• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.448-459
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• 2021

In this paper, we propose a method of predicting the next-day stock price fluctuations of 10 KOSDAQ-listed companies in 5G, autonomous driving, and electricity sectors by training SVM, XGBoost, and LightGBM models from macroeconomic·financial market indicators, technical indicators, social interest indicators, and daily positive indices extracted from online news. In the three experiments to find out the usefulness of social interest indicators and daily positive indices, the average accuracy improved when each indicator and index was added to the models. In addition, when feature selection was performed to analyze the superiority of the extracted features, the average importance ranking of the social interest indicator and daily positive index was 5.45 and 1.08, respectively, it showed higher importance than the macroeconomic financial market indicators and technical indicators. With the results of these experiments, we confirmed the effectiveness of the social interest indicators as alternative data and the daily positive index for predicting stock price fluctuation.

• Asia pacific journal of information systems
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• v.30 no.4
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• pp.719-740
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• 2020

The stock market changes continuously as new information emerges, affecting the judgments of investors. Online news articles are valued as a traditional window to inform investors about various information that affects the stock market. This paper proposed new ways to utilize online news articles with technical indicators. The suggested hybrid model consists of three models. First, a self-attention-based convolutional neural network (CNN) model, considered to be better in interpreting the semantics of long texts, uses news content as inputs. Second, a self-attention-based, bi-long short-term memory (bi-LSTM) neural network model for short texts utilizes news titles as inputs. Third, a bi-LSTM model, considered to be better in analyzing context information and time-series models, uses 19 technical indicators as inputs. We used news articles from the previous day and technical indicators from the past seven days to predict the share price of the next day. An experiment was performed with Korean stock market data and news articles from 33 top companies over three years. Through this experiment, our proposed model showed better performance than previous approaches, which have mainly focused on news titles. This paper demonstrated that news titles and content should be treated in different ways for superior stock price prediction.

• International Journal of Advanced Culture Technology
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• v.11 no.1
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• pp.171-180
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• 2023

This study is to develop a performance analysis model for SMEs.Based on similar performance indicators through previous studies, performance indicators for SMEs were rewritten.Through the Korean Journal Citation Index (KCI), 75 related data were classified and a comprehensive SME performance analysis model was developed.Performance analysis was divided into two axes and classified into tables.The horizontal axis is the spatial performance range, which is divided into three areas: performance management by department/function, integrated performance management for the entire organization, and governance performance management requiring policy feedback. The vertical axis is subdivided into short-term, mid-term, and long-term by time and growth stage, and is divided into three parts: technical performance according to technological input, economic performance as organizational performance, and social performance for policy utilization. Then, performance indicators were mapped to each column. As a result of the survey, 28% of technical performance was analyzed as a result of frequency analysis, and performance indicators were organized into five categories: IT, R&D, certification, patent, and innovation. Economic performance was divided into 29%, BSC, HRD, logistics, production quality management, financial support, asset management, etc. 6 categories, social performance 43%, ESG, marketing, export, policy support, consulting, cooperation, etc. 7 categories.Limitations of the study include the narrowness of the survey that derived only performance indicators despite being a meta-analysis, and the performance model was mapped and classified according to growth stage and support period.however Insufficiency of validity due to lack of evidence, performance indicators were developed, but there were limitations in utilization for practical use.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.5
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• pp.1759-1764
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• 2015

Purpose: To establish the concept of lung cancer hazard assessment theoretical models, evaluating the degree of lung cancer risk of Beijing for regional population lung cancer hazard assessment to provide a basis for technical support. Materials and Methods: ISO standards were used to classify stratified analysis for the entire population, life cycle, processes and socioeconomic management. Associated risk factors were evaluated as lung cancer hazard risk assessment first class indicators. Study design: Using the above materials, indicators were given the weight coefficients, building lung cancer risk assessment theoretical models. Regional data for Beijing were entered into the theoretical model to calculate the parameters of each indicator and evaluate the degree of local lung cancer risk. Results: Adopting the concept of lung cancer hazard assessment and theoretical models for regional populations, we established a lung cancer hazard risk assessment system, including 2 first indicators, 8 secondary indicators and 18 third indicators. All indicators were given weight coefficients and used as information sources. Score of hazard for lung cancer was 84.4 in Beijing. Conclusions: Comprehensively and systematically building a lung cancer risk assessment theoretical model for regional populations in conceivable, evaluating the degree of lung cancer risk of Beijing, providing technical support and scientific basis for interventions for prevention.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.210-218
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• 2023

The author presents a simple data-driven intraday technical indicator trading approach based on Genetic Programming (GP) for return forecasting in the Bitcoin market. We use five trend-following technical indicators as input to GP for developing trading rules. Using data on daily Bitcoin historical prices from January 2017 to February 2020, our principal results show that the combination of technical analysis indicators and Artificial Intelligence (AI) techniques, primarily GP, is a potential forecasting tool for Bitcoin prices, even outperforming the buy-and-hold strategy. Sensitivity analysis is employed to adjust the number and values of variables, activation functions, and fitness functions of the GP-based system to verify our approach's robustness.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.9-21
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• 2020

The aim of this paper is to predict the Borsa Istanbul (BIST) 30 index movements to determine the most accurate buy and sell decisions using the methods of Artificial Neural Networks (ANN) and Genetic Algorithm (GA). We combined these two methods to obtain a hybrid intelligence method, which we apply. In the financial markets, over 100 technical indicators can be used. However, several of them are preferred by analysts. In this study, we employed nine of these technical indicators. They are moving average convergence divergence (MACD), relative strength index (RSI), commodity channel index (CCI), momentum, directional movement index (DMI), stochastic oscillator, on-balance volume (OBV), average directional movement index (ADX), and simple moving averages (3-day moving average, 5-day moving average, 10-day moving average, 14-day moving average, 20-day moving average, 22-day moving average, 50-day moving average, 100-day moving average, 200-day moving average). In this regard, we combined these two techniques and obtained a hybrid intelligence method. By applying this hybrid model to each of these indicators, we forecast the movements of the Borsa Istanbul (BIST) 30 index. The experimental result indicates that our best proposed hybrid model has a successful forecast rate of 75%, which is higher than the single ANN or GA forecasting models.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.31 no.3
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• pp.103-118
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• 2023

An indicator system is an effective way to monitor ongoing safety status. Current aviation safety measurements account for many qualitative technical and lagging indicators. Conversely, quantitative and leading indicators have only a tiny proportion. This research added more quantitative leading indicators and reviewed them to harmonize lagging and leading indicators to measure airport safety and provide an index. The South Korean national gate, Incheon International Airport's indicators, were applied as primary data to verify this research practically. Then, examples from International and national authorities were reviewed and extracted for use. Fifty-five safety specialists participated in the focus group discussion and three rounds of the Delphi survey. Finally, 51 sub-indicators were newly chosen. After this process, weights for each indicator could be assigned using the AHP (Analytical Hierarchy Process) to provide an integrated index. The result of the simulation with newly added indicators in the past five years (2020-2022) reliable trend showed in indicators and integrated index. Moreover, this allows monitoring the status of the details of indicators and holistic insight. This study considered that it is more suitable for a single company or service provider to use it according to the exact situation than in a macro- and general-purpose at the country or regional level.

• Journal of Navigation and Port Research
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• v.42 no.5
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• pp.341-346
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• 2018

Supramax bulk carriers cover a wide range of ocean transportation requirements, from major to minor bulk cargoes. Market forecasting for this segment has posed a challenge to researchers, due to complexity involved, on the demand side of the forecasting model. This paper addresses this issue by using technical indicators as input features, instead of complicated supply-demand variables. Artificial neural networks (ANN), one of the most popular machine-learning tools, were used to replace classical time-series models. Results revealed that ANN outperformed the benchmark binomial logistic regression model, and predicted direction of the spot market with more than 70% accuracy. Results obtained in this paper, can enable chartering desks to make better short-term chartering decisions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1414-1419
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• 2019

Patent indicators were used to analyze the movements of local industries in order to derive blank technologies due to rapid changes in technology in the 4th Industrial Revolution and to discover promising technologies. Currently, Changwon-si is gathering a lot of technical information to develop hydrogen electric vehicle technology as a future regional flagship industry to discover it as a promising technology in the future. Collecting technical information has many problems in terms of time and cost due to classification methods, technical trends, and similar technologies. Therefore, a systematic classification of technical information and a method for easily deriving technical trends are needed. In this paper, we analyzed the blank technology and promising technology trends for the future core industries of the region through the method of measuring the growth rate of patents and the frequency of patent application through the patent indicators.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.67-75
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• 2023

Stock price prediction is an important topic extensively discussed in the financial market, but it is considered a challenging subject due to numerous factors that can influence it. In this research, performance was compared and analyzed by applying time series prediction models (LSTM, GRU) and non-time series prediction models (RF, SVR, KNN, LGBM) that do not take into account the temporal dependence of data into stock price prediction. In addition, various data such as stock price data, technical indicators, financial statements indicators, buy sell indicators, short selling, and foreign indicators were combined to find optimal predictors and analyze major factors affecting stock price prediction by industry. Through the hyperparameter optimization process, the process of improving the prediction performance for each algorithm was also conducted to analyze the factors affecting the performance. As a result of feature selection and hyperparameter optimization, it was found that the forecast accuracy of the time series prediction algorithm GRU and LSTM+GRU was the highest.