Anomaly Detection and Day-of-the-Week Forecasting of NSE NIFTY Using a Hybridized Neural Network

2026-07-01T11:57:42+00:00

Financial markets, particularly stock markets, exhibit complex behaviors. Calendar anomalies and sudden structural changes pose challenges to standard forecasting models. Linear models cannot predict these changes effectively. One significant calendar anomaly is the day-of-the-week (DoW) effect, which has been particularly studied in emerging markets such as India. This study provides empirical evidence that explores the DoW anomalies in the NSE NIFTY
50 index and proposes a hybrid neural network framework that combines anomaly detection with daily forecasting. This study employs unsupervised anomaly detection using an autoencoder neural network to identify unusual trading days. These indicators were then included in a CNN-LSTM-Attention model for return forecasting. The results provide evidence of significant day-of-the-week effects on the NSE NIFTY 50 index. The proposed hybrid autoencoder-CNN-LSTM-attention framework achieved superior forecasting performance compared with conventional ARIMA and standalone LSTM models, reducing the RMSE by approximately 31.7% (from 1.42 to 0.97) and improving the directional accuracy from 52% to 63%. Future research should incorporate sentiment indicators, macroeconomic variables, and cross-market comparisons to further enhance forecasting performance.

A Study on Exploring Occupational Stress Triggers and Stress Management Practices among Faculties of Higher Education

2026-07-01T11:57:42+00:00

Occupational stress has become a major issue for faculty members in higher-education institutions because of rising academic, administrative, and professional pressures. This study investigates the primary sources of occupational stress faced by faculty in higher education and analyzes the stress management techniques employed to address these issues. This research utilized a qualitative method by conducting a narrative review of the available literature. A systematic review and thematic analysis were conducted on 23 national and international empirical and theoretical studies published from 1990 to 2024 to identify shared stressors, coping mechanisms, and institutional factors impacting faculty’s well-being.
The results indicate that faculty members experience occupational stress due to external and internal factors. Key external stressors include heavy workloads, administrative duties, job instability, technology-related difficulties, workplace dynamics, and classroom management issues, while internal stressors encompass procrastination, perfectionism, emotional fatigue, and lack of engagement. The analysis emphasizes that successful coping methods encompass time management, physical wellness activities, mindfulness techniques, positive self-talk, organizational support and initiatives for work-life balance. The study determined that work-related stress notably influences faculty welfare, job fulfillment, and professional performance, highlighting the necessity for supportive organizational policies and organized stress reduction strategies. Future studies should emphasize the empirical analysis of stress-related mental health issues, such as burnout, anxiety, and obsessive-compulsive behaviors, alongside comparative research in various educational and cultural settings to create more tailored stress management strategies.

Shanlax International Journal of Management

Anomaly Detection and Day-of-the-Week Forecasting of NSE NIFTY Using a Hybridized Neural Network

A Study on Exploring Occupational Stress Triggers and Stress Management Practices among Faculties of Higher Education