TY - GEN
T1 - ADMIRAL
T2 - 1st IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007
AU - Rachlin, Gil
AU - Last, Mark
AU - Alberg, Dima
AU - Kandel, Abraham
PY - 2007/9/25
Y1 - 2007/9/25
N2 - This paper presents a novel framework for predicting stock trends and making financial trading decisions based on a combination of Data and Text Mining techniques. The prediction models of the proposed system are based on the textual content of time-stamped web documents in addition to traditional numerical time series data, which is also available from the Web. The financial trading system based on the model predictions (ADMIRAL) is using three different trading strategies. In this paper, the ADMIRAL system is simulated and evaluated on real-world series of news stories and stocks data using the C4.5 Decision Tree Induction Algorithm. The main performance measures are the predictive accuracy of the induced models and, more importantly, the profitability of each trading strategy using these predictions.
AB - This paper presents a novel framework for predicting stock trends and making financial trading decisions based on a combination of Data and Text Mining techniques. The prediction models of the proposed system are based on the textual content of time-stamped web documents in addition to traditional numerical time series data, which is also available from the Web. The financial trading system based on the model predictions (ADMIRAL) is using three different trading strategies. In this paper, the ADMIRAL system is simulated and evaluated on real-world series of news stories and stocks data using the C4.5 Decision Tree Induction Algorithm. The main performance measures are the predictive accuracy of the induced models and, more importantly, the profitability of each trading strategy using these predictions.
UR - http://www.scopus.com/inward/record.url?scp=34548742455&partnerID=8YFLogxK
U2 - 10.1109/CIDM.2007.368947
DO - 10.1109/CIDM.2007.368947
M3 - Conference contribution
AN - SCOPUS:34548742455
SN - 1424407052
SN - 9781424407057
T3 - Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007
SP - 720
EP - 725
BT - Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007
Y2 - 1 April 2007 through 5 April 2007
ER -