• Journal of Information Technology Applications and Management
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• v.19 no.1
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• pp.1-12
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• 2012

Data mining is the process of searching and analyzing large quantities of data for finding out meaningful patterns and rules. Artificial Neural Network (ANN) is one of the tools of data mining which is becoming very popular in forecasting the future values. Some of the areas where it is used are banking, medicine, retailing and fraud detection. In finance, artificial neural network is used in various disciplines including stock market forecasting. In the stock market time series, due to high volatility, it is very important to choose a model which reads volatility and forecasts the future values considering volatility as one of the major attributes for forecasting. In this paper, an attempt is made to develop two models - one using feed forward back propagation Artificial Neural Network and the other using Autoregressive Conditional Heteroskedasticity (ARCH) technique for forecasting stock market returns. Various parameters which are considered for the design of optimal ANN model development are input and output data normalization, transfer function and neuron/s at input, hidden and output layers, number of hidden layers, values with respect to momentum, learning rate and error tolerance. Simulations have been done using prices of daily close of Sensex. Stock market returns are chosen as input data and output is the forecasted return. Simulations of the Model have been done using MATLAB$^{(R)}$ 6.1.0.450 and EViews 4.1. Convergence and performance of models have been evaluated on the basis of the simulation results. Performance evaluation is done on the basis of the errors calculated between the actual and predicted values.

• Journal of Ecology and Environment
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• v.37 no.4
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• pp.209-216
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• 2014

This study was undertaken to suggest an effective fisheries resources management system by using stock assessment and potential yield analyses of crucian carp population in the mid-upper system of the Seomjin River. Fieldwork was conducted seasonally from 2008 to 2009 in the mid-upper system of the Seomjin River. The stock assessment was carried out by the swept area method and the potential yield was estimated by improved fisheries resource potential estimation system based on the Allowable Biological Catch. Also, the yield-per-recruit analysis was used to review the efficient management implication of the resource, Carassius auratus. As a result, the age at first capture ($t_c$) was estimated as 1.468 year, converted body length (BL) was 10.8 cm. Meaning the current fishing intensities, the instantaneous coefficient of fishing mortality (F) was $0.067year^{-1}$, and the yield-per-recruit analysis showed that the current yield per recruit was estimated to be 15.999 g with F and $t_c$. The instantaneous rate of fishing mortality that provides for Allowable Biological Catch ($F_{ABC}$) based on the current $t_c$ and F was estimated as $0.618year^{-1}$. Therefore, the optimum fishing intensities could be achieved at the higher fishing intensity for Carassius auratus. The calculated annual stock of C. auratus was estimated as 7,608 kg, and the potential yield was estimated as 343 kg with $t_c$ and F at the fixed current level. Using yield-per-recruit analysis, if F and $t_c$ were set at $0.618year^{-1}$ and 2 year, the yield per recruit and total allowable catch would be predicted to increase to 62 g and 2,531 kg by about 3.9 times and 7.3 times, respectively.

• Proceedings of the Korean Society of Plant Pathology Conference
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• 2003.10a
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• pp.73.1-73
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• 2003

In order to cultivate watermelon on farm, grafting of the watermelon seedling to the watermelon stock is necessary because the watermelon root is less viable than the root of watermelon stock. Recently, commercially important watermelon varieties further require a resistant stock against especially CGMMV to control the heavy loss of the total yield of watermelon by CGMMV infection. Therefore, we have set out a project to develop a CGNEMV-resistant watermelon stock. We have successfully transformed dozens of watermelon stocks (gongdae) during last two years especially using a cDNA encoding the coat protein of CGMMV (cucumber green mottle mosaic virus). Recently we have tested levels of resistance of those watermelon stocks against CGMMV infection. For CGMMV inoculation, the leaves of one month old gongdae (T1) were rubbed by carborundum mixed with the CGMMV. A total of 140 plants (T1) were exposed to the CGMMV and we found that ten plants were completely resistant to virus infection. This is the first report that by genetic engineering a cucubitaceae crop resistant to CGMMV infection is ever developed. Further information will be provided in the poster.

• The Journal of Industrial Distribution & Business
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• v.8 no.5
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• pp.87-93
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• 2017

Purpose - This paper investigates whether managerial overconfidence is associated with firm-specific crash risk. Overconfidence leads managers to overestimate the returns of their investment projects, and misperceive negative net present value projects as value creating. They even use voluntary disclosures to convey their optimistic beliefs about the firms' long-term prospects to the stock market. Thus, the overconfidence bias can lead to managerial bad news hoarding behavior. When bad news accumulates and crosses some tipping point, it will come out all at once, resulting in a stock price crash. Research design, data and methodology - 7,385 firm-years used for the main analysis are from the KIS Value database between 2006 and 2013. This database covers KOSPI-listed and KOSDAQ-listed firms in Korea. The proxy for overconfidence is based on excess investment in assets. A residual from the regression of total asset growth on sales growth run by industry-year is used as an independent variable. If a firm has at least one crash week during a year, it is referred to as a high crash risk firm. The dependant variable is a dummy variable that equals 1 if a firm is a high crash risk firm, and zero otherwise. After explaining the relationship between managerial overconfidence and crash risk, the total sample was divided into two sub-samples; chaebol firms and non-chaebol firms. The relation between how I overconfidence and crash risk varies with business group affiliation was investigated. Results - The results showed that managerial overconfidence is positively related to crash risk. Specifically, the coefficient of OVERC is significantly positive, supporting the prediction. The results are strong and robust in non-chaebol firms. Conclusions - The results show that firms with overconfident managers are likely to experience stock price crashes. This study is related to past literature that examines the impact of managerial overconfidence on the stock market. This study contributes to the literature by examining whether overconfidence can explain a firm's future crashes.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.907-920
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• 2016

This study suggests an outlier detection algorithm that uses quantile autoregressive model in time series data, eventually applying it to actual stock manipulation cases by comparing its performance to existing methods. Studies on outlier detection have traditionally been conducted mostly in general data and those in time series data are insufficient. They have also been limited to a parametric model, which is not convenient as it is complicated with an analysis that takes a long time. Thus, we suggest a new algorithm of outlier detection in time series data and through various simulations, compare it to existing algorithms. Especially, the outlier detection algorithm in time series data can be useful in finding stock manipulation. If stock price which had a certain pattern goes out of flow and generates an outlier, it can be due to intentional intervention and manipulation. We examined how fast the model can detect stock manipulations by applying it to actual stock manipulation cases.

• Journal of Climate Change Research
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• v.7 no.4
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• pp.499-506
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• 2016

This study was conducted to estimate carbon stock and uptake for Larix kaempferi Lamb., the single species, which is the most widely distributed one following Pinus densiflora, using data from 6th national forest inventory and forest type map of 1:5,000. Overall distribution area of Larix kaempferi in South Korea was shown as 272,800ha, in detail, Gangwon-do was the most widely distributed region with 39.6% (108,141 ha) of the whole forest area, and Gyeongsangbuk-do was 18.6%(50,839 ha), Chungcheongbuk-do was 15.1%(41,205ha) in order. As the results of analysis in carbon stock and uptake for each province, the values were high with Gyeonggi-do 109.0 tC/ha, $10.3tCO_2/ha/yr$, Gangwon-do 349.1 tC/ha, $9.7tCO_2/ha/yr$ in order, and Jeollabuk-do was the lowest with 78.3 tC/ha, $7.6tCO_2/ha/yr$. Also, the results of estimation in total carbon stocks and uptakes by year (1989~2015) were turned out that total carbon stocks and uptakes were 24,891 thousand tC, $2,428thousand\;tCO_2$ in 2015, increasing about 4.8 times and 3.8 times each compared with 5,238 thousand C/ha, $640thousand\;CO_2$ in 1989. Although forest area was decreased 26.6% with 371,884 ha in 1989 to 272,800 ha in 2015, carbon stocks and uptakes were increased in 2015 in that forest stock was increased 126% compared to 1989.

• Journal of Digital Convergence
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• v.18 no.11
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• pp.259-266
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• 2020

Many developments have been steadily carried out by researchers with applying knowledge-based expert system or machine learning algorithms to the financial field. In particular, it is now common to perform knowledge based system trading in using stock prices. Recently, deep learning technologies have been applied to real fields of stock trading marketplace as GPU performance and large scaled data have been supported enough. Especially, LSTM has been tried to apply to stock price prediction because of its compatibility for time series data. In this paper, we implement stock price prediction using LSTM. In modeling of LSTM, we propose a fitness combination of model parameters and activation functions for best performance. Specifically, we propose suitable selection methods of initializers of weights and bias, regularizers to avoid over-fitting, activation functions and optimization methods. We also compare model performances according to the different selections of the above important modeling considering factors on the real-world stock price data of global major companies. Finally, our experimental work brings a fitness method of applying LSTM model to stock price prediction.

• Ocean and Polar Research
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• v.40 no.4
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• pp.237-247
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• 2018

This study aims to estimate optimal harvests, fishing efforts, and stock levels of hairtail harvested by the large pair bottom trawl, the large otter trawl, the large purse seine, the offshore long line, and the offshore angling fisheries by using the surplus production models and the current value Hamiltonian method. Processes of this study are as follows. First of all, this study estimates the standardized fishing efforts regarding the harvesting of the hairtail by the above five fishing gears based on the general linear model developed by Gavaris. Secondly, this study estimates environmental carrying capacity (k), intrinsic growth rate (r), and catchability coefficient (q) by applying the Clarke Yoshimoto Pooley (CY&P) model among various surplus production models. Thirdly, this study estimates the optimal harvests, fishing efforts, and stock levels regarding the hairtail by the current value Hamiltonian method, including the average landing price, the average unit cost, and the social discount rate. Finally, this study attempts a sensitivity analysis to figure out changes in optimal harvests, fishing efforts, and stock levels due to changes in the average landing price and the average unit cost. As results induced by the current value Hamiltonian method, the optimal harvests, fishing efforts, and stock levels regarding the hairtail caught by several fishing gears were estimated as 33,133 tons, 901,080 horse power, and 79,877 tons, respectively. In addition, from the results of the sensitivity analysis, first of all, if the average landing price of the hairtail constantly increases, the optimal harvests of it increase at a decreasing rate, and then harvests finally slightly decrease as a result of decreases in stock levels. Secondly, if the average unit cost of fishing efforts continuously increases, the optimal fishing efforts decreases, but optimal stock levels increase. Optimal harvests start climbing and then decrease continuously due to increases in the average unit cost. In summary, this study suggests that the optimal harvests (33,133 tons) were larger than actual harvests (25,133 tons), but the optimal fishing efforts (901,080 horse power) were much less than estimated standardized fishing efforts (1,277,284 horse power), corresponding to the average of the recent three years (2014-2016). This result implies that the hairtail has been inefficiently harvested and recently overfished due to excessive fishing efforts. Efficient management and conservation policies on stock levels need to be urgently implemented. Some appropriate strategies would be to include the hairtail in the Korean TAC species or to extend the closed fishing season for this species.

• Advanced Industrial SCIence
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• v.2 no.3
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• pp.22-28
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• 2023

In this study, we conducted research on the automated trading of U.S. stocks through a volatility strategy using the Fear and Greed index. Volatility in the stock market is a common phenomenon that can lead to fluctuations in stock prices. Investors can capitalize on this volatility by implementing a strategy based on it, involving the buying and selling of stocks based on their expected level of volatility. The goal of this thesis is to investigate the effectiveness of the volatility strategy in generating profits in the stock market.This study employs a quantitative research methodology using secondary data from the stock market. The dataset comprises daily stock prices and daily volatility measures for the S&P 500 index stocks. Over a five-year period spanning from 2016 to 2020, the stocks were listed on the New York Stock Exchange (NYSE). The strategy involves purchasing stocks from the low volatility group and selling stocks from the high volatility group. The results indicate that the volatility strategy yields positive returns, with an average annual return of 9.2%, compared to the benchmark return of 7.5% for the sample period. Furthermore, the findings demonstrate that the strategy outperforms the benchmark return in four out of the five years within the sample period. Particularly noteworthy is the strategy's performance during periods of high market volatility, such as the COVID-19 pandemic in 2020, where it generated a return of 14.6%, as opposed to the benchmark return of 5.5%.

• Journal of the Korean Operations Research and Management Science Society
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• v.1 no.1
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• pp.151-170
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• 1976

There are many cases of production processes which intermittently produce several different kinds of products for stock through one set of physical facility. In this case, an important question is what size of production run should be prduced once we do set-up for a product in order to minimize the total cost, that is, the sum of the set-up, carrying, and stock-out costs. This problem is used to be called scheduling of multiple products through a single facility in the production management field. Despite the very common occurrence of this type of production process, no one has yet devised a method for determining the optimal production schedule. The purpose of this study is to develop quantitative analytical models which can be used practically and give us rational production schedules. The study is to show improved models with application to a can-manufacturing plant. In this thesis the economic production quantity (EPQ) model was used as a basic model to develop quantitative analytical models for this scheduling problem and two cases, one with stock-out cost, the other without stock-out cost, were taken into consideration. The first analytical model was developed for the scheduling of products through a single facility. In this model we calculate No, the optimal number of production runs per year, minimizing the total annual cost above all. Next we calculate No$_{i}$ is significantly different from No, some manipulation of the schedule can be made by trial and error in order to try to fit the product into the basic (No schedule either more or less frequently as dictated by) No$_{i}$, But this trial and error schedule is thought of inefficient. The second analytical model was developed by reinterpretation by reinterpretation of the calculating process of the economic production quantity model. In this model we obtained two relationships, one of which is the relationship between optimal number of set-ups for the ith item and optimal total number of set-ups, the other is the relationship between optimal average inventory investment for the ith item and optimal total average inventory investment. From these relationships we can determine how much average inventory investment per year would be required if a rational policy based on m No set-ups per year for m products were followed and, alternatively, how many set-ups per year would be required if a rational policy were followed which required an established total average inventory inventory investment. We also learned the relationship between the number of set-ups and the average inventory investment takes the form of a hyperbola. But, there is no reason to say that the first analytical model is superior to the second analytical model. It can be said that the first model is useful for a basic production schedule. On the other hand, the second model is efficient to get an improved production schedule, in a sense of reducing the total cost. Another merit of the second model is that, unlike the first model where we have to know all the inventory costs for each product, we can obtain an improved production schedule with unknown inventory costs. The application of these quantitative analytical models to PoHang can-manufacturing plants shows this point.int.