Stock Returns and the Business Cycle

CFA Digest ◽  
2005 ◽  
Vol 35 (2) ◽  
pp. 42-43
Author(s):  
Daniel B. Cashion
Keyword(s):  
Business Cycle ◽  
Stock Returns ◽  
The Business Cycle

Introduction

2021 ◽  
pp. 1-6
Author(s):  
Jesper Rangvid
Keyword(s):  
Economic Growth ◽  
Business Cycle ◽  
Stock Returns ◽  
Economic Activity ◽  
Final Part ◽  
Stylized Facts ◽  
Long Run ◽  
Key Concepts ◽  
The Business Cycle

Chapter 1 contains an overview of the book. Part I introduces key concepts, definitions, and stylized facts regarding long–run economic growth and stock returns.Part II analyses the relation between economic growth and stock returns in the long run. Part III examines the shorter-horizon relation between economic growth and stock returns: the relation over the business cycle. Part IV explains how to make reasonable projections for economic activity, both for the short and the long run. Part V deals with expected future stock returns. The final part, a short one including one chapter only, explains how one can use the insights from the book when making investments.

Value, size, sector, and momentum stocks

2021 ◽  
pp. 187-198
Author(s):  
Jesper Rangvid
Keyword(s):  
Stock Market ◽  
Business Cycle ◽  
Stock Returns ◽  
The Business Cycle ◽  
Better Than ◽  
Do So

This chapter studies the characteristics of the most important and well-known factors. Factor portfolios are portfolios of stocks based on certain characteristics, such as the size of the company, the price of the stock in relation to, e.g., the earnings of the company, the sector within which the firm operates, etc.Factors that perform better than the overall stock market tend to suffer more during recessions. To compensate investors for their underperformance during recessions, returns on these factors during expansions are so high that average stock returns over the full business cycle end out being high. Conversely, those factors that provide lower average returns than the overall stock market do so because they perform relatively better during recessions. The business cycle again plays an important role for understanding stock-market patterns.

scholarly journals Industrial Electricity Usage and Stock Returns

2017 ◽  
Vol 52 (1) ◽  
pp. 37-69 ◽  
Author(s):  
Zhi Da ◽  
Dayong Huang ◽  
Hayong Yun
Keyword(s):  
Growth Rate ◽  
Asset Pricing ◽  
Business Cycle ◽  
Stock Returns ◽  
Real Time ◽  
Risk Premium ◽  
Output Gap ◽  
Financial Variables ◽  
The Future ◽  
The Business Cycle

The growth rate of industrial electricity usage predicts future stock returns up to 1 year with an R2 of 9%. High industrial electricity usage today predicts low stock returns in the future, consistent with a countercyclical risk premium. Industrial electricity usage tracks the output of the most cyclical sectors. Our findings bridge a gap between the asset pricing literature and the business cycle literature, which uses industrial electricity usage to gauge production and output in real time. Industrial electricity growth compares favorably with traditional financial variables, and it outperforms Cooper and Priestley’s output gap measure in real time.

Risk-Return Tradeoff in U.S. Stock Returns over the Business Cycle

2011 ◽  
Vol 47 (1) ◽  
pp. 137-158 ◽  
Author(s):  
Henri Nyberg
Keyword(s):  
Stock Market ◽  
Business Cycle ◽  
Stock Returns ◽  
Regime Switching ◽  
Probit Model ◽  
Expected Return ◽  
Stock Market Returns ◽  
Risk Return ◽  
The Business Cycle ◽  
Cycle Indicator

AbstractIn the empirical finance literature, findings on the risk-return tradeoff in excess stock market returns are ambiguous. In this study, I develop a new qualitative response (QR)-generalized autoregressive conditional heteroskedasticity-in-mean (GARCH-M) model combining a probit model for a binary business cycle indicator and a regime-switching GARCH-M model for excess stock market return with the business cycle indicator defining the regime. Estimation results show that there is statistically significant variation in the U.S. excess stock returns over the business cycle. However, consistent with the conditional intertemporal capital asset pricing model (ICAPM), there is a positive risk-return relationship between volatility and expected return independent of the state of the economy.

NONLINEAR RISK

2001 ◽  
Vol 5 (4) ◽  
pp. 621-646 ◽  
Author(s):  
Marcelle Chauvet ◽  
Simon Potter
Keyword(s):  
Business Cycle ◽  
Stock Returns ◽  
Conditional Expectation ◽  
Negative Relationship ◽  
Dynamic Factor ◽  
Expected Return ◽  
Conditional Moments ◽  
Risk Return ◽  
Excess Stock Returns ◽  
The Business Cycle

This paper analyzes the joint time-series properties of the level and volatility of expected excess stock returns. An unobservable dynamic factor is constructed as a nonlinear proxy for the market risk premia with its first moment and conditional volatility driven by a latent Markov variable. The model allows for the possibility that the risk–return relationship may not be constant across the Markov states or over time. We find an overall negative contemporaneous relationship between the conditional expectation and variance of the monthly value-weighted excess return. However, the sign of the correlation is not stable, but instead varies according to the stage of the business cycle. In particular, around the beginning of recessions, volatility rises substantially, reflecting great uncertainty associated with these periods, while expected return falls, anticipating a decline in earnings. Thus, around economic peaks there is a negative relationship between conditional expectation and variance. However, toward the end of a recession expected return is at its highest value as an anticipation of the economic recovery, and volatility is still very high in anticipation of the end of the contraction. That is, the risk–return relation is positive around business-cycle troughs. This time-varying behavior also holds for noncontemporaneous correlations of these two conditional moments.

Are individual stock returns predictable?

2021 ◽  
pp. 031289622110015
Author(s):  
Hui Zeng ◽  
Ben R Marshall ◽  
Nhut H Nguyen ◽  
Nuttawat Visaltanachoti
Keyword(s):  
Business Cycle ◽  
Stock Returns ◽  
Predictive Power ◽  
Jel Classification ◽  
Market Returns ◽  
Predictive Information ◽  
Macroeconomic Variables ◽  
Limits To Arbitrage ◽  
The Business Cycle

We show that the previously documented predictability of macroeconomic and technical variables for market returns is also evident in individual stock returns. Technical variables generate better predictability on firms with high limits to arbitrage (small, illiquid, volatile firms), while macroeconomic variables better predict firms with low limits to arbitrage. Technical predictors show a stronger predictive power for high limits to arbitrage firms across the business cycle, whereas macroeconomic variables capture more predictive information for firms with low limits to arbitrage during recessions. JEL Classification: C58, E32, G11, G12, G17

The behaviour of stock returns and interest rates over the business cycle in the US and UK

2001 ◽  
Vol 8 (4) ◽  
pp. 233-238 ◽  
Author(s):  
Elena Andreou ◽  
Rita Desiano ◽  
Marianne Sensier
Keyword(s):  
Business Cycle ◽  
Interest Rates ◽  
Stock Returns ◽  
The Us ◽  
The Business Cycle
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