Predicting Stock and Bond Market Returns with Emotions: Evidence from Futures Markets

2021 ◽  
pp. 1-12
Author(s):  
Jiancheng Shen ◽  
John Griffith ◽  
Mohammad Najand ◽  
Licheng Sun
Keyword(s):  
Futures Markets ◽  
Bond Market ◽  
Market Returns

The impact of oil price shocks on U.S. bond market returns

2014 ◽  
Vol 44 ◽  
pp. 248-258 ◽  
Author(s):  
Wensheng Kang ◽  
Ronald A. Ratti ◽  
Kyung Hwan Yoon
Keyword(s):  
Bond Market ◽  
Oil Price ◽  
Oil Price Shocks ◽  
Market Returns ◽  
Price Shocks ◽  
The Impact

Are corporate bond market returns predictable?

2012 ◽  
Vol 36 (8) ◽  
pp. 2216-2232 ◽  
Author(s):  
Yongmiao Hong ◽  
Hai Lin ◽  
Chunchi Wu
Keyword(s):  
Bond Market ◽  
Corporate Bond ◽  
Market Returns ◽  
Corporate Bond Market

The Impact of Oil Price Shocks on U.S. Bond Market Returns

2014 ◽  
Author(s):  
Wensheng Kang ◽  
Ronald A. Ratti ◽  
Kyung Hwan Yoon
Keyword(s):  
Bond Market ◽  
Oil Price ◽  
Oil Price Shocks ◽  
Market Returns ◽  
Price Shocks ◽  
The Impact

Seasonality and bond market returns

1985 ◽  
Vol 11 (3) ◽  
pp. 42-44 ◽  
Author(s):  
Michael Smirlock
Keyword(s):  
Bond Market ◽  
Market Returns

scholarly journals What Goliaths and Davids among Swiss firms tell us about expected returns on Swiss asset markets

2019 ◽  
Vol 155 (1) ◽  
Author(s):  
David R. Haab ◽  
Thomas Nitschka
Keyword(s):  
Stock Market ◽  
Predictive Power ◽  
Bond Market ◽  
Asset Markets ◽  
Market Returns ◽  
Stock Market Returns ◽  
Asset Return ◽  
The Us ◽  
Out Of Sample ◽  
Sample Test

AbstractMotivated by recent US evidence, we evaluate the predictive power of changes in the weight of large firms in the aggregate stock market (“Goliath vs David” (GVD)) for Swiss stock market returns and bond market returns. Previous research suggests that the asset return dynamics in the US and Switzerland differ markedly. Forecasting Swiss asset returns hence constitutes a challenging “out-of-sample” test for GVD. Over the sample period from January 1999 to December 2017, we find that the Swiss version of GVD exhibits predictive power for Swiss stock and bond market returns even in the presence of global predictors. However, Swiss bond market returns are best predicted by the US term spread.

scholarly journals Clearing the Fog: The Predictive Power of Weather for Employment Reports and Their Asset Price Responses

2019 ◽  
Vol 1 (3) ◽  
pp. 373-388
Author(s):  
Daniel J. Wilson
Keyword(s):  
Data Model ◽  
Predictive Power ◽  
Asset Price ◽  
Dynamic Panel Data ◽  
Bond Market ◽  
Weather Data ◽  
Market Returns ◽  
Dynamic Panel ◽  
Dynamic Panel Data Model ◽  
Out Of Sample

This paper exploits vast granular data—with over one million county-month observations—to estimate a dynamic panel data model of weather’s local employment effects. The fitted county model is then aggregated and used to generate in-sample and rolling out-of-sample (nowcast) estimates of the weather effect on national monthly employment. These nowcasts, which use only employment and weather data available prior to a given employment report, are significantly predictive not only of the surprise component of employment reports but also of stock and bond market returns on the days of employment reports. (JEL C53, G12, G17, H63, Q54, R23)

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