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International Journal of Electronic Finance (5 papers in press)
Forecasting the Stock Exchange of Thailand using Data Mining techniques 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.
Are digital assets backstop for GCC stock markets in COVID-19 led financial crisis? by Sahar Loukil, Mouna Aloui, Ahmed Jeribi Abstract: This study examines the safe heaven properties of top five crypto currencies, oil and GOLD for the five Gulf Cooperation Council countries in view of COVID-19 period through a non-linear and asymmetric framework NARDL methodology to uncover short- and long-run asymmetries. Using daily data from January 2019 to April 2020, we find that BITCOIN and ETHEREUM are safe haven assets for GCC in instability and BITCOIN is a safe haven for OMAN, SAUDI ARABIA and ABU DHABI. ETHEREUM is a safe haven for BAHRAIN, KUWAIT and QATAR. Further, for KUWAIT, QATAR, SAUDI ARABIA and ABU DHABI, oil is as safe haven asset in mitigated period. We also notice that the strategies of hiding differ interestingly for all countries except for SAUDI ARABIA that does not significantly change its strategies. Thus, portfolio managers may consider few eligible crypto currencies and oil for their inclusion into the portfolio to hedge risk. While, speculators acting in both stock and crypto market may go for spread strategy. Our research is useful for portfolio managers and financial advisors looking for the best of cryptos, gold and oil to hedge shocks in stock market indices. Keywords: Cointegration; Asymmetry; Nonlinearity; GCC; stock market; oil; gold; cryptocurrencies.
Development of A GPS Guided Mobile Shopping System by Nathan Keeton, June Wei Abstract: This paper aims at developing a user-friendly mobile environment to guide mobile shopping via the Guided Position System (GPS) technology. Specifically, it first studied usability features that are crucial to mobile shopping via GPS by developing a framework with integrated user-friendly features. Second, twenty-three data flows are mapped and identified based on the framework to illustrate how to integrate these usability features into development. Third, the system analysis is performed based on both dynamic modeling (using Data Flow Diagramming) and static modeling (using Entity-Relation Diagramming). Fourth, the system design including database design and user interface design are performed. Finally, a prototype is developed with usability testing. This study concludes that the mobile shopping guided by GPS with usability emphasis is crucial to develop the efficient mobile shopping systems. \r\n\r\n Keywords: Guided Position System (GPS); data flow diagram; guided mobile shopping.
Importance of perceived security, perceived privacy and website design in online investment behaviour: An Indian market perspective by Shalini Gautam, Priyanka Malik Abstract: This study was undertaken to explore the determinants which affect the behavior intention of individuals to make online investments. It was proposed to extend the Technology Acceptance Model (TAM) to better explain the online investment behavior. The factors which were added in the TAM model were perceived security, perceived privacy, and website security. The conceptual model of the study was tested on 220 respondents using the structural equation model (SEM). The data for the study was collected using a structured questionnaire consisting of reliable and established scales. The findings indicate that perceived ease of use and security are significantly associated with the intention of the consumer to use online investments whereas perceived usefulness is not found to have a statistically significant impact on the intention of the individuals to make online investments. The website design and perceived security contribute to users perceived ease of use and perceived usefulness but perceived privacy doesnt contribute to either of them. The present research is one of the few studies which examines the overall investment behavior of an individual across a range of investment products. It provides a guide to the organizations which are providing online investment services to the individuals and helps them to identify the factors which make the entire process of online investments smooth for an individual. Keywords: Perceived security; perceived privacy; website design; behaviour intention; online investment behaviour.
Financialization of Agricultural commodity and its trading during COVID-19 by S. Mahalakshmi, Thiyagarajan S, G. Naresh Abstract: The establishment of regularised trading exchanges for agricultural commodities attracted every market participant to benefit from their trade. The pandemic has created massive chaos in every asset class, and agri-commodities are no exception. However, the pandemic also taught lessons for the global traders to focus on the food produces. Therefore, this paper intends to look at agricultural commodity trading behaviour during this COVID-19 pandemic by looking at the movement of the trade in the agri-futures index and other asset classes, including equity, exchange rates, bullion prices, etc. The results show that all the selected asset classes except the exchange rate are influencing Agridex. In addition, the Agridex returns are influenced by the severity of COVID-19 cases. Therefore, the policymakers should keep this in mind and work to prevent the price rise to an uncontrollable extent because this can lead to stagflation. Keywords: agricultural futures index; commodity; futures; assets; volatility; COVID-19. DOI: 10.1504/IJEF.2021.10041554