Modeling Strategic Group Dynamics : A Hidden Markov Approach

With competition playing a critical role in market-based strategic planning and implementation, identifying and understanding competiton and competitive dynamics has become critical. In this vein, the strategic groups perspective has emerged as a powerful means to understand such competitive phenomena. Empirical approaches to model competitive dynamics within the strategic groups framework, however, have been piece meal as researchers typically resort to distinct sequential analysis by time period. To overcome the limitations of these simplistic approaches, we develop a hidden Markov model to study strategic group (competitive) dynamics. In this approach, we explicitly account for competitive dynamics over time by modeling strategic group memberships as latent states that follow a first-order Markov process. Thus, we explictly model the notion that firms adopt their strategy for the next time period based on their current strategy and respective outcomes. We illustrate the model with longitudinal data from COMPUSTAT on 63 public banks from the tri-state region of NY-OH-PA. The results show the proposed model to be superior to a number of viable alternative approaches that have been suggested in the literature. We find the existence of three strategic groups: the leveraged group has low current assets compared to current liabilities, high debt to equity, and high total borrowing to assets. The lending group consists of the largest banks that focus on lending with high ratios of gross loans to securities and gross loans to deposits. The balanced group has the largest number of banks where the values of the financial and product ratios are intermediate compared to the leveraged and lending groups. The asymmetries in the switching probabilites are also evident as there seems to be a higher probability of switching into the balanced group than switching out of this group. The switching probabilites are symmetric between the the leveraged and lending groups