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.
Financial networks reveal systemic risk, suggesting new regulatory strategies.
problem Global financial interconnectedness and inadequacy of traditional risk models.
method Network-based models of financial systems to understand contagion and risk.
result Financial networks exhibit 'robust-yet-fragile' properties, informing cost-effective regulation.
New model predicts financial connectedness via COVID-19 spread.
problem Predicting financial connectedness during COVID-19 spread.
method Semiparametric matrix regression model with Bayesian hierarchical mixture prior.
result Model captures heterogeneity in network responses to risk factors.
New portfolio optimization method considers both asset-specific and systemic risks for financial networks.
problem Optimizing portfolios with both idiosyncratic and systemic risks in financial networks.
method Developed a multi-objective optimization model that incorporates idiosyncratic variance and network clustering coefficient.
result Optimal portfolios outperform in terms of return measures and have less drawdown compared to traditional strategies.
The paper predicts financial markets using news text and semantic network analysis.
problem Predicting financial markets with news data.
method Semantic network analysis of news text to assess economic keywords' importance.
result The index captures financial market phases and predicts returns and volatilities.
M2VN forecasts financial volatility by fusing time series data with news embeddings.
problem Forecasting financial volatility with unstructured news data.
method Combines deep neural networks with open-source market features and news embeddings.
result M2VN outperforms existing models in financial volatility forecasting.
NewsNet-SDF uses deep learning to integrate financial news with financial data for better asset pricing.
problem Combining unstructured text with structured financial data for accurate asset pricing.
method Adversarial networks and pretrained language model embeddings.
result Substantially outperforms alternatives with a Sharpe ratio of 2.80.
New method constructs multilayer networks from financial data, capturing dependencies across different risk factors.
problem Difficult construction of multilayer networks, neglecting time delays and interdependencies.
method Tucker tensor autoregression for direct multilayer network construction.
result Captures within and between connections, identifies strong interconnections between volumes and prices layers.
New method identifies extreme risk propagation in financial networks.
problem Understanding extreme risk in financial networks.
method Max-linear structural equation model, hard-thresholding, Hamming distance.
result Sparse DAG for extreme risk propagation estimated.
A new neural network framework ADNN improves financial feature construction.
problem Constructing highly informative financial features.
method Neural network (ADNN) with domain knowledge, pre-training, and data augmentation.
result ADNN produces more diversified and informative features than genetic programming.
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.
Unified approach for clustering financial multiplex networks.
problem Lack of methods to capture interconnections between assets over time.
method Tensor-based unified local and global clustering coefficients for multiplex networks.
result Unified clustering coefficients effectively describe dependencies between assets over time.
Study introduces new financial ratios for better predicting company performance.
problem Lack of progress in predicting company performance and assessing financial risks.
method Developed new financial and macroeconomic ratios, supervised learning models, and Bayesian models.
result New proposed variables improve model accuracy and FNN performs best across multiple 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.
FININ predicts financial markets by modeling news interactions and influence.
problem Complex diffusion of financial news into market prices.
method FININ is a novel model that captures news links and interactions, integrating market data and news articles.
result FININ outperforms advanced models with a 0.429 and 0.341 improvement in daily Sharpe ratio for S&P 500 and NASDAQ 100 respectively.
The paper proposes a method of financial time series forecasting taking into account the semantics of news. For the semantic analysis of financial news the sampling of negative and positive words in economic sense was formed based on Loughran McDonald Master Dictionary. The sampling included the words with high frequen…
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.
A new framework predicts stock movements using news sentiment and relational data.
problem Predicting stock prices from textual information is challenging due to market uncertainty and natural language complexity.
method Multi-Graph Recurrent Network (MGRN) combining textual sentiment from financial news and relational data.
result The model outperforms benchmarks in predicting stock movements.
A new model calculates optimal clearing payments in dynamic financial networks.
problem Determining fair clearing payments in networks with potential defaults.
method Extends Eisenberg-Noe model to multiple time periods, solving linear programs for optimal payments.
result Proves the model satisfies the priority of debt claims requirement and finds unique optimal payments.
Study uses neural networks for fast Hawkes model parameter estimation in finance.
problem Estimating parameters of Hawkes models from high-frequency financial data.
method Recurrent neural networks for parameter estimation.
result Significantly faster computational performance compared to traditional methods.
Global balance index measures systemic risk in financial networks.
problem Measuring systemic risk in financial networks.
method Defined global balance index based on a diffusive process and linear system.
result Global balance index correlates with systemic risk measures.
DP-LSTM predicts stock prices using financial news with improved accuracy and privacy.
problem Predicting stock prices with financial news articles.
method Integrates financial news articles into a sentiment-ARMA model, then uses an LSTM network with differential privacy.
result Achieves up to 65.79% improvement in MSE for S&P 500 prediction.
Paper uses neural nets for financial optimization problems.
problem Financial optimization and derivative pricing problems.
method Neural networks and deep reinforcement learning for solving PDEs and dynamic optimization.
result Efficient resolution of nonlinear PDEs and dynamic optimization in finance.
Analyzes incentives and strategies in financial networks.
problem Deciding default status and liabilities in a network of banks.
method Refined model of financial systems with priority assignments.
result Actions by banks can influence their own outcomes.
This thesis models financial contagion and stability, providing insights for systemic risk management.
problem Systemic risk in financial networks through default contagion and fire sales.
method Developed mathematical models for default contagion in weighted financial networks, derived asymptotic expressions for total damage.
result Explicit asymptotic expressions for total damage and stability criteria for financial systems.
Predict stock prices using financial news sentiment analysis.
problem Predicting stock market trends for better investment returns.
method Deep Learning (MLP, LSTM, FinBERT-LSTM) integrating news sentiment.
result FinBERT-LSTM model predicts stock prices more accurately.
ChatGPT enhances GNN for stock movement prediction.
problem Predicting stock movements using textual data.
method Integrates ChatGPT's graph inference into GNN for stock movement forecasting.
result Model outperforms state-of-the-art benchmarks in stock movement forecasting.
New method filters large networks from financial data to reveal key subnetworks.
problem Filtering large dimensional networks to isolate key constituents.
method Exploits spectral properties of high-dimensional data networks, tuning for sparsity and consistency.
result Shows method can interpolate between zero and maximal filtering, preserving spectral properties.
Uses news sentiment scores for direct reinforcement trading in financial markets.
problem Incorporating news data into quantitative trading remains challenging.
method Directly uses news sentiment scores and raw data as inputs for reinforcement learning, processed by sequence models.
result Achieves superior performance compared to market benchmarks.
Proposes a new normalization method for deep neural networks in financial forecasting.
problem Deep neural networks are sensitive to input variable range and prone to numerical issues, especially with financial time-series.
method Bilinear input normalization method that handles high-frequency financial time-series without expert knowledge.
result Significant improvements in forecasting future stock price dynamics over other normalization techniques.
In this work, we develop a novel framework to measure the similarity between dynamic financial networks, i.e., time-varying financial networks. Particularly, we explore whether the proposed similarity measure can be employed to understand the structural evolution of the financial networks with time. For a set of time-v…
A new neural network model simulates financial markets without assuming underlying dynamics.
problem Modeling financial time series without assuming underlying dynamics.
method Neural network based generative model using a parsimonious Variational Autoencoder framework.
result Works reliably in small data environments, providing a new performance evaluation metric.
This paper reviews transfer learning for financial data predictions, highlighting its potential.
problem Accurate stock price prediction in financial time series is challenging due to noise and non-linear relationships.
method Transfer Learning applied to financial market predictions.
result Transfer Learning can improve financial prediction capability.
Method detects and visualizes changes in financial markets' asset relationships.
problem Detecting and explaining changes in financial markets' asset relationships.
method Construct co-occurrence networks, calculate Graph-Based Entropy, apply Differential Network.
result Visualization of changes in financial markets with high interpretability.
Financial markets modeled like brain networks using dMNC.
problem Understanding latent dynamics in financial markets.
method Biologically inspired framework using dMNC.
result Structural persistence, regime shifts, and early warning signals identified.
Many new models for measuring financial contagion have been presented recently. While these models have not been specified for investment funds directly, there are many similarities that could be explored to extend the models. In this work we explore ideas developed about financial contagion to create a network of inve…
New financial volatility models capture dynamic volatility better.
problem Traditional volatility models miss important volatility dynamics.
method Integrate recurrent neural networks into GARCH models.
result Improved in-sample and out-of-sample volatility forecasting.
In this paper we develop a novel neural network model for predicting implied volatility surface. Prior financial domain knowledge is taken into account. A new activation function that incorporates volatility smile is proposed, which is used for the hidden nodes that process the underlying asset price. In addition, fina…
The paper analyzes how news sentiment of companies can affect market movements.
problem Understanding how news sentiment impacts market performance and volatility.
method Applied NLP techniques to analyze news sentiment of 87 companies over 7 years.
result Strong media sentiment towards one company can indicate significant changes in sentiment towards related companies.
New deep learning architecture learns martingales efficiently.
problem Efficiently learning martingales in financial derivatives pricing.
method High-order weak approximation algorithms of Runge-Kutta type.
result Deep neural networks based on this architecture learn martingales effectively.
Deep learning improves credit risk assessment without new data.
problem Improving credit risk assessment in banking without new data.
method Sequential deep learning using temporal convolutional networks.
result Sequential deep learning outperformed tree-based models in credit risk assessment.
CoCos can increase financial fragility in certain network structures.
problem The effectiveness of CoCos in enhancing financial stability depends on the network structure.
method Analysis of phase transitions in a network of interconnected banks.
result CoCos can increase financial fragility under certain network structures.
Financial networks have become extremely useful in characterizing the structure of complex financial systems. Meanwhile, the time evolution property of the stock markets can be described by temporal networks. We utilize the temporal network framework to characterize the time-evolving correlation-based networks of stock…
We introduce an event based framework of directional changes and overshoots to map continuous financial data into the so-called Intrinsic Network - a state based discretisation of intrinsically dissected time series. Defining a method for state contraction of Intrinsic Network, we show that it has a consistent hierarch…
Study finds market inefficiencies vary by time scale, with news uncertainty key.
problem Evaluating scale-dependent informational efficiency of stock markets.
method Tensor-eigenvalue-based Financial Chaos Index, Granger causality, network analysis.
result Semi-strong form of EMH rejected at daily frequency, but not at monthly.
In this paper we focus our attention on the exploitation of the information contained in financial news to enhance the performance of a classifier of bank distress. Such information should be analyzed and inserted into the predictive model in the most efficient way and this task deals with all the issues related to tex…
Analyzes news graphs to predict financial market dislocations.
problem Predicting financial market dislocations using news content.
method Extracts entities from news articles, aggregates them into graphs, applies network analysis, and uses sentiment analysis.
result Identifies high entropy in news graphs correlates with financial market dislocations.
MassMutual uses neural network embeddings from financial news to predict downgrade risk.
problem Predicting downgrade risk in financial institutions using alternative data sources.
method Proposes a predictive downgrade model using neural network embeddings of financial news.
result Improves performance of benchmark model by more than 5 percent in terms of AUC and recall rate.