TL;DR
Experimental results show that the CSI 300 Index is more stable in bear market and risk control; Ablation tests confirmed the key role of factor formation and adaptive selection in IS-MDP.
The complexity and dynamics of the stock market make traditional absolute price prediction models based on supervised learning face great risks and limitations in practical business applications. This report proposes an innovative Hybrid Deep Reinforcement Learning trading framework to shift the forecast target from st...
It is essential for market-making strategies to optimally capture the spread, balance their inventory, and adjust to changing order-book dynamics. In this paper, we explore the use of Proximal Policy Optimization (PPO) to perform adaptive market making in a simulated limit-order-book setting using the MSFT data from th...
Financial markets are challenging to navigate due to changing regimes, high volatility, and unpredictable investor behavior, often leading to model misspecification in classical stationary frameworks like Moving Average (MA) and Autoregressive (AR) models. To address this, we propose an uncertainty-aware framework that...
Results from experiments show that RL-DynTrade outperforms both traditional approaches and RL models that use PPO or DQN alone in terms of stability, flexibility, and profitability.
This study proposes a hybrid artificial intelligence-based trading framework that integrates deep learning and reinforcement learning to develop an adaptive Bitcoin trading strategy. The proposed approach combines an Attention Long Short-Term Memory model to estimate the probability of future price movements with a Sof...
A new machine-learning approach broke records for weather forecasting for an important time frame in emergency planning.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
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