Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65
-
pISSN : 1598-849X / eISSN : 2383-9945
- https://journal.kci.go.kr/jksci
KJCKorea
Journal Central
A Study on Stock Trend Determination in Stock Trend Prediction
- Journal of The Korea Society of Computer and Information
- Abbr : JKSCI
- 2020, 25(12), pp.35-44
- DOI : 10.9708/jksci.2020.25.12.035
- Publisher : The Korean Society Of Computer And Information
- Research Area : Engineering > Computer Science
- Received : August 31, 2020
- Accepted : November 19, 2020
- Published : December 31, 2020
1한국교통대학교
Accredited
ABSTRACT
In this study, we analyze how stock trend determination affects trend prediction accuracy. In stock markets, successful investment requires accurate stock price trend prediction. Therefore, a volume of research has been conducted to improve the trend prediction accuracy. For example, information extracted from SNS (social networking service) and news articles by text mining algorithms is used to enhance the prediction accuracy. Moreover, various machine learning algorithms have been utilized. However, stock trend determination has not been properly analyzed, and conventionally used methods have been employed repeatedly. For this reason, we formulate the trend determination as a moving average-based procedure and analyze its impact on stock trend prediction accuracy. The analysis reveals that trend determination makes prediction accuracy vary as much as 47% and that prediction accuracy is proportional to and inversely proportional to reference window size and target window size, respectively.
Statistics
-
298 Viewed
-
111 Downloaded
Tools
Issue List
Citation status
* References for papers published after 2023 are currently being built.
[journal]
H. S. Sim
/ 2019
/ Is deep learning for image recognition applicable to stock market prediction?
/ Complexity
2019
: 1~10
[journal]
Xiangzhou Zhang
/ 2014
/ A causal feature selection algorithm for stock prediction modeling
/ Neurocomputing
/ Elsevier BV
142
: 48~59
[journal]
Xi Zhang
/ 2018
/ Improving stock market prediction via heterogeneous information fusion
/ Knowledge-Based Systems
/ Elsevier BV
143
: 236~247
[journal]
Asil Oztekin
/ 2016
/ A data analytic approach to forecasting daily stock returns in an emerging market
/ European Journal of Operational Research
/ Elsevier BV
253(3)
: 697~710
[journal]
Deepak Kumar
/ 2016
/ Proximal support vector machine based hybrid prediction models for trend forecasting in financial markets
/ Journal of Computational Science
/ Elsevier BV
17
: 1~13
[confproc]
Z. Lei
/ 2015
/ Price trend prediction of stock market using outlier data mining algorithm
/ Proceedings of IEEE International Conference on Big Data and Cloud Computing
: 93~98
[journal]
Jing Zhang
/ 2018
/ A novel data-driven stock price trend prediction system
/ Expert Systems with Applications
/ Elsevier BV
97
: 60~69
[journal]
Wen-Chyuan Chiang
/ 2016
/ An adaptive stock index trading decision support system
/ Expert Systems with Applications
/ Elsevier BV
59
: 195~207
[journal]
Yingjun Chen
/ 2018
/ Integrating principle component analysis and weighted support vector machine for stock trading signals prediction
/ Neurocomputing
/ Elsevier BV
321
: 381~402
[journal]
Ehsan Hoseinzade
/ 2019
/ CNNpred: CNN-based stock market prediction using a diverse set of variables
/ Expert Systems with Applications
/ Elsevier BV
129
: 273~285
[journal]
Wen Long
/ 2019
/ Deep learning-based feature engineering for stock price movement prediction
/ Knowledge-Based Systems
/ Elsevier BV
164
: 163~173
[confproc]
R. Socher
/ 2012
/ Convolutional-recursive deep learning for 3D object classification
/ Proceedings of International Conference on Neural Information Processing Systems
: 656~664
[confproc]
A. Graves
/ 2013
/ Speech recognition with deep recurrent neural networks
/ Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing
: 6645~6649
[confproc]
D. Chicco
/ 2014
/ Deep autoencoder neural networks for gene ontology annotation predictions
/ Proceedings of ACM Conference on Bioinformatics, COmputational Biology, and Health Informatics
: 533~540
[journal]
Andrew Sun
/ 2016
/ Trade the tweet: Social media text mining and sparse matrix factorization for stock market prediction
/ International Review of Financial Analysis
/ Elsevier BV
48
: 272~281
[journal]
Hongping Hu
/ 2018
/ Predicting the direction of stock markets using optimized neural networks with Google Trends
/ Neurocomputing
/ Elsevier BV
285
: 188~195
[confproc]
G. Park
/ 2010
/ Stock price forecasting using semi-supervised learning
/ Proceedings of KORMS Conference
: 110~116
[confproc]
S. Ahn
/ 2010
/ Stock prediction using news text mining and time series analysis
/ Proceedings of KIISE Conference
: 364~369
[journal]
신동근
/ 2011
/
Forecasting Short-Term KOSPI using Wavelet Transforms and Fuzzy Neural Network
/ 한국콘텐츠학회 논문지
/ 한국콘텐츠학회
11(6)
: 1~7
[journal]
김진화
/ 2011
/
A Real-Time Stock Market Prediction Using Knowledge Accumulation
/ 지능정보연구
/ 한국지능정보시스템학회
17(4)
: 109~130
[journal]
허준영
/ 2015
/
SVM based Stock Price Forecasting Using Financial Statements
/ 정보과학회 컴퓨팅의 실제 논문지
/ 한국정보과학회
21(3)
: 167~172
[confproc]
Z. Hu
/ 2013
/ Stocks market prediction using support vector machine
/ Proceedings of International Conference on Information Management, Innovation Management and Industrial Engineering
: 115~118
[confproc]
G. Dong
/ 2013
/ One-step and multi-step ahead stock prediction using backpropagation neural networks
/ Proceedings of International Conference on Information, Communicatio, and Signal Processing
: 1~5
[confproc]
F. Wang
/ 2014
/ Stock volatility prediction using multi-kernel learning based extreme learning machine
/ Proceedings of International Joint Conference on Neural Networks
: 3078~3085
[confproc]
Y. Xu
/ 2013
/ A study on feature selection for the trend prediction of stock trading price
/ Proceedings of International Conference on Computational and Information Sciences
: 579~582
[journal]
Kyoung-jae Kim
/ 2003
/ Financial time series forecasting using support vector machines
/ Neurocomputing
/ Elsevier BV
55(1-2)
: 307~319
[confproc]
Y. Lin
/ 2013
/ An SVM-based approach for stock market trend prediction
/ Proceedings of International Joint Conference on Neural Networks
: 1~7
[confproc]
A. K. Sirohi
/ 2014
/ Multiple kernel learning for stock price direction prediction
/ Proceedings of IEEE International Conference on Advances in Engineering &Technology Research
: 1~4
[confproc]
D. Kato
/ 2012
/ Stock prediction using multiple time series of stock prices and news articles
/ Proceedings of IEEE Symposium on Computers & Informatics
: 11~16
[confproc]
Y. Luo
/ 2014
/ Stock trends prediction based on hypergraph modeling clustering algorithm
/ Proceedings of IEEE International Conference on Progress in Informatics and Computing
: 27~31
[journal]
Kyoung-jae Kim
/ 2000
/ Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index
/ Expert Systems with Applications
/ Elsevier BV
19(2)
: 125~132
[journal]
Jigar Patel
/ 2015
/ Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques
/ Expert Systems with Applications
/ Elsevier BV
42(1)
: 259~268
[journal]
Chih-Fong Tsai
/ 2010
/ Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches
/ Decision Support Systems
/ Elsevier BV
50(1)
: 258~269
[journal]
Ming-Chi Lee
/ 2009
/ Using support vector machine with a hybrid feature selection method to the stock trend prediction
/ Expert Systems with Applications
/ Elsevier BV
36(8)
: 10896~10904
[journal]
Li-Ping Ni
/ 2011
/ Stock trend prediction based on fractal feature selection and support vector machine
/ Expert Systems with Applications
/ Elsevier BV
38(5)
: 5569~5576
[journal]
Arash Negahdari Kia
/ 2018
/ A hybrid supervised semi-supervised graph-based model to predict one-day ahead movement of global stock markets and commodity prices
/ Expert Systems with Applications
/ Elsevier BV
105
: 159~173