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 [1].
Deep Modeling Complex Couplings within Financial Markets
Authors: Wei Cao, Liang Hu, Longbing Cao
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on data of stock and currency markets from three countries show that our approach outperforms other baselines, from both technical and business perspectives. |
| Researcher Affiliation | Academia | Wei Cao Advanced Analytics Institute University of Technology, Sydney EMAIL Liang Hu University of Technology, Sydney and Shanghai Jiaotong University EMAIL Longbing Cao Advanced Analytics Institute University of Technology, Sydney Long EMAIL |
| Pseudocode | No | The paper describes mathematical equations and inference steps but does not provide a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any specific statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | The data set used includes weekly closing prices from Jan 2007 to Dec 2013 2, and the prices are decoded into returns by RIt = P It P It 1 P It 1 100%, here RIt and PIt are, respectively, the return and closing price at time t. As indexes in different markets may appear on different trading days, we delete those days on which some market data is missing and only choose the days with data from all financial markets. 2http://research.stlouisfed.org/ |
| Dataset Splits | Yes | The testing data includes the financial crisis period (2007-2009) and a non-crisis period (2010-2013) (Here we split the data by years, and we use the last five years data as the training set before the testing year so as to learn the model parameters). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers. |
| Experiment Setup | Yes | This is our deep learning approach, where the order n of the both CGRBM and CCRBM are set equal to 2 which yields good results in this experiment. |