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Found 24 papers, 11 papers with code
Microstructure-Empowered Stock Factor Extraction and Utilization
To address these challenges, we propose a novel framework that aims to effectively extract essential factors from order flow data for diverse downstream tasks across different granularities and scenarios.
Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance
Order execution is a fundamental task in quantitative finance, aiming at finishing acquisition or liquidation for a number of trading orders of the specific assets.
Scalable and Safe Remediation of Defective Actions in Self-Learning Conversational Systems
Off-Policy reinforcement learning has been a driving force for the state-of-the-art conversational AIs leading to more natural humanagent interactions and improving the user satisfaction for goal-oriented agents.
Learning Differential Operators for Interpretable Time Series Modeling
In this work, we propose an learning framework that can automatically obtain interpretable PDE models from sequential data.
Towards Applicable Reinforcement Learning: Improving the Generalization and Sample Efficiency with Policy Ensemble
Considering the great performance of ensemble methods on both accuracy and generalization in supervised learning (SL), we design a robust and applicable method named Ensemble Proximal Policy Optimization (EPPO), which learns ensemble policies in an end-to-end manner.
DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation
To handle concept drift, previous methods first detect when/where the concept drift happens and then adapt models to fit the distribution of the latest data.
SHGNN: Structure-Aware Heterogeneous Graph Neural Network
Next, we use a tree-attention aggregator to incorporate the graph structure information into the aggregation module on the meta-path.
KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings
Learning the embeddings of knowledge graphs (KG) is vital in artificial intelligence, and can benefit various downstream applications, such as recommendation and question answering.
HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information
To overcome the shortcomings of previous work, we proposed a novel stock trend forecasting framework that can adequately mine the concept-oriented shared information from predefined concepts and hidden concepts.
DM-CT: Consistency Training with Data and Model Perturbation
The common approach of consistency training is performed on the data-level, which typically utilizes the data augmentation strategy (or adversarial training) to make the predictions from the augmented input and the original input to be consistent, so that the model is more robust and attains better generalization ability.
Deep Ensemble Policy Learning
Ensemble learning, which can consistently improve the prediction performance in supervised learning, has drawn increasing attentions in reinforcement learning (RL).
Instance-wise Graph-based Framework for Multivariate Time Series Forecasting
In this paper, we propose a simple yet efficient instance-wise graph-based framework to utilize the inter-dependencies of different variables at different time stamps for multivariate time series forecasting.
Deep Risk Model: A Deep Learning Solution for Mining Latent Risk Factors to Improve Covariance Matrix Estimation
Modeling and managing portfolio risk is perhaps the most important step to achieve growing and preserving investment performance.
Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport
In this paper, we propose a novel architecture, Temporal Routing Adaptor (TRA), to empower existing stock prediction models with the ability to model multiple stock trading patterns.
Model Complexity of Deep Learning: A Survey
Model complexity is a fundamental problem in deep learning.
REST: Relational Event-driven Stock Trend Forecasting
To remedy the first shortcoming, we propose to model the stock context and learn the effect of event information on the stocks under different contexts.
Universal Trading for Order Execution with Oracle Policy Distillation
As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument.
Learning to Use Future Information in Simultaneous Translation
For wait-k inference, we observe that wait-m training with $m>k$ in simultaneous NMT (i. e., using more future information for training than inference) generally outperforms wait-k training.
ADD: Augmented Disentanglement Distillation Framework for Improving Stock Trend Forecasting
Stock trend forecasting has become a popular research direction that attracts widespread attention in the financial field.
Qlib: An AI-oriented Quantitative Investment Platform
Quantitative investment aims to maximize the return and minimize the risk in a sequential trading period over a set of financial instruments.
Temporally Correlated Task Scheduling for Sequence Learning
In many applications, a sequence learning task is usually associated with multiple temporally correlated auxiliary tasks, which are different in terms of how much input information to use or which future step to predict.
Learning to Reweight with Deep Interactions
Recently, the concept of teaching has been introduced into machine learning, in which a teacher model is used to guide the training of a student model (which will be used in real tasks) through data selection, loss function design, etc.
Measuring Model Complexity of Neural Networks with Curve Activation Functions
Our results demonstrate that the occurrence of overfitting is positively correlated with the increase of model complexity during training.
Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction
Stock trend prediction plays a critical role in seeking maximized profit from stock investment.
