• Proceedings of the Safety Management and Science Conference
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• 2009.04a
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• pp.337-350
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• 2009

Using the Malmquist productivity index, this paper compares the productivity of Korean manufacturing industries from 1999 to 2006. The malmquist productivity index has more advantages than previous methods such as solow model, endogenous growth theory. The malmquist productivity index can be decomposed productivity into two parts. The first part is an index of the technical efficiency change. This index has high value when the elements of production is used more efficiently. The second part is an index of technical change. This index has high value if the advent of new technology and process innovation occur.

• The Journal of Fisheries Business Administration
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• v.41 no.3
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• pp.21-44
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• 2010

The paper estimates changes in total factor productivity and technical efficiency change index and technical change index using Malmquist productivity index(MPI) in fisheries wholesale products over the time period of 2006 through 2008. The model considers a number of employees and operating costs as input factors, and sales and EBIT(earnings before tax and interest) as output factors. The results indicate that, between 2006 and 2007, there is in general technical progress in which TCI(Technical Change Index) indicates 2.7994 in the sale scale of 50 million won through 100 million won, while there are no efficiency in TECI(Technical Efficiency Change Index), PECI(Pure Efficiency Change Index) and SECI(Scale Efficiency Change Index) which are estimated to be around 1. Between 2007 and 2008 technical efficiency and technical progress are generally declined, compared to those of 2006 and 2007. Wilcoxon's rank-sum test shows that there are statistically significant difference of TCI and MPI between two periods at the level of 5%, while there are statistically significant difference of TECI, PECI and SECI between two periods at the level of 5%.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.17 no.32
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• pp.281-296
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• 1994

In this paper, a new index(joint index) which shows the higher confidence than that of the existing technical indices and has simple logical structure and an index which is an applied form of the existing index(OBV-I index) are proposed. The index characteristics which have been known are verified by choosing the three most used technical indices(ADR index, investment psychological rate and VR index) as samples. And the index characteristics of the joint index are compared to those of the OBV-I index. The comparisons are executed not by comparing the investment earning rate of each index but by calculating the unnecessary number of trade days in the total number of trade days according to the index.

• International journal of advanced smart convergence
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• v.11 no.2
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• pp.171-184
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• 2022

The purpose of this paper is output oriented, in order to maximize the output level of sustainable development efficiency of foreign trade in western China with limited input. This paper adopts the relevant input-output indicators of sustainable foreign trade development of 11 provinces and cities in western China from 2016 to 2020, and uses DEA model to measure their technical efficiency, pure technical efficiency and scale efficiency. Malmquist index was used to calculate the total factor productivity change index of each province in western China from 2016 to 2020. We found that, on the whole, the average values of technical efficiency, pure technical efficiency and scale efficiency of provinces and cities in western China from 2016 to 2020 are greater than 0.8, indicating that the western region has high technical efficiency, relatively high management and institutional level and high existing scale level. Scale efficiency is lower than pure technical efficiency on the whole, indicating that the current sustainable development efficiency of foreign trade in western China is mainly limited by its scale level. The technological progress index is higher than the technological efficiency change index, indicating that the total factor productivity of the sustainable development of foreign trade in western China is mainly driven by technological progress and more influenced by external factors. We think the conclusion of this study can provide important reference information for the sustainable development of foreign trade of provinces and cities in western China.

• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.547-553
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• 2022

This study analyzed the changes in the operational productivity of fourteen Korean container lines from 2019 to 2021 using MP I(Malmquist Productivity Index). The results indicated that the operational productivity of the shipping companies has increased by 38.4% annually, representing the TCI (Technical Change Index) increasing by 58.3% and the TECI (Technical Efficiency Change Index) decreasing by 12.6%. The increase in the operational productivity of the container shipping lines was mainly attributed to the high rise in ocean freight rates rather than an increase in fleet size or ship technical efficiency. However, the deep-sea shipping lines (i.e. HMM and SM lines) experienced increases in both the TCI and TECI, which was not the case for other shipping lines(i.e. Intra-Asian short-sea shipping lines). The intra-Asian short-sea shipping lines enhance their productivity due to the TCI but failed to appreciate the cost savings of the increased fleet effects due to the low SECI(Scale Efficiency Change Index) values.

• Journal of Korea Technology Innovation Society
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• v.4 no.3
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• pp.369-382
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• 2001

In this research, the world top steel companies' technical changes were measured by the nonparametric Malmquist index. Steel industry's production frontier which was composed of multiple inputs and multiple outputs was defined by using real data of the companies from 1986 to 1998, and then the variability of technical progress(or regress) was analyzed during that period. The dynamics of individual steel companies\` technical changes, periodical technical changes, and each country's technical chanties were explained. A matrix which was based on technical chanties, technical efficiency changes, and scale efficiency changes was designed to assist to find out current position and In build up future strategies. This research results will be an valuable input to the early stage of formulating competitive strategy because the technical core capability is an important source of the each companies's competitiveness.

• Korean Journal of Agricultural Science
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• v.45 no.4
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• pp.871-878
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• 2018

The government of Gyeongbuk in Korea increased the financial input in the agricultural sector by 43% from 2008 through 2012. However, the productivity of the agricultural sector in Gyeongbuk rose only about 3.6% during the same period. This study evaluated the agricultural competitiveness of each city or county in Gyeongbuk using six evaluation indices (scale, productivity, finance input, location quotient, technical education, and total) and investigated the relationship between the indices and total values of agricultural productions. The results show that Sangju, Gyeongju, Andong, Uiseong, and Seongju have high competitiveness for the scale index. For the productivity index, Gimcheon, Mungyeong, Ulleung, Yeongju, and Seongju were highly evaluated. For the location quotient index, Yeongcheon, Uiseong, Cheongdo, Seongju, and Sangju have high competitiveness. Uiseong, Yeongdeok, Cheongsong, Gunwi, and Yeongyang were highly evaluated for the agricultural finance input index. For the agricultural technical education index, Chilgok, Sangju, Mungyeong, Uiseong, and Gyeongju have high competitiveness. Finally, Sangju, Euiseong, Yeongcheon, Gyeongju, and Andong were highly evaluated for the total competitive index, while Yeongdeok, Yeongyang, Goryeong, Uljin, and Cheongsong have a low competitiveness for the total competitive index. This study also found that out of five indices, scale, location quotient, and technical education indices have a positive and statistically significant effect on the total values of agricultural production. However, we did not find any statistically significant effect of the productivity and finance input indices on the total values of agricultural production. Hence, we can conclude that the government should focus their agricultural policy on scale, location quotient and education to improve total agricultural production.

• Earthquakes and Structures
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• v.9 no.1
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• pp.145-171
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• 2015

Due to earthquakes, many structures suffered extensive damages that were attributed to the torsional effect caused by mass, stiffness or strength eccentricity. Due to this type of asymmetry torsional moments are generated that are imposed by means of additional shear forces developed at the vertical resisting structural elements of the buildings. Although the torsional effect on the response of reinforced concrete buildings was the subject of extensive research over the last decades, a quantitative index measuring the amplification of the shear forces developed at the vertical resisting elements due to lateral-torsional coupling valid for both elastic and elastoplastic response states is still missing. In this study a reliable index capable of assessing the torsional effect is proposed. The performance of the proposed index is evaluated and its correlation with structural response quantities like displacements, interstorey drift, base torque, shear forces and upper diaphragm's rotation is presented. Torsionally stiff, mass eccentric single-story and multistory structures, subjected to bidirectional excitation, are considered and nonlinear dynamic analyses are performed using natural records selected for three hazard levels. It was found that the proposed index provides reliable prediction of the magnitude of torsional effect for all test examples considered.

• Ocean and Polar Research
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• v.32 no.4
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• pp.387-396
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• 2010

This paper estimates changes in the total factor productivity and technical efficiency change index and the technical change index using the Malmquist productivity index (MPI) in fishery-processed wholesale products over the time period spanning 2006-2008. The model considers a number of employees and operating costs as input factors, and sales and EBIT (earnings before tax and interest) as output factors. The results demonstrate that, between 2006 and 2007, there has generally been no technical progress, although a small improvement in productivity was detected in the sales scale of 10 billion won-50 billion won. Between 2007 and 2008, there was technical progress in the majority of DMU, except within the range of 20 million won-10 billion won. Wilcoxon's rank-sum test, however, demonstrates no statistically significant differences in productivity between the two periods.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.187-201
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• 2011

KOSPI200 index is the Korean stock price index consisting of actively traded 200 stocks in the Korean stock market. Its base value of 100 was set on January 3, 1990. The Korea Exchange (KRX) developed derivatives markets on the KOSPI200 index. KOSPI200 index futures market, introduced in 1996, has become one of the most actively traded indexes markets in the world. Traders can make profit by entering a long position on the KOSPI200 index futures contract if the KOSPI200 index will rise in the future. Likewise, they can make profit by entering a short position if the KOSPI200 index will decline in the future. Basically, KOSPI200 index futures trading is a short-term zero-sum game and therefore most futures traders are using technical indicators. Advanced traders make stable profits by using system trading technique, also known as algorithm trading. Algorithm trading uses computer programs for receiving real-time stock market data, analyzing stock price movements with various technical indicators and automatically entering trading orders such as timing, price or quantity of the order without any human intervention. Recent studies have shown the usefulness of artificial intelligent systems in forecasting stock prices or investment risk. KOSPI200 index data is numerical time-series data which is a sequence of data points measured at successive uniform time intervals such as minute, day, week or month. KOSPI200 index futures traders use technical analysis to find out some patterns on the time-series chart. Although there are many technical indicators, their results indicate the market states among bull, bear and flat. Most strategies based on technical analysis are divided into trend following strategy and non-trend following strategy. Both strategies decide the market states based on the patterns of the KOSPI200 index time-series data. This goes well with Markov model (MM). Everybody knows that the next price is upper or lower than the last price or similar to the last price, and knows that the next price is influenced by the last price. However, nobody knows the exact status of the next price whether it goes up or down or flat. So, hidden Markov model (HMM) is better fitted than MM. HMM is divided into discrete HMM (DHMM) and continuous HMM (CHMM). The only difference between DHMM and CHMM is in their representation of state probabilities. DHMM uses discrete probability density function and CHMM uses continuous probability density function such as Gaussian Mixture Model. KOSPI200 index values are real number and these follow a continuous probability density function, so CHMM is proper than DHMM for the KOSPI200 index. In this paper, we present an artificial intelligent trading system based on CHMM for the KOSPI200 index futures system traders. Traders have experienced on technical trading for the KOSPI200 index futures market ever since the introduction of the KOSPI200 index futures market. They have applied many strategies to make profit in trading the KOSPI200 index futures. Some strategies are based on technical indicators such as moving averages or stochastics, and others are based on candlestick patterns such as three outside up, three outside down, harami or doji star. We show a trading system of moving average cross strategy based on CHMM, and we compare it to a traditional algorithmic trading system. We set the parameter values of moving averages at common values used by market practitioners. Empirical results are presented to compare the simulation performance with the traditional algorithmic trading system using long-term daily KOSPI200 index data of more than 20 years. Our suggested trading system shows higher trading performance than naive system trading.