scholarly journals Does Tokyo Stock Exchange Appreciate Corporate Innovations? Role of Patents’ Quality and Research Productivity.

2020 ◽  
Author(s):  
Kostiantyn Ovsiannikov
Keyword(s):  
Stock Exchange ◽  
Manufacturing Sector ◽  
Dominant Role ◽  
Research Question ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Data Set ◽  
Empirical Measures ◽  
Tokyo Stock Exchange

This paper examines the market perception of corporate innovations in Japan. It follows the research question formulated by Hall, Jaffe, and Trajtenberg (2005): "how does innovative activity translate into market value, and what aspects of the underlying process are captured by the empirical measures available?". The novelty of my study is twofold. First, it embraces the longitudinal innovation- and finance-related corporate records to come up with the largest ever combined data-set for Japan that encompasses 632 companies listed at the Tokyo Stock Exchange over the period of 19 years. Second, in addition to linear regressions, it applies the generalized additive models (GAMs). The latter technique allows for realistically capturing nonlinear patterns present in the data while at the same time retaining predictive features of a model. The main finding of the article is following. Amid the dominant role of research and development (R&D), especially for the Pharmaceutical and Chemical industries, market consistently rewards influential patents in the manufacturing sector.

scholarly journals Age-specific cut-off levels of anti-Müllerian hormone can be used as diagnostic markers for polycystic ovary syndrome

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Fahimeh Ramezani Tehrani ◽  
Maryam Rahmati ◽  
Fatemeh Mahboobifard ◽  
Faezeh Firouzi ◽  
Nazanin Hashemi ◽  
...  
Keyword(s):  
Bayesian Method ◽  
Polycystic Ovary ◽  
Characteristic Curve ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Control Group ◽  
Age Group ◽  
Predictive Values ◽  
Pcos Group

Abstract Background The majority of available studies on the AMH thresholds were not age-specific and performed the receiver operating characteristic curve (ROC) analysis, based on variations in sensitivity and specificity rather than positive and negative predictive values (PPV and NPV, respectively), which are more clinically applicable. Moreover, all of these studies used a pre-specified age categorization to report the age-specific cut-off values of AMH. Methods A total of 803 women, including 303 PCOS patients and 500 eumenorrheic non-hirsute control women, were enrolled in the present study. The PCOS group included PCOS women, aged 20–40 years, who were referred to the Reproductive Endocrinology Research Center, Tehran, Iran. The Rotterdam consensus criteria were used for diagnosis of PCOS. The control group was selected among women, aged 20–40 years, who participated in Tehran Lipid and Glucose cohort Study (TLGS). Generalized additive models (GAMs) were used to identify the optimal cut-off points for various age categories. The cut-off levels of AMH in different age categories were estimated, using the Bayesian method. Main results and the role of chance Two optimal cut-off levels of AMH (ng/ml) were identified at the age of 27 and 35 years, based on GAMs. The cut-off levels for the prediction of PCOS in the age categories of 20–27, 27–35, and 35–40 years were 5.7 (95 % CI: 5.48–6.19), 4.55 (95 % CI: 4.52–4.64), and 3.72 (95 % CI: 3.55–3.80), respectively. Based on the Bayesian method, the PPV and NPV of these cut-off levels were as follows: PPV = 0.98 (95 % CI: 0.96–0.99) and NPV = 0.40 (95 % CI: 0.30–0.51) for the age group of 20–27 years; PPV = 0.96 (95 % CI: 0.91–0.99) and NPV = 0.82 (95 % CI: 0.78–0.86) for the age group of 27–35 years; and PPV = 0.86 (95 % CI: 0.80–0.94) and NPV = 0.96 (95 % CI: 0.93–0.98) for the age group of 35–40 years. Conclusions Application of age-specific cut-off levels of AMH, according to the GAMs and Bayesian method, could elegantly assess the value of AMH in discriminating PCOS patients in all age categories.

scholarly journals IMPACT OF DIVIDEND PAYOUT ON FIRM’S PERFORMANCE UNDER LOW GROWTH OPPORTUNITIES

2021 ◽  
Vol 9 (3) ◽  
pp. 1156-1165
Author(s):  
Taymoor Ali ◽  
Muhammad Kashif Khurshid ◽  
Adnan Ali Chaudhary
Keyword(s):  
Stock Exchange ◽  
Manufacturing Sector ◽  
Secondary Data ◽  
Financial Statements ◽  
Manufacturing Firms ◽  
Growth Opportunities ◽  
Dividend Payout ◽  
Relationship Of ◽  
The Relationship

Purpose of the study: The objective of the study was to investigate the relationship of the dividend payout on a firm's performance under low growth opportunities from the manufacturing sector of Pakistan. Methodology: A sample of 251 firms out of 378 manufacturing firms listed at the Pakistan Stock Exchange (PSX), have been carefully chosen for the era of ten years from 2006 to 2015. The secondary data was obtained from the firm’s web financials and analysis of financial statements, published by the statistics department of the State Bank of Pakistan. For the persistence of investigation panel data (fixed effect) analyses were employed in this study. Main Findings: The fallouts of the analysis revealed that the dividend payout ratio has an insignificant relationship with the firm's performance in the low growth perspectives of the study. Applications of this study: The findings of the study are helpful for the financial managers of the firms facing low growth opportunities. Furthermore, the investors in capital markets can use the findings of this while investing. The originality of this study: The study focussed on the role of low growth opportunities while studying the nexus of dividend pay-out and the firm’s financial performance which inherits the novelty and originality of the study.

scholarly journals Assessing Critical Thinking in Open-ended Answers: An Automatic Approach

2020 ◽  
pp. 109-116
Author(s):  
Antonella Poce ◽  
Francesca Amenduni ◽  
Carlo De Medio ◽  
Alessandra Norgini
Keyword(s):  
Critical Thinking ◽  
Language Processing ◽  
Research Question ◽  
Critical Evaluation ◽  
Weighted Kappa ◽  
Automated Scoring ◽  
Data Set ◽  
Constructed Response ◽  
Human Evaluation

The role of Higher Education (HE) is growingly acknowledged for the promotion of Critical Thinking (CT). Constructed-response tasks (CRT) are recognized to be necessary for the CT assessment, though they present problems related to scoring quality and cost (Ku, 2009). Researchers (Liu, Frankel, Roohr, 2014) have proposed using automated scoring to address the above concerns. The present work is aimed at comparing the features of different Natural Language Processing (NLP) techniques adopted to improve the reliability of a prototype designed to automatically assess six sub-skills of CT in CRT: use of language, argumentation, relevance, importance, critical evaluation and novelty (Poce, 2017). We will present the first (1.0) and the second (2.0) version of the CT prototype and their respective reliability results. Our research question is the following: Which level of reliability are shown respectively by the 1.0 and 2.0 automatic CT assessment prototype compared to expert human evaluation? Data collection is realized in two moments, to measure respectively the CT prototype 1.0 and 2.0 reliability from a total of 264 participants and 592 open-ended answers. Two human assessors rated all of these responses on each of the subskills on a scale of 1-5. Similarly, NLP approaches are adopted to compute a feature on each dimension. Quadratic Weighted Kappa and Pearson product-moment correlation were used to evaluate the between-human agreement and human-NLP agreement. Preliminary findings based on the first data set suggest adequate level of between-human rating agreement and a lower level human-NLP agreement (r .43 for the subscales of Relevance and Importance). We are continuing the analysis of the data collected in the 2nd step and expect to complete them in June 2020.

Statistical modelling reveals spatial, temporal, and environmental preferences for white sharks at an oceanic aggregation site

2020 ◽  
Vol 655 ◽  
pp. 171-183
Author(s):  
EE Becerril-García ◽  
RO Martínez-Rincón ◽  
F Galván-Magaña ◽  
O Santana-Morales ◽  
EM Hoyos-Padilla
Keyword(s):  
Feeding Habits ◽  
Generalized Additive Models ◽  
Statistical Modelling ◽  
Time Of Day ◽  
White Shark ◽  
Additive Models ◽  
Environmental Preferences ◽  
Data Set ◽  
Aggregation Site ◽  
Guadalupe Island

Guadalupe Island, Mexico, is one of the most important white shark (Carcharodon carcharias) aggregation sites in the Eastern Pacific. In the waters surrounding Guadalupe Island, cage diving has been carried out since 2001 during August-November; however, there is scarce information regarding the factors associated with this seasonal aggregation. The purpose of this study was to describe the probability of occurrence of white sharks relative to spatial, temporal, and environmental factors in Guadalupe Island. Generalized additive models (GAMs) were used to describe the effect of sea surface temperature, water visibility, tide, moon phase, cloud cover, time of day, and location on white shark occurrence. GAMs were generated from a data set of 6266 sightings of white sharks, classified as immature males, mature males, immature females, and mature females. A sexual segregation related to month was observed, where females arrived after males during late September. GAMs evidenced a segregation of white sharks according to the analysed variables, which is consistent with previous observations in this locality. Environmental preferences for each white shark category are potentially influenced by feeding habits, sexual maturation, and reproduction. This study constitutes a baseline of the effect of the environment on the occurrence of white sharks in Guadalupe Island, which can be used in further studies regarding management and conservation in future climatic and anthropogenic scenarios. Its relevance is related to the understanding of its ecology in oceanic environments and the presence of this threatened species during the ecotourism season.

Stable inversion-based multitrace deabsorption method for spatial continuity preservation and weak signal compensation

Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. V199-V212 ◽  
Author(s):  
Sanyi Yuan ◽  
Shangxu Wang ◽  
Nan Tian ◽  
Zongjun Wang
Keyword(s):  
Dominant Role ◽  
Random Noise ◽  
Linear Matrix ◽  
Weak Signal ◽  
Coherent Noise ◽  
Data Set ◽  
Spatial Continuity ◽  
Singular Vectors ◽  
Stable Inversion

Nonstationary seismic data can be expressed using a linear matrix-vector multiplication system derived from wave theory when anelastic effects of the earth can be quantified by the intrinsic quality factor (or [Formula: see text]) and [Formula: see text] is frequency independent in the seismic bandwidth. On the basis of the linear modeling system and singular value decomposition, we have assessed the stability using weights associated with the left singular vectors, data, and singular values, and we assessed the compensation/resolution limitation of inversion-based deabsorption using the right singular vectors. In addition, a stable inversion-based multitrace deabsorption method was developed by minimizing the [Formula: see text]-norm of coefficients in the frequency-wavenumber ([Formula: see text]) domain of reflectivity subject to the time-domain nonstationary data misfit. The optimum deabsorption result can be obtained by sequentially solving a series of lasso subproblems until the stopping condition is reached. As the number of solving lasso subproblems increases, the role of [Formula: see text] magnitude sparsity constraint relative to data misfit gradually decreases. In this way, the proposed method can highlight the spatial continuities and reduce the influence of noise on the updated result during starting iterations due to [Formula: see text] magnitude sparsity, whereas compensate details including some discontinuities of events and weak reflections during later iterations due to the dominant role of data misfit. We tested the method on a series of data sets, including a synthetic data set, a physical modeling data set, and a field data set. Our results determined that the proposed method can provide stable compensation results, even in the presence of coherent noise and/or strong random noise. Compared with the trace-by-trace [Formula: see text]-norm regularization deabsorption method, our method performed better in spatial continuity preservation and weak signal compensation.

scholarly journals The Audit Quality and Audit Delay: Evidence from Indonesia

2020 ◽  
Vol 2 (3) ◽  
pp. 22-32
Author(s):  
T. Husain ◽  
I Gusti Ayu Intan Saputra Rini
Keyword(s):  
Audit Quality ◽  
Negative Impact ◽  
Stock Exchange ◽  
Manufacturing Sector ◽  
Manufacturing Firms ◽  
Average Delay ◽  
Data Set ◽  
Significant Negative Impact ◽  
The Impact ◽  
Insight Into

Purpose: This study specifically aims to identify the impact of Audit Quality on Audit Delay. Audit Quality is measured by the proxy log natural fee audit (LNFE). Methods: This is a causal research with quantitative analysis. This study involves six companies listed in the sub-sectors of Cable under the manufacturing sector in the Indonesia Stock Exchange for the period of 2013-2019. It applied panel data set in a regression model using STATA MP - Parallel Edition Ver14.00 application. Results: The findings show that the Audit quality has a significant negative impact on the Audit Delay with an average delay of 83.62 days. Implications: This study could be extended further by considering all manufacturing firms of IDX which may provide more insight into the audit quality with other proxies.

scholarly journals Quantile Non‑parametric Additive Models

2019 ◽  
Vol 6 (345) ◽  
pp. 127-139
Author(s):  
Grażyna Trzpiot
Keyword(s):  
Quantile Regression ◽  
Stock Exchange ◽  
Risk Measure ◽  
Additive Models ◽  
Central Tendency ◽  
Data Set ◽  
Response Variable ◽  
Parametric Regression ◽  
Warsaw Stock Exchange ◽  
Non Parametric

Quantile regression allows us to assess different possible impacts of covariates on different quantiles of a response variable. Additive models for quantile functions provide an attractive framework for non‑parametric regression applications focused on functions of the response instead of its central tendency. Total variation smoothing penalties can be used to control the smoothness of additive components. We write down a general approach to estimation and inference for additive models of this type. Quantile regression as a risk measure has been applied in sector portfolio analysis for a data set from the Warsaw Stock Exchange.

scholarly journals Patterns in Invoicing Currency in Global Trade

2020 ◽  
Vol 20 (126) ◽  
Author(s):  
Emine Boz ◽  
Camila Casas ◽  
Georgios Georgiadis ◽  
Gita Gopinath ◽  
Helena Le Mezo ◽  
...  
Keyword(s):  
Dominant Role ◽  
Global Trade ◽  
Data Set ◽  
The Us ◽  
Overall Stability ◽  
Us Dollar ◽  
Pass Through ◽  
Exchange Rate Pass Through ◽  
Invoicing Currency

This paper presents the most comprehensive and up-to-date panel data set of invoicing currencies in global trade. It provides data on the shares of exports and imports invoiced in US dollars, euros, and other currencies for more than 100 countries since 1990. The evidence from these data confirms findings from earlier research regarding the globally dominant role of the US dollar in invoicing – despite the comparatively smaller role of the US in global trade – and the overall stability of invoicing currency patterns. The evidence also points to several novel facts. First, both the US dollar and the euro have been increasingly used for invoicing even as the share of global trade accounted for by the US and the euro area has declined. Second, the euro is used as a vehicle currency in parts of Africa, and some European countries have seen significant shifts toward euro invoicing. Third, as suggested by the dominant currency paradigm, countries invoicing more in US dollars (euros) tend to experience greater US dollar (euro) exchange rate pass-through to their import prices; also, their trade volumes are more sensitive to fluctuations in these exchange rates.

scholarly journals Evaluation of Managerial Overconfidence, Cash Holding, and Investment Efficiency in Companies

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Abdorreza Asadia ◽  
Maryam Oladia ◽  
Mohammad Ghasem Aghela
Keyword(s):  
Fixed Effects ◽  
Stock Exchange ◽  
Financial Statements ◽  
Investment Efficiency ◽  
Cash Holding ◽  
Data Set ◽  
Listed Firms ◽  
Managerial Overconfidence ◽  
Moderating Role

Managers’ overconfidence leads to overestimating their ability to manage cash sources. Holding more cash may result in overinvestment in projects and investment inefficiency consequently. The present study aims to investigate the effect of cash holding on investment efficiency with the moderating role of managerial overconfidence in Iranian companies. All listed firms in Tehran Stock Exchange, excluding banks, insurance, pension funds, and financial intermediaries, are included in the research. We have used data from financial statements of 91 companies over the period from 2010 to 2018 and conducted multiple regression models to test the hypotheses based on pooled and panel data set with fixed effects. The results indicate a positive relationship between managerial overconfidence and cash holding. The effect of cash holding on investment efficiency turns out to be significantly negative. Furthermore, managerial overconfidence has a significant moderating effect on the relation of the variables. This study is almost the first one, which has been done in emerging markets, so the study’s findings not only contribute to the existing literature on managerial overconfidence and investment efficiency but also assist policymakers, managers, and investors in making effective decisions.

scholarly journals A tutorial on regression-based norming of psychological tests with GAMLSS (version 3)

2019 ◽  
Author(s):  
Marieke Timmerman ◽  
Lieke Voncken ◽  
Casper J Albers
Keyword(s):  
Generalized Additive Models ◽  
Reference Population ◽  
Psychological Tests ◽  
Additive Models ◽  
Normative Study ◽  
Data Set ◽  
Composite Scale ◽  
Sample Data ◽  
Proper Interpretation ◽  
Multiple Reference

A norm-referenced score expresses the position of an individual test taker in the reference population, thereby enabling a proper interpretation of the test score. Such normed scores are derived from test scores obtained from a sample of the reference population. Typically, multiple reference populations exist for a test, namely when the norm-referenced scores depend on individual characteristic(s), as age (and sex). To derive normed scores, regression-based norming has gained large popularity. The advantages of this method over traditional norming are its flexible nature, yielding potentially more realistic norms, and its efficiency, requiring potentially smaller sample sizes to achieve the same precision. In this tutorial, we introduce the reader to regression-based norming, using the generalized additive models for location, scale, and shape (GAMLSS). This approach has been useful in norm estimation of various psychological tests. We discuss the rationale of regression-based norming, theoretical properties of GAMLSS, and their relationships to other regression-based norming models. Based on six steps, we describe how to: a) design a normative study to gather proper normative sample data; b) select a proper GAMLSS model for an empirical scale; c) derive the desired normed scores for the scale from the fitted model, including those for a composite scale; and d) visualize the results to achieve insight into the properties of the scale. Following these steps yields regression-based norms with GAMLSS for a psychological test, as we illustrate with normative data of the intelligence test IDS-2. The complete R code and data set is provided as supplemental material.

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