论文标题
通过转移学习预测指数指数方向和因果图作为主要输入
Predicting S&P500 Index direction with Transfer Learning and a Causal Graph as main Input
论文作者
论文摘要
我们提出了一个统一的多任务框架，以代表金融市场动态的复杂和不确定的因果过程，然后在S＆P500指数的月度方向上预测任何类型的索引的运动。我们的解决方案基于三个主要支柱：（i）转移学习在所有金融市场之间共享知识和特征（表示，学习），增加培训样本的规模并保持培训，验证和测试样本之间的稳定性。 （ii）多学科知识（金融经济学，行为金融，市场微观结构和投资组合构建理论）的结合，可以通过图表代表任何金融市场的全球自上而下的动态。 （iii）通过潜在变量/节点集成前瞻性的非结构化数据，不同类型的上下文（长期，中和短期），然后使用唯一的VAE网络（参数共享）同时学习其分布表示。在12年的测试期内，我们获得了高于该行业和其他基准的74.3％，67％和0.42的准确性，F1得分和Matthew相关性，其中包括三个不稳定且难以预测的难度。
We propose a unified multi-tasking framework to represent the complex and uncertain causal process of financial market dynamics, and then to predict the movement of any type of index with an application on the monthly direction of the S&P500 index. our solution is based on three main pillars: (i) the use of transfer learning to share knowledge and feature (representation, learning) between all financial markets, increase the size of the training sample and preserve the stability between training, validation and test sample. (ii) The combination of multidisciplinary knowledge (Financial economics, behavioral finance, market microstructure and portfolio construction theories) to represent a global top-down dynamics of any financial market, through a graph. (iii) The integration of forward looking unstructured data, different types of contexts (long, medium and short term) through latent variables/nodes and then, use a unique VAE network (parameter sharing) to learn simultaneously their distributional representation. We obtain Accuracy, F1-score, and Matthew Correlation of 74.3 %, 67 % and 0.42 above the industry and other benchmark on 12 years test period which include three unstable and difficult sub-period to predict.
