市值结构下的英国股票市场:预测内容的新证据

C. Georgiou
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引用次数: 0

摘要

目的:我们论文的目的是双重的。首先,我们在英国经济中最强的证券的最新数据集上检验了日志账面市场、股息价格、收益价格和股息收益率的预测能力;与本数据集中的大多数研究不同,我们的分析并不局限于回报,而是进一步研究了在最近全球金融衰退的情况下股息和盈利增长的可预测性。其次,我们利用[p_t,d_t,e_t]和[p_t,b_t,e_t]两个系统的长期均衡关系,检验我们新形成的变量[pde]_t和[pbe]_t的预测能力。设计/方法/方法:在本研究中,我们研究了金融时报证券交易所100指数(FTSE 100)的最新数据集,并根据规模投资组合的形成进行了分析。我们主要关注的是该指数的回报率、股息和盈利增长率,以及我们选择的四种财务比率的预测能力,因为它们具有很强的预测能力。我们还根据它们的长期均衡关系制定了两个额外的比率。研究发现:本研究的主要发现可以总结如下。首先,我们检索了样本内收益可预测性在中型和大型投资组合中是明显的证据,并且在35%和47%的相等值下,[pde]_t更好地捕获了样本内收益可预测性。其次,对股息增长的预测与我们采用的规模标准更加相关。第三,连续复合收益增长率的样本内回归表明,在R^2为45%的中等投资组合中,[dp]_t获得了大多数预测收益。研究限制/启示:第一个约束是我们使用的预测者;我们使用了最具指示性的变量,因为它们在类似的数据集中很受欢迎,但还有其他宏观经济变量,如价差和利率,可以在未来的研究中进行测试。此外,我们可以检查我们的结果是否使用名义、超额或实际回报的敏感性,然后,尝试改变我们的数据频率,以解决主要在股息和收益中观察到的季节性效应。原创性/价值:我们认为,我们的论文有助于正在进行的关于使回报可预测的特征以及股息或收益中包含的信息来解释可预测性的辩论。最后,本文的新颖之处在于它试图在一个预测成分尚未完全探索的市场中检索市值与可预测性之间的联系。对于关注短期预测和对投资组合形成规模效应感兴趣的投资者来说,我们的论文可能会提供信息。
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The British Stock Market under the Structure of Market Capitalization Value: New Evidence on its Predictive Content
Purpose: The aim of our paper is twofold. First, we examine the predictive ability of log book-market, dividend-price, earnings-price and dividend-earnings ratios on the most recent data set of the strongest securities in the UK economy; unlike the majority of the studies in this data set, our analysis is not limited on returns but further investigates dividend and earnings growth predictability under the presence of the most recent global financial recession. Second, we exploit the long-run equilibrium relationship in two systems, [p_t,d_t,e_t] and [p_t,b_t,e_t] and examine the predictive ability of our newly formed variables, namely [pde]_t and [pbe]_t. Design/methodology/approach: In this study, we examine the most recent data set of Financial Times Stock Exchange 100 (FTSE 100) and analyze it based on the formation of size portfolios. The main focus is placed on the index’s returns, dividend and earnings growth rates and the predictive ability of the four financial ratios we have selected following their reputation as strong predictors. We also formulate two extra ratios based on their long-run equilibrium relationship. Finding: Our study’s main findings can be summarized as following. First, we retrieve evidence that in-sample return predictability is evident in the medium and large-sized portfolios and is better captured by [pde]_t at 35% and 47% equivalently. Second, forecasts on dividend growth are even more linked to the size criterion we employ. Third, in-sample regressions of continuously compounded earnings growth rate show that most predictive benefits are obtained by [dp]_t in the medium portfolio with an R^2 of 45%. Research limitations/implications: A first constraint is the forecasters we employ; we have used the most indicative ones due to their popularity in similar data sets but there are other macroeconomic variables such as spreads and interest rates that could be tested in future research. Also, we could examine the sensitivity of our results on whether we use nominal, excess or real returns and then, attempt to alter our data’s frequency so as to address the seasonality effect observed mainly in dividends and earnings. Originality/value: We believe that our paper contributes to the ongoing debate of the traits that make return predictable and the information included in either dividends or earnings to explain that predictability. Finally, the novelty of this paper lies in the links it tries to retrieve among market capitalization value and predictability in a market whose predictive components have not been entirely explored. Our paper may prove informative to investors focused on short-term forecasting and interested in the effects of size in portfolio formation.
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