Essays in financial propagation and corporate inventory investment behavior

The three chapters comprising this Ph.D. dissertation focus on how financial propagation affects firms’ inventory investment dynamics. These studies will enrich the current literature on inventory behavior and macroeconomic activities. In the first chapter, I investigate the role of financ ial frictions on corporate inventory investment dynamics. The prevailing production smoothing inventory theory can not explain the empirical findings—why production is more volatile than sales and why inventories and sales are positively correlated? I deve lop a tractable linearized version of equation based on a firm’s optimization problem which describes the joint evolution of inventory investment and financial variables. The empirical results support the significance of financial frictions for firms’ inventory investment behavior. In the second chapter, I incorporate both trade credit and bank loans into the traditional production smoothing inventory model. The main purposes of this study are to first investigate the impact of trade credit for firm’s inventory investment dynamics in imperfect capital markets, and second to test whether the use of trade credit is a substitution or a complement for bank loans. I find that the use of trade credit can significantly mitigates financial stress and helps firms overcome liquidity shortages. This finding illustrates a channel to dampen the impact of contractionary monetary policy and make the consequent reduction of the inventory investment less severe. Moreover, this study provides evidence showing that trade credit and bank loans are not perfect substitutes. These findings have important implications for both monetary policy and corporate financing management. In the last chapter, I present a decision-making process that incorporates a Genetic Algorithm (GA) into a state dependent dynamic portfolio optimization system. A GA solves the model by forward-looking and backward-induction, which incorporates both historical information and future uncertainty when estimating the asset returns. It significantly improves the accuracy of expected return estimation and thus improves the overall portfolio efficiency over the classical mean-variance method.