Forthcoming articles

International Journal of Electronic Finance

International Journal of Electronic Finance (IJEF)

These articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Electronic Finance (6 papers in press)

Regular Issues

  • Estimating Bitcoin and traded asset classes volatility using GARCH Model   Order a copy of this article
    by Timcy Sachdeva 
    Abstract: Bitcoin is the worlds first cryptocurrency which has largest market capitalization. The study aims to analyze the risk measures for the bitcoin and comparing with tradeable asset classes that include the Standard and Poors BSE 500, USD, Euro, GBP and the Gold future prices. The study uses the GARCH models to identify the components of world economies that Bitcoin is sentisitive too as against variables that impact the global financial prudence. The empirical results of the study reveal that against dollar and euro exchange rates bitcoin returns are more sensitive. Bitcoin can be used together with gold to diversify or eliminate explicit market risks. The study presents reasonable justification over the development and relationship between bitcoin and different traded assets that pose new challenges before the global investors. The implication of this paper for the strategic policy makers shows the sensitivity among tradeable assets.
    Keywords: Bitcoin; traded asset classes; volatility; hedging; GARCH Model; FinTech.

  • Internet of Things: Financial Perspective and its associated security concerns.   Order a copy of this article
    by Faheem Masoodi, Bilal Ahmad Pandow 
    Abstract: The Internet of Things (IoT) has expanded at a very rapid rate over the last decade and revolutionized much of Internet and devices technologies. Though much of transformation was driven by IoT, however, its implementation, security issues and other associated aspects still remain a matter of concern. The literature on the financial aspect of IoT is very scarce and this paper aims to fill this void and provide financial perspectives on IoT. The analysis of IoT banking and financial services industry is projected to expand from $249.5 million to USD 2.03 billion by 2023: eight-time rise or a Compound annual growth rate (CAGR) of around 52%. Furthermore, it was noted that the financial results of selected IoT firms had seen decent development over the past many years. In addition, there are several mergers and acquisitions in the IoT market, culminating in a USD 75.44 billion increase in the sector. One of the major challenges in IoT implementation in the financial industry is security and privacy. The inherent vulnerabilities in IoT devices can be exploited by the attacker, which makes it increasingly onerous for financial services firms to safeguard the system against phishing, data breaches, ransomware and other attacks.
    Keywords: Internet; Security; IoT; Finance.

  • A study of confirmation bias among online investors in virtual communities   Order a copy of this article
    by Bhoomika Trehan, Amit Kumar Sinha 
    Abstract: The purpose of this study is to investigate the existence of confirmation bias among online investors participating in virtual communities. It further examines the factors such as perceived knowledge, investment experience, and gender that influence the confirmation bias. In the virtual communities, two types of participants were identified Knowledge Seekers and Knowledge Contributors. An online survey was conducted using structured questionnaire and the data was analysed with the application of relevant statistical tools. Investment-related virtual communities were found to be a great source of stock market-related news and investment ideas. The findings indicate that online investors exhibit confirmation bias as they join virtual communities to seek information that confirms their previous beliefs and opinions. The data was collected from online chat rooms where online investors interact and discuss investment trades. As many investors invest online without taking financial advice and guidance, their investment choices depend on their instinct and knowledge. Therefore, this study is of immense importance for both investors and investment advisors.
    Keywords: behavioural biases; confirmation bias; decision-making process; gender; investment experience; online investors; overconfidence; perceived knowledge; psychology; virtual communities.
    DOI: 10.1504/IJEF.2021.10036026
     
  • Bitcoin prices and rupee-dollar exchange rates during COVID 19   Order a copy of this article
    by Naresh G, Ananda S 
    Abstract: Bitcoin is the primary cryptocurrency in the world that can be stored and traded through the internet. Digital contracts and cryptocurrencies created on blockchains have now been used in exchanging instruments on the networks and are available online readily. This paper's main objective is to investigate the causal relationship between bitcoin prices and rupee-US dollar exchange during COVID 19. The sample data of spread, daily prices of bitcoin trading, and rupee-US dollar exchange rate are collected from yahoo finance and RBI web portal. The data were chosen covers the period of two quarters from December 2019 to May 2020. The study used the Granger Causality model to study the price behavior of bitcoin and the Granger Causality among them. The study found a unidirectional granger causality existed, where the rupee-US dollar exchange rate affected the bitcoin price in the Indian market during COVID-19. The bitcoins are widely considered an investment asset in Indian markets, and the rupee-dollar exchange rate has a significant impact on the Bitcoin prices.
    Keywords: Bitcoin prices; COVID 19; cryptocurrency; rupee-dollar exchange rate; granger causality.

  • DIGITAL FINANCIAL INCLUSION - DEMAND SIDE VS SUPPLY SIDE APPROACH   Order a copy of this article
    by Ashutosh Upadhyay, Shiva Reddy Kalluru 
    Abstract: This paper explores the measurement of Digital Financial Inclusion (DFI) in India by analyzing the parameters such as per capita bank accounts, cards, retail payment systems, internet and broadband connections from both the supply side and demand side data. We observe substantial gaps in the level of DFI brought out by these two datasets, with the supply side seeming to overestimate the level of digital financial inclusion. We also propose a theoretical model for equilibrium in demand and supply sides of DFI. Supply of DFI is divided into two components of autonomous and induced supply. We find that demand for and (induced) supply of DFI is directly proportional to the income level of users and the incentives provided for the usage of DFI products/ services. For a sustainable model of DFI, both the demand and supply sides should balance and complement each other, and supply-side infrastructure should be available and scalable, to meet a higher level of demand for DFI.
    Keywords: payment systems; digital financial inclusion; demand side; supply side; central bank policies; income; incentives.

  • Forecasting the Stock Exchange of Thailand using Data Mining techniques   Order a copy of this article
    by Kanokkarn Snae Namahoot, Viphasiri Jantasri 
    Abstract: Abstract: The prediction of stock price index movement is regarded as a challenging task in financial time series prediction as an accurate forecasting of stock price movement may yield profits for investors. Due to the complexity of stock market data, the development of efficient models for predicting is very difficult. This study attempted to develop three efficient models and compared their performances in predicting the direction of movement in the daily stock exchange market of Thailand (SET). The models are based on three classification techniques: the uses of linear regression, decision trees, and artificial neural networks (ANN). Thirteen technical indicators were selected as inputs for the proposed models. Three comprehensive parameter setting experiments for the models were performed to improve their prediction performances. Experimental results showed that average performance of the ANN model (89.79%) was found to be significantly better than that of the linear regression (89.74%) and decision tree models (88.07%). Consequentially, this research demonstrates rule extraction as a post-processing technique for improving prediction accuracy and for explaining the prediction logic to financial decision makers. Purpose: Data mining is a useful tool that extracts interesting knowledge from a very large database. The Stock Exchange of Thailand (SET) uses data mining techniques in making useful decision in variety of aspects. This paper aims to study and compare the findings of the accuracy of the SET model as classified by industry sectors. Data mining supports making decisions for investment in the stock exchange market of Thailand. It can also investigate factors that impact the rate of change of stock prices in the SET. Design/methodology/approach: This study employs three models (linear regression, decision trees, and artificial neural networks) to forecast the three consecutive periods of rate of change of stock prices. SCG method, comparing the number of epochs, is not as relevant as standard back propagation. Iteration can be checked out for comparing SCG with decision tree and linear regression methods. Back propagation is the most widely used algorithm for supervised learning with neural networks. Findings: The prediction of stock price index movement is crucial for stock trading strategies. It usually affects a financial traders decision. Essentially, a successful prediction of stock prices benefits investors. However, making a prediction is an extremely complicated and difficult process. This study attempts to predict the direction of stock price in the Thailand Stock Exchange. Three prediction models were constructed, and their performances were reviewed for the financial years 20092019 as three consecutive periods of financial industry. The study also discovers that compared to the other three methods, ANN is the most accurate method for predicting stock prices in the stock exchange market of Thailand. Keywords: data mining; linear regression; neural networks; decision tree; SCG, stock exchange
    Keywords: data mining; linear regression; neural networks; decision tree; SCG; stock exchange.