Unexpected Component Research Articles

Application of Multivariate Statistical Analysis to STEM X-ray Spectral Images: Interfacial Analysis in Microelectronics

Multivariate statistical analysis methods have been applied to scanning transmission electron microscopy (STEM) energy-dispersive X-ray spectral images. The particular application of the multivariate curve resolution (MCR) technique provides a high spectral contrast view of the raw spectral image. The power of this approach is demonstrated with a microelectronics failure analysis. Specifically, an unexpected component describing a chemical contaminant was found, as well as a component consistent with a foil thickness change associated with the focused ion beam specimen preparation process. The MCR solution is compared with a conventional analysis of the same spectral image data set.

Probability of Meeting/Beating Analysts' Forecasts and Market Reaction to Earnings Announcements*

In this paper, I hypothesize that market reaction to meeting/beating (missing) earnings expectations depends on its unexpected component, which is related to the ex ante probability that a firm will meet/beat expectations (MBE probability). I first empirically model the ex ante MBE probability using a vector of variables that is available to the market prior to earnings announcements. I then generate out-of-sample estimates of the MBE probability, which I use to explain cross-sectional variation in market reaction to earnings announcements. As predicted, I find that when firms with high MBE probabilities miss (meet/beat) analysts' consensus forecasts, their three-day abnormal returns around earnings announcements are significantly more negative (less positive) than those with low MBE probabilities. These results are robust to controlling for unexpected earnings and other determinants of stock returns around earnings announcements. Overall, I contribute to the literature on meeting/beating expectations by providing a rational explanation for cross-sectional variation in market reaction to meeting/beating or missing earnings expectations.

First Attempt at Spectroscopic Detection of Gravity Modes in the Long‐Period Pulsating Subdwarf B Star PG 1627+017

The first spectroscopic campaign on a PG 1716 variable (long-period pulsating subdwarf B star) has successfully detected low-level velocity variations due to g-mode pulsations in PG 1627+017, superimposed on the known orbital motion. The strongest velocity mode is barely detectable at 1.0-1.5 km s-1, although the target is one of the brightest (V = 12.9) and largest amplitude (~0.03 mag) stars in its class. Forty nights of observations on 2 m class telescopes in Arizona, South Africa, and Australia provided 84 hr of time-series spectroscopy over a time baseline of 53 days, with typical velocity errors of 5-6 km s-1. The derived radial velocity amplitude spectrum, after subtracting the orbital component, shows three potential pulsational modes 3-4 σ above the mean noise level of 0.365 km s-1, at 7201.0 s (138.87 μHz), 7014.6 s (142.56 μHz), and 7037.3 s (142.10 μHz). Only one feature is statistically likely to be real, but all three are tantalizingly close to, or a 1 day alias of, the three strongest periodicities found in the concurrent photometric campaign. The velocity spectrum also shows an unexpected component at twice the orbital frequency of PG 1627+017, possibly evidence of a slightly elliptical orbit, supporting Edelmann et al.'s recent results for other short-period sdB binaries. We further attempted to detect pulsational variations in the Balmer line amplitudes. The single detected periodicity of 7209 s, although weak, is consistent with theoretical expectations as a function of wavelength, and it rules out a degree index of l = 3 or 5 for that mode. Given the extreme weakness of g-mode pulsations in PG1716 stars, we conclude that future efforts will require larger telescopes, higher efficiency spectral monitoring over longer time baselines, improved longitude coverage, and increased radial velocity precision.

Monetary Policy News and Exchange Rate Responses: Do Only Surprises Matter?

This paper shows that exchange rates respond to only the surprise component of an actual US monetary policy change and that failure to disentangle the surprise component from the actual monetary policy change can lead to an underestimation of the impact of monetary policy, or even to a false acceptance of the hypothesis that monetary policy has no impact on exchange rates. This finding implies that there is a need for reexamining the empirical analyses of asset price responses to macro news that do not isolate the unexpected component of news from the expected element. In addition, we add to the debate on how quickly exchange rates respond to news by showing that the exchange rates under study absorb monetary policy surprises within the same day as the news are announced.

Interpreting Velocities from Heat‐Based Flow Sensors by Numerical Simulation

We have carried out numerical simulations of three-dimensional nonisothermal flow around an in situ heat-based flow sensor to investigate how formation heterogeneities can affect the interpretation of ground water flow velocities from this instrument. The flow sensor operates by constant heating of a 0.75-m-long, 5-cm-diameter cylindrical probe, which contains 30 thermistors in contact with the formation. The temperature evolution at each thermistor can be inverted to obtain an estimate of the ground water flow velocity vector using the standard interpretive method, which assumes that the formation is homogeneous. Analysis of data from heat-based flow sensors installed in a sand aquifer at the Former Fort Ord Army Base near Monterey, California, suggested an unexpected component of downward flow. The magnitudes of the vertical velocities were expected to be much less than those of the horizontal velocities at this site because the sensors were installed just above a clay aquitard. Numerical simulations were conducted to examine how differences in thermal conductivities may lead to spurious indications of vertical flow velocities. We found that a decrease in the thermal conductivity near the bottom of the sensor can perturb the temperature profiles along the instrument in such a manner that analyses assuming homogeneous thermal conductivity could indicate a vertical flow component even though flow is actually horizontal. This work demonstrates how modeling can be used to simulate instrument response to formation heterogeneity and shows that caution must be used in interpreting data from such devices.

A closed‐form expression for computing the sensitivity in second‐order bilinear calibration

AbstractA general expression is derived for estimating the sensitivity of second‐order bilinear calibration models, particularly parallel factor analysis (PARAFAC) and bilinear least‐squares (BLLS), whether the second‐order advantage is required or not. In the latter case, the sensitivity is correctly estimated either if the advantage is achieved by processing the unknown sample together with the calibration set (PARAFAC), or by post‐calibration residual bilinearization (BLLS). The expression includes, as special cases, the sensitivity expressions already discussed by Messick, Kalivas and Lang (MKL) and by Ho, Christian and Davidson (HCD). The former one is the maximum achievable sensitivity in a given calibration situation, where all components are present in the calibration set of samples. The latter approach gives the lowest possible sensitivity, corresponding to only calibrating the analyte of interest, leaving the remaining components as uncalibrated constituents of the unknown sample. In intermediate situations, that is more than one calibrated analyte and presence of unexpected components in the unknown sample, only the present approach is able to provide a satisfactory sensitivity parameter, in close agreement with previously described Monte Carlo numerical simulations. Copyright © 2006 John Wiley & Sons, Ltd.

How Forecasts Evolve - The Growth Forecasts of the Federal Reserve and the Bank of England

We investigate how central bank forecasts of GDP growth evolve through time, and how they are adapted in the light of official estimates of actual GDP growth. Using data for 1988–2005, we find that the Federal Open Market Committee (FOMC) has typically adjusted its forecast for growth over the coming four quarters by about a third of the unexpected component of estimated growth in the four quarters most recently ended. We were unable to find any clear signs of systematic errors in the FOMC's forecasts. UK data for 1998–2005 suggest that the Bank of England Monetary Policy Committee (MPC) did not adjust its forecasts in this way, and that there were systematic forecast errors, but the evidence from the latter part of the period 2001–5 tentatively shows a behaviour pattern closer to that of the FOMC, with no clear signs of systematic errors.

The Effect of Futures Trading on Spot Market Liquidity

This study analyzes the effect of stock index futures trading on the price volatility and liquidity of spot markets, It is found that spot price volatility increases significantly after stock index futures are listed, This study partitions the trading activity series of sPOt markets into expected and unexpected components, and documents that unexpected spot-trading activities are associated with smaller sPOt price movements subsequent to the introduction of futures trading, This imolies that spot market liquidity has been increased by the intraduction of futures trading, Furthermore, this study examines the effect of futures-trading activity on the liquidity of spot markets, Results show that active futures markets enhance the liquidity of soot markets.

Deregulated Power Prices: Changes Over Time

We examine price by season and by year in 14 deregulated markets, by looking at diurnal price patterns and price volatility. Power price volatility is measured by price velocity, the daily average of the absolute value of price change per hour, which is broken into a component expected from the average diurnal pattern, and an unexpected component. Deregulated markets can be categorized into three groups: stable markets, markets that experienced one bad period or season of high price excursion, and chaotic markets. Britain, Spain and Scandinavia show consistent price patterns and low unexpected price velocity; a thoughtful consumer could reasonably implement demand side management (DSM) by shaping consumption patterns to reflect price. California, New Zealand and Alberta are examples of markets that had a period of very high price excursion; we discuss factors affecting public reaction to this. Australian power markets have inconsistent price patterns from season to season and year to year, and very high unexpected price velocity. Planned DSM in these markets would be very difficult. We offer four policy considerations for markets considering deregulation in the future.

Electricity deregulation, spot price patterns and demand-side management

This paper examines extensive hourly or half-hourly power price data from 14 deregulated power markets. It analyzes average diurnal patterns, relationship to system load, volatility, and consistency over time. Diurnal patterns indicate the average price spread between off-peak and on-peak and weekend vs. weekday power consumption. Volatility is measured by price velocity: the average normalized hourly change in power price, calculated daily. The calculated price velocity is broken down into an expected component that arises from the diurnal pattern and an unexpected component that arises from unknown factors. The analysis reveals significant differences among markets, suggesting that demand-side management (DSM) of power consumption is far more difficult in some markets than in others. At one extreme, Spain, Britain and Scandinavia show consistent diurnal price patterns, a stable relationship between price and system load, and a low unexplained component of price volatility. A power consumer in these markets could form a reasonable expectation of a reward for DSM of elective power consumption. At the other extreme, two markets in Australia show erratic diurnal price patterns from year to year, low correlation between price and system load, and a high amount of unexpected price velocity. A power consumer in these markets would have far greater difficulty in realizing a benefit from DSM. Markets that experienced one period of very high prices without a clear external cause, such as California and Alberta, appear to have a significant longer-term erosion of public support for deregulation.

Characterization of B‐ and C‐type low molecular weight glutenin subunits by electrospray ionization mass spectrometry and matrix‐assisted laser desorption/ ionization mass spectrometry

Low molecular weight glutenin subunits (LMW-GS) are typically subdivided into three groups, according to their molecular weights and isoelectric points, namely the B-, C-, and D groups. Enriched B- and C-type LMW-GS fractions extracted from the bread wheat cultivar Chinese Spring were characterized using high performance liquid chromatography (HPLC) directly interfaced with electrospray ionization mass spectrometry and HPLC coupled off-line with matrix-assisted laser desorption/ionization mass spectrometry, in order to ascertain the number and relative molecular masses of the components present in each fraction and determine the number of cysteine residues. About 70 components were detected in each of the fractions examined by the combined use of these two techniques, with 18 components common to both fractions. Analysis of the fractions after alkylation with 4-vinylpyridine allowed determination of the number of the cysteines present in about 40 subunits. The proteins detected were tentatively classified based on the relative molecular masses and number of cysteine residues. Cross-contamination was found in both B- and C- fractions, along with the presence of D-type LMW-GS. The two fractions also contained unexpected components, probably lipid transfer proteins and omega-gliadins. The presence of extensive microheterogeneity was suggested by the detection of several co-eluting proteins with minor differences in their molecular masses.

The Relationship Between Macroeconomic Variables and Relative Price Variability in Greek Agriculture

The purpose of this paper is to present a measure of relative price variability (RPV) among 53 agricultural products and to explore the relationships between RPV, aggregate inflation rate, and changes in economic activity for the period 1962–1997. We also estimate the effects of expected and unexpected components of inflation on price variability. The results show that changes in inflation rate and economic activity have a strong positive effect on RPV, in addition, expected inflation is found to be an important determinant for explaining price variability.

Expected Accrual Models: The Impact of Operating Cash Flows and Reversals of Accruals

This paper augments the Jones (1991) model with operating cash flows and lagged accruals to evaluate the impact of (1) the negative association between accruals and concurrent cash flows, (2) the positive association between accruals and lagged cash flows, and (3) the reversal of accruals. I find that operating cash flows greatly improve the explanatory and predictive power of the Jones model; but, lagged accruals do not. A market test of the expected and unexpected components of accruals indicates that unexpected accruals are on average informative with respect to concurrent stock returns; however, the market does not fully understand the implications of accruals anticipated at the beginning of the return period.

The Effects of Expected and Unexpected Volatility on Long‐Run Growth: Evidence from 18 Developed Economies

This article examines the relationship between output volatility and long‐run growth for 18 developed countries between 1880 and 1990. The analysis builds on the existing literature by decomposing output growth volatility into expected and unexpected components and then examining whether the types of volatility have different effects on long‐run growth. The results are consistent with the view that unexpected volatility reduces long‐run growth and that expected volatility increases long‐run growth. The results also suggest that the combined effect of expected and unexpected volatility is to reduce long‐run growth for most countries and most time periods.

Video Stabilization for a Camcorder Mounted on a Moving Vehicle

Vision systems play an important role in many intelligent transportation systems (ITS) applications, such as traffic monitoring, traffic law reinforcement, driver assistance, and automatic vehicle guidance. These systems installed in either outdoor environments or vehicles have often suffered from image instability. In this paper, a video stabilization technique for a camcorder mounted on a moving vehicle is presented. The proposed approach takes full advantage of the a priori information of traffic images, significantly reducing the computational and time complexities. There are four major steps involved in the proposed approach: global feature extraction, camcorder motion estimation, motion taxonomy, and image compensation. We begin with extracting the global features of lane lines and the road vanishing point from the input image. The extracted features are then combined with those detected in previous images to compute the camcorder motion corresponding to the current input image. The computed motion consists of both expected and unexpected components. They are discriminated and the expected motion component is further smoothed. The resulting motion is next integrated with a predicted motion, which is extrapolated from the previous desired camcorder motions, leading to the desired camcorder motion associated with the input image under consideration. The current input image is finally stabilized based on the computed desired camcorder motion using an image transformation technique. A series of experiments with both real and synthetic data have been conducted. The experimental results have revealed the effectiveness of the proposed technique.

The High-Frequency Effects of U.S. Macroeconomic Data Releases on Prices and Trading Activity in the Global Interdealer Foreign Exchange Market

We introduce a new high-frequency foreign exchange dataset from EBS (Electronic Broking Service) that includes trading volume in the global interdealer spot market, data not previously available to researchers. The data also gives live transactable quotes, rather than the indicative quotes that have been used in most previous high frequency foreign exchange analysis. We describe intraday volume and volatility patterns in euro-dollar and dollar-yen trading. We study the effects of scheduled U.S. macroeconomic data releases, first confirming the finding of recent literature that the conditional mean of the exchange rate responds very quickly to the unexpected component of data releases. We next study the effects of data releases on trading volumes. News releases cause volume to rise, and to remain elevated for a longer period. However, in contrast to the result for the level of the exchange rate, even if the data release is entirely in line with expectations, we find that there is still typically a large pickup in trading volume.

Expected Accrual Models: The Impact of Operating Cash Flows and Reversals of Accruals

This paper augments the Jones (1991) model with operating cash flows and lagged accruals to evaluate the impact of (1) the negative association between accruals and concurrent cash flows, (2) the positive association between accruals and lagged cash flows, and (3) the reversal of accruals. I find that operating cash flows greatly improve the explanatory and predictive power of the Jones model; but, lagged accruals do not. A market test of the expected and unexpected components of accruals indicates that unexpected accruals are on average informative with respect to concurrent stock returns; however, the market does not fully understand the implications of accruals anticipated at the beginning of the return period.

Intelligent on-line fault tolerant control for unanticipated catastrophic failures

As dynamic systems become increasingly complex, experience rapidly changing environments, and encounter a greater variety of unexpected component failures, solving the control problems of such systems is a grand challenge for control engineers. Traditional control design techniques are not adequate to cope with these systems, which may suffer from unanticipated dynamic failures. In this research work, we investigate the on-line fault tolerant control problem and propose an intelligent on-line control strategy to handle the desired trajectories tracking problem for systems suffering from various unanticipated catastrophic faults. Through theoretical analysis, the sufficient condition of system stability has been derived and two different on-line control laws have been developed. The approach of the proposed intelligent control strategy is to continuously monitor the system performance and identify what the system's current state is by using a fault detection method based upon our best knowledge of the nominal system and nominal controller. Once a fault is detected, the proposed intelligent controller will adjust its control signal to compensate for the unknown system failure dynamics by using an artificial neural network as an on-line estimator to approximate the unexpected and unknown failure dynamics. The first control law is derived directly from the Lyapunov stability theory, while the second control law is derived based upon the discrete-time sliding mode control technique. Both control laws have been implemented in a variety of failure scenarios to validate the proposed intelligent control scheme. The simulation results, including a three-tank benchmark problem, comply with theoretical analysis and demonstrate a significant improvement in trajectory following performance based upon the proposed intelligent control strategy.

How Are Earnings Managed? An Examination of Specific Accruals*

AbstractThere is relatively little evidence on the specific accruals used to manage earnings. This paper examines this issue by considering the use of specific accruals in three earnings‐management contexts: equity offerings, management buyouts, and firms avoiding earnings decreases. We argue that the costs of managing earnings through different income statement items vary and that the benefits of earnings management through each of these items depend on the context. We thus make differential predictions regarding which specific accrual will be used to manage earnings in each of the three contexts we consider. To measure earnings management for specific accruals, we develop performance‐matched measures to capture the unexpected component of accounts receivable, inventory, accounts payable, accrued liabilities, depreciation expense, and special items. Consistent with our predictions, we find that firms issuing equity appear to prefer managing earnings upward by accelerating revenue recognition. Specifically, we find that accounts receivable for these firms are unexpectedly high. Conversely, for the management buyout context, we predict and find unexpected accounts receivable to be negative. For firms trying to avoid reporting an earnings decrease, we expect firms to be less concerned with earnings persistence and therefore more likely to use more transitory, and less costly, items to achieve their goal. We find that special items are significantly more positive for this group. This paper provides a further step toward understanding how the incentives behind earnings management affect the method used to achieve earnings goals, and it illustrates the usefulness of examining individual accruals in specific contexts.

Inflation Announcements, Federal Reserve Bias And Stock Returns

In this article, we found that the US stock prices react only to the unexpected component of U.S. inflation announcements, with such impact statistically significant only in a situation of economic recession. We also show that the impact of the unexpected component of inflation announcements is also dependent upon the 'signals' that the Federal Reserve sends to the market (the known Federal Reserve Bias). In fact, we found a negative correlation between the unexpected component of macroeconomic announcements and stock price returns, this correlation being statistically significant when the Federal Reserve discloses a Neutral Bias.