Demystifying the trend of the healthcare index: Is historical price a key driver?

2026-01-20

AI Summary

This study investigates whether historical OHLC (Open, High, Low, Close) price data alone suffices to predict the direction of the next-day opening price of healthcare indices. Framing the problem as a supervised classification task within a rolling-window framework, the authors construct a rich feature set that integrates raw prices, volatility-based technical indicators, and novel nowcasting features derived from OHLC ratios. The approach is evaluated on five years of market data from the U.S. and India, spanning multiple economic regimes including the COVID-19 pandemic. The proposed OHLC-ratio-based nowcasting features substantially enhance predictive performance, achieving accuracy above 0.8 and a Matthews correlation coefficient exceeding 0.6. Shapley value-based interpretability analysis further confirms that these new features play a dominant role in driving prediction outcomes.

Technology Category

• Machine Learning: Feature Construction/Reformulation
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Large pretrained models with web data
• Security and Privacy: Data transparency and provenance

Abstract

Healthcare sector indices consolidate the economic health of pharmaceutical, biotechnology, and healthcare service firms. The short-term movements in these indices are closely intertwined with capital allocation decisions affecting research and development investment, drug availability, and long-term health outcomes. This research investigates whether historical open-high-low-close (OHLC) index data contain sufficient information for predicting the directional movement of the opening index on the subsequent trading day. The problem is formulated as a supervised classification task involving a one-step-ahead rolling window. A diverse feature set is constructed, comprising original prices, volatility-based technical indicators, and a novel class of nowcasting features derived from mutual OHLC ratios. The framework is evaluated on data from healthcare indices in the U.S. and Indian markets over a five-year period spanning multiple economic phases, including the COVID-19 pandemic. The results demonstrate robust predictive performance, with accuracy exceeding 0.8 and Matthews correlation coefficients above 0.6. Notably, the proposed nowcasting features have emerged as a key determinant of the market movement. We have employed the Shapley-based explainability paradigm to further elucidate the contribution of the features: outcomes reveal the dominant role of the nowcasting features, followed by a more moderate contribution of original prices. This research offers a societal utility: the proposed features and model for short-term forecasting of healthcare indices can reduce information asymmetry and support a more stable and equitable health economy.

Problem

• healthcare index
• directional prediction
• OHLC data
• short-term forecasting
• market movement

Innovation

• nowcasting features
• OHLC ratios
• healthcare index prediction
• explainable AI
• technical indicators