TY - GEN
T1 - Using artificial neural networks and market profile theory to construct the trading analysis in stock market
AU - Lin, Chien Cheng
AU - Chen, Mu Yen
AU - Hung, Min Chih
AU - Chen, An Pin
AU - Chiang, Hsiu Sen
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/6/25
Y1 - 2018/6/25
N2 - The empirical research on the financial trading market in the past few years is quite important and complex. In addition to discover the characteristics of the market trend movement, that buying and selling timing in the market has a great impact on profitability performance. Therefore, through the extension study of previously published literature, there should be existing relatively low risk buying and selling points in the trading market. Using market profile indicators and financial engineering physical quantities to find trade signals, and using reverse-operation trading strategies to verify whether it is a relatively low-risk buying and selling point. The results of this study show that by statistically significant differences in profitability performance, it proves that there exist relatively low risk buying and selling points in the financial trading market. There are three contributions to this study: 1).This study refutes both the Efficient-market Theory and the Random Walk Theory and there is the existence of relatively low-risk buying and selling points in the market. 2). Verify the financial physics of the trading market 3). Verify the applicability of the new indicator definition for the market profile.
AB - The empirical research on the financial trading market in the past few years is quite important and complex. In addition to discover the characteristics of the market trend movement, that buying and selling timing in the market has a great impact on profitability performance. Therefore, through the extension study of previously published literature, there should be existing relatively low risk buying and selling points in the trading market. Using market profile indicators and financial engineering physical quantities to find trade signals, and using reverse-operation trading strategies to verify whether it is a relatively low-risk buying and selling point. The results of this study show that by statistically significant differences in profitability performance, it proves that there exist relatively low risk buying and selling points in the financial trading market. There are three contributions to this study: 1).This study refutes both the Efficient-market Theory and the Random Walk Theory and there is the existence of relatively low-risk buying and selling points in the market. 2). Verify the financial physics of the trading market 3). Verify the applicability of the new indicator definition for the market profile.
UR - http://www.scopus.com/inward/record.url?scp=85054863175&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054863175&partnerID=8YFLogxK
U2 - 10.1145/3233347.3233385
DO - 10.1145/3233347.3233385
M3 - Conference contribution
AN - SCOPUS:85054863175
SN - 9781450364720
T3 - ACM International Conference Proceeding Series
SP - 122
EP - 126
BT - Proceedings of 2018 the 4th International Conference on Frontiers of Educational Technologies, ICFET 2018 - Workshop 2018 3rd International Conference on Knowledge Engineering and Applications, ICKEA 2018
PB - Association for Computing Machinery
T2 - 4th International Conference on Frontiers of Educational Technologies, ICFET 2018, Jointly with its Workshop the 3rd International Conference on Knowledge Engineering and Applications, ICKEA 2018
Y2 - 25 June 2018 through 27 June 2018
ER -