Impact of Major Health Events on Pharmaceutical Stocks: A Comprehensive Analysis Using Macroeconomic and Market Indicators

TL;DR

This study addresses how major health events influence pharmaceutical stock prices by integrating macroeconomic and market indicators within a four-model OLS framework. It analyzes thirteen events from 2000–2023, modeling stock responses across beginning, peak, and ending phases using event-dummy variables and an aggregated target $ST ext{-}CM$ while controlling for $MI$ and $MA$ factors. Results show robust, phase-dependent effects of health events, with early optimism and later corrections; macroeconomics (GDP, unemployment) and market sentiment (S&P 500, NASDAQ) significantly shape these dynamics, yielding very high $R^2$ in several specifications. The work provides practical guidance for investors and policymakers on risk management and timing during health crises, illustrating that isolating event-driven movements requires accounting for both macroeconomic context and market conditions.

Abstract

This study investigates the impact of significant health events on pharmaceutical stock performance, employing a comprehensive analysis incorporating macroeconomic and market indicators. Using Ordinary Least Squares (OLS) regression, we evaluate the effects of thirteen major health events since 2000, including the Anthrax attacks, SARS outbreak, H1N1 pandemic, and COVID-19 pandemic, on the pharmaceutical sector. The analysis covers different phases of each event beginning, peak, and ending to capture their temporal influence on stock prices. Our findings reveal distinct patterns in stock performance, driven by market reactions to the initial news, peak impact, and eventual resolution of these crises. We also examine scenarios with and without key macroeconomic (MA) and market (MI) indicators to isolate their contributions. This detailed examination provides valuable insights for investors, policymakers, and stakeholders in understanding the interplay between major health events and health market dynamics, guiding better decision-making during future health related disruptions.

Figures (5)

• Figure 1: This figure shows the time series of closing prices for major pharmaceutical companies (JNJ, PFE, MRK, ABBV, TMO, GILD, LLY, GSK, NVO, AMGN, AZN) from 2000 to 2023. Significant health events are marked with dashed vertical lines, including Anthrax Attacks (2001), SARS Outbreak (2003), HPV Vaccine Approval (2006), H1N1 Pandemic (2009), MERS Outbreak (2012), Ebola Outbreak (2014), Zika Virus Outbreak (2016), CAR-T Cell Therapy Approval (2017), COVID-19 Pandemic (2020), Mass Vaccination Campaigns (2021), and COVID-19 Variants (2022). Each event's period (start, peak, and end) is indicated to highlight their impact on stock prices.
• Figure 2: This figure consists of 12 subplots, each zooming in on the average closing price of pharmaceutical stocks during the periods of significant health events. Each subplot highlights the start, peak, and end periods of the events with different colors, providing a detailed view of stock price movements during these critical times. The beginning (30 days) and ending phase (30 days) windows are displayed and for simplicity the peak phase (30 days, starting from the middle of beginning phase and running until the middle of ending phase) is removed.
• Figure 3: Box plots showing the stock prices (ST-CM) segmented by significant health-related events from 2000 to 2023. Each box represents the distribution of stock prices during the specified event period, highlighting the impact of each event on the stock market.
• Figure 4: This figure presents the histograms of key variables, displaying their distribution over the study period. The variables include ST-CM (Stock Closing Price), MA-GP (Gross Private Domestic Investment), MA-IF (Inflation Rate), MI-SP (S&P 500 Index), MI-NS (NASDAQ Index), and MA-UR (Unemployment Rate).
• Figure 5: Correlation heatmap of various stock market and macroeconomic variables. The heatmap depicts the correlation coefficients between different variables, with red indicating strong positive correlations and blue indicating strong negative correlations. Key variables include stock prices, market indices (S&P 500, NASDAQ), and macroeconomic indicators such as GDP, inflation rate, and unemployment rate. The hierarchical clustering on both axes groups variables with similar correlation patterns, highlighting the interrelationships among the factors influencing pharmaceutical stock prices.