股票定价机制现代化:提高股票市场效率的有效途径

Mohamad Hani Saad
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摘要

本研究探讨采用公认的有效股票估值模型来规范股票定价机制是否能提高股票市场效率。这项研究涉及一个模拟的股票市场实验,有65名交易员在不受监管和受监管的情况下为一家虚拟公司提供每日股价预测。在受监管的情况下,交易员同意三种估值模型中的一种来产生股票价格。此外,20名评估者作为“Homo-Economicus”来确定股票的公允价值趋势,作为评估模拟市场信息效率的基准。研究发现,napv管制的市场具有较强的线性关系和较高的r平方,表明信息效率水平最高,而DDM和rim管制的市场具有中等相关性。研究表明,通过规范股东相互同意一种有效的股票估值模型,由公司使用,以产生替代市场价格的公允价值,从而实现股票定价机制的现代化,可以通过关注基本面而不是非理性的投机者,显著提高股票市场的效率。然而,模型选择很重要,因为NAPV解释了更多的变化。研究表明,适当的监管对于实现这一潜力至关重要。虽然结果是有希望的,局限性,如小评估样本,模型不能总是产生股票价值,和交易者的偏见应该考虑。未来使用更大样本和更多模型的研究可以加强这些见解。
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Modernizing the Stock Pricing Mechanism: An Effective Path to Improve the Stock Market Efficiency
This study investigates whether regulating the stock pricing mechanism by adopting an agreed-upon efficient stock valuation model can enhance the stock market efficiency. The study involves a simulated stock market experiment with 65 traders who provide daily stock price predictions for a virtual company under unregulated and regulated scenarios. In the regulated scenario, traders agree on one of three valuation models to generate stock prices. Moreover, 20 evaluators acted as “Homo-Economicus” to determine a stock's fair value trend, serving as a benchmark to assess the information efficiency of the simulated market. The study finds that the NAPV-regulated market shows a strong linear relationship and high R-squared, indicating the highest level of information efficiency, while the DDM- and RIM-regulated markets show moderate correlations. The study suggests that modernizing the stock pricing mechanism, by regulating shareholders to mutually agree on one efficient stock valuation model to be used by the company to generate fair values alternative to market prices, could significantly enhance the stock market efficiency by focusing on fundamentals rather than irrational speculators. However, model choice matters, as NAPV explains more variation. The study suggests that appropriate regulation is crucial for realizing this potential. Although the results are promising, limitations like small evaluator samples, inability of models to always generate stock values, and trader biases should be considered. Future research with larger samples and more models could strengthen these insights.
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