• Journal of Digital Convergence
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• v.17 no.2
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• pp.73-80
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• 2019

This study analyzed the relevance between investment horizons of foreign investors and Book-Tax Difference. To do this, we measured investment horizons of foreign investors as the investment-turnover of foreign investors. We use the sample of 3,025 firm-year Korea listed companies belonging to non-financial corporate sector during 2011-2016. The results of empirical analyses show that investment horizons of foreign investors has a negative relevance with Book-Tax Difference. This result indicates that the firm have a long-term foreign investors has a good Accounting Earning's Quality than the firm have a short-term foreign investors. This study is expected to provide useful information by suggesting the need for more incentive for the long-term foreign investors. And Our Study has a contribution that this study extended prior research on foreign investors by utilizing investment horizons of foreign investors.

• Journal of Digital Convergence
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• v.17 no.3
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• pp.153-160
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• 2019

This paper analyzes the impact of investment tendency of foreign investor on accounting conservatism. We use the sample of 1,527 firm-year Korea listed companies belonging to non-financial corporate sector during 2014-2016. The results of empirical analyses show that investment horizons of foreign investors has a positive relevance with accounting conservatism. This result indicates that the firm have a long-term foreign investors has a good quality of accounting earning than the firm have a short-term foreign investors. This study that verified the relevance between investment tendency of foreign investor and accounting conservatism is expected to provide useful information by suggesting the need for more incentive for the long-term foreign investors. And we expect a follow-up study focused on the discriminative effect of investment tendency of foreign investor on accounting policy.

• Journal of the Korean Operations Research and Management Science Society
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• v.36 no.1
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• pp.13-26
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• 2011

In order to explain the U-shaped pattern of autocorrelations of stock returns i.e., autocorrelations starting around 0 for short-term horizons and becoming negative and then moving toward 0 for long-term horizons, researchers suggested the use of a state-space model consisting of an I(1) permanent component and an AR(1) stationary component, where the two components are assumed to be independent. They concluded that auto-regression coefficients derived from the state-space model follow a U-shape pattern and thus there is mean-reversion in stock prices. In this paper, we show that only negative autocorrelations are feasible under the assumption that the permanent component and the stationary component are independent in the state-space model. When the two components are allowed to be correlated in the state-space model, we show that the sign of the auto-regression coefficients is not restricted as negative. Monthly return data for all NYSE stocks for the period from 1926 to 2007 support the state-space model with correlated noise processes. However, the auto-regression coefficients of the ARIMA process, equivalent to the state-space model with correlated noise processes, do not follow a U-shaped pattern, but are always positive.

• The Korean Journal of Applied Statistics
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• v.25 no.6
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• pp.897-915
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• 2012

This paper examines the relationship between the stock and futures markets in terms of lead-lag relationship, correlation and the hedge ratio using wavelet analysis. The basic finding is that the relationship between the two markets significantly depends on the time-scale. First, there is a feedback relationship between the stock and futures markets in the long-run scale; however, weaker evidence is observed in shorter-run scales. Second, wavelet correlation between the two markets increases for a longer time scale. Third, the hedge ratio and the effectiveness of hedging strategies increase as the investment horizon gets longer. The results in this paper indicate that the stock and futures series are perfectly correlated in the long run and are tied together over long horizons.

• Asia pacific journal of information systems
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• v.7 no.1
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• pp.67-83
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• 1997

The utility of learning techniques in investment analysis has been demonstrated in many areas, ranging from forecasting individual stocks to entire market indexes. To date, however, the application of artificial intelligence to financial forecasting has focused largely on short predictive horizons. Usually the forecast window is a single period ahead; if the input data involve daily observations, the forecast is for one day ahead; if monthly observations, then a month ahead; and so on. Thus far little work has been conducted on the efficacy of long-term prediction involving multiperiod forecasting. This paper examines the impact of alternative procedures for extended prediction using knowledge discovery techniques. One dimension in the study involves temporal granularity: a single jump from the present period to the end of the forecast window versus a web of short-term forecasts involving a sequence of single-period predictions. Another parameter relates to the numerosity of input variables: a technical approach involving only lagged observations of the target variable versus a fundamental approach involving multiple variables. The dual possibilities along each of the granularity and numerosity dimensions entail a total of 4 models. These models are first evaluated using neural networks, then compared against a multi-input jump model using case based reasoning. The computational models are examined in the context of forecasting the S&P 500 index.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.821-830
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• 2023

The advancement of Internet network technology has triggered the emergence of new OTT video content platforms, increasing demand for content and altering consumption patterns. This trend is bringing positive changes to the South Korean animation industry, where diverse and high-quality animation content is becoming increasingly important. As investment in technology grows, video production technology continues to advance. Specifically, 3D animation and VFX production technologies are enabling effects that were previously unthinkable, offering detailed and realistic graphics. The Fourth Industrial Revolution is providing new opportunities for this technological growth. The rise of Artificial Intelligence (AI) is automating repetitive tasks, thereby enhancing production efficiency and enabling innovations that go beyond traditional production methods. Cutting-edge technologies like 3D animation and VFX are being continually researched and are expected to be more actively integrated into the production process. Digital technology is also expanding the creative horizons for artists. The future of AI and advanced technologies holds boundless potential, and there is growing anticipation for how these will elevate the video content industry to new heights.