Online Trading Systems Research Articles

Fortifying Trust, Security, and Privacy in Online Trading Systems: Unlocking their Paramount Significance

The purpose of the study is to examine the impact of perceived security and perceived privacy on customers trust to accept the use online trading system in an extended form of the tam model with TAM constructs. This research paper tries to study the impact of security and privacy perceptions on customer’s trust, also the impact of perceived usefulness and perceived ease of use on customer’s intention to use online trading system. For the purpose of the study total of 201 samples were collected through the online survey and the data so collected was analyzed descriptively in SPSS version 24, and the seven hypotheses were tested using PLS-SEM in SmartPLS. The path modeling through PLS indicated that perceived ease of use and privacy has no significant positive relation with behavioral intention and trust respectively but all others hypothesis were accepted and the research showed that trust plays an important role for customers intention to use online trading system.

An efficient isomorphic CNN-based prediction and decision framework for financial time series

Financial time series prediction and trading decision-making are priorities of computational intelligence for researchers in academia and the finance industry due to their broad application areas and substantial impact. However, these methods remain challenging because they retain various complex statistical properties, and the mechanism behind the processes is unknown to a large extent. A significant number of machine learning-based methods are proposed and demonstrate impressive results, especially deep learning-based models. Nevertheless, due to the high complexity of massive, nonlinear, and nonindependent data and the difficulties and time consumption of complicated training models of deep learning, the performance of online trading decisions is still inadequate for practical application. This paper proposes the Integrated Framework of Forecasting Based Online Trading Strategy (IFF-BOTS) to satisfy better prediction performance and dynamic decisions for real-world online trading systems. Our method adopts a novel isomorphic convolutional neural network (CNN)-based forecaster-classifier-executor architecture to exploit CNN-based price and trend integrated prediction and direct-reinforcement-learning-based trading decision-making. IFF-BOTS can also achieve better real-time performance for online trading. We empirically compare the proposed approach with state-of-the-art prediction and trading methods on real-world S&P and DJI datasets. The results show that the IFF-BOTS outperforms its competitors in predicting metrics, trading profits, and real-time performance.

Metode Alternatif dalam Pencarian Peringkat E-Commerce di Indonesia Berdasarkan Rating Pelanggan

The existence of the internet raises an online trading system using applications. The rise of online trading systems has triggered the emergence of various e-commerce in Indonesia that provide various kinds of customer needs. This also causes problems for customers, namely the difficulty in choosing quality e-commerce. The effort to overcome this problem is to rank e-commerce in Indonesia based on customer ratings. The method commonly used for ranking is the analytical hierarchy process (AHP) method, but in practice there are several variables that are not found in e-commerce so the AHP method cannot be used. The alternative method chosen is the ant colony optimization (ACO) method. The feasibility test of the ACO method in searching rankings for e-commerce data needs to be done because not all variables are in e-commerce. Simulations for ranking search are carried out using 2 generated data scenario with analytical hierarchy process (AHP) and ant colony optimization (ACO) method. The simulation results show that the ACO method is feasible to be used for ranking with blank data, then the application of the ACO method for e-commerce data in Indonesia. The best taboo results are based on the highest opportunity value and the highest correlation coefficient, namely in the second taboo, with three major ratings, namely JD, SP, and TP

Алгоритм вироблення комплексних рекомендацій клієнтам туристичної галузі

На прикладі туристичної галузі розглянуто розроблений алгоритм вироблення комплексних рекомендацій щодо вибору клієнтами товару чи отримання послуги, що максимально мають відповідати їхнім уподобанням і збереженні клієнтів й прибутків туристичними фірмами. З'ясовано, що рекомендаційні алгоритми використовують у багатьох інтернет-системах для надання споживачеві поради стосовно вибору клієнтами товару чи отримання послуги, які найбільше відповідають його уподобанням. Незважаючи на значне поширення рекомендаційних систем у різноманітних галузях (електронна комерція, розваги, послуги, соціальні мережі тощо), залишається невирішеним питання, пов'язане з вибором конкретного алгоритмічного підходу для певної області застосування. Наявні алгоритми здебільшого опрацьовують прості об'єкти і не дають змоги якісно вирішити задачу конструювання рекомендації з окремих складових, враховуючи їх сумісність між собою та шукаючи найкращий варіант з можливих комбінацій. Побудовано новий алгоритм для комплексної рекомендації на підставі удосконалення алгоритму колаборативної фільтрації за рахунок комбінації методів, заснованих на сусідстві, пам'яті та моделі з використанням машинного навчання для коригування ступеня значущості характеристик складових елементів комплексного рішення. Рішення практично реалізовано у формі програмного модуля для рекомендації туристичної подорожі, яка описується місцем відпочинку, маршрутами, готелем та екскурсіями. Особливостями побудованого алгоритму є використання концепції рейтингу користувача для надання збалансованих оцінок елементам турів та коригування коефіцієнтів важливості складових подорожі методом машинного навчання, що дає змогу їх виокремити як параметри моделі рекомендування. Отримані результати порівняно з результатами простіших реалізацій колаборативної фільтрації (засновані на пам'яті та сусідстві). Побудований алгоритм демонструє найкращі результати (30-60) % рекомендацій, що відповідають очікуванням користувача. Недоліки алгоритму виявляються у разі малої кількості даних і їх розрідженості.

Combining Artificial Immune System and Clustering Analysis: A Stock Market Anomaly Detection Model

Artificial intelligence research in the stock market sector has been heavily geared towards stock price prediction rather than stock price manipulation. As online trading systems have increased the amount of high volume and re-al-time data transactions, the stock market has increased vulnerability to at-tacks. This paper aims to detect these attacks based on normal trade behavior using an Artificial Immune System (AIS) approach combined with one of four clustering algorithms. The AIS approach is inspired by its proven ability to handle time-series data and its ability to detect abnormal behavior while only being trained on regular trade behavior. These two main points are essential as the models need to adapt over time to adjust to normal trade behavior as it evolves, and due to confidentiality and data restrictions, real-world manipula-tions are not available for training. This paper discovers a competitive alterna-tive to the leading approach and investigates the effects of combining AIS with clustering algorithms; Kernel Density Estimation, Self-Organized Maps, Densi-ty-Based Spatial Clustering of Applications with Noise and Spectral clustering. The best performing solution achieves leading performance using common clustering metrics, including Area Under the Curve, False Alarm Rate, False Negative Rate, and Computation Time.

User Satisfaction Affecting Factors in the Online Trading Systems

The purpose of this paper is to show how to find a new factor model that affects the satisfaction of online trading system users which is done by analyzing the factors in finding the factors that most influence the online user satisfaction system trading. of the 8 variables that have been determined namely System Quality, Service Quality, Information Quality, Intended use, User Satisfaction, Interface Quality, Performance Quality, and Function Quality which are the basic concepts of the underlying theory. The technique for processing data begins with building a research instrument by constructing and following up on existing concepts and breaking them down into factors that exist in the theory. Through the distribution of questionnaires to obtain primary data, then the data is processed using factor analysis and a number of new factors are obtained, namely application system features, information and knowledge, and comfortable to use that affect the satisfaction of users of online trading systems. It is hoped that this model can help organizational management to improve services better and find out what is desired from users of online trading systems.

Behavior Analysis for Electronic Commerce Trading Systems: A Survey

With the rapid development of the Internet and the continuous progress of computer science and technology, Internet-based online trading has developed rapidly and improved the convenience of everyday life. Increasingly, people conduct business activities through online transactions and payment methods, and the development prospects for online transactions are quite broad. In addition, e-commerce trading systems have attracted considerable attention. Many studies have been conducted to analyze the behaviors of these systems. This paper focuses on analyzing both system behavior and user behavior in e-commerce trading systems. To reflect the characteristics of online trading systems, system behavior reflects the strength of the system, while user behavior reflects various user actions. Consistency, which is a basis for determining whether two objects are similar, provides a reference for designers and modelers. This paper also focuses on analyzing the consistency of system behavior, especially regarding the measurement and diagnosis of e-commerce trading systems. Then, we study the existing user behavior analysis methods for e-commerce trading systems. We divide user behaviors into four groups: web browsing behavior, keystroke behavior, network transaction behavior, and mobile terminal behavior. The performance and correlation analysis techniques of these methods are discussed and compared in detail. Finally, we provide ideas for future research involving behavior analysis for online trading systems.

Examining the effects of information sources on individual earning rates in the South Korean stock market

Since the introduction of the Korea Stock Exchange, individual investors have been some of the most important players in the stock market. With the subsequent introduction of the Internet and online trading systems in South Korea in 1997, the participation of individual investors in the Korean stock market has greatly expanded. However, few studies have investigated their activities and earning rates. To address this research shortcoming, this study investigates the antecedents of individual investors’ earning rates in the Korea stock market by employing multiple constructs of information channels and perceived usefulness. Structural equation modelling (SEM) analysis of survey data collected from 1555 individual investors reveals that the information quality of online trading systems and of companies’ public announcements largely determines the individual investors’ perceptions of their usefulness, whereas the information quality of two Internet channels (Internet news and other web pages) only moderately affects perceived usefulness. Additionally, the information quality of two TV channels significantly affects perceived usefulness, but the effects are weaker than for the other channels. In addition, our findings confirm a close relationship between the perceived usefulness of information and individual investors’ earning rates. Last, we discuss the implications and present suggestions for future research.

멀티 플랫폼 기반 온라인 서비스 전략: 온라인 트레이딩 서비스의 플랫폼 간 비교를 중심으로

As the advance of multi-platform and multi-channel online services, brokerages are now offering three representative online trading systems - HTS(Home Trading Systems), WTS(Web Trading Systems), MTS(Mobile Trading Systems). In this study we investigated and compared the impact of different systems on the performance of brokerages. Using the panel data of 29 brokerages of 4 periods, we empirically tested the impact of online trading systems and the commissions of trading services. We found out that there exist some differences between the impacts of online trading systems based on the platforms. HTS was identified as the main platform for online trading services. However the role of MTS was also significantly identified while WTS showed no significant impact on the brokerage performances. Commission also showed significant negative impact in case of HTS and MTS platforms. Finally, offering MTS was identified as the significant dummy variable influencing the performance of brokerages. The results provides some implication for the multi-platform strategy for online services.

Factors Affecting the Adoption of E-commerce for the Tourism Industry in Taiwan

The tourism industry has successfully pioneered the adoption of e-commerce with respect to its online trading systems. Given the potential for the tourism industry to impart knowledge to other industries, this study examines the acceptance of online trading systems for travel agencies in Taiwan. According to the results of a literature review, this study uses six critical factors that were identified in the literature review as formative influences on the adoption of e-commerce. These factors are then incorporated into a questionnaire designed to verify empirically the extent of their ability to be generalized. This survey reveals that 80% of the travel agencies in the sample have adopted online trading systems. Particularly noteworthy is how the adopters and non-adopters hold different viewpoints on the following factors; “compatibility of adopting innovative technology”, “centralization of organization”, “organizational scale” and “correctness of website transmission”.

Personal innovativeness, security and privacy as determinants of e-trading adoption

Motivated by the growing interest in electronic brokerage firms, we focus our research questions on examining the factors that contribute to acceptance of online trading systems. The research model, based on a Technology Acceptance Model (TAM) and added perceived security, perceived privacy and personal innovativeness, was tested by means of a Partial Least Square (PLS) modelling approach. The findings indicate that perceived security, Perceived Usefulness (PU) and personal innovativeness are positively related to behavioural intentions.

The importance of perceived trust, security and privacy in online trading systems

Purpose – The purpose of this paper is to test an augmented technology acceptance model (TAM) in the online financial trading context. This research aims to investigate how e‐investors are influenced by perceived trust, security, and privacy jointly with traditional TAM constructs.

MULTI-AGENT SYSTEM ARCHITECTURE TO TRADING SYSTEMS

Agents for online trading purpose can be seen as a tool that helps computer users to purchase products from distributed resources based on their interests and preferences. One of the major features that determine the success of trading agent is the ability to negotiate with other agents, because most trading tasks involve interaction among agents. This paper presents a peer-to-peer multi-agent system architecture for online trading. The main objective of this system is to address some of the shortcomings that are present in contemporary online trading systems that focused on providing solutions for specific trading issues, such as single attribute-based negotiation, the requirement of an electronic marketplace and variations and status changes within the network. The proposed system architecture is a multi-tier, multi-agent architecture. The system architecture consists of three types of agents that are classified based on their functionality: interface, resource and retrieval agents. The interface agents are the front-end of the system and able to interact with different users to fulfill their needs. At the middle-tier, the resource agents access and capture the contents and the changes of the local information database. The retrieval agents are the back-end of the system and able to travel and interact with other agents at remote host machines. A prototype of this system is implemented using the IBM Aglet SDK.

Entering the Arena of Wall Street Wizards, Euro Brokers and Cyber-Trading Samurais: A Strategic Imperative for Online Stock Trading

The emergence of discount brokerage firms that allow investors to trade stocks, options, bonds and mutual funds via online trading systems on the Internet is rapidly altering the landscape of global financial securities markets. While 'business-to-business' electronic trading systems for financial securities have existed for two decades exclusively among large institutional/ corporate investors and professional stock traders via proprietary networks, Internetbased online trading systems catering to retail investors is a revolutionary development. By allowing retail investors to trade stocks directly and manage their own financial portfolios electronically, Internetbased trading is enabling a boundary spanning and deregulation process in global financial markets. Since its inception, competition in the retail-based online brokerage sector has been cut-throat due to heavy discounting of brokerage fees/commissions. In the face of mounting competition, our research aim is to explore the challenges online disc...

The strategic intent of on-line trading systems: a case study in national livestock marketing

On-line trading is the buying and selling of goods or services through an interactive telecommunications-based computer network. The paper describes a system which was developed in Australia for the particular conditions that exist in the livestock sector of that country. Known as CALM, the system is claimed by its owners to be the first in the world to offer a national information technology-based livestock trading scheme. The strategic intentions of calm's owners and sponsors are assessed from the perspectives of the market operator and the industry participants, and some cautious generalizations made concerning on-line trading systems generally. Weaknesses in the explanatory power of strategic information systems theory are identified, and the suggestion made that these weaknesses derive from the predominantly competition-oriented approach of contemporary theory.