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.

翻译：医疗保健行业指数综合反映了制药、生物技术和医疗服务公司的经济健康状况。这些指数的短期波动与影响研发投资、药物可及性和长期健康结果的资本配置决策密切相关。本研究探讨历史开盘-最高-最低-收盘（OHLC）指数数据是否包含足够信息，可用于预测下一个交易日的开盘指数方向性变动。该问题被构建为一个涉及一步超前滚动窗口的监督分类任务。我们构建了一个多样化的特征集，包含原始价格、基于波动率的技术指标，以及一类从OHLC相互比率推导出的新型即时预测特征。该框架在美国和印度市场的医疗保健指数数据上进行了评估，数据时间跨度为五年，涵盖包括COVID-19大流行在内的多个经济阶段。结果表明其具有稳健的预测性能，准确率超过0.8，马修斯相关系数高于0.6。值得注意的是，所提出的即时预测特征已成为市场变动的关键决定因素。我们采用了基于沙普利值的可解释性范式来进一步阐明各特征的贡献：结果显示即时预测特征占据主导作用，其次是原始价格的适度贡献。本研究具有社会效用：所提出的用于医疗保健指数短期预测的特征和模型可以减少信息不对称，并支持一个更稳定、更公平的健康经济。