EarnMore uses masked stock representations to train RL agents for customizable stock pools efficiently.
problem Training RL agents for customizable stock pools (CSPs) is computationally expensive and unstable.
method EarnMore introduces a mechanism to mask out stocks outside the target pool, learns meaningful stock representations, and uses a re-weighting mechanism to focus on favorable stocks.
result EarnMore significantly outperforms state-of-the-art baselines in profit metrics with over 40% improvement.
Model uses LLM features to predict stock returns effectively.
problem Predicting stock returns from text data.
method Structured Event Representation (SER) model with attention mechanisms.
result SER-based model outperforms existing models in stock return prediction.
Develops a hybrid deep learning model for stock price prediction.
problem Predicting daily stock prices in the stock market.
method Representation learning with Stock2Vec embedding and temporal convolutional layers.
result Achieves better performance on stock price prediction than benchmarks.
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.
Enhances thematic investing with stock embeddings from textual data.
problem Challenges in constructing thematic portfolios due to overlapping sector boundaries and evolving market dynamics.
method Introduces THEME, a framework that fine-tunes embeddings using hierarchical contrastive learning, aligning themes and stocks using their hierarchical relationship and incorporating stock returns.
result Theme-aligned portfolios demonstrate compelling performance, significantly outperforming large language models in thematic asset retrieval.
Graph-based approach predicts stock trends using dynamic multi-relational graphs.
problem Predicting future stock movements in complex, time-evolving stock relationships.
method Dynamic multi-relational stock graphs, stochastic diffusion process, parallel retention.
result Outperforms state-of-the-art baselines in stock trend forecasting.
We propose a novel investment decision strategy (IDS) based on deep learning. The performance of many IDSs is affected by stock similarity. Most existing stock similarity measurements have the problems: (a) The linear nature of many measurements cannot capture nonlinear stock dynamics; (b) The estimation of many simila…
This research predicts stock market movements using Vision-Language models.
problem Predicting future stock market direction using historical data.
method Utilizing image and byte-based representations of stock data processed with Vision-Language models.
result The proposed approach significantly outperforms deep learning baselines.
Paper uses deep reinforcement learning for optimal stock portfolio management.
problem Optimizing stock portfolio choices in complex market environments.
method Direct deep reinforcement learning to learn factor representations and make optimal decisions.
result Deep learning outperforms average market performance in portfolio allocation.
Many researchers both in academia and industry have long been interested in the stock market. Numerous approaches were developed to accurately predict future trends in stock prices. Recently, there has been a growing interest in utilizing graph-structured data in computer science research communities. Methods that use …
This paper fine-tunes LLMs for stock return prediction using financial news.
problem Improving stock return forecasting accuracy using LLMs.
method Fine-tuning LLMs with text and forecasting modules, comparing encoder-only and decoder-only models, and integrating token-level representations.
result LLMs' aggregated token-level embeddings enhance return predictions for long-only and long-short portfolios.
Novel financial time-series data representation improves industry sector classification.
problem Classifying industries using historical stock returns time-series data.
method Proposed a novel representation based on stock returns embeddings for time-series data, overcoming representational challenges of conventional approaches.
result Substantial performance improvements over baselines using conventional representations.
DGRCL integrates dynamic and static graph relations for financial market prediction.
problem Capturing the evolving nature of stock markets while considering both temporal changes and static relational structures.
method Dynamic Graph Representation with Contrastive Learning (DGRCL) framework, including Embedding Enhancement (EE) and Contrastive Constrained Training (CCT) modules.
result DGRCL significantly outperforms state-of-the-art TGL baselines on NASDAQ and NYSE datasets.
This study reviews text-based stock market analysis methods.
problem Insufficient analysis of unstructured textual data in stock market predictions.
method Reviews existing literature, covers data types, representation techniques, and analysis methods.
result Identifies open problems and suggests future research directions.
We propose improved methods to identify stock groups using the correlation matrix of stock price changes. By filtering out the marketwide effect and the random noise, we construct the correlation matrix of stock groups in which nontrivial high correlations between stocks are found. Using the filtered correlation matrix…
Predict stock movement with news headlines using BERT embeddings.
problem Predicting stock price movement after financial news.
method Fine-Tuned Contextualized-Embedding Recurrent Neural Network (FT-CE-RNN) using BERT.
result Obtains state-of-the-art results on stock movement prediction task.
The paper tackles stock prediction models by improving their generalizability to out-of-sample domains using causal representation learning.
problem Low signal-to-noise ratio and nonstationary nature of financial markets lead to poor performance of stock prediction models.
method The paper investigates Domain Generalization techniques, focusing on causal representation learning to improve model generalizability. It introduces a novel error bound and a causal discovery technique to mitigate spurious correlations.
result The proposed approach enhances the generalizability of stock prediction models, as demonstrated by numerical results.
We propose a methodology for clustering financial time series of stocks' returns, and a graphical set-up to quantify and visualise the evolution of these clusters through time. The proposed graphical representation allows for the application of well known algorithms for solving classical combinatorial graph problems, w…
Study shows stock market efficiency varies over time and can be networked.
problem Understanding the dynamic and collective aspects of stock market efficiency.
method Defined and calculated time-varying efficiency using permutation entropy of log-returns.
result Major world stock markets can be hierarchically classified into groups with similar efficiency profiles, but these rankings are unstable.
Proposes a hybrid model for stock market report classification using graph neural networks.
problem Lack of unified node embeddings for heterogeneous graphs in text datasets.
method Transductive hybrid approach combining unsupervised node representation learning and supervised node classification/edge prediction.
result Demonstrates the model's ability to classify stock market technical analysis reports.
Transfer learning and data augmentation improve stock classification performance.
problem Challenges in stock classification due to noise and volatility.
method Pre-trained model on S&P500 index features, transfer learning to new models, data augmentation on feature space.
result Augmentation on feature space leads to 20% increase in risk-adjusted returns.
This paper is trying to unveil general statistical characteristic of financial; time series data that is subjected to several financial time series data present in Indonesia, e.g. individual index such as stock price of PT. TELKOM, stock price of PT HM SAMPOERNA, and compiled stock price index (Jakarta Stock Exchange I…
NGAT predicts long-term stock trends using graph attention networks.
problem Lack of effective corporate relationship graph comparison methods and model complexity in stock prediction.
method Developed a Node-level Graph Attention Network (NGAT) for corporate relationship graphs.
result Demonstrated the effectiveness of NGAT across two datasets.
TDA improves stock portfolio selection by analyzing data structure.
problem Traditional portfolio selection methods fail to handle stock market data complexities.
method Two-stage method involving time series generation and clustering with TDA features.
result TDA-based portfolio outperforms other methods consistently over different time frames.
SimStock learns stock similarities for better investment management.
problem Challenges in identifying similar stocks due to non-stationary financial markets.
method Temporal self-supervised learning framework combining SSL and temporal domain generalization.
result SimStock outperforms existing methods in finding similar stocks.
The study uses financial events to predict stock market movements.
problem Predicting stock market movements using financial events.
method Combined event extraction method, BERT/ALBERT enhanced event representation, and extended hierarchical attention network.
result Significantly better accuracies and higher simulated returns compared to state-of-the-art models.
A surprising image of the stock market arises if the price time series of all Dow Jones Industrial Average stock components are represented in one chart at once. The chart evolves into a braid representation of the stock market by taking into account only the crossing of stocks and fixing a convention defining overcros…
MDGNN predicts stock prices by capturing multifaceted relations over time.
problem Challenges in predicting stock prices due to dynamic and intricate relations.
method MDGNN uses a discrete dynamic graph and Transformer structure to capture multifaceted relations and temporal evolution.
result MDGNN achieves the best performance in public datasets compared to SOTA methods.
Study shows activist board representation improves Japanese companies' performance.
problem Lack of innovation and improvement in Japanese companies.
method Examined two Japanese companies with activist board representation, analyzing performance metrics.
result Companies with activist board representation experienced significant improvements in stock returns and operational metrics.
Large and stable indices of the world wide stock markets such as NYSE and SP 500 together with NASDAQ -- the index representing markets of new trends, and WIG -- the index of the local stock market of Eastern Europe, are considered. Due to the relation between artificial insymmetrised patterns (AIP) and time series, st…
The paper develops methods to price and hedge options in path-dependent stock models.
problem Pricing and hedging options under complex stock models.
method Develops a path-dependent PDE for option pricing and differentiability of path-dependent SDE solutions.
result Provides formulas for option Greeks and differentiability of path-dependent SDE solutions.
Stock market prediction is still a challenging problem because there are many factors effect to the stock market price such as company news and performance, industry performance, investor sentiment, social media sentiment and economic factors. This work explores the predictability in the stock market using Deep Convolu…
We provided an analytical representation of the price of a barrier option with one type of special moving barrier. We consider the case that risk free rate, dividend rate and stock volatility are time dependent. We get a pricing formula and put call parity for barrier option when the moving barrier has a special relati…
Paper predicts stock volatility using ESG news, showing deep learning's effectiveness.
problem Predicting stock volatility using ESG news.
method ESG news extraction, news representations, and Bayesian inference of deep learning models.
result Deep learning models predict stock volatility better than traditional methods.
This work focuses on the indifference pricing of American call option underlying a non-traded stock, which may be partially hedgeable by another traded stock. Under the exponential forward measure, the indifference price is formulated as a stochastic singular control problem. The value function is characterized as the …
Paper proposes AI for stock market forecasting using external knowledge.
problem Forecasting stock prices influenced by external factors.
method Learning from historical data and external temporal knowledge graphs modeled as Hawkes processes.
result Dynamic representations effectively rank stocks based on returns.
Weibo experts predict stock market better than non-experts.
problem Improving stock market prediction accuracy using sentiment analysis.
method Combining BERT for sentiment classification and LSTM for time-series prediction on Weibo data.
result AFA group users' predictions are 39.67% more accurate than UFA group users.
The properties of q-dependent cross-correlation matrices of stock market have been analyzed by using the random matrix theory and complex network. The correlation structures of the fluctuations at different magnitudes have unique properties. The cross-correlations among small fluctuations are much stronger than those a…
ST-GAN predicts stock trends using financial news and data.
problem Predicting financial trends in stock markets.
method ST-GAN combines NLP and technical indicators using GAN technology.
result Significant improvement over existing models in stock price forecasting.
Paper solves stock loan pricing with finite maturity using integral equations.
problem Valuation of margin-call stock loans with finite maturities.
method Fourier Sine transform and Volterra integral equation approach.
result Integral representation of margin-call stock loan value.
We provide representations of solutions to terminal value problems of inhomogeneous Black-Scholes equations and studied such general properties as min-max estimates, gradient estimates, monotonicity and convexity of the solutions with respect to the stock price variable, which are important for financial security prici…
Unified model predicts stock and systemic risks from diverse financial data.
problem Isolating financial tasks leads to missed cross-scale dependencies.
method Shared Transformer backbone with modular task heads for cross-modal attention and multi-task optimization.
result Uni-FinLLM significantly outperforms baselines in stock forecasting, credit-risk assessment, and systemic-risk detection.
GIFsentiment predicts stock market returns and investor sentiment from social media GIFs.
problem Understanding investor sentiment in the stock market.
method Constructing a sentiment index from social media GIFs and analyzing its correlation with market returns and volume.
result GIFsentiment positively predicts stock market returns and negatively predicts returns for up to four weeks.
The global financial system is highly complex, with cross-border interconnections and interdependencies. In this highly interconnected environment, local financial shocks and events can be easily amplified and turned into global events. This paper analyzes the dependencies among nearly 4,000 stocks from 15 countries. T…
Novel TM-vector model predicts stock market direction using Twitter and market data.
problem Challenging stock market forecasting with equal or ignored user effects.
method TM-vector trained with Twitter features and market information, using IndRNN.
result Significant accuracy in predicting stock market direction, especially for Apple.
Paper proposes a new trading strategy using corporate event detection from news articles.
problem Predicting stock movements based on corporate events from news articles.
method Bi-level event detection model: low-level for token-level event identification, high-level for article-level event identification.
result The proposed strategy outperforms existing models in stock prediction metrics.
We give a microscopic representation of the stock-market in which the microscopic agents are the individual traders and their capital. Their basic dynamics consists in the auto-catalysis of the individual capital and in the global competition/cooperation between the agents mediated by the total wealth invested in the s…
Diffusion-VAE tackles multi-step stock price prediction with stochastic noise.
problem Challenges in multi-step stock price prediction due to stochasticity and target price sequence.
method Combines hierarchical VAE and diffusion probabilistic techniques for seq2seq stock prediction.
result D-Va model outperforms state-of-the-art solutions in prediction accuracy and variance.