reliable assessment of information credibility. I propose a methodology that acts as a preventive tool—a proactive measure to mitigate the impact of social media posts on the financial sector. The correlation between identified fake news and stock market dynamics is 0.5, indicating a noteworthy relationship. Additionally, predictive models were tested, and neural networks proved to be the most effective.

Mitigating the Pitfalls of Income Smoothing on Firm Value: The Double Edges of CSR Moderation and Cost of Debt Mediation

This study investigates the impact of interest rate decisions made by national central banks, including the Federal Reserve (Fed), and the European Central Bank (ECB), on the stock markets of emerging economies. It analyzed stock market reactions to interest rate hikes and cuts through the use of an event study approach. The findings provided evidence of heterogeneous effects of interest rate decisions by national central banks, the Fed, and the ECB on stock markets in emerging economies. Specifically, the results indicated that, in most emerging economies, stock markets reacted negatively to interest rate hikes and positively to cuts by national central banks. However, contrary to expectations, stock markets in many emerging economies reacted positively to interest rate hikes by the Fed and the ECB, but negatively to their interest rate cuts. These findings offer provide policymakers, investors, and portfolio managers with valuable insights into how interest rate decisions impact stock market dynamics in emerging economies.

The prediction of stock market dynamics remains a central challenge in financial economics due to the complexity and volatility of financial time series. Traditional econometric approaches, while useful, struggle to capture nonlinear patterns and long-term dependencies inherent in stock market behavior. Recent advances in artificial intelligence (AI), particularly in deep learning and ensemble learning, offer promising alternatives for improving predictive accuracy and robustness. This study revisits the Moroccan stock market, building upon prior research that tested neural network architectures such as MLP, RNN, CNN, and LSTM. Using daily data from the MASI index and seven sectoral indices from 2017 to 2024, we propose a hybrid methodology combining the Temporal Fusion Transformer (TFT) with gradient boosting models (XGBoost and LightGBM) in a stacking ensemble. The results demonstrate that hybrid models outperform standalone deep learning architectures, offering more reliable forecasts and improved economic backtesting performance. Our findings highlight the potential of probabilistic AI models to enhance financial decision-making and risk management in emerging markets.

Existing similarity measures in stock correlation analysis often overlook the multidimensional nature of stock data and the dynamics of the time-lag effect (TLE) in phase differences. To address these limitations, this paper proposes a novel method, called Multi-Factor Dynamic Temporal Similarity Measure (MFDTSM). The method introduces an enhanced eXtreme Gradient Boosting (XGBoost) model based on Shapley Additive exPlanations (SHAP) to comprehensively evaluate the influence of stock factors. The proposed method effectively categorizes stocks and reveals the heterogeneity of factor influence by clustering the SHAP values of stock factors. Furthermore, MFDTSM is able to successfully quantify the dynamic rate of phase differences in TLE by constructing the cumulative distance matrix and analyzing the optimal alignment paths of time series data, thereby significantly improving the accuracy of the similarity measure. Empirical analysis is performed using 102 stocks from the communication and financial industries, including 12 key stock factors. The experimental results demonstrate that MFDTSM improves the accuracy of the analysis of industry correlation, linear correlation, and stock correlation pricing by 10%, 16%, and 5%, respectively, over existing methods, which highlight the efficiency and stability of MFDTSM in analyzing complex stock market dynamics.

Purpose : The purpose of this study was to review the current literature on investor psychology and its impact on the dynamics of the stock market. Although there has been growing academic interest in the field, no comprehensive review has yet addressed the psychological aspects of market behavior across different contexts. Methodology : The SPAR-4-SLR protocol was used to conduct a systematic literature review of studies indexed in the Scopus database since 2000. The theory, context, characteristics, and methodology (TCCM) framework was adopted for structuring the review of the literature. Findings : The review showed that the theory of behavioral finance and its constructs, such as overconfidence, herding, and sentiment, were prevalent in the domain, and the regression-based models were the most commonly used approach. The focus was disproportionately on developed markets like the United States, and emerging markets and other contexts, such as cryptocurrency and ESG investing, were underrepresented. Implications : It is recommended that future studies need to embrace newer psychological theories like cognitive dissonance, social comparison, etc., cross-cultural and high-frequency data analysis, and more sophisticated methods like machine learning, experimental designs, and causal inference approaches to increase empirical robustness and relevance. Originality : Current literature has a significant gap in terms of a complete synthesis of investor psychology in the wider context of stock market dynamics.