Search results

This paper aims to examine the association between the types of mutual funds, i.e. active versus passive, and the level of earnings manipulation in companies that comprise their stock portfolios.

The authors use Cremers and Petajisto’s (2009) classification of mutual funds by active share and tracking error volatility to differentiate between active and passive mutual funds. To assess the extent of earnings quality at portfolio companies, the authors measure accruals earnings management and real earnings management.

The authors find that the portfolio firms held by active fund managers exhibit lower levels of earnings manipulation. The inverse relationship between earnings management and fund holdings is more pronounced at higher levels of active share selection among concentrated active fund managers.

The degree to which earnings management influences mutual funds’ investment behavior has significant implications for the stability of the US stock market. Based on the findings that earnings management at portfolio companies serves as a potential instrument to guide funds’ investment decisions, future research would examine how these investment preferences exert price pressure (if any) on the stock of the portfolio companies. It would also help to ascertain whether the investment preferences of fund managers with respect to earnings management help to render the stock market more or less efficient.

This paper contributes to the understanding of how actively managed funds perform stock selection. Earnings manipulation leads to negative earnings quality that would inhibit stock performance over time. Active fund managers, who dynamically manage their exposures to systematic and stock-specific risks (in their attempt to outperform their benchmark index), target firms that manage earnings less to form part of their investment portfolios.

This study aims to examine the ability of investors to process signs of real activities manipulations at bidder firms in the quarters leading to the announcement of a merger. It further provides a supplementary explanation for the post-merger underperformance puzzle.

Examining a sample of cash-only, stock swap and mixed mergers completed between 1980 and 2011, it was found that bidder firms increase the use of real activities manipulation in the quarters leading up to the merger announcements. Using average abnormal stock return method, it is shown that the short-term positive effect of real activities manipulation on share prices is stronger than accrual-based earnings management.

While bidders are able to escape investors’ scrutiny in the short run, it is not the case in the long run. It was found that bidders’ long-run stock performance, measured by matched buy-and-hold stock returns, is inversely related to their pre-announcement level of earnings management. This paper contributes to the literature on earnings management by considering how real activities manipulations affect stock prices in mergers and acquisitions.

This study tests whether real activities manipulation, in addition to accrual-based earnings management, explains the underperformance puzzle of the acquiring firms in M&As. Zang (2012) argues that there is a greater likelihood for firms to engage in real activities manipulation, especially when firms are constrained in their use of accrual-based earnings management owing to heightened scrutiny or overuse in prior years.

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.

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.

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.

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.

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.