• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.1
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• pp.75-91
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• 2024

Recently, global business environment is changing dramatically along with the acceleration of technological innovation amid the war, climatic change, and geopolitical instability. Accordingly, it is difficult to predict or plan for the future as the volatility, complexity, ambiguity, and uncertainty of the industrial ecosystem continue to increase. Therefore, organizations are undergoing inevitable restructuring in accordance with their survival strategy, for instance, removing marginal businesses or firing. Accordingly, office workers are seeking a startup as an alternative for their continuous economic activity amid rising anxiety factors that make them think they would lose their jobs unintentionally. Here, this study is aimed to verify through what paths office workers' self-determination influences the process of converting to a startup. For this study, an online survey was carried out, and 310 respondents' valid data were analyzed through SPSS and AMOS. To sum up the results, first, office workers' self-determination did not have significant effects on entrepreneurial intention. However, it was confirmed that self-determination had positive (+) effects on innovativeness and perception of the startup support system. This result shows that their psychology works to prepare step by step by accumulating innovative experiences and increasing perception of the startup support system from a long-term life path perspective rather than challenging startups right way. Second, innovativeness is found to have positive (+) effects on entrepreneurial intention. Also, perception of the startup support system had positive (+) effects on entrepreneurial intention. This implies that when considering startups, they are highly aware of the government's various startup support systems. Third, innovativeness is found to have positive (+) effects on perception of the startup support system. It is judged that perception of the startup support system is valid for prospective founders to exhibit their innovativeness and realize new ideas. Fourth, it was confirmed that innovativeness and perception of the startup support system mediated correlation between self-determination and entrepreneurial intention, and perception of the startup support system mediated correlation between innovativeness and entrepreneurial intention, which shows that it is a crucial factor in entrepreneurial intention. Although previous studies related to startups deal with students mostly, this study targets office workers who form a great part in economic activities, which makes it academically valuable in terms of being differentiated from others and extending the scope of research. Also, when we consider the fact that the motivation for self-determination alone fails to stimulate entrepreneurial intention and the complete mediation of innovativeness and the startup support system, it has great implications in practical aspects such as the government's human and material support systems. In the selection and analysis of samples, this study exhibits a limitation that the problem of common method bias is not completely resolved. Also, additional definitive research is needed on whether entrepreneurial intention is formed and converted into startup behavior. Academically and practically, this study deals with the relationship between humans' psychological motives and startups which has not been handled sufficiently in previous studies. The conversion of office workers to startups is expected to have effects on individuals' economic stability and the state's job creation; therefore, it needs to be investigated continuously for its great value.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.