Task-Based Learning via Task-Oriented Prediction Network with Applications in Finance
Authors: Di Chen, Yada Zhu, Xiaodong Cui, Carla Gomes
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the performance of TOPNet on two real-world financial prediction tasks, revenue surprise forecasting and credit risk modeling. The experimental results demonstrate that TOPNet significantly outperforms both traditional modeling with standard losses and modeling with hand-crafted heuristic differentiable surrogate losses. |
| Researcher Affiliation | Collaboration | Di Chen 1 , Yada Zhu2 , Xiaodong Cui2 and Carla P. Gomes1 1Cornell University 2IBM T. J. Watson Research Center di@cs.cornell.edu, {yzhu, cuix}@us.ibm.com, gomes@cs.cornell.edu |
| Pseudocode | Yes | Algorithm 1 End-to-End learning process for TOPNet |
| Open Source Code | No | Due to business confidentiality, we are not allowed to share the datasets. |
| Open Datasets | No | Due to business confidentiality, we are not allowed to share the datasets. |
| Dataset Splits | Yes | We split the whole dataset chronologically into training set (01-01-2004 to 06-30-2015, 3,267,584 data points), validation set (07-01-2015 to 06-302017, 465,383 data points) and test set (07-01-2017 to 0630-2019, 421,225 data points) to validate the performance of models. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. It only mentions the training process details like optimizer and batch size. |
| Software Dependencies | No | The paper mentions using "Adam optimizer [Kingma and Ba, 2014]" and "Long Short-Term Memory (LSTM) networks [Hochreiter and Schmidhuber, 1997]" but does not specify software versions for these or any other libraries or frameworks. |
| Experiment Setup | Yes | General Experimental Setup: For all models in our experiments, the training process was done for 50 epochs, using a batch size of 1024, an Adam optimizer [Kingma and Ba, 2014] with a learning rate of 3e-5, and early stopping to accelerate the training process and prevent overfitting. |