Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Deep Learning for Event-Driven Stock Prediction
Authors: Xiao Ding, Yue Zhang, Ting Liu, Junwen Duan
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our model can achieve nearly 6% improvements on S&P 500 index prediction and individual stock prediction, respectively, compared to state-of-the-art baseline methods. |
| Researcher Affiliation | Academia | Research Center for Social Computing and Information Retrieval Harbin Institute of Technology, China EMAIL Singapore University of Technology and Design yue EMAIL |
| Pseudocode | Yes | Algorithm 1: Event Embedding Training Process |
| Open Source Code | No | The paper does not explicitly provide a link or statement about the availability of its source code. Footnote 1 refers to the dataset released by Ding et al. [2014], not the code for this paper's methodology. |
| Open Datasets | Yes | We use financial news from Reuters and Bloomberg over the period from October 2006 to November 2013, released by Ding et al. [2014]1. (Footnote 1: http://ir.hit.edu.cn/~xding/index_english.htm) |
| Dataset Splits | Yes | Detail statistics of training, development (tuning) and test sets are shown in Table 1. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions tools like 'Re Verb' and 'ZPar' and algorithms like 'skip-gram' but does not specify version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | The standard L2 regularization weight λ is set as 0.0001. The iteration number N is set to 500. The input of neural tensor network is word embeddings and the output is event embeddings. We learn the initial word representation of d-dimensions (d = 100) from large-scale financial news corpus, using the skip-gram algorithm [Mikolov et al., 2013]. We use a feedforward neural network with one hidden layer and one output layer. |