Dataset Information

The reaction of sponsor stock prices to clinical trial outcomes: An event study analysis.

ABSTRACT: We perform an event study analysis that quantifies the market reaction to clinical trial result announcements for 13,807 trials from 2000 to 2020, one of the largest event studies of clinical trials to date. We first determine the specific dates in the clinical trial process on which the greatest impact on the stock prices of their sponsor companies occur. We then analyze the relationship between the abnormal returns observed on these dates due to the clinical trial outcome and the properties of the trial, such as its phase, target accrual, design category, and disease and sponsor company type (biotechnology or pharmaceutical). We find that the classification of a company as "early biotechnology" or "big pharmaceutical" had the most impact on abnormal returns, followed by properties such as disease, outcome, the phase of the clinical trial, and target accrual. We also find that these properties and classifications by themselves were insufficient to explain the variation in excess returns observed due to clinical trial outcomes.

SUBMITTER: Singh M

PROVIDER: S-EPMC9439234 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The reaction of sponsor stock prices to clinical trial outcomes: An event study analysis.

Singh Manish M Rocafort Roland R Cai Cathy C Siah Kien Wei KW Lo Andrew W AW

PloS one 20220902 9

We perform an event study analysis that quantifies the market reaction to clinical trial result announcements for 13,807 trials from 2000 to 2020, one of the largest event studies of clinical trials to date. We first determine the specific dates in the clinical trial process on which the greatest impact on the stock prices of their sponsor companies occur. We then analyze the relationship between the abnormal returns observed on these dates due to the clinical trial outcome and the properties of ...[more]

PMID: 36054103

Similar Datasets

Project description:BackgroundFor biopharmaceutical companies, investments in research and development are risky, and the results from clinical trials are key inflection points in the process. Few studies have explored how and to what extent the public equity market values clinical trial results.MethodsOur study dataset matched announcements of clinical trial results for investigational compounds from January 2011 to May 2013 with daily stock market returns of large United States-listed pharmaceutical and biotechnology companies. Event study methodology was used to examine the relationship between clinical research events and changes in stock returns.ResultsWe identified public announcements for clinical trials of 24 investigational compounds, including 16 (67%) positive and 8 (33%) negative events. The majority of announcements were for Phase 3 clinical trials (N = 13, 54%), and for oncologic (N = 7, 29%) and neurologic (N = 6, 24%) indications. The median cumulative abnormal returns on the day of the announcement were 0.8% (95% confidence interval [CI]: -2.3, 13.4%; P = 0.02) for positive events and -2.0% (95% CI: -9.1, 0.7%; P = 0.04) for negative events, with statistically significant differences from zero. In the day immediately following the announcement, firms with positive events were associated with stock price corrections, with median cumulative abnormal returns falling to 0.4% (95% CI: -3.8, 12.3%; P = 0.33). For firms with negative announcements, the median cumulative abnormal returns were -1.7% (95% CI: -9.5, 1.0%; P = 0.03), and remained significantly negative over the two day event window. The magnitude of abnormal returns did not differ statistically by indication, by trial phase, or between biotechnology and pharmaceutical firms.ConclusionsThe release of clinical trial results is an economically significant event and has meaningful effects on market value for large biopharmaceutical companies. Stock return underperformance due to negative events is greater in magnitude and persists longer than abnormal returns due to positive events, suggesting asymmetric market reactions.

Project description:ObjectiveTo investigate whether the prices of new anticancer drugs correlated with their relative benefit despite negotiation.DesignRetrospective cross-sectional study correlating new anticancer drugs prices with clinical outcomes.SettingWe did a retrospective cross-sectional study including all new anticancer drugs approved by the European Medicines Agency (EMA) (2010-2016) and reimbursed in Italy.Main outcomes and measuresInformation on clinical outcomes-in terms of median overall survival (OS), median progression-free survival (PFS) and objective response rate (ORR)-was extracted from pivotal trials as reported in the European Public Assessment Reports available on the EMA website. Cost of a full course treatment was estimated on negotiated official and discounted prices. Regression coefficients β, their levels of significance p and the coefficients of determination R2 were estimated adjusting by tumour type.ResultsOverall, 30 new anticancer drugs (with 35 indications) were available for analysis. Where data on OS were available, we observed no correlation between the improvement in median OS (in weeks) and negotiated price (R2=0.067, n=16 drugs for 17 indications). When the clinical outcomes were expressed as improvements in the median PFS or ORR, 25 drugs (29 indications) were available for the analysis, and again, there was no correlation with prices (R2=0.004 and 0.006, respectively).Conclusions and relevanceOur results suggest that the prices of anticancer drugs in Italy do not reflect their therapeutic benefit. Drug price negotiations, which is mandatory by law in Italy, do not seem to ensure that prices correlate with clinical benefits provided by the cancer drugs. These results call for further efforts to establish the standard determinants of drug prices available at the time of negotiation. These findings need to be confirmed in other countries where price negotiations are in place. Moreover, further investigations may verify whether outcome data obtained after drug marketing would improve the correlation between prices and therapeutic benefit.

Project description:Pharmaceutical companies operate in a strictly regulated and highly risky environment in which a single slip can lead to serious financial implications. Accordingly, the announcements of clinical trial results tend to determine the future course of events, hence being closely monitored by the public. Most works focus on retrospective analysis of announcement impact on company stock prices, bypassing the consideration of the problem in the predictive paradigm. In this work, we aim to close this gap by proposing a framework that allows predicting the numerical values of announcement-induced changes in stock prices. In fact, it is a problem of the impact prediction of the specific event on the corresponding time series. Our framework includes a BERT model for extracting the sentiment polarity of announcements, a Temporal Fusion Transformer for forecasting the expected return, a graph convolution network for capturing event relationships, and gradient boosting for predicting the price change. We operate with one of the biggest FDA (the Food and Drug Administration) datasets, consisting of 5436 clinical trial announcements from 681 companies for the years 2018-2022. During the study, we get several significant outcomes and domain-specific insights. Firstly, we obtain statistical evidence for the clinical result promulgation influence on the public pharma market value. Secondly, we witness inherently different patterns of responses to positive and negative announcements, reflected in a stronger and more pronounced reaction to negative clinical news. Thirdly, we discover two factors that play a crucial role in a predictive framework: (1) the drug portfolio size of the company, indicating the greater susceptibility to an announcement in the case of low diversification among drug products and (2) the announcement network effect, manifesting through an increase in predictive power when exploiting interdependencies of events belonging to the same company or nosology. Finally, we prove the viability of the forecast setting by getting ROC AUC scores predominantly greater than 0.7 for the classification of price change on historical data. We emphasize the transferability and generalizability of the developed framework on other datasets and domains but on the condition of the presence of two key entities: events and the associated time series.

Dataset Information

The reaction of sponsor stock prices to clinical trial outcomes: An event study analysis.

Publications

The reaction of sponsor stock prices to clinical trial outcomes: An event study analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets