Ritchie Ng
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View article: Temporal Relational Reasoning of Large Language Models for Detecting Stock Portfolio Crashes
Temporal Relational Reasoning of Large Language Models for Detecting Stock Portfolio Crashes Open
Stock portfolios are often exposed to rare consequential events (e.g., 2007 global financial crisis, 2020 COVID-19 stock market crash), as they do not have enough historical information to learn from. Large Language Models (LLMs) now prese…
View article: Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models
Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models Open
Explaining stock predictions is generally a difficult task for traditional non-generative deep learning models, where explanations are limited to visualizing the attention weights on important texts. Today, Large Language Models (LLMs) pre…
View article: Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models
Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models Open
Explaining stock predictions is generally a difficult task for traditional non-generative deep learning models, where explanations are limited to visualizing the attention weights on important texts. Today, Large Language Models (LLMs) pre…
View article: Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction
Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction Open
Multi-step stock price prediction over a long-term horizon is crucial for\nforecasting its volatility, allowing financial institutions to price and hedge\nderivatives, and banks to quantify the risk in their trading books.\nAdditionally, m…
View article: Time horizon-aware modeling of financial texts for stock price prediction
Time horizon-aware modeling of financial texts for stock price prediction Open
Financial texts (e.g., economic news) play an important role in predicting stock prices. The effects of texts of different semantics (e.g., launching a product and reporting a small product bug) last for different time horizons. Despite th…
View article: Hybrid Learning to Rank for Financial Event Ranking
Hybrid Learning to Rank for Financial Event Ranking Open
The financial markets are moved by events such as the issuance of administrative orders. The participants in financial markets (e.g., traders) thus pay constant attention to financial news relevant to the financial asset (e.g., oil) of int…
View article: Deep Learning Wizard
Deep Learning Wizard Open
This code base contains the entire website's assets of Deep Learning Wizard covering deep learning theories, concepts and Python, C++, and PyTorch code. This release contains stable versions for deep learning tutorials and initial versions…
View article: Deep Reinforcement Learning for Accelerating the Convergence Rate
Deep Reinforcement Learning for Accelerating the Convergence Rate Open
In this paper, we propose a principled deep reinforcement learning (RL) approach that is able to accelerate the convergence rate of general deep neural networks (DNNs). With our approach, a deep RL agent (synonym for optimizer in this work…