TY - JOUR
T1 - Empirical evaluation of an automated intraday stock recommendation system incorporating both market data and textual news
AU - Geva, Tomer
AU - Zahavi, Jacob
N1 - Funding Information:
We conducted this analysis while taking into account the joint effect of data representation and forecasting algorithm on intraday stock recommendation decisions. Our contention is that, due to the strong interrelationship that commonly exists between the data representation and the forecasting algorithm, this joint effect has considerable influence on the results of recommendation models. It is well known that a data representation that produces superior results with a certain data-mining algorithm might underperform when a different type of data-mining algorithm is employed. This contention is also supported by a previous pilot study [24] conducted as part of this research.
PY - 2014/1
Y1 - 2014/1
N2 - In this study we evaluate the effectiveness of augmenting numerical market data with textual-news data, using data mining methods, for forecasting stock returns in intraday trading. Integrating these two sources of data not only enriches the information available for the forecasting model, but it can potentially capture joint patterns that may not otherwise be identified when each data source is employed separately. We start with market data and then gradually add various textual data representations, going from simple representations, such as word counts, to more advanced representations involving sentiment analysis. To find the incremental value of each data representation, we build an end-to-end recommendation process including data preprocessing, modeling, validation, trade recommendations and economic evaluation. Each component of the modeling process is optimized to remove human bias and to allow us to impartially compare the results of the various models. Additionally, we experiment with several forecasting algorithms to find the one that yields the "best" results according to a variety of performance criteria. We employ data representation procedures and modeling improvements beyond those used in previous related studies. The economic evaluation of the results is conducted using a simulation procedure that inherently accounts for transaction costs and eliminates biases that have potentially affected previous related data-mining studies. This research is one of the largest-scale data-mining studies for evaluating the effectiveness of integrating market data with textual news data for the purpose of stock investment recommendations. The results of our study are promising in that they show that augmenting market data with advanced textual data representation significantly improves stock purchase decisions. Best results are achieved when the approach is implemented with a nonlinear neural network forecasting algorithm.
AB - In this study we evaluate the effectiveness of augmenting numerical market data with textual-news data, using data mining methods, for forecasting stock returns in intraday trading. Integrating these two sources of data not only enriches the information available for the forecasting model, but it can potentially capture joint patterns that may not otherwise be identified when each data source is employed separately. We start with market data and then gradually add various textual data representations, going from simple representations, such as word counts, to more advanced representations involving sentiment analysis. To find the incremental value of each data representation, we build an end-to-end recommendation process including data preprocessing, modeling, validation, trade recommendations and economic evaluation. Each component of the modeling process is optimized to remove human bias and to allow us to impartially compare the results of the various models. Additionally, we experiment with several forecasting algorithms to find the one that yields the "best" results according to a variety of performance criteria. We employ data representation procedures and modeling improvements beyond those used in previous related studies. The economic evaluation of the results is conducted using a simulation procedure that inherently accounts for transaction costs and eliminates biases that have potentially affected previous related data-mining studies. This research is one of the largest-scale data-mining studies for evaluating the effectiveness of integrating market data with textual news data for the purpose of stock investment recommendations. The results of our study are promising in that they show that augmenting market data with advanced textual data representation significantly improves stock purchase decisions. Best results are achieved when the approach is implemented with a nonlinear neural network forecasting algorithm.
KW - Data-mining
KW - Investment decisions
KW - Prediction
KW - Recommendation
KW - Stock returns
UR - http://www.scopus.com/inward/record.url?scp=84892369130&partnerID=8YFLogxK
U2 - 10.1016/j.dss.2013.09.013
DO - 10.1016/j.dss.2013.09.013
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AN - SCOPUS:84892369130
SN - 0167-9236
VL - 57
SP - 212
EP - 223
JO - Decision Support Systems
JF - Decision Support Systems
IS - 1
ER -