IEICE TRANSACTIONS on Information
lcyanalysis: An R Package for Technical Analysis in Stock Markets
Summary :
With advances in information technology and the development of big data, manual operation is unlikely to be a smart choice for stock market investing. Instead, the computer-based investment model is expected to bring investors more accurate strategic analysis and more effective investment decisions than human beings. This paper aims to improve investor profits by mining for critical information in the stock data, therefore helping big data analysis. We used the R language to find the technical indicators in the stock market, and then applied the technical indicators to the prediction. The proposed R package includes several analysis toolkits, such as trend line indicators, W type reversal patterns, V type reversal patterns, and the bull or bear market. The simulation results suggest that the developed R package can accurately present the tendency of the price and enhance the return on investment.
- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.7 pp.1332-1341
- Publication Date
- 2019/07/01
- Publicized
- 2019/03/26
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018EDK0004
- Type of Manuscript
- PAPER
- Category
- Office Information Systems, e-Business Modeling
Authors
Chun-Yu LIU
National Taipei University
Shu-Nung YAO
National Taipei University
Ying-Jen CHEN
National Taipei University
Keyword
Latest Issue
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Chun-Yu LIU, Shu-Nung YAO, Ying-Jen CHEN, "lcyanalysis: An R Package for Technical Analysis in Stock Markets" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 7, pp. 1332-1341, July 2019, doi: 10.1587/transinf.2018EDK0004.
Abstract: With advances in information technology and the development of big data, manual operation is unlikely to be a smart choice for stock market investing. Instead, the computer-based investment model is expected to bring investors more accurate strategic analysis and more effective investment decisions than human beings. This paper aims to improve investor profits by mining for critical information in the stock data, therefore helping big data analysis. We used the R language to find the technical indicators in the stock market, and then applied the technical indicators to the prediction. The proposed R package includes several analysis toolkits, such as trend line indicators, W type reversal patterns, V type reversal patterns, and the bull or bear market. The simulation results suggest that the developed R package can accurately present the tendency of the price and enhance the return on investment.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDK0004/_p
Copy
@ARTICLE{e102-d_7_1332,
author={Chun-Yu LIU, Shu-Nung YAO, Ying-Jen CHEN, },
journal={IEICE TRANSACTIONS on Information},
title={lcyanalysis: An R Package for Technical Analysis in Stock Markets},
year={2019},
volume={E102-D},
number={7},
pages={1332-1341},
abstract={With advances in information technology and the development of big data, manual operation is unlikely to be a smart choice for stock market investing. Instead, the computer-based investment model is expected to bring investors more accurate strategic analysis and more effective investment decisions than human beings. This paper aims to improve investor profits by mining for critical information in the stock data, therefore helping big data analysis. We used the R language to find the technical indicators in the stock market, and then applied the technical indicators to the prediction. The proposed R package includes several analysis toolkits, such as trend line indicators, W type reversal patterns, V type reversal patterns, and the bull or bear market. The simulation results suggest that the developed R package can accurately present the tendency of the price and enhance the return on investment.},
keywords={},
doi={10.1587/transinf.2018EDK0004},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - lcyanalysis: An R Package for Technical Analysis in Stock Markets
T2 - IEICE TRANSACTIONS on Information
SP - 1332
EP - 1341
AU - Chun-Yu LIU
AU - Shu-Nung YAO
AU - Ying-Jen CHEN
PY - 2019
DO - 10.1587/transinf.2018EDK0004
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2019
AB - With advances in information technology and the development of big data, manual operation is unlikely to be a smart choice for stock market investing. Instead, the computer-based investment model is expected to bring investors more accurate strategic analysis and more effective investment decisions than human beings. This paper aims to improve investor profits by mining for critical information in the stock data, therefore helping big data analysis. We used the R language to find the technical indicators in the stock market, and then applied the technical indicators to the prediction. The proposed R package includes several analysis toolkits, such as trend line indicators, W type reversal patterns, V type reversal patterns, and the bull or bear market. The simulation results suggest that the developed R package can accurately present the tendency of the price and enhance the return on investment.
ER -
English
