A Study On The Optimal Adopting Timing Of New Technologies

In the era of knowledge economy, technological innovations have become the key point of corporation’s survival and development, the success of technological innovation considerably depends on the optimizing of the adopting timing of new technology. Real option theory is an effective tool for adopting decisions of new technology, however, the existing real option pricing models still have not consider the unique new product’s diffusion characteristic, so the accuracy of the pricing is weakened and adopting timing of new technology is not been optimizing.The goal of this paper is to combine the real options theory and the innovation diffusion theory, as well as constructs new option pricing model whith combined the new product’s diffusion characteristic by combining the option pricing model with the new product diffusion model. On the basis of improving the valuation accuracy of the model, the adopting timing is optimized. This paper’s research focuses on two aspects. Firstly, how to create option pricing models combining the new product’s diffusion characteristic and how to get the optimal adopting timing of new technology. Secondly, how to test the effectiveness of the option pricing models combining the new product’s diffusion characteristic.About models’research, by combing of the diffusion characteristic of new product, option pricing models under discrete time and continuous-time conditions were established based on the initial form of Bass model. Specifically, using initial form of the Bass model to predict the diffusion process of new products, and under the assumptions of the price of new products obeying geometric Brownian motion or mean reversion process, this paper established option pricing models combining diffusion characteristic of new product under a discrete-time condition. Respectively, this paper used the binary tree and the trinomial tree method to obtain optimal adopting timing of new technology. Similarly, based on the initial form of Bass model, this paper used contingent claims approach to establish the option pricing model combining the new product’s diffusion characteristic under continuous-time condition, and used the finite difference method to obtain the optimal adopting timing.Because some assumptions of the initial form of Bass model don’t match the reality, the application of option pricing models based on the initial form of Bass model is limited. Next, the paper relaxed part of the assumptions of the initial form of Bass model. Using the extended models of Bass model to predict the diffusion process of new product, under the circumstances that market potential is variable, a variety of products diffuse at the same time or repurchase is taken place, the paper respectively established an option pricing model combining the new product’s diffusion characteristic, the optimal adopting timing under different circumstances is obtained.As for empirical research, based on the point of view that the value of one project can be viewed as the company value-added in the stock market by this project, the paper chose randomly10listed companies’projects adopting new technology as samples, then analyzed and compared the valuation accuracy of the option pricing model combining diffusion characteristic of new product and of the existing option pricing model. The conclusion is that option pricing model combining the new product’s diffusion characteristic is closer to the company stock value-added and more accurate to grasp the value of the investment of the project. The empirical results show that the option pricing model combining the new product’s diffusion characteristic is more effective than existing models.In addition to the two major studies, the paper also sorted and commented domestic and foreign literature researching the adopting timing of new technologies. Besides, the paper constructed the analysis framework of the option approach combining the new product’s diffusion characteristic, and proposed defects in this paper and future research directions.