Standard financial and economic theories suggest that the stock value of R&D intensive High Technology Small Firms (HTSF) undergo a geometric random walk. Such a model neglects to account for observed periods where firms experience large fluctuations due to uncertainty related to its R&D activities, external competitive or regulatory environments. Empirical evidence also shows that the behavior of these firms is difficult to describe – let alone predict – using this Gaussian process. With ambidexterity as a theoretical basis, we show that the value of HTSF can be statistically described as the result of a combination of two distinct random walks: an exploitative steady state component characterized by Neo-Marshallian equilibrium and low volatility; and a more dynamic component with high volatility reflecting bursts of large and rapid changes associated with Schumpeterian outcomes of explorative processes. A mixture of two normal distributions provides an overall function that is more reflective of the empirical evidence and provides a quantitative measure for the theory that firms engage in concurrent exploration/exploitation activities. A linear relationship between the two components of the mixture distribution that describe the stock value of these firms also emerges. By understanding this dual nature and its impact on stock value, firms can better manage resources and prepare for the increase in variability that are associated with exploration activities. A more accurate financial description of HTSF that reduces or that anticipates uncertainty may lead to financial tools and option pricing methods that put a premium on the value of HTSF markets, incentivizing investors to invest more in such firms.