TY - JOUR
T1 - CashTagNN: Exploiting the Use of CashTags to Predict Stock Market Prices Using Convolutional Networks
AU - Gandy, Lisa
N1 - In this paper, the authors present a system, CashTagNN, which uses the sentiment and subjectivity scores of tweets that include cashtags to model stock market movement, and in particular, predict opening stock market prices. Currently, the system focuses on two companies: Apple and Johnson \ Johnson.
PY - 2020/11/25
Y1 - 2020/11/25
N2 - In this paper, the authors present a system, CashTagNN, which uses the sentiment and subjectivity scores of tweets that include cashtags to model stock market movement, and in particular, predict opening stock market prices. Currently, the system focuses on two companies: Apple and Johnson \& Johnson. The system uses two machine learning methods for prediction, a feed-forward neural network, and a deep convolutional neural network. The authors use stock market prices in March and October 2016 as training data, with stock market prices in November 2016 as test data. The time series used for training and testing consists of stock prices recorded at one minute, five minutes, and one-hour intervals. Results show that the Feed Forward Network Model, in this case, outperforms the Deep Convolutional Network model.
AB - In this paper, the authors present a system, CashTagNN, which uses the sentiment and subjectivity scores of tweets that include cashtags to model stock market movement, and in particular, predict opening stock market prices. Currently, the system focuses on two companies: Apple and Johnson \& Johnson. The system uses two machine learning methods for prediction, a feed-forward neural network, and a deep convolutional neural network. The authors use stock market prices in March and October 2016 as training data, with stock market prices in November 2016 as test data. The time series used for training and testing consists of stock prices recorded at one minute, five minutes, and one-hour intervals. Results show that the Feed Forward Network Model, in this case, outperforms the Deep Convolutional Network model.
KW - computing methodologies
KW - machine learning
KW - machine learning approaches
KW - neural networks
UR - https://dl.acm.org/doi/abs/10.1145/3423390.3423392
U2 - 10.1145/3423390.3423392
DO - 10.1145/3423390.3423392
M3 - Article
JO - ICACS '20: Proceedings of the 4th International Conference on Algorithms, Computing and Systems
JF - ICACS '20: Proceedings of the 4th International Conference on Algorithms, Computing and Systems
ER -