Pages that link to "Item:Q5234368"
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The following pages link to Universal features of price formation in financial markets: perspectives from deep learning (Q5234368):
Displaying 24 items.
- Reinforcement learning and stochastic optimisation (Q2072112) (← links)
- Normalization effects on shallow neural networks and related asymptotic expansions (Q2072629) (← links)
- Analysis of the environmental trend of network finance and its influence on traditional commercial banks (Q2184014) (← links)
- Are markets truly efficient? Experiments using deep learning algorithms for market movement prediction (Q2633259) (← links)
- Credit scoring with drift adaptation using local regions of competence (Q2677348) (← links)
- Learning a functional control for high-frequency finance (Q5051970) (← links)
- A deep learning approach to estimating fill probabilities in a limit order book (Q5051972) (← links)
- Forecasting jump arrivals in stock prices: new attention-based network architecture using limit order book data (Q5120733) (← links)
- Improving Stock Closing Price Prediction Using Recurrent Neural Network and Technical Indicators (Q5157259) (← links)
- Deep Learning for Market by Order Data (Q5165005) (← links)
- Mean Field Analysis of Neural Networks: A Law of Large Numbers (Q5219306) (← links)
- Deep learning for limit order books (Q5234311) (← links)
- Enhancing the momentum strategy through deep regression (Q5234344) (← links)
- Learning multi-market microstructure from order book data (Q5234377) (← links)
- BOUNDS ON MULTI-ASSET DERIVATIVES VIA NEURAL NETWORKS (Q5854317) (← links)
- A two-step framework for arbitrage-free prediction of the implied volatility surface (Q6158370) (← links)
- Analysis and modeling of client order flow in limit order markets (Q6158395) (← links)
- Machine learning architectures for price formation models (Q6166250) (← links)
- Deep order flow imbalance: Extracting alpha at multiple horizons from the limit order book (Q6187364) (← links)
- Normalization effects on deep neural networks (Q6194477) (← links)
- Designing universal causal deep learning models: The geometric (Hyper)transformer (Q6196301) (← links)
- Fin-GAN: forecasting and classifying financial time series via generative adversarial networks (Q6546310) (← links)
- On the universality of the volatility formation process: when machine learning and rough volatility agree (Q6549691) (← links)
- Incorporating causality in energy consumption forecasting using deep neural networks (Q6589090) (← links)
