• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.

• Journal of Animal Science and Technology
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• v.51 no.5
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• pp.395-406
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• 2009

Fifty four Hanwoo steers in final fattening period were assigned to 3 groups control, top dressed $PROSOL^{(R)}$ as an emulsifier (TP) and DSP group (experimental diet made to down spec of nutrients with $PROSOL^{(R)}$, which is sodium stearoyl-2-lactylate), based on the body weight (647.96${\pm}$41.31 kg) and months of age (27.3${\pm}$0.8 mo), and the experiment was conducted to establish the reasonable fattening method of Hanwoo steers for 91 days. Average daily body gains during trial were 0.94, 0.84 and 0.98 kg in control, TP and DSP, respectively (P=0.011). DDMI/ADG of TP group tend to lightly improving compared to control and DSP group (P=0.692). There was no effect of supplementation of the $PROSOL^{(R)}$ in concentrates on growth rate, feed efficiency and shrinkage in Hanwoo steers during the short final fattening period. MQI from TP and DSP was not different based on the rib-eye area and carcass weight in carcass than that from control. Back fat thickness tended to be thicker than control (P>0.05). Marbling score, texture and maturity for TP and DSP was abundantly to increase compared to control (P<0.003). There was significantly increment in supplemented $PROSOL^{(R)}$ (P<0.0001). The meat quality grade of control, TP and DSP were 2.94, 3.78 and 4.50, respectively. Related to this result, the auction price (carcass/kg) were gained significantly (P<0.003) from control (17,560), TP (18,586) and DSP (19,266 won) so which the monetary return was the highest in DSP and the differences was recognized between TP and DSP. Percentage over 1st grade appeared in control, TP and DSP were 55.4, 88.9 and 100.0%, respectively. These results supported the hypothesis that supplementation of emulsifier improve the marbling score and the carcass quality grade by increased digestibilities of the feed fat in Hanwoo steers in fattening period.

• Korean Journal of Heritage: History & Science
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• v.38
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• pp.179-222
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• 2005

Studies prosecuted on relics in those 17 bronze workshops that have been thus far excavated show that these workshops may be roughly classified into a royal workshop, a state-operated workshop and a private workshop depending upon by whom they were operated. Workshops in the Gyeongju area developed from a small royal handicraft manufacturing to a large state-operated handicraft manufacturing scale, and then later on gradually changed to a private handicraft manufacturing industry. The royal bronze workshops were operated in a small scale, as shown from the relics excavated at Wolseong(月城), Imhaejeonji(Anapji:雁鴨池) and their neighborhood places around Hwangnam_dong(皇南洞). The state-operated bronze workshops are concentrated upon one point around Dongcheon-dong(東川洞), Gyeongju city. On the other hand, in the state-operated workshop stage, a broad street was built by a workshop, which is presumed to aim to thoroughly transport materials needed for the workshop. And the point that wastes from bronze workshops were used for road repairs indicates that road repair works were carried at the bronze workshops near the road. The private workshop as a new type of workshop was operated by the aristocracy. For that purpose, craftsmen belonging to state-operated workshops or individual artisans were absorbed into the aristocracy-operated workshops. These types of workshops were pervaded throughout the city. When private workshops came to emerge in the houses of the aristocracy, the operating subjects of workshops began to change from state-operated to private workshops. Temple workshops were located at a Buddhist temple within the Court and directly produced things needed for the court, including bronze foundries. As aforementioned, through the presence of bronze workshops operated in the Silla Court, we can identify the relationships between their technical level and trading areas and among their origin, supply and demand sources, along with phases of social life in those days.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.237-262
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• 2022

There has been a growing interest in IPOs (Initial Public Offerings) due to the profitable returns that IPO stocks can offer to investors. However, IPOs can be speculative investments that may involve substantial risk as well because shares tend to be volatile, and the supply of IPO shares is often highly limited. Therefore, it is crucially important that IPO investors are well informed of the issuing firms and the market before deciding whether to invest or not. Unlike institutional investors, individual investors are at a disadvantage since there are few opportunities for individuals to obtain information on the IPOs. In this regard, the purpose of this study is to provide individual investors with the information they may consider when making an IPO investment decision. This study presents a model that uses machine learning and text analysis to predict whether an IPO stock price would move up or down after the first 5 trading days. Our sample includes 691 Korean IPOs from June 2009 to December 2020. The input variables for the prediction are three tone variables created from IPO prospectuses and quantitative variables that are either firm-specific, issue-specific, or market-specific. The three prospectus tone variables indicate the percentage of positive, neutral, and negative sentences in a prospectus, respectively. We considered only the sentences in the Risk Factors section of a prospectus for the tone analysis in this study. All sentences were classified into 'positive', 'neutral', and 'negative' via text analysis using TF-IDF (Term Frequency - Inverse Document Frequency). Measuring the tone of each sentence was conducted by machine learning instead of a lexicon-based approach due to the lack of sentiment dictionaries suitable for Korean text analysis in the context of finance. For this reason, the training set was created by randomly selecting 10% of the sentences from each prospectus, and the sentence classification task on the training set was performed after reading each sentence in person. Then, based on the training set, a Support Vector Machine model was utilized to predict the tone of sentences in the test set. Finally, the machine learning model calculated the percentages of positive, neutral, and negative sentences in each prospectus. To predict the price movement of an IPO stock, four different machine learning techniques were applied: Logistic Regression, Random Forest, Support Vector Machine, and Artificial Neural Network. According to the results, models that use quantitative variables using technical analysis and prospectus tone variables together show higher accuracy than models that use only quantitative variables. More specifically, the prediction accuracy was improved by 1.45% points in the Random Forest model, 4.34% points in the Artificial Neural Network model, and 5.07% points in the Support Vector Machine model. After testing the performance of these machine learning techniques, the Artificial Neural Network model using both quantitative variables and prospectus tone variables was the model with the highest prediction accuracy rate, which was 61.59%. The results indicate that the tone of a prospectus is a significant factor in predicting the price movement of an IPO stock. In addition, the McNemar test was used to verify the statistically significant difference between the models. The model using only quantitative variables and the model using both the quantitative variables and the prospectus tone variables were compared, and it was confirmed that the predictive performance improved significantly at a 1% significance level.