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1 – 10 of 797
Article
Publication date: 5 November 2019

Yi Sun, Quan Jin, Qing Cheng and Kun Guo

The purpose of this paper is to propose a new tool for stock investment risk management through studying stocks with what kind of characteristics can be predicted by…

Abstract

Purpose

The purpose of this paper is to propose a new tool for stock investment risk management through studying stocks with what kind of characteristics can be predicted by individual investor behavior.

Design/methodology/approach

Based on comment data of individual stock from the Snowball, a thermal optimal path method is employed to analyze the lead–lag relationship between investor attention (IA) and the stock price. And machine learning algorithms, including SVM and BP neural network, are used to predict the prices of certain kind of stock.

Findings

It turns out that the lead–lag relationships between IA and the stock price change dynamically. Forecasting based on investor behavior is more accurate only when the IA of the stock is stably leading its price change most of the time.

Research limitations/implications

One limitation of this paper is that it studies China’s stock market only; however, different conclusions could be drawn for other financial markets or mature stock markets.

Practical implications

As for the implications, the new tool could improve the prediction accuracy of the model, thus have practical significance for stock selection and dynamic portfolio management.

Originality/value

This paper is one of the first few research works that introduce individual investor data into portfolio risk management. The new tool put forward in this study can capture the dynamic interplay between IA and stock price change, which help investors identify and control the risk of their portfolios.

Details

Industrial Management & Data Systems, vol. 120 no. 2
Type: Research Article
ISSN: 0263-5577

Keywords

Content available
Article
Publication date: 26 August 2014

398

Abstract

Details

Asian Review of Accounting, vol. 22 no. 3
Type: Research Article
ISSN: 1321-7348

Article
Publication date: 9 August 2013

Pan Ji and W. Wayne Fu

This study aims to examine how information and social gratifications sought by Internet users affect their affinity for the Internet or for particular types of online content.

3077

Abstract

Purpose

This study aims to examine how information and social gratifications sought by Internet users affect their affinity for the Internet or for particular types of online content.

Design/methodology/approach

A survey was administered in Singapore to collect data. A correlation analysis, a paired‐sample t test, and hierarchical regression analyses are conducted to address the research questions and hypotheses.

Findings

Affinity for the Internet and affinity for particular types of online content are correlated and distinct. Both relate positively to social gratifications. The passive social gratification of Internet access and the active pursuit of interactions exert similar impact on both types of affinity. Information affects neither after social gratifications are controlled.

Practical implications

Constant access to online contacts or quality online interaction may facilitate social gratifications, thereby boosting user affinity for the Internet or for particular types of online content. Online information should be presented interactively to attract and retain users. The selection of online content and applications should also be made easier to cultivate a loyal user market.

Originality/value

This study contributes to U&G theory by adapting a television‐based proposition to cyberspace, and examining the attitudinal effect of online social gratifications involving different levels of user activity.

Details

Internet Research, vol. 23 no. 4
Type: Research Article
ISSN: 1066-2243

Keywords

Article
Publication date: 28 September 2020

Satish Kumar, Riza Demirer and Aviral Kumar Tiwari

This study aims to explore the oil–stock market nexus from a novel angle by examining the predictive role of oil prices over the excess returns associated with the market…

Abstract

Purpose

This study aims to explore the oil–stock market nexus from a novel angle by examining the predictive role of oil prices over the excess returns associated with the market, size, book-to-market and momentum factors via bivariate cross-quantilograms.

Design/methodology/approach

This study makes use of the bivariate cross-quantilogram methodology recently developed by Han et al. (2016) to analyze the predictability patterns across the oil and stock markets by focusing on various quantiles that formally distinguish between normal, bull and bear as well as extreme market states.

Findings

The study analysis of systematic risk premia across the four regions shows that crude oil returns indeed capture predictive information regarding excess factor returns in stock markets, particularly those associated with market, size and momentum factors. However, the predictive power of oil return over excess factor returns is asymmetric and primarily concentrated on extreme quantiles, suggesting that large fluctuations in oil prices capture markedly different predictive information over stock market risk premia during up and down states of the oil market.

Practical implications

The findings have significant implications for the profitability of factor- or style-based active portfolio strategies and suggest that the predictive information contained in oil market fluctuations could be used to enhance returns via conditional strategies based on these predictability patterns.

Originality/value

This study contributes to the vast literature on the oil–stock market nexus from a novel perspective by exploring the effect of oil price fluctuations on the risk premia associated with the systematic risk factors including market, size, value and momentum.

Details

Studies in Economics and Finance, vol. 37 no. 4
Type: Research Article
ISSN: 1086-7376

Keywords

Open Access
Article
Publication date: 6 November 2018

Candace Borders, Frank Hsu, Alexander J. Sweidan, Emily S. Matei and Robert G. Bota

Studies suggest deep brain stimulation (DBS) as a treatment modality for the refractory obsessive-compulsive disorder (OCD). It is unclear where to place the DBS. Various…

Abstract

Studies suggest deep brain stimulation (DBS) as a treatment modality for the refractory obsessive-compulsive disorder (OCD). It is unclear where to place the DBS. Various sites are proposed for placement with the ventral capsule/ventral striatum (VC/VS) among the most studied. Herein, we aim to summarize both quantitative Yale-Brown Obsessive-Compulsive Scale (YBOCS) data and qualitative descriptions of the participants' symptoms when given. A literature search conducted via PubMed yielded 32 articles. We sought to apply a standard based on the utilization of YBOCS. This yielded 153 distinct patients. The outcome measure we focused on in this review is the latest YBOCS score reported for each patient/cohort in comparison to the location of the DBS. A total of 32 articles were found in the search results. In total, 153 distinct patients' results were reported in these studies. Across this collection of papers, a total of 9 anatomic structures were targeted. The majority of studies showed a better response at the last time point as compared to the first time point. Most patients had DBS at nucleus accumbens followed by VC/VS and the least patients had DBS at the bilateral superolateral branch of the median forebrain bundle and the bilateral basolateral amygdala. The average YBOCS improvement did not seem to directly correlate with the percentile of patients responding to the intervention.

Well-controlled, randomized studies with larger sample sizes with close follow up are needed to provide a more accurate determination for placement of DBS for OCD.

Details

Mental Illness, vol. 10 no. 2
Type: Research Article
ISSN: 2036-7465

Keywords

Article
Publication date: 28 June 2022

Yi-Chung Hu and Geng Wu

Given that the use of Google Trends data is helpful to improve forecasting performance, this study aims to investigate whether the precision of forecast combination can…

Abstract

Purpose

Given that the use of Google Trends data is helpful to improve forecasting performance, this study aims to investigate whether the precision of forecast combination can benefit from the use of Google Trends Web search index along with the encompassing set.

Design/methodology/approach

Grey prediction models generate single-model forecasts, while Google Trends index serves as an explanatory variable for multivariate models. Then, three combination sets, including sets of univariate models (CUGM), all constituents (CAGM) and constituents that survive the forecast encompassing tests (CSET), are generated. Finally, commonly used combination methods combine the individual forecasts for each combination set.

Findings

The tourism volumes of four frequently searched-for cities in Taiwan are used to evaluate the accuracy of three combination sets. The encompassing tests show that multivariate grey models play a role to be reckoned with in forecast combinations. Furthermore, the empirical results indicate the usefulness of Google Trends index and encompassing tests for linear combination methods because linear combination methods coupled with CSET outperformed that coupled with CAGM and CUGM.

Practical implications

With Google Trends Web search index, the tourism sector may benefit from the use of linear combinations of constituents that survive encompassing tests to formulate business strategies for tourist destinations. A good forecasting practice by estimating ex ante forecasts post-COVID-19 can be further provided by scenario forecasting.

Originality/value

To improve the accuracy of combination forecasting, this research verifies the correlation between Google Trends index and combined forecasts in tourism along with encompassing tests.

Google 搜尋趨勢指標與涵蓋性檢定對於旅遊需求組合預測的影響

目的

過去的研究顯示 Google 搜尋趨勢資料有助於改善旅遊需求預測的準確度,本研究就此進一步探討 Google 搜尋趨勢網頁搜尋指標與涵蓋性檢定的使用對於組合預測準確度所造成的影響。

設計/方法論/方法

本研究以 Google 搜尋趨勢指標做為多變量灰色預測模式的解釋變數,並以單變量與多變量灰色模式產生各別預測值。在分別產生由所有的單變量模式 (CUGM)所有的模式 (CAGM), 以及經過涵蓋性檢定所留存下來之模式 (CSET) 所組成之集合後,就各別的組合集以常用的組合方法產生預測值。

發現

以台灣的四個熱搜旅遊城市的旅遊人數進行三個組合集的預測準確度分析。涵蓋性檢定顯示多變量灰色模式在組合預測中扮演重要的角色,而結果亦呈現線性組合方法在 CSET優於在 CUGMCAGM 的準確度,突顯搜尋趨勢指標與涵蓋性檢定對於線性組合方法的有用性。

實踐意涵

藉由 Google 搜尋趨勢網頁搜尋指標與涵蓋性檢定,旅遊部門應可透過線性組合方法的預測規劃旅遊目的地的經營策略。新冠疫情下於各季的事前預測亦可結合情境預測具體呈現。

原創性/價值

為提升組合預測在旅遊需求的預測準確度,本研究結合涵蓋性檢定以分析 Google 搜尋趨勢指標與組合預測準確度之間的關聯性。

關鍵字

旅遊需求,涵蓋性檢定,Google 搜尋趨勢,灰色預測,組合預測

文章类型

研究型论文

El impacto de Google Trends en la previsión de viajes combinados y su evidencia relacionada

Propósito

Dado que el uso de los datos de Google Trends es útil para mejorar la precisión de las predicciones, este estudio examina si el uso del índice de búsqueda web de Google Trends combinado con la agregación de relevancia puede mejorar la precisión del predictor.

Diseño/metodología/enfoque

El modelo predictivo gris genera predicciones bajo un único modelo, mientras que el modelomultivariado utiliza el indicador Google Trends como variable explicativa. Se generaron tresensamblajes generales, incluido el Modelo armónico único (CUGM), los ensamblajes de todos loscomponentes (CAGM) y la prueba de presencia de componentes con predicción (CSET). Laspredicciones individuales encada grupo luego se combinan utilizando métodos de correlación deuso común.

Recomendaciones

Utilizando el número de turistas en las cuatro ciudades más investigadas de Taiwán, los tresgrupos combinados se clasificaron según su precisión. Las pruebas incluidas muestran que losmodelos multivariados en escala de grises son importantes para la predicción. Además, losresultados de las pruebas muestran que el índice de Google Trends y las pruebas que incluyenmétodos de suma lineal son útiles porque los métodos combinados con CSET funcionan majorque los métodos combinados con CSET. CAGM y VCUG.

Implicaciones practices

La industria de viajes puede usar el índice de búsqueda web de Google Trends para desarrollarestrategias comerciales para atracciones basadas en un conjunto lineal de componentes.

Autenticidad/valor

Con el objetivo de mejorar la precisión de los pronósticos agregados, este estudio investiga larelación entre el índice de tendencias de Google y las expectativas generales de viaje junto con laevidencia global.

Palabras clave

Demanda de viajes, Experiencia global, Tendencias de Google, Predicción gris

Tipo de papel

Trabajo de investigación

Open Access
Article
Publication date: 6 September 2021

Gerd Hübscher, Verena Geist, Dagmar Auer, Nicole Hübscher and Josef Küng

Knowledge- and communication-intensive domains still long for a better support of creativity that considers legal requirements, compliance rules and administrative tasks…

474

Abstract

Purpose

Knowledge- and communication-intensive domains still long for a better support of creativity that considers legal requirements, compliance rules and administrative tasks as well, because current systems focus either on knowledge representation or business process management. The purpose of this paper is to discuss our model of integrated knowledge and business process representation and its presentation to users.

Design/methodology/approach

The authors follow a design science approach in the environment of patent prosecution, which is characterized by a highly standardized, legally prescribed process and individual knowledge study. Thus, the research is based on knowledge study, BPM, graph-based knowledge representation and user interface design. The authors iteratively designed and built a model and a prototype. To evaluate the approach, the authors used analytical proof of concept, real-world test scenarios and case studies in real-world settings, where the authors conducted observations and open interviews.

Findings

The authors designed a model and implemented a prototype for evolving and storing static and dynamic aspects of knowledge. The proposed solution leverages the flexibility of a graph-based model to enable open and not only continuously developing user-centered processes but also pre-defined ones. The authors further propose a user interface concept which supports users to benefit from the richness of the model but provides sufficient guidance.

Originality/value

The balanced integration of the data and task perspectives distinguishes the model significantly from other approaches such as BPM or knowledge graphs. The authors further provide a sophisticated user interface design, which allows the users to effectively and efficiently use the graph-based knowledge representation in their daily study.

Details

International Journal of Web Information Systems, vol. 17 no. 6
Type: Research Article
ISSN: 1744-0084

Keywords

Article
Publication date: 31 May 2013

Behzad Taheri and Edmond Richer

Autonomous Underwater Vehicles (AUVs) play a crucial role in marine biology research and oceanic natural resources exploration. Since most AUVs are underactuated they…

Abstract

Purpose

Autonomous Underwater Vehicles (AUVs) play a crucial role in marine biology research and oceanic natural resources exploration. Since most AUVs are underactuated they require sophisticated trajectory planning and tracking algorithms. The purpose of this paper is to develop a new method that allows an underactuated AUV to track a moving object while constraining the approach to a direction tangent to the path of the target. Furthermore, the distance at which the AUV follows the target is constrained, reducing the probability of detection and unwanted behavior change of the target.

Design/methodology/approach

First, a kinematic controller that generates a trajectory tangent to the path of the moving target is designed such that the AUV maintains a prescribed distance and approaches the target from behind. Using a Lyapunov based method the stability of the kinematic controller is proven. Second, a dynamic sliding mode controller is employed to drive the vehicle on the trajectory computed in the first step.

Findings

The kinematic and dynamic controllers are shown to be stable and robust against parameter uncertainty in the dynamic model of the vehicle. Results of numerical simulations for equidistant tracking of a target on both smooth and discontinuous derivatives trajectories for a variety of relative initial positions and orientations are shown.

Originality/value

The contribution of this research is development of a new method for path planning and tracking of moving targets for underactuated AUVs in the horizontal plane. The method allows control of both the direction of approach and the distance from a moving object.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 6 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 30 April 2021

Shaofei Wang and Depeng Dang

Previous knowledge base question answering (KBQA) models only consider the monolingual scenario and cannot be directly extended to the cross-lingual scenario, in which the…

Abstract

Purpose

Previous knowledge base question answering (KBQA) models only consider the monolingual scenario and cannot be directly extended to the cross-lingual scenario, in which the language of questions and that of knowledge base (KB) are different. Although a machine translation (MT) model can bridge the gap through translating questions to the language of KB, the noises of translated questions could accumulate and further sharply impair the final performance. Therefore, the authors propose a method to improve the robustness of KBQA models in the cross-lingual scenario.

Design/methodology/approach

The authors propose a knowledge distillation-based robustness enhancement (KDRE) method. Specifically, first a monolingual model (teacher) is trained by ground truth (GT) data. Then to imitate the practical noises, a noise-generating model is designed to inject two types of noise into questions: general noise and translation-aware noise. Finally, the noisy questions are input into the student model. Meanwhile, the student model is jointly trained by GT data and distilled data, which are derived from the teacher when feeding GT questions.

Findings

The experimental results demonstrate that KDRE can improve the performance of models in the cross-lingual scenario. The performance of each module in KBQA model is improved by KDRE. The knowledge distillation (KD) and noise-generating model in the method can complementarily boost the robustness of models.

Originality/value

The authors first extend KBQA models from monolingual to cross-lingual scenario. Also, the authors first implement KD for KBQA to develop robust cross-lingual models.

Details

Data Technologies and Applications, vol. 55 no. 5
Type: Research Article
ISSN: 2514-9288

Keywords

Article
Publication date: 16 August 2022

Tingneyuc Sekac, Sujoy Kumar Jana and Indrajit Pal

The climate change and related impacts are experienced around the world. There arise different triggering factors to climate change and impact. The purpose of this study…

13

Abstract

Purpose

The climate change and related impacts are experienced around the world. There arise different triggering factors to climate change and impact. The purpose of this study is to figure out how changes in vegetation cover may or may not have an impact to climate change. The research will produce ideas for vegetation preservation and replant.

Design/methodology/approach

The investigation was probed for 34 years’ time period starting from the year 1981 to 2015. After testing and checking for serial autocorrelation in the vegetation data series, Mann–Kendal nonparametric statistical evaluation was carried out to investigate vegetation cover trends. Sen’s method was deployed to investigate the magnitude of vegetation cover change in natural differential vegetation index (NDVI) unit per year. Furthermore, the ArcGIS spatial analysis tools were used for the calculation of mean NDVI distribution and also for carrying out the spatial investigation of trends at each specific location within the study region.

Findings

The yearly mean NDVI during the study period was observed to have a decreasing trend. The mean NDVI value ranges between 0.32 and 0.98 NDVI unit, and hence, this means from less or poor vegetated zones to higher or healthier vegetated zones. The mean NDVI value was seen decreasing toward the highlands regions. The NDVI-rainfall correlation was observed to be stronger than the NDVI-temperature correlation. The % area coverage of NDVI-rainfall positive correlation was higher than the negative correlation. The % area coverage of NDVI-temperature negative correlation was higher than the positive correlation within the study region. Rainfall is seen as a highly influencing climatic factor for vegetation growth than the temperature within the study region.

Originality/value

This study in this country is a new approach for climate change monitoring and planning for the survival of the people of Papua New Guinea, especially for the farmer and those who is living in the coastal area.

Details

International Journal of Disaster Resilience in the Built Environment, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1759-5908

Keywords

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