• Information Systems Review
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• v.26 no.2
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• pp.45-57
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• 2024

This study develops a deep learning-based methodology for predicting Crude Palm Oil (CPO) prices. Palm oil is an essential resource across various industries due to its yield and economic efficiency, leading to increased industrial interest in its price volatility. While numerous studies have been conducted on palm oil price prediction, most rely on time series forecasting, which has inherent accuracy limitations. To address the main limitation of traditional methods-the absence of stationarity-this research introduces a novel model that uses the ratio of future prices to current prices as the dependent variable. This approach, inspired by return modeling in stock price predictions, demonstrates superior performance over simple price prediction. Additionally, the methodology incorporates the consideration of lag values of independent variables, a critical factor in multivariate time series forecasting, to eliminate unnecessary noise and enhance the stability of the prediction model. This research not only significantly improves the accuracy of palm oil price prediction but also offers an applicable approach for other economic forecasting issues where time series data is crucial, providing substantial value to the industry.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.7
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• pp.699-709
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• 2014

This paper proposes a new method for predicting the derailment of a running train under cross-wind conditions, using the single wheelset derailment theory. The conventional theories used for predicting the derailment due to cross-winds were developed under the assumption that derailment will always be of the roll-over type, thus neglecting other possible types such as wheel-climbing, which may occur under special driving conditions. In addition, these theories do not consider running conditions such as dynamic wheel-rail interactions and friction effects. The new method considers the effects of dynamic wheel-rail interaction as well as those of lateral acceleration, rail cant, and cross-winds. The results of this method were compared and verified with those of the conventional methods and numerical simulations.

• KDI Journal of Economic Policy
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• v.27 no.2
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• pp.123-154
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• 2005

Using Korean household data, this paper examines how consumption of durable goods is determined. Previous studies report that the standard Permanent Income Hypothesis (PIH), while being broadly consistent with non-durable goods consumption, provides little explanation for durable goods consumption. In this paper, we consider the (S, s) model as an alternative to the standard PIH. The (S, s) model predicts that, because of fixed adjustment costs, consumers make no adjustment to the durable goods stock until deviation from the optimal level becomes large. When the adjustments are made, the durable goods stock attains the optimal level. In order to test this prediction, we examine the intra-temporal relationship between non-durable goods and durable goods consumption and intertemporal changes in durable goods consumption, using data from the Korean Household Panel Study. The results show that, while the standard PIH is rejected by the data, the (S, s) model is not.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.227-230
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• 2015

For analyzing and prediction of economic trends, it is necessary to collect particular economic news and stock data. Typical Web crawler to analyze the page content, collects document and extracts URL automatically. On the other hand there are forms of crawler that can collect only document of a particular topic. In order to collect economic news on a particular Web site, we need to design a crawler which could directly analyze its structure and gather data from it. The wrapper-based web crawler design is required. In this paper, we design a crawler wrapper for Economic news analysis system based on big data and implemented to collect data. we collect the data which stock data, sales data from USA auto market since 2000 with wrapper-based crawler. USA and South Korea's economic news data are also collected by wrapper-based crawler. To determining the data update frequency on the site. And periodically updated. We remove duplicate data and build a structured data set for next analysis. Primary to remove the noise data, such as advertising and public relations, etc.

• Korean Business Review
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• v.16
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• pp.145-168
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• 2003

This study is mainly aimed at analyzing the influence of the divergency(mispricing) between KOSPI 200 theoretical prices and its real prices of KOSPI 200 spot index, considering the existence of arbitrage opportunity from the mispricing. The data in this study are the daily prices of 1262 days, from 3 May 1996 to 14 December 2000. The results of our empirical study represent that the real prices in KOSPI 200 Stock Index Futures are continuously undervalued relative to their corresponding theoretical prices. Our study reconfirms the results from previous studies conducted at the domestic and overseas markets. We conclude that the undervaluation, especially in the market opening period, could come from fear of investors, whose experiences in the stock index futures market are limited, chiefly because of loss and uncertainty of prediction toward interest rates and dividends. Our study also represents that KOSPI 200 index shows more volatilities during days with mispricing relative to days without mispricing.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.52-56
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• 2004

The Information of classification and estimate about KOSPI200 index`s up and down in the stock market becomes an important standard of decision-making in designing portofolio in futures and option market. Because the coming trend of time series patterns, an economic indicator, is very subordinate to the most recent economic pattern, it is necessary to study the recent patterns most preferentially. This paper compares classification and estimated performance of SVM(Support Vector Machine) and Fuzzy SVM model that are getting into the spotlight in time series analyses, neural net models and various fields. Specially, it proves that Fuzzy SVM is superior by presenting the most suitable dimension to fuzzy membership function that has time series attribute in accordance with learning Data Base.

• Journal of Energy Engineering
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• v.24 no.1
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• pp.17-23
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• 2015

Electrical door system is one of the most essential items for the successful commercial operation of the railway vehicles. Nowadays, reliability values of electrical door system have a tendency to be included in technical requirements for design and manufacturing of rolling stocks. Manufacturer shall meet the reliability target values of electrical door system which is proposed by railway operator in procurement contract book. Railway operator shall approve the supplier's the reliability target values based on maintenance operation data. Railway operators are in the transition stage from the framework of maintenance interval based on time to the framework of maintenance interval based on distance. In this study, failure rates of the electrical door system currently used in railway vehicles are collected from maintenance field data. Failure rates are analyzed by using Minitab. Several kinds of plan for improving reliability are also suggested. It is necessary to keep studying on reliability prediction methodology, applying it in the field and implementing on improvement of reliability through feedback as well. Further, it will be useful for determining new maintenance policies or changing maintenance intervals for existing railway vehicles.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.32-41
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• 2023

Recently, many studies are being conducted to extract emotion from text and verify its information power in the field of finance, along with the recent development of big data analysis technology. A number of prior studies use pre-defined sentiment dictionaries or machine learning methods to extract sentiment from the financial documents. However, both methods have the disadvantage of being labor-intensive and subjective because it requires a manual sentiment learning process. In this study, we developed a financial sentiment dictionary that automatically extracts sentiment from the body text of analyst reports by using modified Bayes rule and verified the performance of the model through a binary classification model which predicts actual stock price movements. As a result of the prediction, it was found that the proposed financial dictionary from this research has about 4% better predictive power for actual stock price movements than the representative Loughran and McDonald's (2011) financial dictionary. The sentiment extraction method proposed in this study enables efficient and objective judgment because it automatically learns the sentiment of words using both the change in target price and the cumulative abnormal returns. In addition, the dictionary can be easily updated by re-calculating conditional probabilities. The results of this study are expected to be readily expandable and applicable not only to analyst reports, but also to financial field texts such as performance reports, IR reports, press articles, and social media.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.