2015/08/12 by Ines Wilms, Wilms, Ines, Sarah Gelper +3
Decision Sciences · Economics, Econometrics and Finance · Mathematics · #Complex Systems and Time Series Analysis #Market Dynamics and Volatility #Stock Market Forecasting Methods #stat.AP
paper · pdf · doi:10.48550/arxiv.1508.02846
arxiv created 2015/08/12 · arxiv updated 2015/08/13
We study the predictive power of industry-specific economic sentiment indicators for future macro-economic developments. In addition to the sentiment of firms towards their own business situation, we study their sentiment with respect to the banking sector - their main credit providers. The use of industry-specific sentiment indicators results in a high-dimensional forecasting problem. To identify the most predictive industries, we present a bootstrap Granger Causality test based on the Adaptive Lasso. This test is more powerful than the standard Wald test in such high-dimensional settings. Forecast accuracy is improved by using only the most predictive industries rather than all industries.