These theses whether examine the property stock investors are overconfident or underconfident. This thesis firstly use VAR model and the empirical result shows that the property stock investors existed overconfidence investment behavior in the bull market, especially in low-price stock. However, this kind of behavior does not exist in the bear market. The result also shows that most of turnover rate are driven by market return rather than property stock return. this represents investors purchase (or sell) the property stock is caused by the market goes up (or down) rather than have perspective in the property stock. The result implied the property stock investor had speculative trading in short term. In long term, investors have insufficient faith in the real estate market. Next, this thesis use EGARCH and the result shows that the overconfidence investors¡¦ excessive trading will increase volatility, and will harm the development of industry. Finally, we discussed the main factor of investors¡¦ underconfidence in property industry, found political environment as well as long recession are the factors.

Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves 107-109). Also available on the Internet.

Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 89-94).

Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves 107-109). Also available on the Internet.

Thesis (Ph. D.)--University of California, San Diego, 2003. / Vita. Includes bibliographical references.

published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy

published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy

published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy

No description available.

In this thesis, we propose a novel trust management scheme for improving routing reliability in wireless ad hoc networks. It is grounded on two classic autoregression models, namely Autoregressive (AR) model and Autoregressive with exogenous inputs (ARX) model. According to this scheme, a node periodically measures the packet forwarding ratio of its every neighbor as the trust observation about that neighbor. These measurements constitute a time series of data. The node has such a time series for each neighbor. By applying an autoregression model to these time series, it predicts the neighbors future packet forwarding ratios as their trust estimates, which in turn facilitate it to make intelligent routing decisions. With an AR model being applied, the node only uses its own observations for prediction; with an ARX model, it will also take into account recommendations from other neighbors. We evaluate the performance of the scheme when an AR, ARX or Bayesian model is used. Simulation results indicate that the ARX model is the best choice in terms of accuracy.