• Asia-Pacific Journal of Business
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• v.14 no.4
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• pp.343-356
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• 2023

Purpose - This study attempted to predict short-term transportation demand using trains and getting off at Gangchon Station. Through this, we present numerical data necessary for future tourist inflow policies in the Gangchon area of Chuncheon and present related implications. Design/methodology/approach - This study collected and analyzed transportation demand data from Gangchon Station using the Gyeongchun Line and ITX-Cheongchun Train from January 2014 to August 2023. Winters exponential smoothing model and ARIMA model were used to reflect the trend and seasonality of the raw data. Findings - First, transportation demand using trains to get off at Gangchon Station in Chuncheon City is expected to show a continuous increase from 2020 until the forecast period is 2024. Second, the number of passengers getting off at Gangchon Station was found to be highest in May and October. Research implications or Originality - As transportation networks are improving nationwide and people's leisure culture is changing, the number of tourists visiting the Gangchon area in Chuncheon City is continuously decreasing. Therefore, in this study, a time series model was used to predict short-term transportation demand alighting at Gangchon Station. In order to calculate more accurate forecasts, we compared models to find an appropriate model and presented forecasts.

• The Korean Journal of Financial Management
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• v.25 no.4
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• pp.145-182
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• 2008

As the life expectancy increases resulting in the aged society, the post-retirement life became one of the most important concerns of people. The long-term investment vehicles such as retirement savings and pension plans have been introduced to meet such demand of society. This paper examines the impact of asset allocation strategies on the long-term investment performance. Because of the unusually long investment horizon and the compounding effect, a suboptimal asset mix in a retirement plan can be a very costly and irreversible mistake. Instead of relying on anecdotal evidence to evaluate the merits of different allocation strategies, this paper performs various tests including stochastic dominance tests using both actual data and Monte Carlo simulated data that best fit the historical experience. The results indicate 1) the long-term investments perform better than the short-term investments, 2) the optimal asset allocation strategy for the long-term investments should be highly equity dominated.

• Asia-Pacific Journal of Business
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• v.14 no.1
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• pp.73-91
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• 2023

Purpose - The purpose of this study is to re-examine the disclosure effect of stock splits and long-term performance after stock splits using stock split data over the past 10 years, and infer the motivation (signal or opportunism) of stock splits. In addition, we focus on exploring the determinants of the short- and long-term market response to stock splits. Design/methodology/approach - We measure the short-term market response to a stock split and the long-term stock performance after the stock split announcement using the event study method. We analyze whether there is a difference in the long-term and short-term market response to a stock split according to various company characteristics through univariate analysis and regression analysis. Findings - In the case of the entire sample, a statistically significant positive excess return is observed on the stock split announcement date, and the excess return during the 24-month holding period after the stock split do not show a difference from zero. In particular, the difference between short-term and long-term returns on stock splits is larger in companies with a large stock split ratio, small companies, large growth potential, and companies with a combination of financial events after a stock split. Research implications or Originality - The results of this study suggest that at least the signal hypothesis for a stock split does not hold in the Korean stock market. On the other hand, it suggests that there is a possibility that a stock split can be abused by the manager's opportunistic motive, and that this opportunism can be discriminated depending on the size of the stock split, corporate characteristics, and financing plan.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.377-396
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• 2023

As the cryptocurrency market continues to grow, it has developed into a new financial market. The need for investment strategy research on the cryptocurrency market is also emerging. This study aims to conduct an empirical analysis on an investment methodology of cryptocurrency that combines short-term trading strategy and deep learning. Daily price data of the Ethereum was collected through the API of Upbit, the Korean cryptocurrency exchange. The investment performance of the experimental model was analyzed by finding the optimal parameters based on past data. The experimental model is a volatility breakout strategy(VBS), a Long Short Term Memory(LSTM) model, moving average cross strategy and a combined model. VBS is a short-term trading strategy that buys when volatility rises significantly on a daily basis and sells at the closing price of the day. LSTM is suitable for time series data among deep learning models, and the predicted closing price obtained through the prediction model was applied to the simple trading rule. The moving average cross strategy determines whether to buy or sell when the moving average crosses. The combined model is a trading rule made by using derived variables of the VBS and LSTM model using AND/OR for the buy conditions. The result shows that combined model is better investment performance than the single model. This study has academic significance in that it goes beyond simple deep learning-based cryptocurrency price prediction and improves investment performance by combining deep learning and short-term trading strategies, and has practical significance in that it shows the applicability in actual investment.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.2
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• pp.15-24
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• 2019

This study analyzes the effect of the capital structure of Korean manufacturing firms on default risk based on Moody's KMV option pricing model where the probability of default is obtained by measuring the distance to default as a covariant in logit model developed by Merton (1974). Based on the panel data of manufacturing firms, this study achieves its primary objective, using a fixed effect regression model and examines the effect of a firm's capital structure on default risk amongst publicly listed firms on Korea exchange during 2005-2016. Empirical results obtained suggest that the rise in short-term debt to assets leads to increase the risk of default whereas the increase in long-term debt to assets leads to decrease the default risk. The benefits of short-term debt financing over a short-term period fade out in the presence of information asymmetry. However, long-term debt financing overcomes the information asymmetry and enjoys the paybacks of tax advantage associated with long-term debt. Additionally, size, tangibility and interest coverage ratio are also the important determinants of default risk. Findings support the trade-off theory of capital structure and recommend the optimal use of long-term debt in a firm's capital structure.

• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.515-525
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• 2020

This study investigates the potential of bidirectional long short-term memory (Bi-LSTM) for efficient modeling of temporal information in crop classification using multitemporal remote sensing images. Unlike unidirectional LSTM models that consider only either forward or backward states, Bi-LSTM could account for temporal dependency of time-series images in both forward and backward directions. This property of Bi-LSTM can be effectively applied to crop classification when it is difficult to obtain full time-series images covering the entire growth cycle of crops. The classification performance of the Bi-LSTM is compared with that of two unidirectional LSTM architectures (forward and backward) with respect to different input image combinations via a case study of crop classification in Anbadegi, Korea. When full time-series images were used as inputs for classification, the Bi-LSTM outperformed the other unidirectional LSTM architectures; however, the difference in classification accuracy from unidirectional LSTM was not substantial. On the contrary, when using multitemporal images that did not include useful information for the discrimination of crops, the Bi-LSTM could compensate for the information deficiency by including temporal information from both forward and backward states, thereby achieving the best classification accuracy, compared with the unidirectional LSTM. These case study results indicate the efficiency of the Bi-LSTM for crop classification, particularly when limited input images are available.

• Journal of Information Technology Applications and Management
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• v.29 no.5
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• pp.27-37
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• 2022

This study intends to link agricultural machine history data with related organizations or collect them through IoT sensors, receive input from agricultural machine users and managers, and analyze them through AI algorithms. Through this, the goal is to track and manage the history data throughout all stages of production, purchase, operation, and disposal of agricultural machinery. First, LSTM (Long Short-Term Memory) is used to estimate oil consumption and recommend maintenance from historical data of agricultural machines such as tractors and combines, and C-LSTM (Convolution Long Short-Term Memory) is used to diagnose and determine failures. Memory) to build a deep learning algorithm. Second, in order to collect historical data of agricultural machinery, IoT sensors including GPS module, gyro sensor, acceleration sensor, and temperature and humidity sensor are attached to agricultural machinery to automatically collect data. Third, event-type data such as agricultural machine production, purchase, and disposal are automatically collected from related organizations to design an interface that can integrate the entire life cycle history data and collect data through this.

• Korean Journal of Agricultural Science
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• v.38 no.3
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• pp.583-591
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• 2011

Status survey on short-term agricultural machinery rental business was conducted to provide basic data for effective and sustainable implementation of the rental system. Selected survey samples were 34 rental management institutions such as city and county level government offices and agricultural technology development centers, and Primary Agricultural Cooperatives. Survey was conducted through mailing of questionnaire papers and direct interviews with the officers in charge of the agricultural machinery short-term rental management. Number of agricultural machinery retained by the 34 management institutions for the machinery rental business was 3,699, and numbers of the machinery were 1630 for upland crops, 929 for rice, 542 for orchard farming, 274 for animal husbandry, and 324 for common use. Regarding size of warehouse for rental agricultural machinery, 50% of the institutes were less than 660 $m^2$, 26.5% were greater than 993 $m^2$, and 23.5% were between 663 and 990 $m^2$. Institutes maintaining machinery washing facilities were only 10 (29%) among the 34 rental management institutions. Agricultural machinery rental business was advertised to farmers by 91% of the institutes, and the methods were leaflet (35.2%), village broadcasting (26.5%), call-up education (23.6%), and TV and radio (14.7%). Major contents of the advertisement were rental procedure (52.9%), rental machinery (26.5%), and rental cost (20.6%).

• Journal of Biosystems Engineering
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• v.40 no.3
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• pp.178-185
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• 2015

Purpose: This research suggests a method of establishing criteria for working area and operating cost for a long-term lease of agricultural machinery. Methods: Eight crops were selected-three food crops and five open-field vegetables-and agricultural machines used for sowing, transplanting, and cultivation in dry-field farming were analyzed. Results: The break-even acreage for agricultural machinery under a long-term lease was found to differ by agricultural machine, ranging from 1.0 to 5.8 ha. In terms of arable land area, the break-even acreages for harvesting machinery and transplanters were 15.6 to 26.1 ha and 6.1 to 8.6 ha, respectively. The working area lessees should secure was divided into two cases: (1) 2.0 to 11.6 ha when leasing individual agricultural machines (sowing and transplanting) for a long-term period, and (2) more than 10 ha when farmers who cultivate beans, potatoes, garlic, onions, and so on lease sowing and transplanting machines as a set. When agricultural machinery was leased for a long term, the operating cost and working time were reduced by 27.6 to 74.4% and 2.5 to 21.6%, respectively, indicating considerable effect. Conclusions: A long-term lease project needs to be promoted to overcome the limitation of short-term leases of agricultural machinery. The local government should lead this project and facilitate the mechanization of dry-field farming. The department in charge of agricultural machinery lease projects needs to set the working area to cover the rate and maintenance cost for farmers who lease agricultural machinery for the long term.

• The Journal of the Convergence on Culture Technology
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• v.4 no.4
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• pp.155-160
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• 2018

The article studies the effect of US real effective exchange rate and short-term interest rate on Chnise exports and imports using the EGARCH-GED model. This article analyze the effect of US major economic variables on China's exports and imports as the US pushes for interest rate hikes and worsens trade wars with China. The main results are as follows. The US short-term interest rate has a significant positive effect on China's trade volume. Even in the case of China's exports, US short-term interest rate has a significant positive effect. However, in the case of China's imports, in contrast to exports, US short-term interest rate do not have a significant effects and US real effective exchange rate has a significant positive effect. On the other hand, China's policy interest rate has a negative impact on China's imports and not statistically significant, but it has a significant positive effect on China's exports.