On the Scope and Drivers of the Asset Growth Effect

2011 ◽  
Vol 46 (6) ◽  
pp. 1651-1682 ◽  
Author(s):  
Marc L. Lipson ◽  
Sandra Mortal ◽  
Michael J. Schill
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Negative Correlation ◽  
Small Firms ◽  
Explanatory Power ◽  
Analyst Forecasts ◽  
Growth Effect ◽  
Idiosyncratic Volatility ◽  
Asset Growth ◽  
Return Effect

AbstractRecent papers have debated whether the negative correlation between measures of firm asset growth and subsequent returns is of little importance since it applies only to small firms, is justified as compensation for risk, or is evidence of mispricing. We show that the asset growth effect is pervasive, and evidence to the contrary arises due to specification choices; that one measure of asset growth, the change in total assets, largely subsumes the explanatory power of other measures; that the ability of asset growth to explain either the cross section of returns or the time series of factor loadings is linked to firm idiosyncratic volatility (IVOL); that the return effect is concentrated around earnings announcements; and that analyst forecasts are systematically higher than realized earnings for faster growing firms. In general, there appears to be no asset growth effect in firms with low IVOL. Our findings are consistent with a mispricing-based explanation for the asset growth effect in which arbitrage costs allow the effect to persist.

The Determinants of Idiosyncratic Volatility

2017 ◽  
Vol 25 (4) ◽  
pp. 509-545
Author(s):  
Jaeuk Khil ◽  
Song Hee Kim ◽  
Eun Jung Lee
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Cross Section ◽  
Growth Rates ◽  
Idiosyncratic Volatility ◽  
Return Volatility ◽  
Firm Level ◽  
Cross Sectional ◽  
Asset Growth ◽  
The Cross

We investigate the cross-sectional and time-series determinants of idiosyncratic volatility in the Korean market. In particular, we focus on the empirical relation between firms’ asset growth rate and idiosyncratic stock return volatility. We find that, in the cross-section, companies with high idiosyncratic volatility tend to be small and highly leveraged, have high variance of ROE and Market to Book ratio, high turnover rate, and pay no dividends. Furthermore, firms with extreme (either high positive or negative) asset growth rates have high idiosyncratic return volatility than firms with moderate growth rates, suggesting the V-shaped relation between asset growth rate and idiosyncratic return volatility. We find that the V-shaped relation is robust even after controlling for other factors. In time-series, we find that firm-level idiosyncratic volatility is positively related to the dispersion of the cross-sectional asset growth rates. As a result, this study is contributed to show that the asset growth is the most important predictor of firm-level idiosyncratic return volatility in both the cross-section and the time-series in the Korean stock market. In addition, we show how the effect of risk factors varies with industries.

scholarly journals Analysis of Asset Growth Anomaly on Cross-Section Stock Returns: Evidence from Indonesia Stock Exchange

2017 ◽  
Vol 20 (1) ◽  
pp. 47
Author(s):  
Muhammad Iqbal ◽  
Buddi Wibowo
Keyword(s):  
Risk Factor ◽  
Stock Returns ◽  
Cross Section ◽  
Stock Prices ◽  
Stock Exchange ◽  
High Growth ◽  
Growth Effect ◽  
Market Anomalies ◽  
Growth Anomaly ◽  
Asset Growth

Assorted types of market anomalies occur when stock prices deviate from the prediction of classical asset pricing theories. This study aims to examine asset growth anomaly where stocks with high asset growth will be followed by low returns in the subsequent periods. This study, using Indonesia Stock Exchanges data, finds that an equally-weighted low-growth portfolio outperforms high-growth portfolio by average 0.75% per month (9% per annum), confirming existence of asset growth anomaly. The analysis is extended at individual stock-level using fixed-effect panel regression in which asset growth effect remains significant even with controlling other variables of stock return determinants. This study also explores further whether asset growth can be included as risk factor. Employing two-stage cross-section regression in Fama and Macbeth (1973), the result aligns with some prior studies that asset growth is not a new risk factor; instead the anomaly is driven by mispricing due to investors’ overreaction and psychological bias. This result imply that asset growth anomaly is general phenomenon that can be found at mostly all stock market but in Indonesia market asset growth anomaly rise from investors’ overreaction, instead of  playing as a factor of risk.

scholarly journals Analysis of Asset Growth Anomaly on Cross-Section Stock Returns: Evidence from Indonesia Stock Exchange

2017 ◽  
Vol 19 (3) ◽  
Author(s):  
Muhammad Iqbal ◽  
Buddi Wibowo
Keyword(s):  
Risk Factor ◽  
Stock Returns ◽  
Cross Section ◽  
Stock Prices ◽  
Stock Exchange ◽  
High Growth ◽  
Growth Effect ◽  
Market Anomalies ◽  
Growth Anomaly ◽  
Asset Growth

Assorted types of market anomalies occur when stock prices deviate from the prediction of classical asset pricing theories. This study aims to examine asset growth anomaly where stocks with high asset growth will be followed by low returns in the subsequent periods. This study, using Indonesia Stock Exchanges data, finds that an equally-weighted low-growth portfolio outperforms high-growth portfolio by average 0.75% per month (9% per annum), confirming existence of asset growth anomaly. The analysis is extended at individual stock-level using fixed-effect panel regression in which asset growth effect remains significant even with controlling other variables of stock return determinants. This study also explores further whether asset growth can be included as risk factor. Employing two-stage cross-section regression in Fama and Macbeth (1973), the result aligns with some prior studies that asset growth is not a new risk factor; instead the anomaly is driven by mispricing due to investors’ overreaction and psychological bias. This result imply that asset growth anomaly is general phenomenon that can be found at mostly all stock market but in Indonesia market asset growth anomaly rise from investors’ overreaction, instead of  playing as a factor of risk.

The Post-Acquisition Returns of Stock Deals: Evidence of the Pervasiveness of the Asset Growth Effect

2015 ◽  
Vol 50 (3) ◽  
pp. 477-507 ◽  
Author(s):  
Sandra Mortal ◽  
Michael J. Schill
Keyword(s):  
Time Series ◽  
Stock Returns ◽  
Growth Rates ◽  
Growth Effect ◽  
Distinguishing Characteristic ◽  
Cross Sectional ◽  
The Poor ◽  
Asset Growth ◽  
High Valuation

AbstractA growing literature finds that firm asset growth rates are negatively correlated with subsequent stock returns. We show that the poor post-deal returns that have been documented for stock acquisitions are more precisely explained by the return effects associated with systematically larger asset growth rates for stock deals. We find a similar result for other cross-sectional and time-series acquisition effects, including poor returns for glamour deals, weakly monitored deals, and deals done during high-valuation periods. We suggest that the distinguishing characteristic associated with poor performing acquisitions is simply their tendency to grow assets.

Pooling of Time Series and Cross Section Data

Econometrica ◽  
1969 ◽  
Vol 37 (3) ◽  
pp. 552
Author(s):  
V. K. Chetty
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Cross Section Data ◽  
Section Data

The Data Science of COVID-19 Spread: Some Troubling Current and Future Trends

2020 ◽  
Vol 26 (3) ◽  
Author(s):  
Rex W. Douglass ◽  
Thomas Leo Scherer ◽  
Erik Gartzke
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Observational Studies ◽  
Data Science ◽  
Future Trends ◽  
Methodological Issues

AbstractOne of the main ways we try to understand the COVID-19 pandemic is through time series cross section counts of cases and deaths. Observational studies based on these kinds of data have concrete and well known methodological issues that suggest significant caution for both consumers and produces of COVID-19 knowledge. We briefly enumerate some of these issues in the areas of measurement, inference, and interpretation.

Predicting US recessions with stock market illiquidity

2016 ◽  
Vol 16 (1) ◽  
Author(s):  
Shiu-Sheng Chen ◽  
Yu-Hsi Chou ◽  
Chia-Yi Yen
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Returns ◽  
Cross Section ◽  
Market Liquidity ◽  
Credit Spreads ◽  
Stock Market Liquidity ◽  
Predictive Content ◽  
Dynamic Link

AbstractIn this paper, we investigate the dynamic link between recessions and stock market liquidity by examining the predictive content of illiquidity for US recessions. After controlling for other commonly featured recession predictors such as term spreads and credit spreads, we find that the illiquidity measure proposed by (Amihud, Y. 2002. “Illiquidity and Stock Returns: Cross-Section and Time-Series Effects.”

Time is Not A Theoretical Variable

2010 ◽  
Vol 18 (3) ◽  
pp. 293-294 ◽  
Author(s):  
Nathaniel Beck
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Duration Dependence ◽  
Cross Section Data ◽  
Section Data ◽  
Data Analyst ◽  
Binary Time Series ◽  
Simple Cubic ◽  
Cubic Polynomial

Carter and Signorino (2010) (hereinafter “CS”) add another arrow, a simple cubic polynomial in time, to the quiver of the binary time series—cross-section data analyst; it is always good to have more arrows in one's quiver. Since comments are meant to be brief, I will discuss here only two important issues where I disagree: are cubic duration polynomials the best way to model duration dependence and whether we can substantively interpret duration dependence.

Sign in / Sign up

Export Citation Format

Share Document