Study on Leverage Ratio in European banks during financial crises.
problem Impact of financial crises on European banks' Leverage Ratio.
method Empirical analysis using regression techniques.
result Leverage Ratio is significantly influenced by financial scenarios.
This paper introduces compositional data analysis for financial ratios, improving industry-level analysis.
problem Statistical issues with standard financial ratios at industry level.
method Compositional data analysis techniques for financial ratios.
result Improved analysis of financial ratios using compositional data methods.
New financial ratios using compositional data improve analysis of firm health.
problem Statistical issues with standard financial ratios, especially skewness and outliers.
method Compositional data (CoDa) methodology to analyze financial statements.
result Outliers and skewness reduced, results invariant to numerator and denominator permutation.
Study shows death ratio of COVID-19 deaths increases financial volatility.
problem Impact of COVID-19 deaths on financial markets.
method Analysis of VIX index based on daily case reports and death ratios.
result Death ratio positively influences financial volatility.
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.
The study uses CoDa to analyze family business financial ratios, highlighting methodological issues.
problem Asymmetry, non-normality, and non-linearity in financial ratios of family businesses.
method Compositional data analysis (CoDa) and classical analysis strategies.
result Results are sensitive to the methodology used, emphasizing the need for appropriate methodologies.
The paper optimizes portfolios by selecting financial ratios via PCA for better value investment.
problem Embedding value investment in portfolio optimization models.
method Principal Component Analysis (PCA) to filter out dominant financial ratios, then applying portfolio optimization model with second-order stochastic dominance criteria.
result PCA-SPO(B) strategy outperforms other models in terms of downside deviation, CVaR, VaR, Sortino, Rachev, and STARR ratios.
The study visualizes Spanish fish and meat processing companies using financial, environmental, and social ratios.
problem Mapping financial, environmental, and social performance of Spanish processing companies.
method Used compositional data and principal-component analysis biplot for statistical analysis.
result Identified clusters of companies with similar financial, environmental, and social performance.
It had been believed in the conventional practice that the risk of a bank going bankrupt is lessened in a straightforward manner by transferring the risk of loan defaults. But the failure of American International Group in 2008 posed a more complex aspect of financial contagion. This study presents an extension of the …
The study predicts bankruptcy in Indian companies using financial ratios.
problem Predicting early signs of corporate bankruptcy in Indian companies.
method Logistic regression considering profitability, leverage, and efficiency ratios for one and two years before bankruptcy.
result The model accurately predicts bankruptcy with 81.4% and 85.1% accuracy one and two years before filing, respectively.
In the context of the current financial crisis, when more companies are facing bankruptcy or insolvency, the paper aims to find methods to identify distressed firms by using financial ratios. The study will focus on identifying a group of Romanian listed companies, for which financial data for the year 2008 were availa…
Study reveals clusters of resilient and vulnerable Spanish agri-food firms post-Ukraine-Russia war.
problem Financial resilience of agri-food companies in Spain during the Ukraine-Russia conflict.
method Cluster analysis using centred log-ratios for compositional data of financial ratios.
result Increase in resilient firms by 2023, highlighting sectoral adaptation to economic challenges.
Firm financials are well established as return predictors, being the inspiration for a large set of anomalies in the asset pricing literature. Employing topological data analysis we revisit the question of association between seven of the most commonly studied financial ratios and stock returns. Specifically the TDA Ba…
The paper tests if optimal hedge ratios for Bitcoin are position-dependent.
problem Testing if optimal hedge ratios for Bitcoin are position-dependent.
method Explicit and efficient method for testing symmetric vs. asymmetric optimal hedge ratios in a multivariate setting.
result The optimal hedge ratio for Bitcoin is position-dependent, with long positions having a higher ratio than short positions.
The paper examines Indian market bubbles using financial ratios.
problem Detecting bubbles in emerging markets like India is challenging.
method Utilizes financial ratios and the Philips et al 2015 right-tailed unit test.
result Identifies various bubble episodes in the Indian market.
Adapts Altman's model to compositional data for bankruptcy prediction.
problem Predicting business default using standard financial ratios has issues.
method Uses compositional data methodology with log-ratios and machine learning.
result Compositional methods improve predictive performance, especially random forests.
A new framework assesses financial and ESG risks for sustainable investing.
problem Measuring risk and reward in sustainable investing considering environmental, social, and governance factors.
method Proposes axiomatic definitions for ESG-coherent risk measures and reward-risk ratios based on bivariate random variables.
result Empirical analysis ranks stocks using the proposed measures.
This study presents an ANWSER model (asset network systemic risk model) to quantify the risk of financial contagion which manifests itself in a financial crisis. The transmission of financial distress is governed by a heterogeneous bank credit network and an investment portfolio of banks. Bankruptcy reproductive ratio …
This paper evaluates financial competitiveness of Indian real estate companies using entropy method.
problem Improving financial competitiveness of Indian real estate companies in a competitive market.
method Financial competitiveness evaluation index system using key financial ratios and a scoring system.
result Companies with high scores have strong profitability and operational capacity, while those with lower scores struggle with solvency and working capital.
Modeling financial markets with sandpile model to understand price volatility and arbitrage constraints.
problem Understanding price volatility and arbitrage constraints in financial markets.
method Uses a sandpile model to represent information and price changes, linking size of price volatility to the scaling law of avalanches.
result Identifies a structural tension between non-arbitrage condition and price adjustments consistent with a constant Sharpe ratio.
A3T-GCN model forecasts FTSE100 stock prices using technical indicators and financial ratios.
problem Forecasting closing stock prices of FTSE100 constituents.
method Hybrid A3T-GCN architecture using technical indicators, financial ratios, and sector correlations.
result A3T-GCN model improves prediction accuracy with annualized log-returns and shorter sequence lengths.
The paper optimizes portfolios using clustering and Sharpe ratio-based optimization.
problem Optimizing portfolio performance in financial modeling.
method Combines K-Means clustering for asset segmentation and Sharpe ratio-based optimization.
result Optimized portfolios outperform traditional equal-weighted benchmarks.
Study finds CNNs perform better with financial ratio data than fundamental data.
problem Improving CNN performance with financial data.
method Developed and analyzed three image encoding methods for financial data.
result Image encoding methods improve CNN performance for financial ratio data but not significantly for fundamental data.
Paper introduces lexical ratio to measure portfolio diversification.
problem Traditional diversification metrics overlook non-numerical relationships.
method Uses textual data to capture diversification dimensions through entropy-based insights.
result Lexical ratio (LR) outperforms traditional metrics in optimizing portfolio returns.
We study the various sectors of the Bombay Stock Exchange (BSE) for a period of eight years from January 2006 to March 2014. Using the data of the daily returns of a period of eight years we investigate the financial cross correlation co-efficients among the sectors of BSE and Price by Earning (PE) ratio of BSE Sensex.…
Paper presents a deep learning method for estimating asset return precision matrices in noisy financial markets.
problem Estimating precision matrices of asset returns in low signal-to-noise ratio environments.
method Non-linear factor model within deep learning framework, consistent estimator with error covariance estimator.
result Superior accuracy in simulations and empirical data.
Study defines and optimizes bank reliability using LR and PSO.
problem Lack of reliability concept in financial services.
method Logistic Regression (LR) for initial estimation, Particle Swarm Optimization (PSO) for optimization.
result Optimal financial ratios maximize bank reliability.
L2GMOM learns financial networks and optimizes momentum strategies.
problem Expensive databases and financial expertise limit network construction accessibility.
method End-to-end machine learning framework (L2GMOM) that learns networks and optimizes trading signals.
result Significant improvement in portfolio profitability and risk control with Sharpe ratio of 1.74.
New method identifies uncertainty shocks in financial markets using revised VIX.
problem Traditional VIX fails to capture non-Gaussian, heavy-tailed asset returns.
method Fit a double-subordinated Normal Inverse Gaussian Levy process to S&P 500 option prices to construct a revised VIX.
result Revised VIX provides a more comprehensive measure of volatility reflecting extreme movements and heavy tails.
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.
End-to-end framework optimizes financial metrics using neural networks.
problem Difficult portfolio optimization in financial markets due to non-stationarity and high costs.
method Directly optimizes differentiable financial metrics via neural networks, incorporating realistic costs and rebalancing.
result Best model achieves +7.86% total return, outperforming S&P 500 by 12.38 percentage points.
Enhances financial data signal-to-noise ratio using auto-encoders and mutual regularization.
problem Improving signal-to-noise ratio in financial data.
method Combining target and context variables, using auto-encoders with mutual regularization to learn common ground.
result Discover new regularities in financial time-series data.
X-Trend quickly adapts to new financial regimes, increasing Sharpe ratio by 18.9%.
problem Adapting to rapidly changing financial market conditions.
method Few-shot learning and cross-attention mechanism.
result X-Trend increases Sharpe ratio by 18.9% over a neural forecaster and 10-fold over a conventional strategy.
The paper compares traditional regression with modern neural network methods for financial hedging and risk compression.
problem Finding optimal hedge ratios and managing portfolio risk using traditional regression methods has limitations.
method The paper introduces regularization techniques and common factor analyses using neural networks to improve upon regression methods.
result Neural network methods provide better performance in hedge ratio estimation and risk compression compared to traditional regression.
Financial econometrics has become an increasingly popular research field. In this paper we review a few parametric and nonparametric models and methods used in this area. After introducing several widely used continuous-time and discrete-time models, we study in detail dependence structures of discrete samples, includi…
Study examines credit risk's impact on Vietnamese banks' financial performance.
problem Impact of credit risk on commercial banks' financial performance in Vietnam.
method Dynamic Difference Generalized Method of Moments (dynamic Difference GMM) approach to address autocorrelation, non-constant variance, and endogeneity issues.
result ROE and NIM persist from one year to the next, while NPLR negatively affects ROA and ROE.
Benchmarking deep learning models for financial time series, focusing on risk-adjusted performance.
problem Optimizing risk-adjusted performance in financial time series prediction.
method Evaluation of various deep learning architectures including linear models, RNNs, transformers, state space models, and sequence representation approaches.
result Hybrid models like VSN with LSTM and xLSTM achieve the highest overall Sharpe ratio and superior downside adjusted characteristics.
Paper optimizes DC pension fund management with VaR and relative performance constraints.
problem Optimizing DC pension fund performance under VaR and relative performance constraints.
method Introduced an auxiliary process to transform the problem into a self-financing problem, combined linearization, Lagrange dual, martingale, and concavification methods.
result Explicit investment strategies obtained for certain penalty and reward functions.
Paper uses Transformers to predict intraday volume ratio with high accuracy.
problem Accurate prediction of intraday volume ratio for VWAP strategies.
method Transformer architecture with log-normal transformation and external features.
result Probabilistic forecasting captures mean and standard deviation of volume ratios.
Deep RL optimizes dynamic portfolio weights in China's stock market.
problem Traditional portfolio optimization methods struggle with dynamic asset weight adjustments.
method Developed a deep reinforcement learning framework with novel reward functions and random sampling.
result Model outperforms traditional methods in portfolio optimization and risk mitigation.
We study hedging and pricing of unattainable contingent claims in a non-Markovian regime-switching financial model. Our financial market consists of a bank account and a risky asset whose dynamics are driven by a Brownian motion and a multivariate counting process with stochastic intensities. The interest rate, drift, …
Paper extends ranking metrics theory for financial positions.
problem Developing a new class of functionals for evaluating financial positions.
method Axiomatic framework based on monotonicity and cash-quasiconcavity.
result Linking ranking metrics to families of acceptance sets and risk measures.
Study uses MTD model to optimize portfolios by capturing complex financial asset relationships.
problem Capturing nonlinear and directional relationships in financial markets.
method Directed and weighted financial networks using Mixture Transition Distribution (MTD) model.
result Portfolio optimization with network-based assortativity measures outperforms classical methods.
Paper extends ranking metrics theory for financial positions.
problem Developing a new class of performance evaluation methods.
method Axiomatic framework based on monotonicity and cash-quasiconcavity.
result Linking ranking metrics to families of acceptance sets and risk measures.
Study uses VC correlation to uncover directional financial relationships.
problem Understanding causal relationships between financial variables.
method Volatility constrained correlation (VC correlation) method.
result Operating income is most influential, while market capitalization and revenue are most susceptible.
TraderTalk uses LLMs to simulate human trading interactions in financial markets.
problem Simulating realistic human trading interactions in financial markets.
method Hybrid ABM with LLM-generated behaviors for detailed conversations.
result Successfully replicates trade-to-order volume ratios in financial markets.
This paper provides a framework for modeling the financial system with multiple illiquid assets when liquidation of illiquid assets is caused by failure to meet a leverage requirement. This extends the network model of Cifuentes, Shin & Ferrucci (2005) which incorporates a single asset with fire sales and capital adequ…
New techniques identify shifts in financial market sectors.
problem Identifying shifts in financial market structure and composition.
method Developed new mathematical techniques to identify nonlinear shifts in market sectors.
result Identified meaningful sector-to-sector mappings and optimal portfolio styles.