Browsing by Author "Verona, Fabio"
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- Forecasting stock market returns by summing the frequency-decomposed partsPublication . Faria, Gonçalo; Verona, FabioWe forecast stock market returns by applying, within a Ferreira and Santa-Clara (2011) sum-of-the-parts framework, a frequency decomposition of several predictors of stock returns. The method delivers statistically and economically significant improvements over historical mean forecasts, with monthly out- of-sample R2 of 3.27% and annual utility gains of 403 basis points. The strong performance of this method comes from its ability to isolate the frequencies of the predictors with the highest predictive power from the noisy parts, and from the fact that the frequency-decomposed predictors carry complementary information that captures both the long-term trend and the higher frequency movements of stock market returns.
- Forecasting stock market returns by summing the frequency-decomposed partsPublication . Faria, Gonçalo; Verona, FabioWe generalize the Ferreira and Santa-Clara (2011) sum-of-the-parts method for forecasting stock market returns. Rather than summing the parts of stock returns, we suggest summing some of the frequency-decomposed parts. The proposed method significantly improves upon the original sum-of-the-parts and delivers statistically and economically gains over historical mean forecasts, with monthly out-of-sample of 2.60% and annual utility gains of 558 basis points. The strong performance of this method comes from its ability to isolate the frequencies of the parts with the highest predictive power, and from the fact that the selected frequency-decomposed parts carry complementary information that captures different frequencies of stock market returns.
- Forecasting the equity risk premium with frequency-decomposed predictorsPublication . Faria, Gonçalo; Verona, FabioWe show that the out-of-sample forecast of the equity risk premium can b e signi cantly improved by taking into account the frequency-domain relationship b etween the equity risk premium and several p otential predictors. We consider fteen predictors from the existing literature, for the out-of-sample forecasting p erio d from January 1990 to Decemb er 2014. The b est result achieved for individual predictors is a monthly out-of-sample R 2 of 2.98 % and utility gains of 549 basis p oints p er year for a mean-variance investor. This p erformance is improved even further when the individual forecasts from the frequency- decomp osed predictors are combined. These results are robust for di erent subsamples, including the Great Mo deration p erio d, the Great Financial Crisis p erio d and, more generically, p erio ds of bad, normal and go o d economic growth. The strong and robust p erformance of this metho d comes from its ability to disentangle the information aggregated in the original time series of each variable, which allows to isolate the frequencies of the predictors with the highest predictive p ower from the noisy parts.
- The yield curve and the stock market: mind the long runPublication . Faria, Gonçalo; Verona, FabioWe extract cycles from the term spread and study their role for predicting the equity premium using linear models. When properly extracted, the trend of the term spread is a strong and robust out-of-sample equity premium predictor, both from a statistical and an economic point of view. It outperforms several variables recently proposed as good equity premium predictors. Our results support recent findings in the asset pricing literature that the low-frequency components of macroeconomic variables play a crucial role in shaping the dynamics of equity markets. Hence, for policymakers and financial market participants interested in gauging equity market developments, the trend of the term spread is a promising variable to look at.
- Time-frequency forecast of the equity premiumPublication . Faria, Gonçalo; Verona, FabioAny time series can be decomposed into cyclical components fluctuating at different frequencies. Accordingly, in this paper, we propose a method to forecast the equity premium which exploits the frequency relationship between the equity premium and several predictor variables. We evaluate a large set of models and find that, by selecting the relevant frequencies for equity premium forecasting purposes, this method significantly improves in a statistical and economic way upon standard time series forecasting methods. This outperformance is robust regardless of the predictor used, the out-of-sample period considered, and the frequency of the data used.