Forecasting stock returns with industry volatility concentration

IF 3.4 3区 经济学 Q1 ECONOMICS Journal of Forecasting Pub Date : 2024-05-14 DOI:10.1002/for.3150
Yaojie Zhang, Mengxi He, Zhikai Zhang
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Abstract

In this paper, we show that industry volatility concentration is a strong predictor for aggregate stock market returns. Our monthly industry volatility concentration (IVC) index displays significant predictive ability, with in-sample and out-of-sample R2 statistics of 0.686% and 0.712%, respectively, which outperforms a host of prevailing return predictors. Moreover, the IVC index can generate high utility gains of 143.8 basis points above the historical average benchmark for mean–variance investors. We find that the IVC index is countercyclical. Furthermore, the predictive source of the IVC index not only stems from the cash flow and discount rate channels but is also explained by the channels of investor attention and sentiment. The predictive ability of our IVC index also remains significant under a broad range of robustness tests.

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利用行业波动集中度预测股票回报率
本文表明,行业波动集中度是股市总回报的有力预测指标。我们的月度行业波动性集中度(IVC)指数显示出显著的预测能力,样本内和样本外 R2 统计量分别为 0.686% 和 0.712%,优于一系列流行的收益预测指标。此外,IVC 指数还能为均值方差投资者带来高于历史平均基准 143.8 个基点的高效用收益。我们发现 IVC 指数具有反周期性。此外,IVC 指数的预测来源不仅来自现金流和贴现率渠道,还来自投资者关注和情绪渠道。在广泛的稳健性测试中,我们的 IVC 指数的预测能力也依然显著。
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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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