This paper explores how combining quantitative factors and news from LLMs improves stock return prediction.
problem Improving stock return prediction using quantitative factors and news.
method Introduces a fusion learning framework to learn unified representations from factors and LLM-generated newsflow, comparing combination, summation, and attentive methods. Explores mixture models and decoupled training approaches.
result Effective multimodal modeling of factors and news improves stock return prediction and selection.
AutoAlpha efficiently discovers effective alpha factors for quantitative investment.
problem Mining effective alpha factors for successful quantitative investment models.
method Hierarchical evolutionary algorithm with PCA-QD search, warm start, and replacement methods.
result AutoAlpha discovers and generates effective formulaic alphas for portfolio optimization.
QRAFTI uses multi-agent framework to improve equity factor research.
problem Replicating and developing new equity factors in large financial datasets.
method Integrates a research toolkit with MCP servers for data access and custom coding operations.
result Improves performance and explainability in multi-step empirical tasks.
Paper proposes NNAFC for automatic financial factor construction.
problem Manual factor construction is time-consuming and prone to bias.
method NNAFC uses neural networks to automatically construct diversified financial factors.
result NNAFC outperforms GP in constructing more informative and diversified factors.
Factor Engine simplifies financial factor computation and analysis in Python.
problem Efficient computation and analysis of financial factors.
method Modular, extensible Python library with decorators, integrates with data science ecosystem.
result Mispricing factors computed by Factor Engine and Stata implementation are highly similar.
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.
AlphaForge mines and dynamically combines alpha factors for better investment performance.
problem Inconsistency and inflexibility of fixed factor weights in alpha factor mining.
method Generative-predictive neural network for factor generation and dynamic weight adjustment.
result Demonstrated superior performance in formulaic alpha factor mining and portfolio returns.
Improved stock selection through predictive fundamentals and uncertainty estimates.
problem Selecting stocks based on future financial data to outperform traditional factor models.
method Train deep nets to forecast future fundamentals, incorporate uncertainty estimates, and adjust portfolios to manage risk.
result Simulated annualized return of 17.7% and Sharpe ratio of 0.84 for uncertainty-aware model, significantly higher than 14.0% and 0.52 for standard factor models.
AlphaCFG discovers alpha factors using grammar-guided search.
problem Discovering formulaic alpha factors in finance.
method AlphaCFG uses a grammar-based framework to define and discover alpha factors with syntactic and semantic constraints.
result AlphaCFG outperforms state-of-the-art methods in trading profitability and efficiency.
Quantformer uses transformer to predict stock returns, outperforming traditional strategies.
problem Predicting stock returns in a dynamic financial market.
method Transfer learning from sentiment analysis to build investment factors using a transformer-based neural network.
result Quantformer outperforms other 100-factor-based quantitative strategies in predicting stock trends.
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.
We quantitatively relate the Patterson-Sullivant currents and generic stretching factors for free group automorphisms to the asymmetric Lipschitz metric on Outer space and to Guirardel's intersection number.
Framework uses LLMs to automate strategy finding in quantitative finance.
problem Brittleness of traditional deep learning models in financial applications.
method Three-stage framework with prompt-engineered LLMs, multimodal agent-based evaluation, and dynamic weight optimization.
result Robust performance in Chinese & US markets, superior risk-adjusted performance.
EFS uses LLMs to optimize sparse portfolios by evolving alpha factors.
problem Sparse portfolio optimization in dynamic market regimes.
method Evolutionary feedback loop with LLM-generated alpha factors.
result Significantly outperforms baselines in diverse datasets.
We present a novel method for extracting cancer signatures by applying statistical risk models (http://ssrn.com/abstract=2732453) from quantitative finance to cancer genome data. Using 1389 whole genome sequenced samples from 14 cancers, we identify an "overall" mode of somatic mutational noise. We give a prescription …
On a periodic basis, publicly traded companies are required to report fundamentals: financial data such as revenue, operating income, debt, among others. These data points provide some insight into the financial health of a company. Academic research has identified some factors, i.e. computed features of the reported d…
In this paper, we quantitative convergence in W2 for a family of Langevin-like stochastic processes that includes stochastic gradient descent and related gradient-based algorithms. Under certain regularity assumptions, we show that the iterates of these stochastic processes converge to an invariant distribution at a…
Quantitative Investment, built on the solid foundation of robust financial theories, is at the center stage in investment industry today. The essence of quantitative investment is the multi-factor model, which explains the relationship between the risk and return of equities. However, the multi-factor model generates e…
Study evaluates LLMs for predicting Chinese stock movements using financial news sentiments.
problem Evaluating LLMs' ability to predict stock price movements using financial news sentiments.
method Standardized experimental procedure with three LLMs, each with unique performance enhancement methods.
result Developed quantitative trading strategies and conducted back-tests to assess LLMs' performance.
FactorMiner discovers financial alpha factors with low redundancy.
problem Finding novel financial alpha factors in a vast search space.
method Modular Skill Architecture and Experience Memory to distill and guide exploration.
result FactorMiner constructs a diverse library of high-quality factors with competitive performance.
We report quantitative relations between corruption level and economic factors, such as country wealth and foreign investment per capita, which are characterized by a power law spanning multiple scales of wealth and investments per capita. These relations hold for diverse countries, and also remain stable over differen…
Paper proposes a reinforcement learning method for trading using expert trajectories.
problem Inability of existing methods to handle long-term goals and delayed rewards in futures trading.
method Modeling futures trading as MDP, using reinforcement learning with expert trajectories and multiple short-term alpha factors.
result The proposed method outperforms traditional and deep learning methods in trading performance.
AlphaForgeBench evaluates LLMs as quantitative researchers, not trading agents, to address instability in financial decision-making.
problem Behavioral instability of LLMs in sequential decision-making under financial uncertainty.
method Proposes AlphaForgeBench, a framework that requires LLMs to generate executable alpha factors and compose factor-based trading strategies.
result Eliminates execution-induced instability and provides a rigorous benchmark for evaluating financial reasoning.
Although Bayesian Optimization (BO) has been employed for accelerating materials design in computational materials engineering, existing works are restricted to problems with quantitative variables. However, real designs of materials systems involve both qualitative and quantitative design variables representing materi…
Enhances genetic programming for stock alpha discovery with warm start and structural constraints.
problem Overwhelming search space and computational burden in traditional genetic programming for alpha factor discovery.
method Proposes a new GP framework with warm start and structural constraints to enhance search performance and interpretability.
result Superior out-of-sample prediction results and higher portfolio returns compared to benchmarks.
The Clifford torus minimizes Willmore energy closely for small perturbations.
problem Finding the closest shape to the Clifford torus under small perturbations of Willmore energy.
method Analyzing integral 2-varifolds with specific properties and showing quantitative closeness to the Clifford torus.
result The support of the varifold is quantitatively close to the Clifford torus after a conformal transformation.
In this paper we bring to bear some new tools from statistical learning on the analysis of roll call data. We present a new data-driven model for roll call voting that is geometric in nature. We construct the model by adapting the "Partition Decoupling Method," an unsupervised learning technique originally developed fo…
We establish blow-up profiles for any blowing-up sequence of solutions of general conformally invariant fully nonlinear elliptic equations on Euclidean domains. We prove that (i) the distance between blow-up points is bounded from below by a universal positive number, (ii) the solutions are very close to a single stand…
This paper improves credit risk analysis by incorporating state-dependent recovery rates into a factor model.
problem Accurate default forecasting in credit risk analysis.
method Extends a one-factor Gaussian copula model to include state-dependent recovery rates and a common factor.
result The proposed model outperforms other models in default prediction, especially during hectic periods.
Quantum codes with optimal distance and dimension for n-dimensional space.
problem Designing efficient quantum codes in high dimensions.
method Combining asymptotically good codes, manifold construction, and embedding theorem.
result Optimal quantum codes with distance and dimension for n-dimensional space.
Stock price prediction is a challenging task, but machine learning methods have recently been used successfully for this purpose. In this paper, we extract over 270 hand-crafted features (factors) inspired by technical and quantitative analysis and tested their validity on short-term mid-price movement prediction. We f…
Revealing latent structure in data is an active field of research, having introduced exciting technologies such as variational autoencoders and adversarial networks, and is essential to push machine learning towards unsupervised knowledge discovery. However, a major challenge is the lack of suitable benchmarks for an o…
This paper fine-tunes BERT for stock market sentiment analysis and improves trading performance.
problem Improving trading performance in non-strongly efficient markets.
method Fine-tuning BERT on annotated data, combining with Alpha191 model for regression and prediction.
result Emotional factors significantly improve trading performance, increasing return rates by 73.8% compared to baseline.
Study explores factors influencing saving behavior among Dhaka employees.
problem Factors influencing saving behavior among Dhaka employees.
method Quantitative approach with cross-sectional survey design, structured questionnaire, descriptive statistics, reliability analysis, regression analysis.
result Only financial management practices had a significant positive relationship with saving behavior.
The MAXFLAT low-pass filter improves factor adjustment for better portfolio performance in China's stock market.
problem Improving factor adjustment for better portfolio performance in China's stock market.
method Using MAXFLAT low-pass volatility model to adjust factors and construct portfolios.
result Adjusted factors by MAXFLAT volatility model show better performance in both large and small cap universes.
In this article, we focus on decomposing latent representations in generative adversarial networks or learned feature representations in deep autoencoders into semantically controllable factors in a semisupervised manner, without modifying the original trained models. Particularly, we propose factors' decomposer-entang…
We propose a family of novel hierarchical Bayesian deep auto-encoder models capable of identifying disentangled factors of variability in data. While many recent attempts at factor disentanglement have focused on sophisticated learning objectives within the VAE framework, their choice of a standard normal as the latent…
Research examines motivations and factors influencing retailers' payment method choices.
problem Understanding motivations and factors affecting retailers' payment method choices.
method Qualitative and quantitative analysis of various factors including regulatory constraints, merchant service providers, and demographic variables.
result Lower interchange fees and regulatory constraints make card payment adoption financially feasible for merchants.
FactorGCL uses hypergraph learning to predict stock returns by mining hidden factors.
problem Mining effective factors in data-driven models is challenging due to low signal-to-noise ratio in market data.
method FactorGCL employs a hypergraph structure and temporal residual contrastive learning to extract hidden factors.
result FactorGCL outperforms existing methods and mines effective hidden factors for predicting stock returns.
HireVAE adapts to market regimes for online stock prediction.
problem Building an online and adaptive factor model for stock prediction.
method HireVAE uses a hierarchical latent space to estimate latent factors from historical market information.
result HireVAE outperforms previous methods in active returns across benchmarks.
We discuss some methods to quantitatively investigate the properties of correlation matrices. Correlation matrices play an important role in portfolio optimization and in several other quantitative descriptions of asset price dynamics in financial markets. Specifically, we discuss how to define and obtain hierarchical …
Study uses deep learning to predict stock trends with superior performance.
problem Predicting short-term equity trends with high accuracy.
method Dual-task multilayer perceptron (MLP) integrating technical signals and deep learning.
result Deep learning model outperforms linear baselines in multi-factor stock selection.
TradeExpert uses a mix of LLMs to predict stock movements.
problem Synthesizing insights from diverse financial data sources.
method A mix of four specialized LLMs analyzing different data types, with a General Expert LLM synthesizing the insights.
result TradeExpert outperforms existing benchmarks in stock movement prediction.
Paper defines conditions for feasible correlation matrices from factor structures.
problem Feasibility of option implied correlation matrices in non-FX markets.
method Quantitative and economic approaches to solve the nearest correlation matrix problem.
result Introduces methods to ensure feasible correlation matrices from factor structures.
Computer simulations often involve both qualitative and numerical inputs. Existing Gaussian process (GP) methods for handling this mainly assume a different response surface for each combination of levels of the qualitative factors and relate them via a multiresponse cross-covariance matrix. We introduce a substantiall…
This paper clarifies VAE's property through geometric and information-theoretic interpretations.
problem The transparency of VAE model is an underlying issue.
method Quantitative understanding of VAE through differential geometry and information theory.
result VAE can be mapped to an implicit isometric embedding with a scale factor derived from the posterior parameter.
Study decomposes uncertainty in HK-distribution parameter estimation for QUS.
problem Uncertainty in HK-distribution parameter estimation for quantitative ultrasound.
method Bayesian Neural Networks (BNNs) for parameter estimation and uncertainty decomposition.
result Decomposes total predictive uncertainty into epistemic and aleatoric components.
We study the asymptotic behavior of the difference between the values at risk VaR(L) and VaR(L+S) for heavy tailed random variables L and S for application in sensitivity analysis of quantitative operational risk management within the framework of the advanced measurement approach of Basel II (and III). Here L describe…