Search Results (500)

Nutrition is a fundamental aspect of consumer behavior, closely linked to the satisfaction of basic household needs and strategies for purchasing food products. This study aimed to examine how fundamental food needs—specifically survival (daily food) and food security (food stocks)—shape purchasing behaviors, enabling the identification of vulnerable consumer segments and the delineation of patterns useful for producers and retailers. Data were collected through a cross-sectional survey (N = 1060) and analyzed using the Rao & Scott-adjusted Pearson chi-square test (R, version 4.4.3), considering key socio-demographic factors including gender, age, educational level, marital status, residence, and income. Results indicate that gender, age, and education significantly influence food purchases driven by the need for food security, whereas marital status is a significant factor only for survival-related purchases. Differences observed in other contexts were not statistically significant. Additionally, two multinomial logistic regression models were developed to predict consumer food purchases driven by fundamental needs, demonstrating high explanatory power. Each socio-demographic factor emerged as a significant predictor for at least one response category on the Likert scale, and the relative influence of each predictor was quantified. These models provide actionable insights for marketing strategies, including the identification of optimal store locations and the adjustment, diversification, or optimization of product ranges based on the characteristics of specific consumer segments and geographic areas. Full article

Present financial markets are characterized by great volatility and nonlinear dynamics since they are driven by both quantitative forces and qualitative mood. Traditional trading practices cannot capture such nuance. This study proposes an automated trading system based on machine learning that uses technical analysis as well as sentiment factors for better decision-making. Historical OHLCV stock price data from 2000 to 2025 was augmented with financial indicators such as SMA, EMA, RSI, and Bollinger Bands, as well as sentiment scores based on real-time news via natural language processing. LightGBM regression for predicting the price range and Histogram-Based Gradient Boosting classification for directional prediction were employed. Signals were generated with volatility-adjusted thresholds and classifier confirmation, and a risk management layer enforced position sizing, stop-loss triggering, and drawdown constraint. Back testing demonstrated improved Sharpe ratio, Sortino ratio, and win rates versus baseline strategies. The findings emphasize that the combination of machine learning and sentiment analysis with risk-conscious design improves predictive accuracy, dependability, and preservation of capital in automated trading systems. Full article

Sustainable finance increasingly relies on Environmental, Social, and Governance (ESG) data, yet forecasting ESG-based stock indices remains challenging in an emerging-market context. Using Thailand as a representative case due to limited historical information, this study constructs a realistic simulated SET ESG Index using free-float-adjusted market capitalization and semiannual rebalancing rules that reflect the methodology of the Stock Exchange of Thailand. Using this index as the forecasting target, this study compares traditional statistical time series models (ARIMA, SARIMA, SARIMAX) with seven deep learning architectures (RNN, GRU, LSTM, DF-RNN, DeepAR, DSSM, Deep Renewal) to evaluate performance in multi-step (36-day) prediction. Results reveal that deep learning models significantly outperform statistical approaches, with GRU delivering the highest accuracy and the most consistent robustness across reduced-data scenarios. These findings highlight the ability of advanced AI techniques to capture nonlinear ESG market dynamics better. This study provides a replicable modeling pipeline for ESG index forecasting in data-constrained contexts, with practical implications for sustainable investment decision-making, risk management, and market resilience in emerging economies. Full article

Stock return prediction in emerging markets remains difficult due to the gap between theoretical efficiency and empirical irregularities. This study assesses the statistical and economic performance of Linear Regression, Ridge Regression, Random Forest, and XGBoost in forecasting 5-day and 21-day returns for six LQ45 stocks (2016–2025). Momentum, volatility, trend, and volume indicators are used as predictors, while model performance is evaluated using MAE, RMSE, R², and backtested trading metrics that include transaction costs. All models yield near-zero or negative R², directional accuracy of 49–54%, and AUC around 0.50–0.53, indicating weak signals overshadowed by noise. XGBoost offers the lowest statistical errors, but Ridge Regression achieves slightly better risk-adjusted outcomes (Sharpe 0.1232), although every strategy underperforms Buy & Hold. SHAP results show volatility and volume features as most influential, but with minimal absolute impact. Overall, the LQ45 market exhibits semi-efficiency: patterns exist but fail to translate into profitable trading once real-world frictions are considered, underscoring the gap between statistical predictability and economic viability in algorithmic trading. This research was conducted in order to support the achievement of various goals through SDG 8 (Decent Work and Economic Growth). Full article

Forecasting stock prices remains a central challenge in financial modelling, as markets are influenced by market sentiment, firm-level fundamentals and complex interactions between macroeconomic and microeconomic factors, for example. This study evaluates the predictive performance of both classical statistical models and advanced attention-based deep learning architectures for daily stock price forecasting. Using a dataset of major U.S. equities and Exchange Traded Funds (ETFs) covering 2012–2024, we compare traditional statistical approaches, Seasonal Autoregressive Integrated Moving Average (SARIMA) and Exponential Smoothing (ES) in the Error, Trend, Seasonal (ETS) framework, with deep learning architectures such as the Temporal Fusion Transformer (TFT), and a novel hybrid model, the TFT-Graph Neural Network (TFT-GNN), which incorporates relational information between assets. All models are assessed under consistent experimental conditions in terms of forecast accuracy, computational efficiency, and interpretability. Our results indicate that while statistical models offer strong baselines with high stability and low computational cost, the TFT outperforms them in capturing short-term nonlinear dependencies. The hybrid TFT-GNN achieves the highest overall predictive accuracy, demonstrating that relational signals derived from inter-asset connections provide meaningful enhancements beyond traditional temporal and technical indicators. These findings highlight the advantages of integrating relational learning into temporal forecasting frameworks and emphasise the continued relevance of statistical models as interpretable and efficient benchmarks for evaluating deep learning approaches in high-frequency financial prediction. Full article

Short-term trading presents a high-dimensional prediction problem, where the profitability of trading signals depends not only on model accuracy but also on how financial labels are defined and aligned with market dynamics. Traditional approaches often apply uniform modeling choices across assets, overlooking the asset-specific nature of volatility, liquidity, and market response. In this work, we introduce a structured, label-aware machine learning pipeline aimed at maximizing short-term trading profitability across four major benchmarks: S&P 500 (SPX), NASDAQ-100 (NDX), Dow Jones Industrial Average (DJI), and the Tadāwul All-Share Index (TASI and twelve of their most actively traded constituents). Our solution systematically evaluates all combinations of six model types (logistic regression, support vector machines, random forest, XGBoost, 1-D CNN, and LSTM), eight look-ahead labeling windows (3 to 10 days), and four feature subset sizes (44, 26, 17, 8 variables) derived through Random Forest permutation-importance ranking. Backtests are conducted using realistic long/flat simulations with zero commission, optimizing for Percentage Profit and Profit Factor on a 2005–2021 train/2022–2024 test split. The central contribution of the framework is a labeling-aware search mechanism that assigns to each asset its optimal combination of model type, look-ahead horizon, and feature subset based on out-of-sample profitability. Empirical results show that while XGBoost performs best on average, CNN and LSTM achieve standout gains on highly volatile tech stocks. The optimal look-ahead window varies by market from 3-day signals on liquid U.S. shares to 6–10-day signals on the less-liquid TASI universe. This joint model–label–feature optimization avoids one-size-fits-all assumptions and yields transferable configurations that cut grid-search cost when deploying from index level to constituent stocks, improving data efficiency, enhancing robustness, and supporting more adaptive portfolio construction in short-horizon trading strategies. Full article

This study examines whether the external financing anomaly exists in an emerging-market setting. Using data on Korean listed firms from 1994 to 2023, we find that firms with higher net external financing subsequently earn significantly lower stock returns, consistent with behavioral misvaluation and market-timing explanations. A hedge portfolio long in net repurchasers and short in net issuers delivers an average annual return of about 12 percent. Decomposing financing flows show that both equity and debt issuance predict lower future returns, and further separating debt into bonds and loans reveals a stronger negative return association for bond-financed firms, consistent with greater sentiment sensitivity in market-based financing. We also document subsequent declines in operating performance, indicating that external financing aligns with temporary overvaluation rather than growth opportunities. Overall, our findings extend evidence on the external financing anomaly to an emerging market and provide further support for the behavioral interpretation of corporate financing decisions. Full article

Rising ocean temperatures driven by climate change are impacting the distribution of fish stocks. In the Northeastern United States, fish scientists predict that well-known local species will shift further north and will be replaced by lesser-known southern species in the local waters. It is unclear whether New England seafood consumers will accept these unfamiliar species when they enter the market, posing a threat to the resiliency of fishing communities. This paper investigates how New England seafood consumers might respond to a shifting supply of seafood by conducting an online stated preference survey. The choice experiment leveraged in the survey revealed that, compared to Atlantic Cod, consumers are willing to pay less for the unfamiliar fish species. However, significant heterogeneity was detected in the consumers’ preferences for purchasing these species. We find the varying degree of willingness to pay being affected by factors such as the type of venues they purchase seafood from and whether they fish recreationally. Our results suggest there will be a challenge in marketing these species, although with proper marketing strategies and coordination among the industry, these challenges may be reduced. Full article

This paper investigates the predictability of stock returns in the Chinese market through the lens of consumption–wealth dynamics within a broader financial system. We focus on two key state variables derived from modern consumption-based asset pricing models: the ratio of log surplus consumption (scr), from the habit-formation framework, and the log consumption–wealth ratio (cay), from the long-run cointegration framework. Using quarterly data from the CSI 300 index between 2012Q1 and 2018Q4, our system-based analysis reveals a horizon-dependent pattern of predictability. The results show that scr is a strong short-term predictor of excess stock returns, reflecting cyclical changes in risk aversion, whereas cay demonstrates superior predictive power over mid- to long-term horizons, consistent with its role as a proxy for long-run expectations. Interestingly, combining scr and cay does not improve predictive performance, suggesting that the economic mechanisms they capture are distinct rather than complementary in the Chinese market. These findings provide evidence on how interconnected macro-financial variables shape stock return dynamics, highlighting the importance of considering temporal horizons when modeling financial systems. Full article

The stability of life purpose and coherence as dimensions of life attitude shapes the cognitive structures underpinning financial decision-making. This study examines how cognitive ability mediates the effect of life attitude profile on the intention to stock-market participation of 195 randomly selected young professionals in the Philippines. This study adopted a quantitative, cross-sectional framework, employing Partial least squares–Structural Equation Modeling to evaluate both predictive and mediating influence. The findings revealed that individuals with stronger life purpose, greater goal-seeking tendencies, and an overall positive outlook toward life exhibit a higher propensity to participate in stock market investments. Cognitive ability, proxied by financial literacy, emerged as a crucial mechanism that reinforces this relationship—suggesting that psychological readiness alone is not sufficient unless complemented by the knowledge and capacity to make informed financial decisions. This study contributes to the intersection of psychology and finance by demonstrating that investment intentions are not solely products of rational calculation but are shaped by the individual’s sense of meaning, life orientation, and cognitive preparedness. Full article

The ability to accurately predict future time series behavior in multiple steps, known as multi-horizon forecasting, is a vital aspect in various industries, including retail sales, energy consumption, server load, healthcare, weather, and others. We have proposed, in this paper, the use of statistical forecasters as covariates in a Deep Neural Network (DNN) model and evaluated its impact on forecast metrics. Our analysis covered four diverse datasets: M5, Stallion, Stock Market, and Synthetic. The results demonstrated that the inclusion of statistical predictors in the DNN model led to varying degrees of improvement in forecast performance, depending on the dataset and the chosen evaluation metric. In general, our findings suggest that incorporating statistical prediction as a covariate can be a valuable approach to improving multi-horizon prediction, especially in scenarios with data scarcity and intermittency. The hybrid model achieved consistent improvements, particularly on Symmetric Mean Absolute Percentage Error (SMAPE) across datasets, with statistically significant gains on synthetic and stock market series. Specifically, SMAPE was reduced by approximately 33% on synthetic and stock market datasets, by 15–20% on Stallion, and by around 6% on M5. These results confirm that integrating statistical forecasts as covariates can substantially enhance predictive accuracy, especially for volatile or synthetic series. Full article

This paper investigates the volatility spillover effects from the crude oil market to domestic stock markets using high-frequency data. We propose an enhanced methodology, the MS-HAR-TVP model, which extends the standard HAR framework. Our model decomposes crude oil price impacts on domestic financial markets into trend and jump volatility spillover components via the TVP framework, while incorporating a Markov switching mechanism to capture regime changes in volatility dynamics. This paper selects the CSI coal index and the CSI new energy index as the representatives of the domestic energy stock market, uses the rolling window method and the MCS test method to evaluate the predictive performance of the model, and compares it with other commonly used models. The empirical results show that (1) the decomposed high-frequency volatility spillover has obvious volatility clustering and asymmetry and the trend and jump spillover have significant improvement in the predictive ability of future volatility; (2) the short-term trend of crude oil is opposite to the trend of the new energy index, but the same as the short-term trend of the coal index, indicating that the impact of crude oil prices on different energy stock markets is different; and (3) the MS-HAR-TVP model and MS-HAR-TVP-J/TCJ model combined with the crude oil volatility spillover have significantly higher in-sample and out-of-sample prediction accuracy than other models in high volatility periods, indicating that the model proposed in this paper can better characterize and predict the volatility characteristics of the domestic energy stock market. Full article

Stock trend prediction is a complex and crucial task due to the dynamic and nonlinear nature of stock price movements. Traditional models struggle to capture the non-stationary and volatile characteristics of financial time series. To address this challenge, we propose the Hybrid Relational Transformer (HRformer), which specifically decomposes time series into multiple components, enabling more accurate modeling of both short-term and long-term dependencies in stock data. The HRformer mainly comprises three key modules: the Multi-Component Decomposition Layer, the Component-wise Temporal Encoder (CTE), and the Inter-Stock Correlation Attention (ISCA). Our approach first employs the Multi-Component Decomposition Layer to decompose the stock sequence into trend, cyclic, and volatility components, each of which is independently modeled by the CTE to capture distinct temporal dynamics. These component representations are then adaptively integrated through the Adaptive Multi-Component Integration (AMCI) mechanism, which dynamically fuses their information. The fused output is subsequently refined by the ISCA module to incorporate inter-stock correlations, leading to more accurate and robust predictions. Extensive experiments on the NASDAQ100 and CSI300 datasets demonstrate that HRformer consistently outperforms state-of-the-art methods, e.g., achieving about 0.83% higher Accuracy and 1.78% higher F1-score than TDformer on NASDAQ100, with Sharpe Ratios of 1.5354 on NASDAQ100 and 0.5398 on CSI300, especially in volatile market conditions. Backtesting results validate its practical utility in real-world trading scenarios, showing its potential to enhance investment decisions and portfolio performance. Full article

Clustering techniques are widely applied to equity markets to uncover sectoral structures and regime shifts, yet most studies rely solely on empirical returns. This paper introduces a novel perspective by using diffusion-based parameters from the Black–Scholes model, namely monthly drift and diffusion, as clustering features. Using SET100 stocks in 2020, we applied k-means clustering and evaluated performances with silhouette scores, the Adjusted Rand Index, Wilcoxon tests, and an out-of-sample portfolio exercise. The results showed that diffusion-based features achieved higher silhouette scores in turbulent months, where they revealed sectoral divergence that log-returns failed to capture. The partition for November 2020 provided clearer sector separation and smaller portfolio losses, demonstrating predictive value beyond in-sample fit. Practically, the findings indicate that diffusion-based parameters can signal early signs of market stress, guide sector rotation decisions during volatile regimes, and enhance portfolio risk management by isolating persistent volatility structures across sectors. Theoretically, this model-based framework bridges equity clustering with stochastic diffusion representations used in derivatives valuation, offering a unified and interpretable tool for data-driven market monitoring. Full article

Investment in equity assets is characterized by high volatility, both in prices and returns, which poses a constant challenge for the efficient management of risk and profitability. In this context, investors continuously seek innovative strategies that enable them to maximize their returns within acceptable risk levels, in accordance with their investment profile. The purpose of this research is to develop a model with a high predictive capacity for equity asset returns through the application of artificial intelligence techniques that integrate genetic algorithms and neural networks. The methodology is framed within a technical analysis-based investment approach, using the Relative Strength Index as the main indicator. The results show that more than 58% of the predictions generated with the proposed methodology outperformed the results obtained through the traditional technical analysis approach. These findings suggest that the incorporation of genetic algorithms and neural networks constitutes an effective alternative for optimizing investment strategies in equity assets, by providing superior returns and more accurate predictions in most of the analyzed cases. Full article