BUSINESS CYCLE AND OPTIMAL TIMING FOR INVESTMENT

2012 ◽  
Vol 6 (1) ◽  
pp. 31-41
Author(s):  
Joseph Cheng ◽  
Jeffery Lippitt
Keyword(s):  
Standard Deviation ◽  
Business Cycle ◽  
Risk Ratio ◽  
Equity Capital ◽  
Optimal Timing ◽  
Expected Return ◽  
Gdp Growth Rate ◽  
The Business Cycle ◽  
Risk Free Rate ◽  
Free Rate

The objective of this paper is to determine which point of the business cycle offers investors the best reward to risk ratio in the stock market.  Expected reward is defined as expected return in excess of the risk free rate, whereas risk is defined as the standard deviation of return.  Thus, the expected reward to risk ratio is measured by expected return in excess of risk free rate relative to the standard deviation of return.  Expected return in excess of the risk free rate and standard deviation of return are generated on a continuum of time periods and the GDP growth rate.  The point where the expected reward to risk ratio peaks, would signify the best time for investment.  Being able to identify this point could help investors in deciding the best time to invest as well as help firms in choosing a favorable time for raising equity capital.  While most people think that the best time to invest is near the bottom, it is not clear whether the best time for investing is before, at, or after the economic trough.  The interesting finding in our model is that the best time is after the point of the economic trough.

scholarly journals Habit Persistence, Asset Returns, and the Business Cycle

2001 ◽  
Vol 91 (1) ◽  
pp. 149-166 ◽  
Author(s):  
Michele Boldrin ◽  
Lawrence J Christiano ◽  
Jonas D. M Fisher
Keyword(s):  
Business Cycle ◽  
Interest Rates ◽  
Substantial Improvement ◽  
Output Growth ◽  
Habit Persistence ◽  
Business Cycle Model ◽  
The Business Cycle ◽  
Risk Free Rate ◽  
Mean Risk ◽  
Free Rate

Two modifications are introduced into the standard real-business-cycle model: habit preferences and a two-sector technology with limited intersectoral factor mobility. The model is consistent with the observed mean risk-free rate, equity premium, and Sharpe ratio on equity. In addition, its business-cycle implications represent a substantial improvement over the standard model. It accounts for persistence in output, comovement of employment across different sectors over the business cycle, the evidence of “excess sensitivity” of consumption growth to output growth, and the “inverted leading-indicator property of interest rates,” that interest rates are negatively correlated with future output. (JEL D10, E10, E20, G12)

THE SPIRIT OF CAPITALISM, ASSET RETURNS, AND THE BUSINESS CYCLE

2007 ◽  
Vol 11 (2) ◽  
pp. 214-230 ◽  
Author(s):  
MARTIN BOILEAU ◽  
REBECCA BRAEU
Keyword(s):  
Business Cycle ◽  
Equity Premium ◽  
Asset Return ◽  
Spirit Of Capitalism ◽  
Business Cycle Model ◽  
The Business Cycle ◽  
Premium Puzzle ◽  
Risk Free Rate ◽  
Free Rate ◽  
Real Business Cycle Model

We evaluate whether the spirit of capitalism improves the ability of the real business cycle model to explain the main features of both asset return and the business cycle. In our model, the spirit of capitalism is embodied in the assumption that individuals have preferences for financial wealth. Our simulation results suggest that this assumption may improve the model's ability to explain the risk-free rate puzzle but not the equity premium puzzle. This assumption also markedly deteriorates the model's ability to account for the main features of the business cycle.

scholarly journals The Shape of Eurozone’s Uncertainty: Its Impact and Predictive Value on GDP

2020 ◽  
Vol 7 (6) ◽  
pp. 1
Author(s):  
Ralf Fendel ◽  
Nicola Mai ◽  
Oliver Mohr
Keyword(s):  
Standard Deviation ◽  
Business Cycle ◽  
Financial Markets ◽  
Impulse Response ◽  
Two Dimensions ◽  
Point Estimate ◽  
Impulse Response Functions ◽  
Response Functions ◽  
The Business Cycle ◽  
The Impact

This paper examines the role of uncertainty in the context of the business cycle in the euro area. To gain a more granular perspective on uncertainty, the paper decomposes uncertainty along two dimensions: First, we construct the four different moments of uncertainty, including the point estimate, the standard deviation, the skewness and the kurtosis. The second dimension of uncertainty spans along three distinct groups of economic agents, including consumers, corporates and financial markets. Based on this taxonomy, we construct uncertainty indices and assess the impact on real GDP via impulse response functions and further investigate their informational value in rolling out-of-sample GDP forecasts. The analysis lends evidence to the hypothesis that higher uncertainty expressed through the point estimate, a larger standard deviation among confidence estimates, positive skewness and a higher kurtosis are all negatively correlated with the business cycle. The impulse response functions reveal that in particular the first and the second moment of uncertainty cause a permanent effect on GDP with an initial decline and a subsequent overshoot. We find uncertainty in the corporate sector to be the main driver behind this observation, followed by financial markets’ uncertainty whose initial effect on GDP is comparable but receding much faster. While the first two moments of uncertainty improve GDP forecasts significantly, both the skewness and the kurtosis do not augment the forecast quality any further.

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.

Strategic Timing of Investment over the Business Cycle: Machine Replacement in the US Rental Industry

2018 ◽  
Vol 238 (3-4) ◽  
pp. 295-351 ◽  
Author(s):  
John McClelland ◽  
John Rust
Keyword(s):  
Business Cycle ◽  
Programming Model ◽  
Optimal Timing ◽  
Data Set ◽  
Replacement Strategy ◽  
Equipment Replacement ◽  
Optimal Replacement ◽  
The Business Cycle ◽  
The Cost ◽  
Machine Replacement

Abstract We analyze a data set containing rental revenues, maintenance costs, and sale prices of five different types of rental machines to econometrically estimate key relationships needed to implement a dynamic programming model of the optimal timing of replacement of rental equipment owned by a large multi-location firm in the equipment rental industry. The model reveals significant potential to improve rental company profitability by improving the strategic timing of equipment replacement. The gains from the optimal replacement strategy come from exploiting seasonal variation in rental demand and the timing of the business cycle due to their effects on rental revenues and the cost of replacement. For some machines we find the optimal replacement strategy is procyclical, but for others we find that a countercyclical replacement strategy –- where replacements are concentrated in slow periods of the business cycle –- can significantly increase firm profits.

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.

Rich Pickings? Risk, Return, and Skill in Household Wealth

2020 ◽  
Vol 110 (9) ◽  
pp. 2703-2747 ◽  
Author(s):  
Laurent Bach ◽  
Laurent E. Calvet ◽  
Paolo Sodini
Keyword(s):  
Idiosyncratic Risk ◽  
Household Wealth ◽  
Expected Return ◽  
Cross Sectional ◽  
Balance Sheets ◽  
Net Worth ◽  
Average Return ◽  
Risk Free Rate ◽  
Free Rate

We investigate wealth returns on an administrative panel containing the disaggregated balance sheets of Swedish residents. The expected return on household net wealth is strongly persistent, determined primarily by systematic risk, and increasing in net worth, exceeding the risk-free rate by the size of the equity premium for households in the top 0.01 percent. Idiosyncratic risk is transitory but generates substantial long-term dispersion in returns in top brackets. Systematic and idiosyncratic risk both drive the cross-sectional distribution of the geometric average return over a generation. Furthermore, wealth returns explain most of the historical increase in top wealth shares. (JEL D31, G11, G51)

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