This paper applies a real option approach to analyze the optimal decisions of mergers, stock offers, and cash offers. We use the two-stage approach to investigate the optimal decisions of mergers and acquisitions. At the first stage, the merger company has to choose the target company to obtain the largest synergy, which comes from the increasing return to scale, improved performance, acquired R&D, and increased market power. At the second stage, the main work is to determine the takeover threshold (timing), exchange rate of stocks or bid premium under the three forms of mergers and acquisitions. We find that the increasing return to scale, improved performance, and increased market power will lower takeover threshold and speed up merger activity. Finally, the forms of mergers and acquisitions will affect the timing and the returns of the acquirer and acquiree. Cash offers will happen even later than mergers and stock offers. This thesis also constructs a model to study the multi-firms¡¦ merger strategies and derives the multi-firms¡¦ synergy value, timing and terms of merges. In addition, we study the effect of firms¡¦ competitive intensity, market power, fixed cost, and demand shocks on the decisions of merges. We find that the increased competitive intensity, increased market power, higher fixed cost, and lower demand shocks will enhance the motives of merges and accelerate merger activities.

This dissertation assumes the R&D investment future cash flows of a firm which follows an arithmetic Brownian motion and Poisson (jump) process. This study evaluates the R&D investment decisions under different market structure while considering the stochastic impact scales are the normal, negative exponential, and Laplace distributions, respectively. The first model of this dissertation aims to build monopoly R&D investment decisions under different stochastic impact scales. The result of this study is different from Cossin et al. (2002), since it shows that the outcome of Cossin et al. (2002) has underestimated decision values in assessing lump-sum investment, staging investment, and liquidation decisions. Sensitivity analysis reveals the following: (1) the positive relation parameter for the lump-sum investment is the cash flow growth rate of project, frequency of jump event, time of jump event, mean and deviation of normal distribution, and initial cost. (2) The positive relation parameter for liquidation decisions is the cash flow growth rate of project, frequency of jump event, time of jump event, and mean and deviation of normal distribution. The second model of this dissertation extends the monopoly to duopoly, and it aims to build the duopoly R&D investment decisions under different stochastic impact scales. The result of the study accords with Tsekrekos (2003) that with more uncertainty, there are more duopoly investment thresholds. Sensitivity analysis reveals the following: (1) the positive relation parameter for the leading R&D investment thresholds is deviation, frequency of jump event, discount rate, investment cost, and mean and deviation of normal distribution, while the negative relation parameter is the growth rate and market share. (2) The positive relation parameter for the follower R&D investment thresholds is deviation, market share, frequency of jump event, discount rate, investment cost, and mean and deviation of normal distribution, while the negative relation parameter is the growth rate. The third model of this dissertation extends to oligopoly, and it aims to build the oligopoly R&D investment decisions under different stochastic impact scales. The result of the study accords with the expectancy of Grenadier (2002), that while other things being equal, the more industry's competition degree, the lower oligopoly investment thresholds. Namely the higher the numbers of firms in an industry, those oligopoly firms have more incentives to invest early. Sensitivity analysis shows the following: (1) The positive relation parameter for the oligopoly R&D investment thresholds is deviation, frequency of jump event, discount rate, unitary investment cost, and mean and oligopoly supply, while the negative relation parameter is the growth rate and market share. (2) The negative relation parameter is the number of firms in the industry, growth rate, and demand elasticity.