Search results

The purpose of the financial statements is to present the situations of the enterprises, such as the financial situation, results of operations, etc., in the decision-making processes to the related parties in a suitable, comparable, and realistic manner. In recent years, professional frauds in enterprises have been seriously reflected in financial statements and this has resulted in the manipulation of financial information.

In this study, we aim to determine financial firms, which might manipulate the financial information, by applying the Beneish model and then determine the financial indicators of possible financial statement manipulation, using logistic regression. For this purpose, companies included constantly in Borsa İstanbul-50 (BIST-50) 2015, 2016, and 2017 were examined. After the enterprises which have the possibility of financial manipulation are determined by the Beneish model, it is understood that there is a positive relationship between the probability of manipulating financial information and the Asset Quality Index and Sales, general and administrative expenses index.

The purpose of this study is to review and evaluate the salient features of stock market manipulation in Malaysia. The research questions used are: Who was involved? How it happened? What were the consequences?

This study has been conducted using content and thematic analysis. This study includes multiple sources of information to help establish the stylized facts and it uses cases that have been prosecuted in Malaysia for 2005-2015.

This study presents arguments and empirical data supporting the view that the stock market manipulation was conducted by those in a privileged position and with access to information. Ethical failure, involving greed, self-interest, dishonesty and a preoccupation with a quick profit, could explain why stock market manipulation happened. Manipulation harms legitimate investors, as share prices and earnings of companies are affected.

A better understanding about the prevalence, characteristics and consequences of the market manipulation problems will be useful for stakeholders, investors and policymakers in the financial industry for promoting and maintaining a fair, efficient and transparent stock market.

The originality of this paper lies in examining and presenting interpretations based on contemporary phenomenon within the real-life context of Malaysia. There is little study or literature that focuses on Malaysia, especially in examining stock market manipulation by integrating finance and management perspectives to form a comprehensive understanding of the issue.

Financial manipulation means the modification made knowingly and willfully by businesses in accounting records and transactions, in financial statements, through addition and subtraction, for the purpose of misleading financial information users. Financial manipulations are expected to have an effect on the decisions of financial information users. The present study was established on the basis of two main objectives. The first objective is to determine whether banks, which are Public Interest Entities (PIE), manipulate their financial statements. As for the second objective, it is to reveal whether the detected financial manipulations have an effect on investor decisions. The research conducted to achieve the first objective is based on the examination of independent audit reports for the periods between 2009 and 2017, pertaining to 45 banks registered to the Banks Association of Turkey, in terms of presented opinions. Data acquired from examined reports were subjected to content analysis via the Microsoft Excel program. In line with the second objective of the study, investor numbers for the periods between 2010 and 2017, of 13 banks, which are within the scope of BIST BANK, were included in the analysis, according to data acquired from the Central Registry Agency. Financial statements of banks, with audit reports in which a qualified opinion is expressed, were considered to have been manipulated. SPSS 22.0 statistics pack software was used to analyze whether investment demands toward these banks had an effect on decisions of domestic and foreign investors. In the analysis, frequency and One-Way ANOVA tests were used. In consequence of the analyses conducted, it was determined that, around one fifth of financial statements of PIE banks, pertaining to the periods between 2009 and 2017, were manipulated; it was mostly committed by private banks, and majority of the manipulations were committed due to free provisions made. It was also observed that manipulations did not have an effect on decisions of neither domestic nor foreign investors. The reason behind the latter is the fact that while the level of manipulations in financial statements is significant, it is not a widespread occurrence.

An increasing amount of attention is being paid to the human side of information security programs, leading to research designs that require the manipulation of study variables. The purpose of this paper is to highlight a traditional assessment of such designs, the manipulation check, and examine how its absence can undermine otherwise solid research efforts.

This paper reviews literature from the fields of research methods, organizational behavior and information systems for extant perspectives and viewpoints on manipulation checks, which are then brought into the realm of information security research.

The possible risks involved with failing to perform manipulation checks are discussed, which include a possibility of making Type II errors. The paper provides further insight on the timing, method and manner in which manipulation checks can be performed.

A disappointing number of research articles in the area of information security fail to report manipulation checks when they should. This paper seeks to remind researchers to perform this vital assessment and to use the results accordingly.

The purpose of this study was to investigate the online shopping intention of customers by watching artificial intelligence (AI)–based deepfake video advertisements using media richness (MR) theory and Information Manipulation Theory 2 (IMT2).

A conceptual model was developed to understand customers' online shopping intention by watching deepfake videos. A quantitative survey was conducted among the 1,180 customers using a structured questionnaire to test the conceptual model, and data were analyzed with partial least squares structural equation modeling.

The outcome of this research provides the antecedents of the online shopping intention of customers after watching AI-based deepfake videos. These antecedents are MR, information manipulation tactics, personalization and perceived trust. Perceived deception negatively influences customers' online shopping intention, and cognitive load has no effect. It also elucidates the manipulation tactics used by the managers to develop AI-based deepfake videos.

The distinctive model that emerged is insightful for senior executives and managers in the e-commerce and retailing industry to understand the influence of AI-based deepfake videos. This provides the antecedents of online shopping intention due to deepfakes, which are helpful for designers, marketing managers and developers.

The authors amalgamate the MR and IMT2 theory to understand the online shopping intention of the customers after watching AI-based deepfake videos. This work is a pioneer in examining the effect of AI-based deepfakes on the online shopping intention of customers by providing a framework that is empirically validated.

Manipulation checks are a recommended for experimental accounting research. Usage of information gained by manipulation checks varies. In some studies, participants who failed the manipulation check are removed from the sample. Other studies report the results of the manipulation checks but still use the full sample. Some authors recommend removing participants who failed the manipulation check as a means to increase the power of the statistical analysis. Others warn that removing these participants endangers the randomization as a crucial precondition for gaining valid insights from experimental research. Until now, there is little research on how sensitive results react to exclusion of participants. The purpose of this paper is to study the effects of removing participants failing the manipulation checks on the evaluation of a hypothesis and the development of alternative usages of the information gained from manipulation checks.

Based on an analytical model and a simulation, the authors show how removing participants who fail the manipulation check affects experimental findings.

Simulations show that statistical results and conclusions drawn from an experiment differ substantially, depending on whether participants who failed the manipulation check are removed from the sample. As the participants who are removed are no random sub-sample, but share a certain property, the experimental results react strongly, typically showing significant results, where there are actually none.

This paper is, to the best of the authors’ knowledge, the first to address the sensitivity of experimental results to removing participants who fail the manipulation check from the sample and the implications for the validity of conclusions drawn from experimental accounting research. This paper’s contribution is a better way of using information gained in the manipulation check in the statistical analysis of the experimental data.

This study aims to examine the tourists' visit intention by watching deepfake destination videos, using Information Manipulation and Media Richness Theory.

This study conducted a primary survey utilizing a structured questionnaire. In total, 1,360 tourists were surveyed, and quantitative data analysis was done using PLS-SEM.

The results indicate that the factors that affect the tourists' visit intention after watching deepfake videos include information manipulation tactics, trust and media richness. This study also found that perceived deception and cognitive load do not influence the tourists' visit intention.

The originality/salience of this study lies in the fact that this is possibly among the first to combine the Media Richness Theory and Information Manipulation for understanding tourists' visit intention and post-viewing deepfake destination videos.

Research on fairly‐traded products has shown that changing consumers' attitudes may not be the best strategy to bring consumers to purchase these products. The objective of this study is to examine a different, non‐cognitive approach based on the utilization of behavioral influence strategies.

A field experiment was conducted involving 168 consumers. The experiment took place in the context of a commercial stand in which fairly‐traded products such as coffee, sugar, chocolate, and so on, were sold. Three factors were manipulated: the concreteness of the information provided to visitors; the provision of information about the popularity of fairly‐traded products among relevant others; and the possibility of receiving concrete feedback from a producer.

The paper finds that, contrary to what was expected, abstract information led to a greater amount of money spent on average by visitors. In addition, knowing that fairly‐traded products were popular among relevant others had a significant impact on money spent only when feedback was not offered to the participants.

A field experiment does not offer a high degree of control over nuisance variables. The application of the manipulations and the randomization of participants in this study were therefore not optimal.

Managers involved in the marketing of fairly‐traded products who communicate with potential buyers using concrete messages should make sure that consumers are attentive to their messages. These messages should inform consumers that fairly‐traded products are purchased by relevant others.

The paper provides useful information on how to influence consumers' attitudes to purchasing fairly‐traded products.

This paper attempts to develop a hybrid model using advanced data mining techniques for the detection of Stock Price Manipulation. The hybrid model detailed in this article elucidates the application of a Genetic Algorithm based Artificial Neural Network to classify stocks witnessing activities that are suggestive of potential manipulation.

Price, volume and volatility are used as the variables for this model to capture the characteristics of stocks. An empirical analysis of this model is carried out to evaluate its ability to predict stock price manipulation in one of the largest emerging markets – India, which has a large number of securities and significant trading volumes. Further, the article compares the performance of this hybrid model with a conventional standalone model based on Quadratic Discreminant Function (QDF).

Based on the results obtained, the superiority of the hybrid model over the conventional model in its ability to predict manipulation in stock prices has been established.

The classification by the proposed model is agnostic of the type of manipulation – action‐based, information‐based or trade‐based.

The market regulators can use these techniques to ensure that sufficient deterrents are in place to identify a manipulator in their market. This helps them carry out their primary function, namely, investor protection. These models will help effective monitoring for abnormal market activities and detect market manipulation.

Implementing this model at a regulator or SRO helps in strengthening the integrity and safety of the market. This strengthens investor confidence and hence participation, as the investors are made aware that the regulators implementing market manipulation detection techniques ensure that the markets they monitor are secure and protects investor interest.

This is the first time a hybrid model has been used to detect market manipulation.