Using sentiment surveys to predict GDP growth and stock returns

This study sheds new light on the question of whether or not sentiment surveys, and the expectations derived from them, are relevant to forecasting economic growth and stock returns, and whether they contain information that is orthogonal to macroeconomic and financial data. I examine 16 sentiment surveys of distinct respondent universes and employ the technique of principal components analysis to extract the common signals from the surveys. I show that the ability of different population groups to anticipate correctly economic growth and excess stock returns is not identical, implying that not all sentiment is the same, although there exist some common components. I demonstrate that sentiment surveys have significant predictive power for both GDP growth and excess stock returns, and that the results are robust to the inclusion of information pertaining to the macroeconomic environment and momentum. Furthermore, the findings reject the conventional wisdom that the effect of sentiment is apparent exclusively in small-capitalization stocks

Using sentiment surveys to predict GDP growth and stock returns

This study sheds new light on the question of whether or not sentiment surveys, and the expectations derived from them, are relevant to forecasting economic growth and stock returns, and whether they contain information that is orthogonal to macroeconomic and financial data. I examine 16 sentiment surveys of distinct respondent universes and employ the technique of principal components analysis to extract the common signals from the surveys. I show that the ability of different population groups to anticipate correctly economic growth and excess stock returns is not identical, implying that not all sentiment is the same, although there exist some common components. I demonstrate that sentiment surveys have significant predictive power for both GDP growth and excess stock returns, and that the results are robust to the inclusion of information pertaining to the macroeconomic environment and momentum. Furthermore, the findings reject the conventional wisdom that the effect of sentiment is apparent exclusively in small-capitalization stocks

Using sentiment to predict GDP growth and stock returns

This study sheds new light on the question of whether or not sentiment surveys, and the expectations derived from them, are relevant to forecasting economic growth and stock returns, and whether they contain information that is orthogonal to macroeconomic and financial data. I examine 16 sentiment surveys of distinct respondent universes and employ the technique of principal components analysis to extract the common signals from the surveys. I show that the ability of different population groups to anticipate correctly economic growth and excess stock returns is not identical, implying that not all sentiment is the same, although there exist some common components. I demonstrate that sentiment surveys have significant predictive power for both GDP growth and excess stock returns, and that the results are robust to the inclusion of information pertaining to the macroeconomic environment and momentum. Furthermore, the findings reject the conventional wisdom that the effect of sentiment is apparent exclusively in small-capitalization stocks

The case for higher frequency inflation expectations

I present evidence that higher frequency measures of inflation expectations outperform lower frequency measures of inflation expectations in tests of accuracy, predictive power, and rationality. For decades, the academic literature has focused on three survey measures of expected inflation: the Livingston Survey, the Survey of Professional Forecasters, and the Michigan Surveys of Consumers. While these measures have been useful in developing models of forecasting inflation, the data are low frequency measures that are anachronistic in the modern era of high frequency and real-time data. I present a collection of 37 different measures of inflation expectations, including many previously unexploited monthly and real-time measures of inflation expectations. These higher frequency measures tend to outperform the standard three low frequency survey measures in tests of accuracy, predictive power, and rationality, indicating that there are benefits to using higher frequency measures of inflation expectations. Out of sample forecasts confirm the findings

Using sentiment to predict GDP growth and stock returns

This study sheds new light on the question of whether or not sentiment surveys, and the expectations derived from them, are relevant to forecasting economic growth and stock returns, and whether they contain information that is orthogonal to macroeconomic and financial data. I examine 16 sentiment surveys of distinct respondent universes and employ the technique of principal components analysis to extract the common signals from the surveys. I show that the ability of different population groups to anticipate correctly economic growth and excess stock returns is not identical, implying that not all sentiment is the same, although there exist some common components. I demonstrate that sentiment surveys have significant predictive power for both GDP growth and excess stock returns, and that the results are robust to the inclusion of information pertaining to the macroeconomic environment and momentum. Furthermore, the findings reject the conventional wisdom that the effect of sentiment is apparent exclusively in small-capitalization stocks

An inflation expectations horserace

For decades, the academic literature has focused on three survey measures of expected inflation: the Livingston Survey, the Survey of Professional Forecasters, and the Michigan Survey. While these measures have been useful in developing models of forecasting inflation, the data are low frequency measures which appear anachronistic in the modern era of high frequency and real-time data. I present a collection of 37 different measures of inflation expectations, including many previously unexploited monthly and real-time measures of inflation expectations. These higher frequency measures tend to outperform the standard three low frequency survey measures in tests of accuracy, predictive power, and rationality, indicating that there are benefits to using higher frequency measures of inflation expectations. Out of sample forecasts confirm the findings

An inflation expectations horserace

For decades, the academic literature has focused on three survey measures of expected inflation: the Livingston Survey, the Survey of Professional Forecasters, and the Michigan Survey. While these measures have been useful in developing models of forecasting inflation, the data are low frequency measures which appear anachronistic in the modern era of high frequency and real-time data. I present a collection of 37 different measures of inflation expectations, including many previously unexploited monthly and real-time measures of inflation expectations. These higher frequency measures tend to outperform the standard three low frequency survey measures in tests of accuracy, predictive power, and rationality, indicating that there are benefits to using higher frequency measures of inflation expectations. Out of sample forecasts confirm the findings

Using sentiment surveys to predict GDP growth and stock returns

This study sheds new light on the question of whether or not sentiment surveys, and the expectations derived from them, are relevant to forecasting economic growth and stock returns, and whether they contain information that is orthogonal to macroeconomic and financial data. I examine 16 sentiment surveys of distinct respondent universes and employ the technique of principal components analysis to extract the common signals from the surveys. I show that the ability of different population groups to anticipate correctly economic growth and excess stock returns is not identical, implying that not all sentiment is the same, although there exist some common components. I demonstrate that sentiment surveys have significant predictive power for both GDP growth and excess stock returns, and that the results are robust to the inclusion of information pertaining to the macroeconomic environment and momentum. Furthermore, the findings reject the conventional wisdom that the effect of sentiment is apparent exclusively in small-capitalization stocks