TY - GEN
T1 - Machine Learning for Earnings Prediction
T2 - 3rd ACM International Conference on AI in Finance, ICAIF 2022
AU - Uddin, Ajim
AU - Tao, Xinyuan
AU - Chou, Chia Ching
AU - Yu, Dantong
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/11/2
Y1 - 2022/11/2
N2 - Successful predictive models for financial applications often require harnessing complementary information from multiple datasets. Incorporating data from different sources into a single model can be challenging as they vary in structure, dimensions, quality, and completeness. Simply merging those datasets can cause redundancy, discrepancy, and information loss. This paper proposes a convolutional neural network-based nonlinear tensor coupling and completion framework (NLTCC) to combine heterogeneous datasets without compromising data quality. We demonstrate the effectiveness of NLTCC in solving a specific business problem - predicting firms' earnings from financial analysts' earnings forecast. First, we apply NLTCC to fuse firm characteristics and stock market information into the financial analysts' earnings forecasts data to impute missing values and improve data quality. Subsequently, we predict the next quarter's earnings based on the imputed data. The experiments reveal that the prediction error decreases by 65% compared with the benchmark analysts' consensus forecast. The long-short portfolio returns based on NLTCC outperform analysts' consensus forecast and the S&P-500 index from three-day up to two-month holding period. The prediction accuracy improvement is robust with different performance metrics and various industry sectors. Notably, it is more salient for the sectors with higher heterogeneity.
AB - Successful predictive models for financial applications often require harnessing complementary information from multiple datasets. Incorporating data from different sources into a single model can be challenging as they vary in structure, dimensions, quality, and completeness. Simply merging those datasets can cause redundancy, discrepancy, and information loss. This paper proposes a convolutional neural network-based nonlinear tensor coupling and completion framework (NLTCC) to combine heterogeneous datasets without compromising data quality. We demonstrate the effectiveness of NLTCC in solving a specific business problem - predicting firms' earnings from financial analysts' earnings forecast. First, we apply NLTCC to fuse firm characteristics and stock market information into the financial analysts' earnings forecasts data to impute missing values and improve data quality. Subsequently, we predict the next quarter's earnings based on the imputed data. The experiments reveal that the prediction error decreases by 65% compared with the benchmark analysts' consensus forecast. The long-short portfolio returns based on NLTCC outperform analysts' consensus forecast and the S&P-500 index from three-day up to two-month holding period. The prediction accuracy improvement is robust with different performance metrics and various industry sectors. Notably, it is more salient for the sectors with higher heterogeneity.
KW - Convolutional Neural Network
KW - FinTech
KW - Firm Earnings Forecast
KW - Nonlinear Tensor Factorization
KW - Sparse Tensor Completion
UR - http://www.scopus.com/inward/record.url?scp=85142538799&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85142538799&partnerID=8YFLogxK
U2 - 10.1145/3533271.3561677
DO - 10.1145/3533271.3561677
M3 - Conference contribution
AN - SCOPUS:85142538799
T3 - Proceedings of the 3rd ACM International Conference on AI in Finance, ICAIF 2022
SP - 282
EP - 290
BT - Proceedings of the 3rd ACM International Conference on AI in Finance, ICAIF 2022
PB - Association for Computing Machinery, Inc
Y2 - 2 November 2022 through 4 November 2022
ER -