Experience Of Analyst Research Articles

The accuracy of a 2D video-based lifting monitor

A widely used risk prediction tool, the revised NIOSH lifting equation (RNLE), provides the recommended weight limit (RWL), but is limited by analyst subjectivity, experience, and resources. This paper describes a robust, non-intrusive, straightforward approach to automatically extract spatial and temporal factors necessary for the RNLE using a single video camera in the sagittal plane. The participant’s silhouette is segmented by motion information and the novel use of a ghosting effect provides accurate detection of lifting instances, and hand and feet location prediction. Laboratory tests using 6 participants, each performing 36 lifts, showed that a nominal 640 pixel × 480 pixel 2D video, in comparison to 3D motion capture, provided RWL estimations within 0.2 kg (SD = 1.0 kg). The linear regression between the video and 3D tracking RWL was R2 = 0.96 (slope = 1.0, intercept = 0.2 kg). Since low definition video was used in order to synchronise with motion capture, better performance is anticipated using high definition video.Practitioner's summary: An algorithm for automatically calculating the revised NIOSH lifting equation using a single video camera was evaluated in comparison to laboratory 3D motion capture. The results indicate that this method has suitable accuracy for practical use and may be, particularly, useful when multiple lifts are evaluated.Abbreviations: 2D: Two-dimensional; 3D: Three-dimensional; ACGIH: American Conference of Governmental Industrial Hygienists; AM: asymmetric multiplier; BOL: beginning of lift; CM: coupling multiplier; DM: distance multiplier; EOL: end of lift; FIRWL: frequency independent recommended weight limit; FM: frequency multiplier; H: horizontal distance; HM: horizontal multiplier; IMU: inertial measurement unit; ISO: International Organization for Standardization; LC: load constant; NIOSH: National Institute for Occupational Safety and Health; RGB: red, green, blue; RGB-D: red, green, blue – depth; RNLE: revised NIOSH lifting equation; RWL: recommended weight limit; SD: standard deviation; TLV: threshold limit value; VM: vertical multiplier; V: vertical distance

How does inventory management affect analyst forecast accuracy

Purpose - This paper aims to examine the association between inventory level and analyst forecast accuracy. Firms can potentially manage earnings through inventory manipulation, and we hypothesize that it is therefore more difficult to forecast earnings in companies with large inventories. Methodology - Analyst forecast accuracy is measured by forecast error, computed as the absolute value of the difference between forecasted earnings per share and actual earnings per share, normalized by the firm's stock price. Univariate tests and regression models are used to examine the relation between forecast error and inventory level. We further investigate whether analyst experience and the level of institutional ownership can reduce the forecast error. Findings - The results show that forecast error is greater in firms with relatively higher levels of inventories and the size of the error decreases as analyst experience and the level of institutional ownership increase. Conclusion - This study demonstrates that it is important for analysts to take extra care in forecasting earnings for companies with a history of high inventory levels.

Detecting ‘poachers’ with drones: Factors influencing the probability of detection with TIR and RGB imaging in miombo woodlands, Tanzania

Conservation biologists increasingly employ drones to reduce poaching of animals. However, there are no published studies on the probability of detecting poachers and the factors influencing detection. In an experimental setting with voluntary subjects, we evaluated the influence of various factors on poacher detection probability: camera (visual spectrum: RGB and thermal infrared: TIR), density of canopy cover, subject distance from the image centreline, subject contrast against the background, altitude of the drone and image analyst. We manually analysed the footage and marked all recorded subject detections. A multilevel model was used to analyse the TIR image data and a general linear model approach was used for the RGB image data. We found that the TIR camera had a higher detection probability than the RGB camera. Detection probability in TIR images was significantly influenced by canopy density, subject distance from the centreline and the analyst. Detection probability in RGB images was significantly influenced by canopy density, subject contrast against the background, altitude and the analyst. Overall, our findings indicate that TIR cameras improve human detection, particularly at cooler times of the day, but this is significantly hampered by thick vegetation cover. The effects of diminished detection with increased distance from the image centreline can be improved by increasing the overlap between images although this requires more flights over a specific area. Analyst experience also contributed to increased detection probability, but this might cease being a problem following the development of automated detection using machine learning.

Holiday effect on stock price reactions to analyst recommendation revisions

The study explores the holiday effect on stock price reactions to analyst recommendation revisions and on post-recommendation price drifts. Based on the Mood Maintenance Hypothesis and on the literature documenting lower stock trading activity before holidays, I hypothesize that if a recommendation revision is issued before a holiday, then investors striving to maintain their positive pre-holiday mood, may be less willing to make influential trading decisions, and therefore, may underreact to the recommendation revision, leading to stronger post-recommendation price drift. Analyzing a large sample of analyst recommendation revisions, I document that, compared to “regular” recommendation revisions, pre-holiday recommendation revisions are followed by: (i) significantly weaker event-day stock price reactions, and (ii) significantly more pronounced post-event price drifts, whose magnitude increases over longer post-event periods (up to 6 months). Both effects are more pronounced for small and more volatile stocks and remain robust after accounting for additional company- (size, market model beta, historical volatility) and event-specific (number of recommendation categories changed in the revision, analyst experience) factors.

The Boss Knows Best: Directors of Research and Subordinate Analysts

Research departments are managed by directors of research (DORs). Subordinate analysts working for higher-quality DORs provide superior earnings forecasts that elicit stronger market reactions, provide better investment recommendations, and have better career outcomes. For the broker, higher-quality DORs drive more trading commissions. Economically, analysts benefit the most from DOR–analyst industry alignment resulting from DORs’ former analyst experience. We provide several tests to mitigate endogeneity concerns and explore various mechanisms to explain these results. Overall, our article identifies a unique channel whereby the industry-specific and general human capital of top management filters through to individual subordinates and consequently improves organizational performance.

Effect of investor inattention on price drifts following analyst recommendation revisions

AbstractThe study explores stock price dynamics after analyst recommendation revisions. Following the previous literature that documents significant post‐recommendation stock price drifts and attributes them to investor inattention to company‐specific events, I hypothesize that if on the day when a recommendation revision with respect to a stock was issued, the sign of the stock's abnormal return was opposite to the direction of the revision, then it means that investors were especially inattentive to the revision and that the subsequent price drift should be more pronounced. Analysing a large sample of analyst recommendation revisions, I document that recommendation revisions accompanied by the opposite‐sign event‐day abnormal returns are followed by significantly greater post‐event price drifts in the direction of the revision, the magnitude of the drifts increasing for longer post‐event periods. This effect is stronger for small and more volatile stocks and remains robust after accounting for additional company‐specific (size, Market Model beta, and historical volatility) and event‐specific (number of recommendation categories changed in the revision and analyst experience) factors.

MobSTer: A model‐based security testing framework for web applications

SummaryWeb applications have become one of the preferred means for users to perform a number of crucial and security‐sensitive operations such as selling and buying goods or managing bank accounts, official documents, personal health records, and smart houses. The pervasive adoption of such web applications calls for an extensive security analysis in order to avoid attacks. Penetration testing is the most common approach for testing the security of web applications, but model‐based security testing has been steadily maturing into a viable alternative and/or complementary approach. Penetration testing is very efficient, but the experience of the security analyst is crucial; model‐based security testing relies on formal methods, but the security analyst has to first create a suitable model of the web application. In this paper, we introduce MobSTer, a formal and flexible model‐based security testing framework that contributes to filling the gap between these two security testing approaches. The main idea underlying this framework is that the use of model‐checking techniques can automate the search for possible vulnerable entry points in the web application, ie, it permits an analyst to perform security testing without missing important checks. Moreover, the framework also allows for reuse: The analyst can collect her expertise into the framework and (re)use it during future tests on possibly different web applications. We have implemented MobSTer as a prototype and applied it to test a number of case studies to assess its strength and concretely evaluate it with respect to four state‐of‐the‐art tools normally used by penetration testers.

Storage at the 2018 Consumer Electronics Show [The Art of Storage

Consumer electronic devices are enabled by digital storage, whether local or in the cloud. Several digital-storage device suppliers showed their storage products at the 2018 Consumer Electronics Show (CES). In addition, many show representatives used digital storage to enable their applications, including companies at the media and analyst events, ShowStoppers and Digital Experience.

On the Weighting of Individual Analyst Forecasts in the Consensus

A consensus forecast commonly represents a simple average, or equal weighting, of individual analyst forecasts. We demonstrate, however, that an equal-weight consensus generally does not aggregate information in individual analyst forecasts efficiently and that an aggregation scheme informed by forecast and analyst attributes results in a more accurate consensus forecast. For example, in the case of forecasts issued in the sixty days immediately preceding an earnings announcement, the mean squared error (MSE) of an informed consensus forecast whose component weights are based on forecast age, broker size, and analyst experience is significantly less than that of a naive consensus. In this case, MSE-improvement ranges from three to fifteen percent, respectively, as the number of forecasts in the consensus varies from four to twenty.

Factors influencing the selection of delay analysis methods in construction projects in UAE

ABSTRACTSeveral delay analysis methods (DAMs) have been developed and used in the construction industry in order to analyse the causes and effects of delay events. In this research, a number of commonly used DAMs, in the specific context of UAE, are investigated by exploring the factors influencing their selection decisions as well as the process of making such a decision. A total of eight expert respondents from five different projects in the UAE were selected who provided critical insight into the decision-making process adopted in practice to select a DAM. The individual project case analysis as well as the cross case analysis helped to identify a number of factors that influence the selection of DAMs in UAE projects. Some of the main identified factors were the attitude of the client, experience of the delay analyst, reputation and impartiality of the delay analyst, complexity of the project, and cost and timing of performing the analysis. The research argues that such an important decision process that can have a serious impact on the success of a commercial venture requires individual organizations to develop and adopt clear guidelines on how such decisions are made to protect its commercial interests.

Identificación de Patrones Emocionales Básicos en Publicidad Audiovisual Utilizando Modelos Vectoriales por Adaptación

At present, the analysis of the results of advertising and marketing studies is done qualitatively in terms of the experience of a marketing analyst, thus generating little certainty and uncertainty of the effectiveness of the feelings and the message emitted. Brands are connected with the idea that the sender wants to transmit. For this, the marketing has studied the behavior of the consumer when exposed to different advertising stimuli, in order to understand the behavior at the time of the stimulus and to achieve alignment of the message to be transmitted with what is actually perceived. In this paper we propose a vector model based on computational intelligence and Neuromarketing studies that allows the identification of four basic emotions: joy, fear, anger and sadness from the bioelectric brain activity recorded by a person exposed to a certain audiovisual advertising. The results of the model allowed the identification of emotions in audiovisual advertising, which constitutes a tool that allows companies to create audiovisual advertising that guarantees greater commitment and effectiveness of the advertising segments with which it wants to impact the marketKeywords: Neuromarketing, audiovisual advertising, basic emotions, neural networks, Emotiv-EPOC®, vector support machines

A systematic clinical reasoning tool to support gait analysis interpretation: an SDR case example

1. Introduction The process of clinical gait analysis interpretation is complex and subject to the experience and professional background of the gait analyst. Here we present a novel, systematic way of reasoning, to bridge the critical gap between identifying abnormal gait features and finding their underlying impairments [1], to be addressed by a therapeutic intervention. The method is illustrated using a clinical case of a 7yo girl M. with spastic diplegic cerebral palsy, GMFCS level II, due to peri-ventricular leucomalacia associated with prematurity (26+2 weeks). 2. Research question Can a systematic clinical reasoning tool assist in treatment decision making for an individual patient? 3. Methods M. received pre-treatment 3D gait analysis using a 28 marker set (Vicon Nexus, ISB anatomical definitions [2]), complemented with force plate measurements (AMTI), 16-channel EMG, and standardized physical examination (PE). Kinematic gait features were identified manually. For our clinical reasoning tool, two tables were developed to help group gait features and relating them to their underlying impairments. Table A contained 30 common gait features identifiable from 3D gait graphs or video, each described by side, variable, type, and timing; as well as 1-9 potential causes (impairments) for each of these features. Table B was developed as the reverse of Table A, containing 22 potential impairments, each with 1-7 accompanying gait features. In total, 56 gait feature-impairment pairs were listed. These pairs were based on a combination of clinical experience, biomechanical reasoning and available literature. The systematic reasoning approach consisted of the following steps: (1) select one prominent abnormal gait feature from the gait graphs or video, e.g. 'right ankle plantar flexion increased in stance' (see Fig. 1 Pre SDR); (2) search in Table A for potential underlying impairments and select one, e.g. 'soleus spasticity'; (3) search in Table B for other gait features related to this impairment (e.g. 'knee extension movement in loading response' and 'knee hyperextension in late stance') and check their presence in the gait graphs or video. If present, this strengthens the likelihood of this impairment limiting the gait; (4) search for ad-ditional evidence in PE, kinetic and EMG data; (5) if the impairment not likely, repeat from step 2 until a likely impairment is found; if the impairment is likely, repeat from Step1 until all features are explained. Using this systematic approach, spasticity in soleus, gastrocnemius, hamstrings, and adductors were identified as the main limiting impairments for M., each with a strong effect on gait. These impairments could explain almost all of the abnormal gait features. Combined with additional criteria such as sufficient strength, selective motor control, and motivation, as well as evidence from literature [3-5], a treatment decision for selective dorsal rhizotomy (SDR) surgery was made. 4. Results One year post surgery, all gait features related to spasticity were improved (see Fig. 1 for knee and ankle angles). 5. Discussion This systematic reasoning approach is a promising tool for novel gait analysts to help develop their skills, as well as for experienced users to verify the consistency of their decisions. Further (literature) study is needed to extend the developed tables including possible interactions, and to validate each of the gait feature-impairment pairs. The gait feature-impairment pairs can form the basis of a (semi-) automated reasoning tool.

Seeking Comfort in an Uncomfortable Chair

The author explores the concept of comfort in relation to the setting. The concept of comfort, an unusual word in the psychoanalytic lexicon, describes the intuitive and complex experience of patient and analyst being together in the analytic office. The couch and the chair are not the only tools of the setting, but they are potential instruments with which to study the therapeutic process, both in high-frequency therapy and in lower-frequency treatments. To describe the transformations that an alternative experience of comfort can promote, the author looks at the intersection of this concept with the body–mind relationship and with the Bionian concept of binocular vision.

Intelligent Tool To Design Drilling, Spacer, Cement Slurry, and Fracturing Fluids by Use of Machine-Learning Algorithms

Summary Design of drilling fluids, spacers, cement slurries, and fracturing fluids is often done by trial and error in the laboratory. In the first step, the required properties of these fluids are categorized and then efforts will be started with a rough idea of the optimal composition. This first guess usually depends on the experience of the laboratory analyst or fluid engineer. Afterward, the trial-and-error testing starts, and it continues until the fluid design moves closer to the desired fluid criteria. There are several test data that would not be used in this method, and it is difficult to digest a large amount of information by the user. Trial and error could be time-consuming, very costly, and misleading. Today, there is a need for an intelligent system that uses all the available data (big data), even if the data sets are not close to the desired goal, and offers insights for fluid designs. This paper conducted a study on the application of machine-learning-based methodologies, including Gaussian-process regression (GPR) and artificial neural networks (ANNs), to reduce the costs of testing, integrate available experimental data, and eliminate the need for personnel supervision. These practical nonlinear-regression methods empower efficient and fast prediction tools that do not require including complex physics of the underlying system while integrating all available data from different sources. GPR, which is also known as Kriging in geostatistics literature, has exceptional advantages over traditional regression methods because it does not require a known form for regression function and also has the capability of determining the estimation error and the confidence interval. This machine-learning-based tool offers insights for intelligent fluid design and could reduce costs.

The Boss Knows Best: Directors of Research and Subordinate Analysts

Research departments are managed by Directors of Research (DORs). Subordinate analysts working for higher quality DORs provide superior earnings forecasts that elicit stronger market reactions, better investment recommendations, and have better career outcomes. For the broker, higher quality DORs drive more trading commissions. Economically, analysts benefit the most from DOR-analyst industry alignment resulting from DORs’ former analyst experience. We provide several tests to mitigate endogeneity concerns and explore various mechanisms to explain these results. Overall, our paper identifies a unique channel whereby industry-specific and general human capital of top management filters through to individual subordinates and consequently improves organizational performance.

An open-source solution for advanced imaging flow cytometry data analysis using machine learning

Imaging flow cytometry (IFC) enables the high throughput collection of morphological and spatial information from hundreds of thousands of single cells. This high content, information rich image data can in theory resolve important biological differences among complex, often heterogeneous biological samples. However, data analysis is often performed in a highly manual and subjective manner using very limited image analysis techniques in combination with conventional flow cytometry gating strategies. This approach is not scalable to the hundreds of available image-based features per cell and thus makes use of only a fraction of the spatial and morphometric information. As a result, the quality, reproducibility and rigour of results are limited by the skill, experience and ingenuity of the data analyst. Here, we describe a pipeline using open-source software that leverages the rich information in digital imagery using machine learning algorithms. Compensated and corrected raw image files (.rif) data files from an imaging flow cytometer (the proprietary .cif file format) are imported into the open-source software CellProfiler, where an image processing pipeline identifies cells and subcellular compartments allowing hundreds of morphological features to be measured. This high-dimensional data can then be analysed using cutting-edge machine learning and clustering approaches using “user-friendly” platforms such as CellProfiler Analyst. Researchers can train an automated cell classifier to recognize different cell types, cell cycle phases, drug treatment/control conditions, etc., using supervised machine learning. This workflow should enable the scientific community to leverage the full analytical power of IFC-derived data sets. It will help to reveal otherwise unappreciated populations of cells based on features that may be hidden to the human eye that include subtle measured differences in label free detection channels such as bright-field and dark-field imagery.

Reusing business components and objects for modeling business systems: The influence of decomposition characteristics and analyst experience

Abstract Component-based development (CBD) relies on the use of pre-fabricated business components to develop new application systems, rather than developing them from scratch. It provides an attractive alternative to more established development methods such as object-oriented analysis and design (OOAD). Given the growing demands for using agile methods for software development, we examine if systems analysts are more effective at modeling business systems by reusing business components than by reusing objects. We also examine whether the influence of component or object reuse on modeling performance is moderated by prior experience in systems analysis and design. We evaluate the representational constructs of the two based on a set of decomposition characteristics, and postulate hypotheses comparing the two based on theories in cognitive psychology and human factors. We find that models generated by reusing business components are of higher accuracy than those developed by reusing objects. An interesting finding of our study is that IT professionals who are less experienced in systems analysis and design perform on par with experienced professionals, when modeling business systems by reusing components. We argue that the decomposition characteristics of components—with respect to granularity, quality, and focus—enable less experienced analysts to perform on par with more experienced analysts.

Analyst Experience and Accrual Anomaly: How Do Experienced Analysts Improve Price Efficiency?

We document that the accrual anomaly is mitigated for firms followed by experienced analysts, suggesting a positive link between analyst quality and stock price efficiency. We examine two channels through which analysts may improve price efficiency — the research and monitoring channels. We find analysts and investors respond more positively to the accrual component of earnings for firms followed by experienced analysts, consistent with the monitoring channel, whereby experienced analysts bring about better accrual quality. Direct examination of accrual quality confirms that firms followed by experienced analysts have higher accrual quality; this holds around exogenous events of broker mergers and closures.

Transforming Trauma: The Relational Unconscious and "Chemistry" in the Treatment of a Paraplegic Patient.

Therapeutic action with a traumatized paraplegic patient highlights the evocative-and transformative-influence of the relational unconscious. The patient's triumphant resolution suggests that formative bipersonal dynamics (including transference, countertransference, and mutual projective identifications) create an ongoing intersubjective enactment and relational chemistry pivotal to psychic shift. A broad systems perspective highlights contextual communication and the interweaving of the analyst's etiological contributions and subjective experience of trauma. Ultimately, an unconscious, co-created dynamic challenges traumatic fixations, supporting a reintegration of narcissistic, gender, and erotic representations and ego capacities. An eclectic perspective that illuminates the analyst's role-receptivity encompasses classical, object relations, relational, systems, and self psychological paradigms.

Analysts, Institutional Investors, and Managers' Trade-Off between Earnings and Expectations Management

This study examines whether financial analysts and institutional investors play a disciplinary role in monitoring corporate financial reporting and disclosure. Using a sample of firms that meet or marginally beat analysts’ forecasts, likely through upward earnings management and downward expectations management, this study shows that managers’ use of the two tactics is associated with monitoring measures, including analyst following, analyst experience and independence, institutional ownership, and institutional investors’ experience and investment style. Managers under more effective monitoring by analysts and institutional investors are more likely to manage expectations downward than to manage earnings upward. Overall, the findings are consistent with financial analysts and institutional investors playing a monitoring role in constraining distortions in reported earnings and inducing timely disclosure of bad news.