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Article
Publication date: 29 April 2022

Ye Wang, Fusheng Wang and Shiyu Liu

This paper aims to discuss whether the attention of investors to abnormalities can serve as a mechanism for the influence of online media coverage on earnings management.

Abstract

Purpose

This paper aims to discuss whether the attention of investors to abnormalities can serve as a mechanism for the influence of online media coverage on earnings management.

Design/methodology/approach

Based on Baidu index data of China’s A-share listed companies between 2014 and 2018, this paper studies influencing mechanism of online media reports on earnings management from the perspective on abnormal investor attention.

Findings

The results show that internet media reports can impose pressure on managers of companies by inducing abnormal focus of the public on listed companies and further force the latter to generate more actions on the management of earnings. It is the abnormal rather than normal investor attention that mediates network media reports and earnings management.

Practical implications

This research enriches and refines the theory on influencing mechanism of media effects on earnings management and provides significant empirical evidence for future researches. Meanwhile, the conclusion of the research is of great practical importance for instructing listed firms dealing with media reports, guiding rational investment of investors and intensifying precision regulation of regulators.

Originality/value

By categorizing abnormal investor attention into active spontaneous abnormal attention which is not guided by media report and passive guided abnormal attention which is guided by media reports, the authors clarify the difference between the two categories. The result indicates that it is only the latter that is the influential mechanism of media report on earnings management.

Details

Nankai Business Review International, vol. 13 no. 3
Type: Research Article
ISSN: 2040-8749

Keywords

Article
Publication date: 13 July 2015

Gebeyehu Belay Gebremeskel, Chai Yi, Chengliang Wang and Zhongshi He

Behavioral pattern mining for intelligent system such as SmEs sensor data are vitally important in many applications and performance optimizations. Sensor pattern mining (SPM) is…

Abstract

Purpose

Behavioral pattern mining for intelligent system such as SmEs sensor data are vitally important in many applications and performance optimizations. Sensor pattern mining (SPM) is also dynamic and a hot research issue to pervasive and ubiquitous of smart technologies toward improving human life. However, in large-scale sensor data, exploring and mining pattern, which leads to detect the abnormal behavior is challenging. The paper aims to discuss these issues.

Design/methodology/approach

Sensor data are complex and multivariate, for example, which data captured by the sensors, how it is precise, what properties are recorded or measured, are important research issues. Therefore, the method, the authors proposed Sequential Data Mining (SDM) approach to explore pattern behaviors toward detecting abnormal patterns for smart space fault diagnosis and performance optimization in the intelligent world. Sensor data types, modeling, descriptions and SPM techniques are discussed in depth using real sensor data sets.

Findings

The outcome of the paper is measured as introducing a novel idea how SDM technique’s scale-up to sensor data pattern mining. In the paper, the approach and technicality of the sensor data pattern analyzed, and finally the pattern behaviors detected or segmented as normal and abnormal patterns.

Originality/value

The paper is focussed on sensor data behavioral patterns for fault diagnosis and performance optimizations. It is other ways of knowledge extraction from the anomaly of sensor data (observation records), which is pertinent to adopt in many intelligent systems applications, including safety and security, efficiency, and other advantages as the consideration of the real-world problems.

Details

Industrial Management & Data Systems, vol. 115 no. 6
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 18 May 2023

Lanhui Cai, Kum Fai Yuen, Mingjie Fang and Xueqin Wang

The COVID-19 pandemic has resulted in significant changes in consumer behaviour, which has had a cascading effect on consumer-centric logistics. As a result, this study conducts a…

Abstract

Purpose

The COVID-19 pandemic has resulted in significant changes in consumer behaviour, which has had a cascading effect on consumer-centric logistics. As a result, this study conducts a focused literature review of pandemic-related consumer behaviour research to address two research questions: 1) what are the pandemic's direct effects on consumer consumption behaviour, with an emphasis on changes in their basic and psychological needs? and 2) what are the consequences of behavioural changes on consumer-centric logistics?

Design/methodology/approach

The scientific procedure and rationales for systematic literature review (SPAR-4-SLR) protocol and the theory, context, characteristics and methodology (TCCM) framework were adopted as a guideline to map, refine, evaluate and synthesise the literature. A total of 53 research articles were identified for further analysis.

Findings

Using Maslow's hierarchy of human needs as a theoretical guide, this review synthesises the COVID-19 pandemic's effects on consumer behaviour into four categories: abnormal buying behaviour, changes in consumer preferences, digitalisation of shopping behaviour and technology-related behaviour. Furthermore, four consumer-centric logistics propositions are proposed based on the four aspects of consumer behavioural changes.

Originality/value

This study outlines the significant behavioural changes in consumers in the face of the COVID-19 pandemic and how these changes impact consumer-centric logistics, with implications for managing consumers' involvement in logistics and pointing out future research directions.

Details

Asia Pacific Journal of Marketing and Logistics, vol. 35 no. 11
Type: Research Article
ISSN: 1355-5855

Keywords

Article
Publication date: 3 July 2020

Xiaoyun Ye and Myung-Mook Han

By using a new feature extraction method on the Cert data set and using a hidden Markov model (HMM) to model and analyze the behavior of users to distinguish whether the behavior

Abstract

Purpose

By using a new feature extraction method on the Cert data set and using a hidden Markov model (HMM) to model and analyze the behavior of users to distinguish whether the behavior is normal within a continuous period.

Design/methodology/approach

Feature extraction of five parts of the time series by rules and sorting in chronological order. Use the obtained features to calculate the probability parameters required by the HMM model and establish a behavior model for each user. When the user has abnormal behavior, the model will return a very low probability value to distinguish between normal and abnormal information.

Findings

Generally, HMM parameters are obtained by supervised learning and unsupervised learning, but the hidden state cannot be clearly defined. When the hidden state is determined according to the data set, the accuracy of the model will be improved.

Originality/value

This paper proposes a new feature extraction method and analysis mode, which determines the shape of the hidden state according to the situation of the data set, making subsequent HMM modeling simple and efficient and in turn improving the accuracy of user behavior detection.

Details

Information & Computer Security, vol. 30 no. 1
Type: Research Article
ISSN: 2056-4961

Keywords

Article
Publication date: 10 July 2009

Alexander Joel‐Carbonell and Nico B. Rottke

This paper seeks to research potential evidence of capital market irregularities by scrutinizing whether the IPO (Initial Public Offering) phenomenon can be found in Real Estate…

2038

Abstract

Purpose

This paper seeks to research potential evidence of capital market irregularities by scrutinizing whether the IPO (Initial Public Offering) phenomenon can be found in Real Estate Investment Trusts (REITs).

Design/methodology/approach

The study employs stock price data of 90 US REITs and derives their performance on the first trading day, but also on a one‐, three‐, and five‐year basis.

Findings

The primary offerings puzzle frequently observed in traditional IPOs is a market imperfection that also exists for REITs from 1991 to 2008. REITs display, on average, both significant first trading day under‐pricing and negative aftermarket performance, predominantly on a five‐year basis.

Research limitations/implications

The research at hand offers evidence that stock irregularities can be found within the US REIT industry, albeit these do not necessarily serve as evidence against efficient markets. Notwithstanding the fact that it may be difficult to exploit the abnormal performance on the first day, investors can nonetheless earn substantial profits by shorting IPO stocks on a long‐term basis. Even net of transaction costs, such a strategy should have a positive abnormal return. However, these investments have to be executed cautiously as the profitability of such a strategy has to pay attention to the reputation of the underwriter, the cycle in which the IPO takes place and various other important factors.

Originality/value

The research at hand offers evidence that REIT market irregularities oppose underlying rational human behavior.

Details

Journal of Property Investment & Finance, vol. 27 no. 4
Type: Research Article
ISSN: 1463-578X

Keywords

Article
Publication date: 14 September 2022

Tooba Akram, Suresh A/I Ramakrishnan and Muhammad Naveed

This paper aims to provide a comprehensive conceptual framework and strong arguments with an intent to examine the stock market variables (predictors) indicating the money…

Abstract

Purpose

This paper aims to provide a comprehensive conceptual framework and strong arguments with an intent to examine the stock market variables (predictors) indicating the money laundering (ML) and terrorism financing (FT) proceeds.

Design/methodology/approach

This paper provides a comprehensive review of ML/FT through the stock market across developed, developing and emerging jurisdictions, sheds light on the existing literature and critically evaluates the gap in the relevant studies. Moving forward, this paper develops the conceptual framework and formulates hypotheses to explore the empirical relationship.

Findings

This paper advocates and finds a basis to carry out much-needed empirical research between the ML/FT and stock market keeping in view the growing criminal cases in the developing countries. This paper suggests mining proxies from the publically available stock market data and the results of existing seminal research as variables of the study. These data and results carry information about the ML determinants. After developing hypothetical research providing concepts, this paper also finds that using a suitable methodology, preferable Bayesian logistic and linear regression models, it is possible to find the typologies and factors that can indicate and endorse the use of the stock market for ML/FT. Broadly, it is found that the significance of this study will be two-pronged: empirical development and policy implications.

Research limitations/implications

This paper mainly focuses on the developing region, a newly emerging market and, peculiarly, a grey-listed region by the Financial Action Task Force (FATF).

Practical implications

In light of the existing literature and to the best of the researchers’ knowledge, this study will bring into focus the new age of the action research on the ML regime in the securities markets of the developing countries, hence, the emerging markets. Moreover, this research shall have a sheer significance for the policy measures on FATF recommendations on ML and FT, especially for the countries listed as “grey”.

Social implications

The research based on comprehensive review will help in controlling the social behaviours aiding the proceeds of ML.

Originality/value

This research is extremely novel to the best of the researcher's knowledge.

Details

Journal of Money Laundering Control, vol. 26 no. 5
Type: Research Article
ISSN: 1368-5201

Keywords

Article
Publication date: 1 January 2002

Javier Estrada and J.Ignacio Peña

Between 1988 and 1994 ten European countries introduced or modified their regulations on insider trading. We evaluate in this article the impact of such regulatory changes on the…

Abstract

Between 1988 and 1994 ten European countries introduced or modified their regulations on insider trading. We evaluate in this article the impact of such regulatory changes on the risk, return, and some other characteristics of these ten markets. After extensive testing, we find that the evidence suggests that these regulations have had little (if any) impact on the market characteristics we examine, and briefly speculate about the reasons that justify our findings.

Details

Studies in Economics and Finance, vol. 20 no. 1
Type: Research Article
ISSN: 1086-7376

Open Access
Article
Publication date: 28 June 2022

Wenhao Yu, Jun Li, Li-Ming Peng, Xiong Xiong, Kai Yang and Hong Wang

The purpose of this paper is to design a unified operational design domain (ODD) monitoring framework for mitigating Safety of the Intended Functionality (SOTIF) risks triggered…

1457

Abstract

Purpose

The purpose of this paper is to design a unified operational design domain (ODD) monitoring framework for mitigating Safety of the Intended Functionality (SOTIF) risks triggered by vehicles exceeding ODD boundaries in complex traffic scenarios.

Design/methodology/approach

A unified model of ODD monitoring is constructed, which consists of three modules: weather condition monitoring for unusual weather conditions, such as rain, snow and fog; vehicle behavior monitoring for abnormal vehicle behavior, such as traffic rule violations; and road condition monitoring for abnormal road conditions, such as road defects, unexpected obstacles and slippery roads. Additionally, the applications of the proposed unified ODD monitoring framework are demonstrated. The practicability and effectiveness of the proposed unified ODD monitoring framework for mitigating SOTIF risk are verified in the applications.

Findings

First, the application of weather condition monitoring demonstrates that the autonomous vehicle can make a safe decision based on the performance degradation of Lidar on rainy days using the proposed monitoring framework. Second, the application of vehicle behavior monitoring demonstrates that the autonomous vehicle can properly adhere to traffic rules using the proposed monitoring framework. Third, the application of road condition monitoring demonstrates that the proposed unified ODD monitoring framework enables the ego vehicle to successfully monitor and avoid road defects.

Originality/value

The value of this paper is that the proposed unified ODD monitoring framework establishes a new foundation for monitoring and mitigating SOTIF risks in complex traffic environments.

Details

Journal of Intelligent and Connected Vehicles, vol. 5 no. 3
Type: Research Article
ISSN: 2399-9802

Keywords

Article
Publication date: 16 October 2009

Hung‐Gay Fung, Pei‐Shan Tsai and Chin‐Ping Yu

We use over 200 firms in Taiwan to examine two issues: (1) how investors in Taiwan react to seasoned equity offerings (SEOs) when firms plan to make capital investments, and (2…

Abstract

We use over 200 firms in Taiwan to examine two issues: (1) how investors in Taiwan react to seasoned equity offerings (SEOs) when firms plan to make capital investments, and (2) factors driving the underwriting price of the stock offerings. Our results indicate that investors seem to have strong negative reactions to the SEOs announcement; they prefer alternative financings for firms with good profitability when they make capital investments. Several interesting results related to underpricing of SEOs are noted. First, larger firms and larger underwriters appear to have more underpricing. The number of underwriters will have a net positive effect on underpricing. Finally, the brokerage firm in comparing to the bank as the underwriter would yield a net positive effect on the underwriting price.

Details

Journal of Asia Business Studies, vol. 4 no. 1
Type: Research Article
ISSN: 1558-7894

Keywords

Article
Publication date: 15 May 2019

Haoqiang Shi, Shaolin Hu and Jiaxu Zhang

Abnormal changes in temperature directly affect the stability and reliability of a gyroscope. Predicting the temperature and detecting the abnormal change is great value for…

Abstract

Purpose

Abnormal changes in temperature directly affect the stability and reliability of a gyroscope. Predicting the temperature and detecting the abnormal change is great value for timely understanding of the working state of the gyroscope. Considering that the actual collected gyroscope shell temperature data have strong non-linearity and are accompanied by random noise pollution, the prediction accuracy and convergence speed of the traditional method need to be improved. The purpose of this paper is to use a predictive model with strong nonlinear mapping ability to predict the temperature of the gyroscope to improve the prediction accuracy and detect the abnormal change.

Design/methodology/approach

In this paper, an double hidden layer long-short term memory (LSTM) is presented to predict temperature data for the gyroscope (including single point and period prediction), and the evaluation index of the prediction effect is also proposed, and the prediction effects of shell temperature data are compared by BP network, support vector machine (SVM) and LSTM network. Using the estimated value detects the abnormal change of the gyroscope.

Findings

By combined simulation calculation with the gyroscope measured data, the effect of different network hyperparameters on shell temperature prediction of the gyroscope is analyzed, and the LSTM network can be used to predict the temperature (time series data). By comparing the performance indicators of different prediction methods, the accuracy of the shell temperature estimation by LSTM is better, which can meet the requirements of abnormal change detection. Quick and accurate diagnosis of different types of gyroscope faults (steps and drifts) can be achieved by setting reasonable data window lengths and thresholds.

Practical implications

The LSTM model is a deep neural network model with multiple non-linear mapping levels, and can abstract the input signal layer by layer and extract features to discover deeper underlying laws. The improved method has been used to solve the problem of strong non-linearity and random noise pollution in time series, and the estimated value can detect the abnormal change of the gyroscope.

Originality/value

In this paper, based on the LSTM network, an double hidden layer LSTM is presented to predict temperature data for the gyroscope (including single point and period prediction), and validate the effectiveness and feasibility of the algorithm by using shell temperature measurement data. The prediction effects of shell temperature data are compared by BP network, SVM and LSTM network. The LSTM network has the best prediction effect, and is used to predict the temperature of the gyroscope to improve the prediction accuracy and detect the abnormal change.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 12 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

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