Value-at-Risk Evaluation in Stock Price-A Comparison between Extended Ohlson Model and Geometric Brownian Motion Model

• Main Authors: Pei-Shan Hsue, 徐珮珊
• Other Authors: Po-Chin Wu
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/42730979032342347888

碩士 === 中原大學 === 國際貿易研究所 === 96 === In his equity valuation model, Ohlson (1995) shows that stock price can be expressed as the summation of abnormal earnings, book value, and non-accounting information. However, numbers shown in financial statement (such as abnormal earnings and book value) can only provide a limited measure of stock price, because they do not reflect other important information about firms’ fundamentals. Especially, Ohlson did not define the non-accounting information very clearly, so this thesis first revises Ohlson equity valuation model by replacing his “non-accounting information” with several important factors, including “macroeconomic variables” and “company factors” and re-evalute the stock price. Secondly, based on the revised Ohlson equity evaluation model, I use Monte Carlo simulation to calculate Value-at-Risk (VaR) for corporate. Finally, to measure and compare the VaRs of the revised Ohlson model and Brownian Motion, then choose the model that can forecast stock price and VaR more officiently. In empirical study, I use Random effect model with fixed slope to evaluate the stock price in Taiwan 50 index constituents. To differentiate between empirical studies and forecast perform evaluation, sample periods spans from 1999 Q4 to 2006 Q3 and 2006 Q4 to 2007 Q3 separately. The empirical results show as follows: 1.Financial indices, including book value and abnormal profit in original Ohlson equity valuation model all have positive effect on stock price. 2.By adding macroeconomic variables and company factors to represent non-accounting information in original Ohlson equity valuation model, the fitness of the revised model is improved. 3.In most sample companies, the VaR calculated from revised Ohlson equity evaluation model is lower than that from Geometric Brownian movement. In addition, for out-of-sample forecast performance, the revised Ohlson equity evaluation model has better VaR forecast performance than Geometric Brownian movement. However, in stock price forecasting, the result is complete opposite. Keywords: