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Stock markets are essential for households for wealth creation and for firms for raising financial resources for capacity expansion and growth. Market participants, therefore, need an understanding of stock price movements. Stock market indices and individual stock prices reflect the macroeconomic environment and are subject to external and internal shocks. It is important to disentangle the impact of macroeconomic shocks, market uncertainty and speculative elements and examine them separately for prediction. To aid households, firms and policymakers, the paper proposes a granular decomposition-based prediction framework for different time periods in India, characterized by different market states with varying degrees of uncertainty.

Ensemble empirical mode decomposition (EEMD) and fuzzy-C-means (FCM) clustering algorithms are used to decompose stock prices into short, medium and long-run components. Multiverse optimization (MVO) is used to combine extreme gradient boosting regression (XGBR), Facebook Prophet and support vector regression (SVR) for forecasting. Application of explainable artificial intelligence (XAI) helps identify feature contributions.

We find that historic volatility, expected market uncertainty, oscillators and macroeconomic variables explain different components of stock prices and their impact varies with the industry and the market state. The proposed framework yields efficient predictions even during the COVID-19 pandemic and the Russia–Ukraine war period. Efficiency measures indicate the robustness of the approach. Findings suggest that large-cap stocks are relatively more predictable.

The paper is on Indian stock markets. Future work will extend it to other stock markets and other financial products.

The proposed methodology will be of practical use for traders, fund managers and financial advisors. Policymakers may find it useful for assessing the impact of macroeconomic shocks and reducing market volatility.

Development of a granular decomposition-based forecasting framework and separating the effects of explanatory variables in different time scales and macroeconomic periods.

The authors test several hypotheses to analyze the influences of community-mindedness, ethical and social responsibility, culture, and gender on performance and job satisfaction.

Using PLS-SEM the authors employ data gathered from a survey administered to a panel of 192 small business owners from across the US We compare the findings across ethnic groups, which result in significant path coefficients.

Greater community mindedness and corporate social responsibility (CSR) lead to better performance, and that leads to greater satisfaction. Collectivism enhances these relationships, while female owners who are more community minded experience greater performance.

One limitation of the current study is the self-reporting nature of all the questionnaire items. In an effort to limit the potential negative effects of self-reporting, the authors conduct necessary validation to help ensure that our instrument measures what it is supposed to measure conceptually.

As hypothesized, the influences are mostly positive relationships. Stronger community-mindedness leads to stronger business performance for Hispanic owners. On the contrary, White owners' performance significantly decreases.

The findings recommend that collectivism is the way to go. Small business owners should have a collective feeling for the community they live in and do business with. This generally leads to better business performance and subsequent satisfaction in life.

One major contribution of this study is to compare and contrast the findings across the two largest ethnic groups in the US – Whites and Hispanics. Another contribution of the study is to determine how social and ethical responsibilities affect business performance and satisfaction.

This paper investigates clinical outcomes of children with asthma treated in a hospital clinic and thus develop a method for the monitoring of clinical performance. Prospective, before‐and‐after analyses of clinic information for first hospital visits and long‐term follow‐ups. For initial referrals we assessed the change in asthma symptom scores between initial and first follow‐up visits. For long‐term follow‐up yearly changes in symptom scores, hospital admissions, school absences and oral steroid use were measured. Multiple regression was used to study the role of possible predicting factors, where applicable. The subjects studied were children attending hospital children’s asthma clinics in Tayside. Initial referral to hospital asthma clinic was associated with a significant improvement in mean symptom scores. In contrast, children on long‐term hospital follow‐up were maintained at a relatively steady state. Measurement of these outcomes in children’s asthma clinics could facilitate the assessment of clinical performance and monitor changes on a longitudinal basis.

This study aims to numerically analyse the impact of an inclined magnetic field and Joule heating on the conjugate heat transfer because of the mixed convection of an Al₂O₃–water nanofluid in a thick wall enclosure.

A horizontal temperature gradient together with the shear-driven Flow creates the mixed convection inside the enclosure. The nonhomogeneous model, in which the nanoparticles have a slip velocity because of thermophoresis and Brownian diffusion, is adopted in the present study. The thermal performance is evaluated by determining the entropy generation, which includes the contribution because of magnetic field. A control volume method over a staggered grid arrangement is adopted to compute the governing equations.

The Lorentz force created by the applied magnetic field has an adverse effect on the flow and thermal field, and consequently, the heat transfer and entropy generation attenuate because of the presence of magnetic force. The Joule heating enhances the fluid temperature but attenuates the heat transfer. The impact of the magnetic field diminishes as the angle of inclination of the magnetic field is increased, and it manifests as the volume fraction of nanoparticles is increased. Addition of nanoparticles enhances both the heat transfer and entropy generation compared to the clear fluid with enhancement in entropy generation higher than the rate by which the heat transfer augments. The average Bejan number and mixing-cup temperature are evaluated to analyse the thermodynamic characteristics of the nanofluid.

This literature survey suggests that the impact of an inclined magnetic field and Joule heating on conjugate heat transfer based on a two-phase model has not been addressed before. The impact of the relative slip velocity of nanoparticles diminishes as the magnetic field becomes stronger.