• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.1059-1062
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• 2021

투자에 대한 관심 증가에 따라 적은 비용과 시간으로 객관적인 정보 제공의 필요성 증가와 함께 인공지능 기술을 활용한 로보어드바이저 서비스가 확대되었다. 또한, 최근 ETF 를 통한 안정적인 투자에 대한 선호도가 증가함에 따라 ETF 중심의 AI 로보어드바이저 추천 서비스가 필요할 것으로 보인다. 하지만, 기존의 투자 어플리케이션에서는 뉴스 기반의 감성적인 요인이 반영되지 않은 추천 방식으로 주가에 영향을 미치는 다양한 요인들을 고려하지 못하는 문제점이 있다. 이에 본 연구에서는 뉴스의 감성분석을 통한 감성지수를 기반으로 새로운 주가 예측 모델을 제안하고, 사용자의 투자 성향 분석을 통한 맞춤 추천 서비스를 통해 개인화된 ETF 서비스를 제공한다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.572-578
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• 2020

The stock market is affected by unexpected factors, such as politics, society, and natural disasters, as well as by corporate performance and economic conditions. In recent days, artificial intelligence has become popular, and many researchers have tried to conduct experiments with that. Our study proposes an experiment using not only stock-related data but also other various economic data. We acquired a year's worth of data on stock prices, the percentage of foreigners, interest rates, and exchange rates, and combined them in various ways. Thus, our input data became diversified, and we put the combined input data into a nonlinear autoregressive network with exogenous inputs (NARX) model. With the input data in the NARX model, we analyze and compare them to the original data. As a result, the model exhibits a root mean square error (RMSE) of 0.08 as being the most accurate when we set 10 neurons and two delays with a combination of stock prices and exchange rates from the U.S., China, Europe, and Japan. This study is meaningful in that the exchange rate has the greatest influence on stock prices, lowering the error from RMSE 0.589 when only closing data are used.

• Annual Conference on Human and Language Technology
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• 2020.10a
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• pp.450-452
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• 2020

기술의 발전과 함께 사용자에게 가까이 자리 잡은 소셜 네트워크 서비스는 이미지, 동영상, 텍스트 등 활용 가능한 데이터의 수를 폭발적으로 증가시켰다. 작성자의 감정을 포함하고 있는 텍스트 데이터는 시장 조사, 주가 예측 등 다양한 분야에서 이용할 수 있으며, 이로 인해 긍부정의 이진 분류가 아닌 다중 감정 분석의 필요성 또한 높아지고 있다. 본 논문에서는 딥러닝 기반 감정 분류에 심리학 이론의 기반 감정 모델을 활용한 결합 모델과 단일 모델을 비교한다. 학습을 위해 AI Hub에서 제공하는 데이터와 노래 가사 데이터를 복합적으로 사용하였으며, 결과에서는 대부분의 경우에 결합 모델이 높은 결과를 보였다.

• International Area Studies Review
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• v.13 no.3
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• pp.287-308
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• 2009

This article examines the distributional characteristics of the return of Chinese stock market indices. The majority of previous empirical researches have tended to focus upon the simple stock market index. However, this study focuses on the four indices which represent the characteristics of each stock market index. The empirical findings indicate that the returns of the four chinese indices are not normally distributed at conventional levels. The Ljimg-Box -statistics indicate the returns of the index of A shares are not serially autocorrelated. However, the returns of the index of B shares are serially autocorrelated. The empirical findings also indicate returns of the four chinese indices are not serially autocorrelated. The statistics of Regression Specification Error Test and ARCH indicate the returns of all four indices are not serially linear. The findings also indicate that E- GARCH model is the most fittest model for the returns of the four chinese indices and the forecast error can be reduced by using student t distribution rather normal distribution.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.23-30
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• 2022

Deep learning analyzes data to discover a series of rules and anticipates the future, helping us in various ways in our lives. For example, prediction of stock prices and agricultural prices. In this research, the results of solar photovoltaic power generation accompanied by weather are analyzed through deep learning in situations where the importance of solar energy use increases, and the amount of power generation is predicted. In this research, we propose a model using LSTM(Long Short Term Memory network) that stand out in time series data prediction. And we compare LSTM's performance with CNN(Convolutional Neural Network), which is used to analyze various dimensions of data, including images, and CNN-LSTM, which combines the two models. The performance of the three models was compared by calculating the MSE, RMSE, R-Squared with the actual value of the solar photovoltaic power generation performance and the predicted value. As a result, it was found that the performance of the LSTM model was the best. Therefor, this research proposes predicting solar photovoltaic power generation using LSTM.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.547-549
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• 2012

Forex trading participants, due to the intensified economic internationalization exchange risk avoidance measures are needed. In this research, Model suitable for estimation of time-series data, such as stock prices and exchange rates, through the concealment of HMM and estimate the short-term exchange rate forecasting model is applied to the prediction of the future. Estimated by applying the optimal model if the real exchange rate data for a certain period of the future will be able to predict the movement aspect of it. Alleged concealment of HMM. For the estimation of the model to accurately estimate the number of states of the model via Bayesian Information Criterion was confirmed as a model predictive aspect of physical exercise aspect and predict the movement of the two curves were similar.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.81-94
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• 2022

Real estate and artworks were considered challenging investment targets for individual investors because of their relatively high average transaction price despite their long investment history. Recently, the so-called fractional investment, generally known as investing in a share of the ownership right for real-life assets, etc., and most investors perceive that they actually own a piece (fraction) of the ownership right through their investments, is gaining popularity. Founded in 2016, Musicow started the first service that allows users to invest in copyright fees related to music distribution. Using the LSTM algorithm, one of the deep learning algorithms, this research predict the price of right to participate in copyright fees traded in Musicow. In addition to variables related to claims such as transfer price, transaction volume of claims, and copyright fees, comprehensive indicators indicating the market conditions for music copyright fees participation, exchange rates reflecting economic conditions, KTB interest rates, and Korea Composite Stock Index were also used as variables. As a result, it was confirmed that the LSTM algorithm accurately predicts the transaction price even in the case of fractional investment which has a relatively low transaction volume.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1435-1440
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• 2021

With the advent of the low interest rate era, many investors are flocking to the stock market. In the past stock market, people invested in stocks labor-intensively through company analysis and their own investment techniques. However, in recent years, stock investment using artificial intelligence and data has been widely used. The success rate of stock prediction through artificial intelligence is currently not high, so various artificial intelligence models are trying to increase the stock prediction rate. In this study, we will look at various artificial intelligence models and examine the pros and cons and prediction rates between each model. This study investigated as stock prediction programs using artificial intelligence artificial neural network (ANN), deep learning or hierarchical learning (DNN), k-nearest neighbor algorithm(k-NN), convolutional neural network (CNN), recurrent neural network (RNN), and LSTMs.

• Journal of KIISE:Databases
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• v.34 no.3
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• pp.179-192
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• 2007

This paper addresses an approach that recommends investment types for stock investors by discovering useful rules from past changing patterns of stock prices in databases. First, we define a new rule model for recommending stock investment types. For a frequent pattern of stock prices, if its subsequent stock prices are matched to a condition of an investor, the model recommends a corresponding investment type for this stock. The frequent pattern is regarded as a rule head, and the subsequent part a rule body. We observed that the conditions on rule bodies are quite different depending on dispositions of investors while rule heads are independent of characteristics of investors in most cases. With this observation, we propose a new method that discovers and stores only the rule heads rather than the whole rules in a rule discovery process. This allows investors to define various conditions on rule bodies flexibly, and also improves the performance of a rule discovery process by reducing the number of rules. For efficient discovery and matching of rules, we propose methods for discovering frequent patterns, constructing a frequent pattern base, and indexing them. We also suggest a method that finds the rules matched to a query issued by an investor from a frequent pattern base, and a method that recommends an investment type using the rules. Finally, we verify the superiority of our approach via various experiments using real-life stock data.

• Journal of KIISE:Software and Applications
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• v.32 no.8
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• pp.693-703
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• 2005

Given the local traders with pattern-based multi-predictors of stock prices, we study a method of dynamic asset allocation to maximize the trading performance. To optimize the proportion of asset allocated to each recommendation of the predictors, we design an asset allocation strategy called meta policy in the reinforcement teaming framework. We utilize both the information of each predictor's recommendations and the ratio of the stock fund over the total asset to efficiently describe the state space. The experimental results on Korean stock market show that the trading system with the proposed meta policy outperforms other systems with fixed asset allocation methods. This means that reinforcement learning can bring synergy effects to the decision making problem through exploiting supervised-learned predictors.