Investigating Structural Change in Predictive Regressions with Applications to Forecasting Stock Returns

大量涉及计量经济学方法的实证研究已经开始调查股票收益的可预测性。这些方法主要是基于预测回归模型，并调查了广泛的金融和宏观经济变量作为假定的预测回报。为预测回报率而进行调查的流行变量包括估值比率，如股息价格比率、收益价格比率、账面市值比率和各种利差。总的来说，这些实证研究往往没有发现或只有相对较弱的统计证据样本中的股票收益率的可预测性。然而，对于许多变量，有很好的理论理由期待它们提供某种形式的回报预测能力。例如，价格-股息比率对回报具有一定的预测能力，这一发现与正统的金融理论是一致的，因为如果股息-价格比率是随时间变化的，那么根据现值模型，它必须在一定程度上预测股息增长率或回报。对可预测性的一个更普遍的解释是，预期收益率随预期的商业条件而变化，而这些变量具有一定的预测能力，是很好的代理人。绝大多数关于股票收益率可预测性的实证研究都假设预测回归模型中的系数不会随着样本数据的变化而变化。然而，市场效率的论点表明，如果股票回报是可预测的，那么它可能是一个暂时的现象，而不是一个永久的现象。更具体地说，如果利用回归模型的预测能力可以产生异常交易利润，那么在有效市场中，该模型将被大量投资者利用，从而导致相关预测器的预测能力被消除。如果一个变量开始对股票收益具有预测能力，那么在投资者了解该变量与收益之间的新关系之前，可能存在一个短暂的可预测窗口，但市场效率意味着它最终会消失。在这种情况下，基于可用数据的完整样本的标准预测回归模型几乎没有能力检测这些短期预测机制。因此，似乎有理由考虑预测关系可能随时间而变化的可能性，因此在一个长的数据跨度上，人们可以观察到出现可预测性的时间窗口和不出现可预测性的时间窗口。该项目将制定严格的计量经济学方法，以便：（i）当应用于全部数据样本时，能够有效地检测预测回归模型的系数中结构变化的存在，及（ii）使用次样本数据分析方法，以有效揭示可预测性的窗口，并使真实的-时间监测，以便在这些可预测窗口出现时及早发现它们。该项目还将调查在使用这些方法时所考虑的预测范围（短期，如季度或月度，或更长）的作用和影响。理论发展将使用大样本计量经济学理论进行，并将涉及最先进的自助方法。理论结果的实际相关性将使用模拟实验进行探索。还将通过工作实例和将这些技术应用于关键的国际股票数据集，向经验研究人员提供明确的指导。该项目的实证方面将调查传统的预测指标（如上文所讨论的）以及最近考虑的所谓技术分析指标（仅使用价格或数量数据来预测回报）。

A vast body of empirical research involving econometric methods has been undertaken investigating stock return predictability. These methods have largely been based on predictive regression models and have investigated a wide array of financial and macroeconomic variables as putative predictors for returns. Popular variables investigated for their predictive ability for returns have included valuation ratios such as the dividend-price ratio, earnings-price ratio, book-to-market ratio and various interest rate spreads. Overall, these empirical studies have tended to find either no or only relatively weak statistical evidence of in-sample predictability in stock returns. Yet for many variables there are good theoretical reasons to expect them to provide some form of predictive power for returns. For example, a finding that the price-dividend ratio has some predictive power for returns is consistent with orthodox financial theory, because if the dividend-price ratio is time-varying then according to the present value model it must forecast either the dividend growth rate or returns to some extent. A more general explanation for predictability is that expected returns vary with expected business conditions for which those variables found to have some predictive ability are good proxies.The overwhelming majority of empirical studies of stock return predictability have assumed that the coefficients in the prediction regression models do not change over the available sample data. However, market efficiency arguments suggest that if stock returns are predictable then it is likely to be a temporary rather than a permanent phenomenon. More specifically, if exploiting the predictive power of a regression model can be used to generate abnormal trading profits, then in an efficient market the model will be exploited by large numbers of investors thereby causing the predictive power of the relevant predictor to be eliminated. If a variable begins to have predictive power for stock returns, then a short window of predictability might exist before investors learn about the new relationship between that variable and returns, but market efficiency implies that it will eventually disappear. In such cases, standard predictive regression models based on the full sample of available data will have almost no power to detect these short predictive regimes. It therefore seems reasonable to consider the possibility that the predictive relationship might change over time, so that over a long span of data one may observe windows of time during which predictability occurs and others where it does not. This project will develop the rigorous econometric methods that are needed to: (i) be capable of effectively detecting the presence of structural change in the coefficients of predictive regression models when applied to the full sample of data, and (ii) use methods of sub-sample data analysis designed to be effective in uncovering such windows of predictability and to enable real-time monitoring for early detection of these windows of predictability as they occur. The project will additionally investigate the role and impact of the forecasting horizon (either short, eg quarterly or monthly, or longer) being considered when using these methods. The theoretical developments will be conducted using large sample econometric theory and will involve state-of-the-art bootstrap methods. The practical relevance of the theoretical results will be explored using simulation experiments. Clear guidance will also be given to empirical researchers through worked examples and applications of the techniques to key international equity data sets. The empirical aspects of the project will investigate both traditional predictors (such as those discussed above) as well as more recently considered so-called technical analysis indicators (where only price or volume data is used to predict returns).