Quant GANs model financial time series using GANs with TCNs.
problem Modeling financial time series with stochastic processes.
method Quant GANs use a generator and discriminator with TCNs to capture long-range dependencies.
result Quant GANs generate financial time series with distributional and dependence properties in high fidelity.
Quant 4.0 uses AI to automate, explain, and incorporate knowledge in investment.
problem Limitations of deep learning in quant investment.
method Automated AI, Explainable AI, Knowledge-driven AI.
result Improves investment decision-making through automation, interpretability, and prior knowledge integration.
Quant strategies lost during market selloff.
problem Why equities Statistical Arbitrage strategies failed during the COVID-19 market downturn.
method Explains strategies' normal performance and market regimes, discusses their limitations.
result Quant strategies are vulnerable to extreme market events.
Survey of AI in quant finance, from deep learning to LLMs.
problem Improving predictive modeling and automation in asset management.
method Exploring AI contributions to quant investment pipeline, from human-crafted features to LLMs.
result AI has enabled scalable modeling and autonomous agents in quant finance.
Study finds long-range dependence in financial markets, but deep generative models struggle to replicate it.
problem Long-range dependence in financial markets and challenges of deep generative models.
method Empirical analysis of financial data from three sectors, including LRD through various statistical methods and deep learning models.
result Deep generative models can reproduce stylized features but fail to capture long-range dependence structures.
Degree-Quant improves GNN efficiency by quantizing them without losing accuracy.
problem Efficiency of graph neural networks at inference time.
method Architecturally-agnostic method, Degree-Quant, for quantizing GNNs.
result Degree-Quant trained models perform as well as full-precision models and achieve up to 26% gains.
Quant firms manipulate stock markets overnight and intraday.
problem Unexplained consistent overnight and intraday returns in stock markets.
method Analysis of trading patterns and market movements.
result Large quant firms expand and contract portfolios to create mark-to-market gains.
Alpha-GPT mines new trading signals with human-AI interaction.
problem Mining new alphas for effective trading signals.
method Human-AI interaction and prompt engineering algorithmic framework.
result Demonstrates Alpha-GPT's effectiveness in generating creative, insightful, and effective alphas.
In a recent comment (Johansen A 2003 An alternative view, Quant. Finance 3: C6-C7, cond-mat/0302141), Anders Johansen has criticized our methodology and has questioned several of our results published in [Sornette D and Zhou W-X 2002 The US 2000-2002 market descent: how much longer and deeper? Quant. Finance 2: 468-81,…
Paper introduces solving financial problems using time-stepped FBSDE and deep learning.
problem Quantitative finance problems under specific dynamics and instruments.
method Formulate as FBSDE, turn into control problems, time-step, solve with optimization and deep learning.
result Solves financial problems with new methods and deep learning.
For the eight-dimensional Riemannian manifold comprised by the three-level quantum systems endowed with the Bures metric, we numerically approximate the integrals over the manifold of several functions of the curvature and of its (anti-)self-dual parts. The motivation for pursuing this research is to elaborate upon the…
Paper uses LLMs to analyze annual reports for stock investment, improving efficiency.
problem Manual analysis of annual reports is time-consuming and requires expertise.
method Leverages Large Language Models to extract and analyze annual reports.
result Machine Learning model trained on LLM outputs outperforms S&P500 returns.
Survey of GANs and autoencoders, addressing mode collapse and likelihood issues.
problem Addressing mode collapse and likelihood issues in GANs and autoencoders.
method Explains various GAN and autoencoder variants, their applications, and methods to resolve issues.
result Various methods to resolve mode collapse and improve likelihood in GANs and autoencoders.
We give an explicit algorithm and source code for extracting expected returns for stocks from expected returns for alphas. Our algorithm altogether bypasses combining alphas with weights into "alpha combos". Simply put, we have developed a new method for trading alphas which does not involve combining them. This yields…
RD-Agent(Q) automates quantitative finance research and development.
problem Challenges in asset return prediction due to high dimensionality and volatility.
method Data-centric multi-agent framework for automated research and development of quantitative strategies.
result Up to 2X higher annualized returns with 70% fewer factors.
A new GAN model α-GAN with tunable loss function addresses gradient vanishing and mode collapse issues.
problem Addressing vanishing gradients and mode collapse in GANs.
method Introduced a tunable GAN α-GAN using a supervised α-loss function. result Holistic understanding of α-GAN related to Arimoto divergence and convergence properties. Unbalanced GANs stabilize GAN training by pre-training the generator with VAE.
problem Stable training of GANs to avoid mode collapses and improve image quality.
method Pre-train GAN generator with VAE, balance generator and discriminator training, prevent discriminator's early convergence.
result Unbalanced GANs reduce mode collapses and outperform ordinary GANs in stability, convergence, and image quality.
GANs can approximate SDEs for large time steps.
problem Approximating SDEs for large time steps using GANs.
method Proposed a conditional GAN architecture to enable strong approximation of SDEs.
result Supervised GAN outperformed standard GAN and other schemes in strong error.
Paper bridges f-GANs and WGANs for better image generation.
problem Learning high-dimensional distributions using GANs.
method List constraints, minimize Lagrangian relaxation, propose KL-Wasserstein GAN.
result Empirical success on synthetic and real-world image generation benchmarks.
We discuss when and why custom multi-factor risk models are warranted and give source code for computing some risk factors. Pension/mutual funds do not require customization but standardization. However, using standardized risk models in quant trading with much shorter holding horizons is suboptimal: 1) longer horizon …
City-GAN learns city architecture styles using a custom GAN architecture.
problem Learning architectural styles of cities using standard GAN and CGAN architectures.
method Proposes a custom GAN architecture to improve learning of city architectural styles.
result Demonstrates superior performance of custom GAN architecture for city architectural style learning.
WGAN uses a smoother metric to train GANs better.
problem Difficulty in training GANs.
method Introducing Wasserstein GAN to improve GAN training.
result Wasserstein GAN improves training stability and effectiveness.
SR-GANs combat mode collapse in GANs by monitoring and compensating spectral distributions.
problem Mode collapse in GANs.
method Spectral regularization (SR-GANs) to combat spectral collapse.
result SR-GANs prevent mode collapse and outperform SN-GANs in experiments.
FCC-GAN combines fully connected and convolutional layers for improved GAN performance.
problem Lack of understanding in choosing GAN network architectures.
method Proposes FCC-GAN, a hybrid architecture combining fully connected and convolutional layers.
result FCC-GAN outperforms traditional GAN architectures in terms of learning speed and sample quality.
Prb-GAN uses dropout and variational inference to improve GAN performance.
problem GANs struggle with mode loss and training instability.
method Introduces Prb-GANs with dropout and variational inference for parameter distribution.
result Improves GAN performance through dropout and variational inference.
xAI-GAN improves GANs by providing richer feedback, enhancing image quality.
problem Challenges in GAN training, especially resource-intensive and data-intensive.
method Integrates xAI systems to provide richer corrective feedback from discriminators to generators.
result Improves image quality by up to 23.18% on MNIST and FMNIST datasets.
This paper reviews GANs algorithms, theory, and applications.
problem Lack of comprehensive study on GANs connections and evolution.
method Detailed introduction of GANs algorithms, theoretical investigation, and application illustrations.
result Comprehensive review of GANs from algorithms, theory, and applications perspectives.
Generative models create paintings that match training data.
problem Creating realistic paintings using machine learning.
method Used Spectral Normalization GAN (SN-GAN) and SN-GAN with Gradient Penalty to generate paintings.
result SN-GAN produced paintings most comparable to the training dataset.
New method compresses GANs by 1669:1 ratio on MNIST, 58:1 on CIFAR-10, 87:1 on Celeb-A.
problem Expensive GANs for low SWAP hardware and real-time applications.
method Knowledge distillation between a student and teacher GAN.
result Compressed GANs outperform standard training methods with a fixed parameter budget.
Generative Adversarial Networks (GANs) are powerful models for learning complex distributions. Stable training of GANs has been addressed in many recent works which explore different metrics between distributions. In this paper we introduce Fisher GAN which fits within the Integral Probability Metrics (IPM) framework f…
GANs may not have Nash equilibria, but proximal training can find solutions.
problem Existence of Nash equilibria in GANs optimization.
method Proximal training approach to find solutions.
result Proximal training finds solutions to GAN problems.
A recent technical breakthrough in the domain of machine learning is the discovery and the multiple applications of Generative Adversarial Networks (GANs). Those generative models are computationally demanding, as a GAN is composed of two deep neural networks, and because it trains on large datasets. A GAN is generally…
We investigate the training and performance of generative adversarial networks using the Maximum Mean Discrepancy (MMD) as critic, termed MMD GANs. As our main theoretical contribution, we clarify the situation with bias in GAN loss functions raised by recent work: we show that gradient estimators used in the optimizat…
Empirical study shows GANs overfit and drop modes when training is deterministic.
problem Understanding overfitting and mode drop in GAN training.
method Empirical analysis of GAN training with and without stochasticity.
result GANs overfit and drop modes when training is deterministic.
GANs generate images of emotions from datasets.
problem Creating realistic images of emotions from datasets.
method Used GANs and StarGAN to train on datasets of emotions.
result StarGAN successfully generated images of 7 basic emotions.
GANs analyzed for performance and training issues.
problem Performance and training issues of GANs.
method SDE approximations for training GANs.
result Improved understanding of GANs through analytical perspectives.
TAC-GAN improves image diversity in AC-GAN by minimizing class distribution divergence.
problem Low diversity in AC-GAN's generated samples as class count increases.
method TAC-GAN introduces twin auxiliary classifiers to address class separability issues.
result TAC-GAN effectively minimizes divergence between generated and real data distributions.
Vanilla GANs are connected to Wasserstein distance for better understanding.
problem Understanding the statistical properties of Vanilla GANs.
method Connecting Vanilla GANs to Wasserstein distance and proving an oracle inequality.
result An oracle inequality for Vanilla GANs in Wasserstein distance is obtained.
Several dihedral angles prediction methods were developed for protein structure prediction and their other applications. However, distribution of predicted angles would not be similar to that of real angles. To address this we employed generative adversarial networks (GAN). Generative adversarial networks are composed …
Generative adversarial networks (GAN) have been effective for learning generative models for real-world data. However, existing GANs (GAN and its variants) tend to suffer from training problems such as instability and mode collapse. In this paper, we propose a novel GAN framework called evolutionary generative adversar…
Unified framework compresses GANs up to 47x with minimal quality loss.
problem High parameter complexity of GANs for resource-constrained devices.
method Unified optimization framework combining model distillation, channel pruning, and quantization.
result 47x compression of CartoonGAN with minimal quality degradation.
NR-GANs learn clean images from noisy data.
problem Learning clean images from noisy training data.
method Introduced a noise generator and distribution/transformation constraints.
result NR-GANs can generate clean images from noisy data.
New method improves GANs by estimating density ratios in feature space with SP loss.
problem Filtering out unrealistic images from GANs trained with suboptimal discriminators.
method Develops DRE-F-SP method based on Softplus loss for density ratio estimation in feature space, and proposes three subsampling methods.
result Empirically shows substantial improvement over existing methods on synthetic and CIFAR-10 datasets.
Fence GAN improves anomaly detection by modifying GAN loss.
problem Anomaly detection on complex high-dimensional data.
method Modified GAN loss to encourage generated samples at the boundary of real data distribution.
result Fence GAN yields the best anomaly classification accuracy.
This work improves GAN stability with theoretical conditions.
problem GANs exhibit unstable behavior during training.
method Developed a theoretical framework and conditions for GAN stability.
result Constructs a GAN that fulfills stability conditions.
This paper uses SDEs to analyze GANs training and long-run behavior.
problem Understanding the training process and long-run behavior of GANs.
method Established SDE approximations for GANs training and analyzed long-run behavior via invariant measures.
result The long-run behavior of GANs training can be studied via the invariant measures of its SDE approximations.
Perturbative GAN reduces training complexity and improves image quality.
problem Training complexity and image quality in GANs.
method Replaces convolution layers with perturbation layers that add fixed noise masks.
result Higher inception score and faster convergence of generated images.
New control theory approach stabilizes GANs training.
problem Stability issues in GANs training.
method Control theory applied to GANs function space dynamics.
result Effective stabilization of GANs training with CLC and squared L2 regularizer.