• 한국멀티미디어학회논문지
• /
• 제24권3호
• /
• pp.448-459
• /
• 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
• /
• 제30권4호
• /
• pp.719-740
• /
• 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
• /
• 제11권1호
• /
• pp.171-180
• /
• 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
• /
• 제16권5호
• /
• pp.1759-1764
• /
• 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
• /
• 제23권7호
• /
• pp.210-218
• /
• 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
• /
• 제7권10호
• /
• pp.9-21
• /
• 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.

• 한국항공운항학회지
• /
• 제31권3호
• /
• pp.103-118
• /
• 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.

• 한국항해항만학회지
• /
• 제42권5호
• /
• pp.341-346
• /
• 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.

• 한국정보통신학회논문지
• /
• 제23권11호
• /
• pp.1414-1419
• /
• 2019

4차 산업시대의 기술 급변에 따른 공백기술을 도출하고 유망기술을 발굴하기 위한 지역산업의 움직임을 특허지표를 활용하여 분석하였다. 현재 창원시는 미래 지역주력산업으로 수소 전기차 기술을 중점 육성하여 미래 지역 유망기술로 발굴하고자 많은 기술 정보를 수집하고 있다. 기술 정보 수집은 분류방법, 기술 동향, 유사기술 등으로 인해 시간과 비용 측면에서 많은 문제를 가지고 있다. 따라서 기술 정보의 체계적인 분류와 기술 동향을 쉽게 도출할 방법이 필요하다. 본 논문에서는 특허지표를 통해 출원 성장률 측정 방법과 특허 출원 빈도 산정 과정을 통해 지역의 미래 주력산업에 대한 공백기술과 유망기술 동향을 분석하였다. 창원시의 미래 지역주력 산업인 수소 전기차의 기술 분류별, 국가별, 주요기업별 특허 동향을 조사하여, 특허 출원의 추세성과 기술의 혁신성 및 집중도인 기술성과 활동성을 분석하였다. 중복기술을 제거하고 기술 연관성을 고려하여 634건에 대해 특허 정보를 기반으로 기술 동향 분석한 결과 향후 지역산업을 위해 특허 출원을 확보하여 지역산업과 기업 경쟁력 제고가 필요한 시점으로 분석되었다.

• 한국컴퓨터정보학회논문지
• /
• 제28권8호
• /
• pp.67-75
• /
• 2023

주가 예측은 금융시장에서 중요하게 다뤄지고 있는 주제이지만 영향을 미칠 수 있는 다수의 요소들로 인해 어려운 주제로 고려되고 있다. 본 논문에서는 시계열 예측 모델 (LSTM, GRU)과 데이터의 시간적 의존성을 고려하지 않는 비 시계열 예측 모델 (RF, SVR, KNN, LGBM)을 주가 예측에 적용하여 성능을 비교하고 분석하였다. 또한 주가 데이터와 기술적 분석 보조지표, 재무제표 지표, 매수매도 지표, 공매도, 외국인 지표 등 다양한 데이터를 조합 및 활용하여 최적의 예측 요소를 찾아내고 업종별로 주가 예측에 영향을 미치는 주요 요소들을 분석했다. 하이퍼파라미터 최적화 과정을 통해 알고리즘별 예측 성능을 향상 시키는 과정도 진행하여 성능에 영향을 주는 요인을 분석하였다. 변수 선택과 하이퍼 파라미터 최적화 과정을 거친 결과, 시계열 예측 알고리즘인 GRU, 그리고 LSTM+GRU의 예측 정확도가 가장 높은 것으로 나타났다.