Empirical Study on the Difference in Analyst’ Tendency for earning forecast and Impact on Stock Price by Industry Types

FinanceWhen analysts forecast the future earnings of a firm, the method used in analyzing and forecasting such a firm differs according to the industry that it participates in due to the various characteristics each industry possesses. This study is motivated by this point. Firstly this study investigates whether the overestimating tendency and forecast accuracy of an analyst are different among industries, as well as running a cross-sectional regression using dummy variables to test the effect of the industry to forecast errors. In addition, this study examines whether there exists difference in stock price impact of analysts according to industry types using CAR for 20 days before and after changing the recommendations. It turned out that there exists a significant difference in the tendency for over-forecast and the accuracy of forecasts of analysts according to each industry. In addition, a tendency for the under-prediction has been identified especially for the Bank Industry. Interestingly even in the same industry, analysts are more likely to overestimate earnings about net income compared to sales and operating profit. Furthermore, in regards to the difference in the influence on the stock price in cases where an analyst changes its target price/investment recommendation, the study has observed a significant difference depending on the industry that an analyst is participating in. At the end of the study, by comparing the results of the influence on stock prices and the accuracy of forecasts of an analyst in the earlier section of this research, it has identified that the industry where an analyst had lower forecasting accuracy showed a lower influence towards the stock price of an analyst. It seems that investors tend not to trust analysts’ who have already presented a relatively less accurate forecast.ope

Automatic 3D Model Generation based on a Matching of Adaptive Control Points

Abstract The use of a 3D model helps to diagnosis and accurately locate a disease where it is neither available, nor can be exactly measured in a 2D image. Therefore, highly accurate software for a 3D model of vessel is required for an accurate diagnosis of patients. We have generated standard vessel because the shape of the arterial is different for each individual vessel, where the standard vessel can be adjusted to suit individual vessel. In this paper, we propose a new approach for an automatic 3D model generation based on a matching of adaptive control points. The proposed method is carried out in three steps. First, standard and individual vessels are acquired. The standard vessel is acquired by a 3D model projection, while the individual vessel of the first segmented vessel bifurcation is obtained. Second is matching the corresponding control points between the standard and individual vessels, where a set of control and corner points are automatically extracted using the Harris corner detector. If control points exist between corner points in an individual vessel, it is adaptively interpolated in the corresponding standard vessel which is proportional to the distance ratio. And then, the control points of corresponding individual vessel match with those control points of standard vessel. Finally, we apply warping on the standard vessel to suit the individual vessel using the TPS (Thin Plate Spline) interpolation function. For experiments, we used angiograms of various patients from a coronary angiography in Sanggye Paik Hospital

A machine learning approach to discover migration modes and transition dynamics of heterogeneous dendritic cells

Dendritic cell (DC) migration is crucial for mounting immune responses. Immature DCs (imDCs) reportedly sense infections, while mature DCs (mDCs) move quickly to lymph nodes to deliver antigens to T cells. However, their highly heterogeneous and complex innate motility remains elusive. Here, we used an unsupervised machine learning (ML) approach to analyze long-term, two-dimensional migration trajectories of Granulocyte-macrophage colony-stimulating factor (GMCSF)-derived bone marrow-derived DCs (BMDCs). We discovered three migratory modes independent of the cell state: slow-diffusive (SD), slow-persistent (SP), and fast-persistent (FP). Remarkably, imDCs more frequently changed their modes, predominantly following a unicyclic SD→FP→SP→SD transition, whereas mDCs showed no transition directionality. We report that DC migration exhibits a history-dependent mode transition and maturation-dependent motility changes are emergent properties of the dynamic switching of the three migratory modes. Our ML-based investigation provides new insights into studying complex cellular migratory behavior

Characteristics of Adolescent Patients Admitted to the Emergency Department due to Attempted Suicide by Poisoning; a Brief Report

Introduction: In the background of the increased suicide rate in the second decade of life, analysis of the characteristics of poisoning-related attempted suicide in adolescents and evaluation of the differences from adults may form an important basis for establishing measures to prevent deaths from poisoning. Objective: We aimed to investigate the types of toxic substances ingested for attempted suicide by poisoning in adolescents admitted to the emergency department (ED). Method: This cross-sectional study retrospectively analyzed and investigated the medical records of patients aged 13 or older, admitted to the ED of a tertiary medical institute over a period of 3 years, for attempted suicide by poisoning. Results: The psychiatric diagnoses among patients in the adolescent group included depression (75.8%), bipolar disorder (12.5%), and panic disorder (12.5%). In terms of the type of drug used for poisoning, antidepressants or anti-psychotics and sleeping pills were the most commonly used in the adolescent (43 subjects, 45.2%) and adult (286 subjects, 37.6%) groups, respectively. Conclusion: As there is a higher chance of poisoning by easily accessible drugs, the emergency physician needs to investigate any preceding diagnoses of psychiatric or medical illnesses in the adolescent patients attempting suicide with unknown drugs

Broussonetia papyrifera Root Bark Extract Exhibits Anti-inflammatory Effects on Adipose Tissue and Improves Insulin Sensitivity Potentially Via AMPK Activation

The chronic low-grade inflammation in adipose tissue plays a causal role in obesity-induced insulin resistance and its associated pathophysiological consequences. In this study, we investigated the effects of extracts of Broussonetia papyrifera root bark (PRE) and its bioactive components on inflammation and insulin sensitivity. PRE inhibited TNF-alpha-induced NF-kappa B transcriptional activity in the NF-kappa B luciferase assay and pro-inflammatory genes' expression by blocking phosphorylation of I kappa B and NF-kappa B in 3T3-L1 adipocytes, which were mediated by activating AMPK. Ten-week-high fat diet (HFD)-fed C57BL6 male mice treated with PRE had improved glucose intolerance and decreased inflammation in adipose tissue, as indicated by reductions in NF-kappa B phosphorylation and pro-inflammatory genes' expression. Furthermore, PRE activated AMP-activated protein kinase (AMPK) and reduced lipogenic genes' expression in both adipose tissue and liver. Finally, we identified broussoflavonol B (BF) and kazinol J (KJ) as bioactive constituents to suppress pro-inflammatory responses via activating AMPK in 3T3-L1 adipocytes. Taken together, these results indicate the therapeutic potential of PRE, especially BF or KJ, in metabolic diseases such as obesity and type 2 diabetes