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展开，以考察股票回报的可预测性。这些方法在很大程度上是以预测回归模型为基础的，并调查了一系列金融和宏观经济变量，作为假定的回报预测因素。就其对回报的预测能力而受到调查的流行变量包括股息市盈率、市盈率、账面市净率和各种利差等估值比率。总体而言，这些实证研究往往要么没有发现样本内可预测性的统计证据，要么只发现了相对较弱的统计证据。然而，对于许多变量，有充分的理论理由期待它们为回报提供某种形式的预测力。例如，价格-股息率对收益具有一定的预测能力的发现与正统的财务理论是一致的，因为如果股息-价格比是时变的，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收益。对可预测性的一个更普遍的解释是，预期收益随着预期的商业状况而变化，那些被发现具有一定预测能力的变量是很好的代表。绝大多数关于股票收益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可用样本数据的变化而变化。然而，市场效率理论表明，如果股票回报是可预测的，那么它很可能是暂时的，而不是永久的现象。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，从而导致相关预测者的预测能力被消除。如果一个变量开始对股票回报具有预测能力，那么在投资者了解该变量与回报之间的新关系之前，可能存在一个很短的可预测性窗口，但市场效率意味着它最终会消失。在这种情况下，基于可用数据全样本的标准预测回归模型将几乎没有能力检测这些短期预测机制。因此，考虑预测关系可能随时间变化的可能性似乎是合理的，这样，在长时间的数据跨度中，人们可以观察到发生可预测性的时间窗口，而其他时间窗口则没有。该项目将制定严格的计量经济学方法，以：(1)能够有效地检测预测回归模型系数在应用于全部数据样本时是否存在结构性变化，以及(2)使用旨在有效揭示这些可预测窗口的方法，并使实时监测能够在这些可预测窗口出现时及早发现这些可预测窗口。该项目还将调查在使用这些方法时正在考虑的预测期(短期，例如季度或每月，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，并将涉及最先进的自举方法。理论结果的实际相关性将通过模拟实验来探索。还将通过对关键国际股票数据集的实际范例和技术应用，向经验研究人员提供明确的指导。该项目的经验方面将调查传统预测指标(如上文讨论的指标)以及最近被认为的所谓技术分析指标(只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).