TY - GEN
T1 - Earnings prediction with deep leaning
AU - Elend, Lars
AU - Tideman, Sebastian A.
AU - Lopatta, Kerstin
AU - Kramer, Oliver
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - In the financial sector, a reliable forecast the future financial performance of a company is of great importance for investors’ investment decisions. In this paper we compare long-term short-term memory (LSTM) networks to temporal convolution network (TCNs) in the prediction of future earnings per share (EPS). The experimental analysis is based on quarterly financial reporting data and daily stock market returns. For a broad sample of US firms, we find that both LSTMs outperform the naive persistent model with up to 30.0% more accurate predictions, while TCNs achieve and an improvement of 30.8%. Both types of networks are at least as accurate as analysts and exceed them by up to 12.2% (LSTM) and 13.2% (TCN).
AB - In the financial sector, a reliable forecast the future financial performance of a company is of great importance for investors’ investment decisions. In this paper we compare long-term short-term memory (LSTM) networks to temporal convolution network (TCNs) in the prediction of future earnings per share (EPS). The experimental analysis is based on quarterly financial reporting data and daily stock market returns. For a broad sample of US firms, we find that both LSTMs outperform the naive persistent model with up to 30.0% more accurate predictions, while TCNs achieve and an improvement of 30.8%. Both types of networks are at least as accurate as analysts and exceed them by up to 12.2% (LSTM) and 13.2% (TCN).
KW - EPS forecasts
KW - Earnings prediction
KW - Finance
KW - Long short term memory
KW - Temporal convolutional network
UR - http://www.scopus.com/inward/record.url?scp=85091157640&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85091157640&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-58285-2_22
DO - 10.1007/978-3-030-58285-2_22
M3 - Conference contribution
AN - SCOPUS:85091157640
SN - 9783030582845
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 267
EP - 274
BT - KI 2020
A2 - Schmid, Ute
A2 - Wolter, Diedrich
A2 - Klügl, Franziska
PB - Springer Science and Business Media Deutschland GmbH
T2 - 43rd German Conference on Artificial Intelligence, KI 2020
Y2 - 21 September 2020 through 25 September 2020
ER -