New Evidence on Stock Price Effects Associated with Changes in the S&P 500 Index

1997 ◽  
Vol 70 (3) ◽  
pp. 351-383 ◽  
Author(s):  
Anthony W. Lynch ◽  
Richard R. Mendenhall
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yang Zhao ◽  
Zhonglu Chen

PurposeThis study explores whether a new machine learning method can more accurately predict the movement of stock prices.Design/methodology/approachThis study presents a novel hybrid deep learning model, Residual-CNN-Seq2Seq (RCSNet), to predict the trend of stock price movement. RCSNet integrates the autoregressive integrated moving average (ARIMA) model, convolutional neural network (CNN) and the sequence-to-sequence (Seq2Seq) long–short-term memory (LSTM) model.FindingsThe hybrid model is able to forecast both linear and non-linear time-series component of stock dataset. CNN and Seq2Seq LSTMs can be effectively combined for dynamic modeling of short- and long-term-dependent patterns in non-linear time series forecast. Experimental results show that the proposed model outperforms baseline models on S&P 500 index stock dataset from January 2000 to August 2016.Originality/valueThis study develops the RCSNet hybrid model to tackle the challenge by combining both linear and non-linear models. New evidence has been obtained in predicting the movement of stock market prices.


2015 ◽  
Vol 44 (3) ◽  
pp. 291-310 ◽  
Author(s):  
Christopher S. Carpenter ◽  
Michael T. Mathes

1985 ◽  
Vol 14 (2) ◽  
pp. 165-194 ◽  
Author(s):  
Wayne H. Mikkelson ◽  
M.Megan Partch
Keyword(s):  

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