50 research outputs found
Dividends, trust, and firm value
We find evidence that investors value dividends differently depending on their level of trust. Our tests indicate that investor demand for dividend-paying stocks increases as trust decreases, and that this relationship affects market values. We begin with survey evidence showing that people think accounting fraud is less likely among dividend payers and that people with low trust are more likely to hold dividend-paying stocks. We then empirically exploit accounting fraud discoveries within a mutual fundâs portfolio as a shock to trust. In response to these shocks, we show that mutual funds tilt their portfolios toward dividend-paying stocks. This result is not explained by a shift in risk preferences, indicating that these institutional investors are seeking dividends in particular rather than stable firms that just happen to pay dividends. Finally, we provide evidence that dividend payers experience a premium in their market values relative to non-payers when their investor base becomes less trusting
Earnings announcement return extrapolation
We propose that extrapolative beliefs about earnings announcement (EA) returns may contribute to our understanding of EA return patterns. We construct a theoretically-motivated measure of extrapolative investorsâ expectations based on a stockâs recent history of EA returns. We then show that this measure explains cross-sectional variation in stock returns and investor behavior around EAs. Stocks expected to have high EA returns according to our measure experience predictable increases in prices before EAs and predictable decreases afterwards. These patterns are economically significant: investors that buy (sell) a portfolio that is long firms with high recent EA returns and short firms with low recent EA returns in the pre-EA (post-EA) period earn daily five-factor abnormal returns of 16.1 bps (18.3 bps). Using individual investor trades data and a measure of institutional trading, we find that individual and institutional investors are more likely to purchase stocks with high recent EA returns, consistent with at least a subset of investors forming extrapolative beliefs about EA returns
Guaranteed Conditional Performance of Control Charts via Bootstrap Methods
To use control charts in practice, the in-control state usually has to be
estimated. This estimation has a detrimental effect on the performance of
control charts, which is often measured for example by the false alarm
probability or the average run length. We suggest an adjustment of the
monitoring schemes to overcome these problems. It guarantees, with a certain
probability, a conditional performance given the estimated in-control state.
The suggested method is based on bootstrapping the data used to estimate the
in-control state. The method applies to different types of control charts, and
also works with charts based on regression models, survival models, etc. If a
nonparametric bootstrap is used, the method is robust to model errors. We show
large sample properties of the adjustment. The usefulness of our approach is
demonstrated through simulation studies.Comment: 21 pages, 5 figure
Reversal of threatening blindness after initiation of eculizumab in Purtscher-like retinopathy secondary to atypical hemolytic uremic syndrome
Corrected diffusion theory approximations in evaluating properties of SPRT charts for monitoring a process mean
Multivariate CUSUM and EWMA Control Charts for Skewed Populations Using Weighted Standard Deviations
Robust kernel distance multivariate control chart using support vector principles
It is important to monitor manufacturing processes in order to improve product
quality and reduce production cost. Statistical Process Control (SPC) is the
most commonly used method for process monitoring, in particular making
distinctions between variations attributed to normal process variability to
those caused by âspecial causesâ. Most SPC and multivariate SPC (MSPC) methods
are parametric in that they make assumptions about the distributional properties
and autocorrelation structure of in-control process parameters, and, if
satisfied, are effective in managing false alarms/-positives and false-
negatives. However, when processes do not satisfy these assumptions, the
effectiveness of SPC methods is compromised. Several non-parametric control
charts based on sequential ranks of data depth measures have been proposed in
the literature, but their development and implementation have been rather slow
in industrial process control. Several non-parametric control charts based on
machine learning principles have also been proposed in the literature to
overcome some of these limitations. However, unlike conventional SPC methods,
these non-parametric methods require event data from each out-of-control process
state for effective model building. The paper presents a new non-parametric
multivariate control chart based on kernel distance that overcomes these
limitations by employing the notion of one-class classification based on support
vector principles. The chart is non-parametric in that it makes no assumptions
regarding the data probability density and only requires ânormalâ or in-control
data for effective representation of an in-control process. It does, however,
make an explicit provision to incorporate any available data from out-of-control
process states. Experimental evaluation on a variety of benchmarking datasets
suggests that the proposed chart is effective for process mo