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    Factors Influencing the Behavioural Intention to Use Cryptocurrency in Emerging Economies During the COVID-19 Pandemic: Based on Technology Acceptance Model 3, Perceived Risk, and Financial Literacy
    (2022-02-09)
    Jariyapan, Prapatchon
    ;
    Mattayaphutron, Suchira
    ;
    Gillani, Syeda Noorzahrah
    ;
    Shafique, Owais
    Cryptocurrency could redefine the interplay of Internet-connected world markets by eliminating constraints set by traditional local currencies and exchange rates. It has the potential to revolutionise digital markets through the use of duty-free trading. This study investigates the factors which influence the behavioural intention to use cryptocurrency based on the Technology Acceptance Model 3 (TAM 3) during the COVID-19 (SARS-COV-2) pandemic. Data were collected through a cross-sectional questionnaire from 357 Pakistani business-educated adults, including investors who had a rudimentary understanding of the technology and financial instruments. Partial least square (PLS)-based structural equation modeling (SEM) was used to test the developed theoretical framework based on the Technology acceptance model 3. The PLS model has explained 72.1% of what constitutes the behavioural intention to use cryptocurrency. Surprisingly, risk was not a major consideration. This might be due to the fact that the majority of respondents thought working with cryptocurrency was hazardous. Willingness to handle cryptocurrency risk, on the other hand, might be a stumbling block to acceptance. The most essential aspect of a cryptocurrency's success was the perceived usefulness. Moreover, the moderating role of experience was not substantiated in this study. However, perceived usefulness was identified as a partial mediator of subjective norm and the perceived ease to use. This study contributed to the literature through the application of TAM 3 (an extension of the technology acceptance models) to investigate the fundamental qualities a cryptocurrency should have in order to influence investor's behavioural intention to use it. These findings provide revolutionary insights for the present and future market players for investment planning and for improved cryptocurrencies development.
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    A Machine Learning Model for Healthcare Stocks Forecasting in the US Stock Market during COVID-19 Period
    (2022-01-01)
    Jariyapan, Prapatchon
    ;
    Singvejsakul, Jittima
    ;
    Chaiboonsri, Chukiat
    This paper study the nowcasting and forecasting for the healthcare stock price in the united states during the Covid-19 period including the google trend data information. The data is collected in monthly data from 2015 to 2020 which are five interested stock price indexes in the healthcare sector. Empirically, the finding reveals that the Bayesian structural time series analysis can be used to investigate the stock price indexes with the google trend data is becoming useful for the prediction in term of current movement. In term of the machine learning algorithms, the unsupervised learning k-Mean algorithm is employed to cluster the cycle regimes of the stock market which provided three regimes such as Bull market, Sideways and Bear market. There are twenty-nine months stand for bull market, thirty-seven months are predictively provided sideways market and five months are referred as the bear market. Additionally, the supervised learning algorithms by using the Linear Discriminant Analysis (LDA), k-Nearest Neighbors (kNN) and Support vector machine (SVM) are used to investigate the cycle regimes of healthcare stock in next five year. The results indicated that LDA is chosen by the highest coefficient validation which represented the the regimes of stock in the healcare sector of the unites states of America will stay on the sideways periods in the next five years. Thus, the finding in this paper can be the useful information for investor to manage their portfolio especially, in healthcare sector during the Covid-19 period.
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    Macroeconomic Impact of Mandatory Retirement Age Policy to Population Aging in Thailand
    (2021-09-01)
    Mattayaphutron, Suchira
    ;
    Tam, Buithiminh
    ;
    Jariyapan, Prapatchon
    This paper develops a stochastic overlapping generations (OLG) model to investigate the effects of mandatory retirement age policy in an aging economy. The model has been constructed using a calibration and simulation approach with a view to analyze the impact of extending the retirement age on macroeconomic variables in Thailand. The results show that a higher mandatory age of retirement is always beneficial in the long run for PAYG pension budgets, government transfers for the elderly, and lifetime consumption. In this way, the future generations enjoy more consumption than the current generations. On the contrary, the policy of increasing the mandatory age may be harmful not only in the long run-in terms of capital accumulation but also in terms of the output. The mechanisms for driving this are two-fold: (1) there is a direct positive effect consisting in an increase of labor supply and lower the length of the retirement period and (2) there is an indirect effect due to the negative change in the wage.
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