How Is This Research Useful to Practitioners?
A firm’s investment in innovation has high information asymmetry given its long-term nature, uncertain outcome, and confidentiality. Derivatives, including options, contribute to informed trades, and an informational benefit is linked to the trading volume. The authors study the link between financial derivatives—specifically, options and innovation. They find that an active market for options reduces information asymmetry related to investment in innovation and thereby creates managerial incentives for innovation that lead to better resource allocation.
The factors that affect innovation have been an important area of research. Many studies link innovation to a number of financial market characteristics, including institutional ownership, analyst coverage, credit supply, liquidity of stocks, leveraged buyouts, the decision to go public, investor risk tolerance, and so forth. Few studies explore the relationship between options trading and innovation. The authors fill that gap by linking options trading volume and resource allocation to innovation and its productive outcome. Their research complements research by Roll, Schwartz, and Subrahmanyam (Journal of Financial Economics 2009) linking options trading activity to firm value. This research also adds to the literature relating an increase in firm value to increased information efficiency and its role in creating managerial incentives.
The authors find that a 200% increase in the dollar value of options trading volume (with the median increasing from $15 million to $45 million) has led to a 31% increase in the number of citation-weighted patents (from 21 to 28)—a proxy for innovation. Increased volume is linked not only to an increase in the R&D spend but also to the quality and productivity of the R&D spend (citations per dollar of spend).
An active options market is also associated with the change in direction of innovation, as indicated by the diversity of patents across technological classes, patent originality, and risk-taking behavior as captured in the standard deviation of citations.
The positive impact of options trading volume on innovation is greater for firms with more competitive product markets, less entrenched CEOs, younger CEOs, and declining profitability—even after adjusting for executive compensation.
The authors suggest two explanations for this link: (1) Information conveyed by an active options market “forces” managers to innovate when they are otherwise reluctant to take risks associated with innovation, or (2) increased monitoring through active options trading “shields” managers from the potential negative consequences of decisions regarding innovation—the more strongly supported hypothesis.
How Did the Authors Conduct This Research?
The authors primarily measure innovation by using the count of US patents, weighted by future citations to better reflect “innovative” output. Additional innovation metrics used in the study include R&D expenditure, patents weighted by non-self forward citations, and changes in the direction of innovative efforts.
The authors fill that gap by linking options trading volume and resource allocation to innovation and its productive outcome. The baseline sample contains 3,271 observations for 548 firms, with median sales of $494 million and 2,400 employees. The options trading volume average is $157 million, with a median of $8.5 million.
Compustat and OptionMetrics data are used for firm financials and options, respectively. The patent data are from the National Bureau of Economic Research patent citation database. The authors examine the period from 1996, when the options data start, to 2004, the last year for which patent data are available is examined.
The authors’ econometric model used focuses on the relationship between the logarithmic form of a firm’s future citation-weighted patents and annual options trading volume, using a range of such firm-specific control variables as size/sales, capital-to-labor ratio, and deflated R&D stock. The authors also make use of the firm’s long pre-sample experience in innovative activity. The coefficient on options trading indicates the elasticity of the innovation. When the control term includes a firm’s R&D stock, the model becomes the production function that relates past R&D investment to innovation output. The authors use ordinary least squares to estimate the model.
Quantifying and modeling innovation has its challenges. The authors acknowledge that patent generation and citation frequently fail to completely capture the value of innovation. For instance, many innovations do not meet patentability criteria, some are not patented for strategic reasons, firms differ in their patent propensity, the effects of managerial signaling motives vary, and so on. The authors address these challenges by carefully selecting the sectors they examine and the statistical methods they use in their study.