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Yield Prediction

Stock Trend Prediction with Multi-Granularity Data: A Contrastive Learning Approach with Adaptive Fusion

  1. Multi-granularity
  2. Cross-Granularity and Cross-Temporal contrast learning

Stock Selection via Spatiotemporal Hypergraph Attention Network: A Learning to Rank Approach

  1. Modeling relations between related stocks' temporal price movement
  2. Using profitable metrics and NDCG for ranking
  3. Using Hawkes process as temporal attention

Stock market forecasting using a multi-task approach integrating long short-term memory and the random forest framework

CCF None

  • An awesome table (table 1) Describes 43 technical indicators.

Relation-aware dynamic attributed graph attention network for stocks recommendation

Using normalized Mutual Information as relations?

NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-task Financial Forecasting

  1. d
  2. Multi-task

Robust Dual Recurrent Neural Networks for Financial Time Series Prediction

  1. Classify noise-free samples and noisy samples
  2. Cross-updating inspired bt co-training to overcome sample-selection bias

Multi-scale Two-way Deep Neural Network for Stock Trend Prediction

  1. Considering 2 different scale behavior by wavelet-based XGBoost and down-sampling-based RCNN
  2. Datasets: FI-2010 and CSI-2016

Long-term, Short-term and Sudden Event: Trading Volume Movement Prediction with Graph-based Multi-view Modeling

  • valuable target for further study
  1. Predict trading volume instead of price
  2. Emphasis on multi-view information, including long-term stock relation, short-term fluctuations and sudden events

Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport

  • model multiple stock trading patterns
  1. the lack of explicit identifiers makes it quite challenging to train a router. So they design a learning algorithm based on Optimal Transport

Investment Behaviors Can Tell What Inside: Exploring Stock Intrinsic Properties for Stock Trend Prediction

  • leverage stock intrinsic properties
  • extract stock intrinsic properties from mutual fund portfolios
  • use static stock properties in a dynamic way by measuring the correlation between the market and the stock

  1. the representation of stock properties

    $Q^j$

    extracted from fund managers' preference

  2. dynamic correlation

    Why not $\hat{r}_{t+1}^j=MLP([\hat{D}_t^j,Q^j])$? The static stock properties cannot explicitly predict the stock trend in dynamic market

    market state: take $Q^j$s whose return ratio is ranked within top-$K_r$ at time t and average those $K_r$ $Q^j$s.

    then correlation $D_t^j=S_tQ^j$

    1. dynamic market state $\hat{D}t^j \approx D{t-1}^j$
    2. dynamic market trend predict $\hat{S}t=LSTM(S{<t})$

  3. predict return ratio

    $\hat{r}_{t+1}^j=MLP([\hat{D}_t^j,Z_t^j])$

    1. $\hat{D}_t^j$ is the correlation
    2. $Z_t^j$ is embedding of stock's past performance, e.g. history price

Individualized Indicator for All: Stock-wise Technical Indicator Optimization with Stock Embedding

  • graph embedding method

    Shaosheng Cao, Wei Lu, and Qiongkai Xu. 2016. Deep neural networks for learning graph representations. In Thirtieth AAAI Conference on Artificial Intelligence. 1145–1152. Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable Feature Learning for Networks. 2016 (2016), 855–864. Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov. 2016. Learning convolutional neural networks for graphs. In International Conference on International Conference on Machine Learning. 2014–2023. Bryan Perozzi, Rami Alrfou, and Steven Skiena. 2014. DeepWalk: online learning of social representations. (2014), 701–710. Daixin Wang, Peng Cui, and Wenwu Zhu. 2016. Structural Deep Network Embedding. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1225–1234.

  • Heterogeneous Graph

“ 稳定 ” 还是 “ 扰动 ” ———基于上证50ETF期权的实证分析

事件研究法