440. Using financial and network analytics to elucidate firm performance in the pharmaceutical industry
Invited abstract in session MB-38: Optimization in contexts with multi-media signals or data security, stream Data Science meets Optimization.
Monday, 10:30-12:00Room: Michael Sadler LG19
Authors (first author is the speaker)
| 1. | Penina Orenstein
|
| Stillman School of Business, Seton Hall University | |
| 2. | Rongrong Zhang
|
| Georgia Southern University |
Abstract
We explore the effect of network measures on firm performance using Tobin’s Q and ROA and examine their impact on specific network structural metrics (e.g. average degree, eigenvector centrality), focusing on the pharmaceutical industry. Using a data visualization approach combined with a regression analysis, we verify that suppliers’ network structures significantly impact their customers’ performance in the pharmaceutical industry.
We expand the analysis to include the cash conversion cycle (CCC) and demonstrate how CCC appears as a potential channel through which network structure affects firm performance. CCC provides the number of days that a firm’s cash is tied up during the operating activities of a firm. It is commonly used to measure a firm’s operational efficiency.
Prior literature generally finds a negative relationship between CCC and firm performance. In the pharmaceutical setting, we expand the baseline firm performance regression model by adding CCC and the interaction term between CCC and the multiple network structural metrics. First, we document a negative and significant relation between CCC and Tobin’s Q, in line with our position that operational inefficiency (i.e., high CCC) detracts from firm performance. Additionally, we document several other significant relationships between CCC and network structural variables. We then explain the implications of these findings for this industry.
Keywords
- Analytics and Data Science
- Financial Modelling
- Graphs and Networks
Status: accepted
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