不同于对股票涨还是跌的分类，以及对股票价格的回顾预测问题，本文利用股票之间增益多少（如，赚差价）对股票进行排序（即，L2R objection）
技术部分，分为两个部分：
（1）LSTM：与传统的没什么区别，设置好输入输出（输入是不同时刻的股票特征，输出是下一个的股票特征）
（2）Temporal Graph Convolution（TGC）：本文宣称是GCN的引入temporal的变种，本人认为，虽然形式上很像（计算方法），但GCN所依赖的一些数学性质，TGC是否成立，不一定。更何况GCN已经在原理很强的数学基础上，做了很多track的简化和改变。这里，本文构造了显式股票与股票之间的图关系（如同属科技板块等，所以是相似的，但这种相似关系却是通过LSTM的输出量进行计算的，所以是动态的。注意，这里的连接关系是构造的，用binary value刻画）。所以这里就有了动态的当前点和周围点的关系计算公式，再通过求和的方式，来影响当前点，这点跟卷积的概念很像。
（3）最后用一个pairwise loss function求参。
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Temporal Relational Ranking for Stock Prediction | |||||||||||
| 文献作者 | FULI FENG, XIANGNAN HE | ||||||||||
| 文献发表年限 | 2019 | ||||||||||
| 文献关键字 | |||||||||||
| Stock Prediction, Learning to Rank, Graph-based Learning, GCN； 股票收益预测 | |||||||||||
| 摘要描述 | |||||||||||
| Stock prediction aims to predict the future trends of a stock in order to help investors to make good investment decisions. Traditional solutions for stock prediction are based on time-series models. With the recent success of deep neural networks in modeling sequential data, deep learning has become a promising choice for stock prediction. However, most existing deep learning solutions are not optimized towards the target of investment, i.e., selecting the best stock with highest expected revenue. Specifically, they typically formulate stock prediction as a classification (to predict stock trend) or a regression problem (to predict stock price). More importantly, they largely treat the stocks as independent of each other. The valuable signal in the rich relations between stocks (or companies), such as two stocks are in the same sector and two companies have a supplier-customer relation, is not considered. In this work, we contribute a new deep learning solution, named Relational Stock Ranking (RSR), for stock prediction. Our RSR method advances existing solutions in two major aspects: 1) tailoring the deep learning models for stock ranking, and 2) capturing the stock relations in a time-sensitive manner. The key novelty of our work is the proposal of a new component in neural network modeling, named Temporal Graph Convolution, which jointly models the temporal evolution and relation network of stocks. To validate our method, we perform back-testing on the historical data of two stock markets, NYSE and NASDAQ. Extensive experiments demonstrate the superiority of our RSR method. It outperforms state-of-the-art stock prediction solutions achieving an average return ratio of 98% and 71% on NYSE and NASDAQ, respectively. | |||||||||||
