Business cycles and the pre-holiday effect in stock returns

1994 ◽  
Vol 4 (3) ◽  
pp. 171-174 ◽  
Author(s):  
Kartono Liano ◽  
Larry R. White
Keyword(s):  
Business Cycles ◽  
Stock Returns ◽  
Holiday Effect

The value relevance of capital expenditures and the business cycle

2018 ◽  
Vol 35 (3) ◽  
pp. 386-406 ◽  
Author(s):  
Sungsoo Kim ◽  
Brandon byunghwan Lee
Keyword(s):  
Business Cycles ◽  
Stock Returns ◽  
Value Relevance ◽  
Financial Data ◽  
Capital Expenditures ◽  
Capital Investments ◽  
Content Type ◽  
Market Participants ◽  
Business Conditions

Purpose This paper aims to clarify the relationship between corporate capital investments and business cycles. Specifically, a major purpose of this paper is to investigate whether there are inherent differences in corporate investment patterns and whether the stock market exhibits different reactions to the value relevance of capital expenditures across different business conditions. Design/methodology/approach The authors use pooled ordinary least square regressions with archival stock price data and financial data from CRSP and Compustat. The authors regress buy and hold returns on the main test variables and control variables that are identified to be related to the investment literature. Findings This paper provides empirical evidence that US firms’ capital expenditures are more value relevant to capital market participants during expansionary business cycles and, conversely, less value relevant during contractionary business cycles. This evidence validates previous literature that has found the information content of capital expenditures to be uncertain and cyclical in nature. Research limitations/implications The main limitation of this paper, as with other work dealing with stock returns and archived financial data, is that the authors try to match stock returns with contemporaneous financial data in an association study context. The precise mapping in this methodology is always challenging and has been questioned in the literature. Practical implications This paper has various implications for capital market participants. Capital expenditures are good news for investors, but they will make a better investment when firms make capital investments during an expansionary period. Creditors deciding whether to extend credit to firms would benefit from more accurate information on the viability of long-term investment. The results also suggest to creditors that an excessive number of loans during the contractionary period may be suboptimal because firms’ returns on capital investment are smaller in that period than in the expansionary period. Social implications Given the valuation of implications of long-term capital investments across different business conditions, this paper sheds light on asset allocations for mutual funds, institutional investors who are entrusted with investors’ investments including retirement funds. Originality/value This paper fulfils an identified need to study how capital investments are valued differently across different business conditions.

THE HOLIDAY EFFECT IN STOCK RETURNS: EVIDENCE FROM THE OTC MARKET

1992 ◽  
Vol 2 (1) ◽  
pp. 45-54 ◽  
Author(s):  
Kartono Liano ◽  
Patrick H. Marchand ◽  
Gow-Cheng Huang
Keyword(s):  
Stock Returns ◽  
Holiday Effect

VIX and the variance of Dow Jones industrial average stocks

2015 ◽  
Vol 41 (3) ◽  
pp. 226-243
Author(s):  
Andre Mollick
Keyword(s):  
Business Cycles ◽  
Stock Returns ◽  
Data Set ◽  
Content Type ◽  
Dummy Variable ◽  
Aggregate Uncertainty ◽  
Dow Jones Industrial Average ◽  
Variance Equation ◽  
The Impact ◽  
Mixture Of Distribution Hypothesis

Purpose – The purpose of this paper is to examine what happens to the variance of individual stocks forming the Dow Jones Industrial Average (DJIA) allowing for aggregate uncertainty measured by VIX, the “fear gauge index” of US options contracts. In examining each individual stock belonging to DJIA in 2011, the authors reconsider aggregate market uncertainty (VIX) as the mixing variable. In contrast to studies on the effects of VIX on the aggregate equity market, the data set used in this paper allow a further look at the proposition that market aggregate uncertainty should have varying impact on individual stock variance. Design/methodology/approach – GARCH-M models estimate individual stock returns belonging to the DJIA in 2011 on its lags and on the ARCH-M term in the mean equation linking stock returns to the variance equation. The longest time span has 5,738 observations for most stocks under daily frequency from January 3, 1990 to December 30, 2011. The authors use one lag for the VIX2 term to address simultaneity problems in the variance equation. In order to allow for interactions between volatility and business cycles, the authors include a dummy variable for the three recessions identified by the NBER over the period. Findings – Adding the “fear gauge” VIX index and a dummy variable for recessions to the variance equation in GARCH-M models, the VIX coefficient always increases variance and the recession dummy has mixed effects. Overall, VIX acts as expected as mixing variable. Supporting the mixture of distribution hypothesis, the impact of VIX is always positive (1.039 on market variance) and GARCH effects vanish completely for the index and almost as much for 24 stocks. Research limitations/implications – In theory, the effects of VIX on stock variance should be positive and statistically significant, together with reductions of GARCH persistence. The authors find this to be the case for the aggregate stock market and for 24 out of its 29 DJIA stocks. The authors leave for further work extensions to estimating the variance equation for companies very exposed to idiosyncratic changes, such as oil price fluctuations or stock buybacks. The implication of this research for the academic or financial community relies on the estimation of VIX effects on individual stock variance, controlling for business cycles. Originality/value – Due to its benchmark in equities, stocks in the Dow Jones Industrials make it a very interesting case study. This paper reconsiders the aggregate uncertainty hypothesis for two main reasons. First, the financial press and traders keep a very close track on the daily evolution of VIX. Second, recent research emphasizes the formal predictive power of VIX in US stock markets. For the variance equation, existing works report positive values for the VIX-coefficient on the S&P 500 index but they have not examined individual stocks as the authors do in this paper.

Confucius confusion: analyst forecast dispersion and business cycles

2018 ◽  
Vol 10 (2) ◽  
pp. 130-145
Author(s):  
Raymond Cox ◽  
Ajit Dayanandan ◽  
Han Donker ◽  
John R. Nofsinger
Keyword(s):  
Business Cycles ◽  
Business Cycle ◽  
Stock Returns ◽  
Turning Points ◽  
Analyst Forecast ◽  
Economic Data ◽  
Forecast Dispersion ◽  
Content Type ◽  
The Us ◽  
Analyst Forecast Dispersion

PurposeFinancial analysts have been found to be overconfident. The purpose of this paper is to study the ramifications of that overconfidence on the dispersion of earnings estimates as a predictor of the US business cycle.Design/methodology/approachWhether aggregate analyst forecast dispersion contains information about turning points in business cycles, especially downturns, is examined by utilizing the analyst earnings forecast dispersion metric. The primary analysis derives from logit regression and Markov switching models. The analysis controls for sentiment (consumer confidence), output (industrial production), and financial indicators (stock returns and turnover). Analyst data come from Institutional Brokers Estimate System, while the economic data are available at the Federal Reserve Bank of St Louis Economic Data site.FindingsA rise in the dispersion of analyst forecasts is a significant predictor of turning points in the US business cycle. Financial analyst uncertainty of earnings estimate contains crucial information about the risks of US business cycle turning points. The results are consistent with some analysts becoming overconfident during the expansion period and misjudging the precision of their information, thus over or under weighting various sources of information. This causes the disagreement among analysts measured as dispersion.Originality/valueThis is the first study to show that analyst forecast dispersion contributions valuable information to predictions of economic downturns. In addition, that dispersion can be attributed to analyst overconfidence.

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