Paper models financial contagion with fire sales and borrowing.
problem Financial contagion and systemic risk in interconnected financial networks.
method Modeling financial contagion in a network with fire sales and borrowing, considering both uncollateralized and collateralized loans.
result Existence and uniqueness of clearing solutions (payments, liquidations, and borrowing) are provided under certain conditions, and these solutions are Nash equilibria.
Paper models financial contagion via leverage requirements and fire sales.
problem Financial contagion through leverage requirements and fire sales.
method Network model with multiple illiquid assets, proving equilibrium existence.
result Calibrated models show systemic risk varies with leverage requirements.
Paper analyzes fire sales in a network of banks using VWAP and LOB pricing.
problem Optimal asset liquidation and borrowing strategies in a network of banks.
method Nash equilibrium model with two market clearing mechanisms.
result Existence and uniqueness of clearing solutions for liquidations, borrowing, prices, and haircuts.
Complex contagion model explains financial fire sales through continuous asset prices.
problem Modeling financial fire sales with a continuum of asset prices.
method Developed a threshold model of continuous-state cascades using real values for asset prices.
result Discretization approach accurately replicates the distribution of defaulted banks and asset prices.
Paper assesses systemic risk using fire sales and portfolio data.
problem Assessing systemic risk with limited data.
method Maximum Entropy principle for network reconstruction.
result Effectiveness of method validated on US bank data.
Mathematical framework investigates fire sales amplification and stability.
problem Market instability caused by fire sales amplification.
method Developed a mathematical framework to investigate system characteristics and resilience.
result Characterized systems resilient to small shocks for financial stability assessment.
Study shows how capital constraints can lead to systemic crises in financial systems.
problem Impact of regulatory capital constraints on fire sales and financial stability.
method Mean field game model with banks adjusting holdings via trading strategies under regulatory constraints.
result Capital constraints can lead to simultaneous defaults in a substantial proportion of the banking system.
Study shows self-exciting shocks increase systemic risk in interbank networks.
problem Systemic risk in interbank lending networks with self-exciting shocks.
method Mean-field model, weak convergence analysis, measure-valued process, law of large numbers.
result Self-exciting shocks increase systemic risk in interbank networks.
The paper validates overlaps between financial institutions to assess systemic risk.
problem Systemic risk from fire sales in financial institutions.
method Statistical validation of portfolio overlaps to build a network of contagion channels.
result Systemic risk increased before the 2007-2008 financial crisis and accelerated in 2013.
New method identifies algo trading strategies as liquidity consumers or providers.
problem Determining if algo trading strategies consume or provide liquidity.
method Analyzes trade and price history to classify strategies as liquidity consumers or providers.
result Identifies net liquidity consumption or provision of algo trading strategies.
Better investment strategies identified through a network metric of asset commonality.
problem Identifying investment strategies based on fund portfolio asset popularity.
method Bipartite network analysis of mutual funds and their holdings, calculating the Average Commonality Coefficient (ACC).
result Funds investing in less popular assets outperform those in more popular ones, even after adjusting for standard factors.
The scope of financial systemic risk research encompasses a wide range of interbank channels and effects, including asset correlation shocks, default contagion, illiquidity contagion, and asset fire sales. This paper introduces a financial network model that combines the default and liquidity stress mechanisms into a "…
Market stability depends on a fundamental value anchor, not price crashes.
problem Stability of order-book markets under fundamental anchoring.
method Analytical model and empirical analysis of six transmission channels.
result Fundamental anchoring stabilizes markets by mean-reverting prices and refilling books; removing the anchor leads to market failure.
Optimizes financial portfolios to minimize systemic risk.
problem Minimizing systemic risk in financial markets.
method Network optimization to rearrange overlapping portfolios.
result Systemic risk can be reduced by more than a factor of two without harming individual banks.
Model financial contagion in a multi-layered network with obligations in illiquid assets.
problem Model financial contagion in a multi-layered network with obligations in illiquid assets.
method Developed a multi-layered financial network model with fire sales, utility maximization, and tâtonnement process.
result Existence and uniqueness of equilibrium portfolio holdings and market prices in a multi-layered financial system.
Enhanced CAPM reconstructs financial network topology and systemic risk.
problem Reconstructing financial interconnection patterns from partial information.
method Constrained entropy maximization tailored for bipartite financial networks.
result ECAPM outperforms traditional CAPM and MECAPM in network topology and systemic risk estimation.
The paper studies price impacts in asset liquidation markets.
problem Understanding price impacts in asset liquidation markets.
method Equilibrium formulation and analysis of price impacts.
result Existence and uniqueness of clearing prices for portfolio liquidation.
Study creates open-access wildfire dataset for Russia.
problem Data scarcity for comprehensive Eurasian wildfire research.
method Machine learning for exploratory data analysis and predictive modeling.
result Identified key environmental factors influencing fire behavior.
SVMs improve forest fire detection accuracy on challenging datasets.
problem Rapid and accurate detection of forest fires.
method Training SVMs on labeled fire image datasets, focusing on data preprocessing, feature extraction, and model training.
result SVMs enhance detection accuracy on complex datasets, revealing key parameters affecting performance.
Unsupervised fire and smoke segmentation from IR videos.
problem Early detection of fire from infrared videos.
method Spatial, temporal, and motion information fusion using optical flow, divergence, and intensity values.
result Markov Random Field outperforms other clustering algorithms in fire and smoke segmentation.
FIRE extracts interpretable rules from tree ensembles.
problem Building sparse and interpretable rule sets from tree ensembles.
method Optimization-based framework with fusion regularization and sparsity-inducing penalties.
result FIRE outperforms state-of-the-art rule ensemble algorithms.
Dynamic risk assessment method for WUI fires improves upon static frameworks.
problem Static risk assessment methods fail to capture dynamic changes in WUI fire risks.
method Dynamic evaluation matrix, grey incidence analysis, optimization model.
result The proposed method effectively captures dynamic risk evolution patterns.
Paper explores wash sales using variations of the birthday problem.
problem Modeling and probability of wash sales in securities.
method Variations of the birthday problem to model wash sales.
result Necessary conditions for wash sales are derived.
Enhances DES by removing noise and defining regions more accurately.
problem Incompetent classifier selection in noisy regions and true indecision regions.
method FIRE-DES++ uses equal number of samples from each class and removes noise to define regions more accurately.
result FIRE-DES++ outperforms FIRE-DES and state-of-the-art DES frameworks.
Machine learning improves wildfire science and management, but requires expert knowledge.
problem Improving wildfire science and management through AI.
method Review of popular ML approaches and their application in six wildfire science domains.
result Opportunities exist for applying more advanced ML methods in wildfire science.
DeFi exploits lead to reduced CP spreads, contrary to contagion hypothesis.
problem Vulnerabilities in DeFi destabilize traditional short-term funding markets.
method Analysis of commercial paper spreads and regulatory segmentation.
result DeFi exploits lead to a 'Flight-to-Quality' pattern, narrowing rather than widening CP spreads.
Forest Fire Clustering discovers cell types from single-cell data.
problem Discovering cell types from large-scale single-cell sequencing data.
method Iterative label propagation and parallelized Monte Carlo simulation.
result Forest Fire Clustering outperforms state-of-the-art methods on diverse benchmarks.
Model predicts short-term Amazon rainforest fires with high accuracy.
problem Accurate short-term forecasting of Amazon rainforest fires is challenging.
method Used Seasonal and Trend decomposition based on Loess combined with multi-month-ahead load forecasting algorithms.
result Proposed decomposition-ensemble models provide more accurate forecasts than other models.
Paper proposes a machine learning method to predict sale efficacy.
problem Determining the efficacy of online sales from discounts alone.
method Machine learning-based heuristic using Support Vector Machine.
result Predicts sale efficacy with 91.11% accuracy.
To investigate the actual phenomena of transport on a complex network, we analysed empirical data for an inter-firm trading network, which consists of about one million Japanese firms and the sales of these firms (a sale corresponds to the total in-flow into a node). First, we analysed the relationships between sales a…
Study finds sales forecasters overreact to extreme news.
problem Understanding how forecasters react to sales growth news.
method Proposes a framework with fat-tailed dynamics and linear forecasting rule.
result Forecasters overreact to significant sales growth news.
Model explains capital allocation and wealth distribution dynamics in a frictional economy.
problem Understanding capital allocation and wealth distribution dynamics in a frictional economy.
method Mean-field game approach to model interactions between expert and household groups.
result Experts accumulate capital during booms and quickly reverse behavior in busts, even without macro-shocks.
AI-driven sales prioritization boosts renewal bookings by 8.08%.
problem Manual sales account prioritization is inefficient and under-invested.
method Developed an AI-based Account Prioritizer using machine learning and explanation algorithms.
result Generated a +8.08% increase in renewal bookings.
The note shows equivalence between polynomial growth and polynomial containment for certain groups.
problem Equivalence between polynomial growth and polynomial containment for Cayley graphs of groups.
method Analyzes results from the literature on Cayley graphs of groups.
result Equivalence holds for elementary amenable groups and non-amenable groups.
The paper uses transformed ANOVA to identify important fire detection variables.
problem Identifying key variables for forest fire detection.
method Developed a complete orthonormal system for standard normal distribution, applied Z-score transformation, and used ANOVA approximation.
result Attribute ranking reveals important variables for fire detection.
SAE-FiRE extracts key financial info from long documents, improving earnings surprise predictions.
problem Predicting earnings surprises from long, redundant financial documents.
method Sparse Autoencoder feature selection to filter out noise and identify key dimensions.
result SAE-FiRE significantly outperforms baseline approaches in financial datasets.
The paper proposes a ML workflow for B2B sales prediction.
problem Predicting business to business sales outcomes using subjective human evaluations.
method A two-pipeline ML approach on Azure ML, including data enrichment and model training.
result ML predictions improve accuracy and increase sales value.
Predicting car sales using maximum entropy principle and proportional growth dynamics.
problem Predicting the sales of new cars over time.
method Analyzing 10 years of Spanish car sales data, applying Gibrat's law, and using the Maximum Entropy Principle.
result Car sales distribution follows predictions from the Maximum Entropy Principle for proportional growth systems.
Paper proposes CNN for automatic sales forecast in e-commerce.
problem Challenges in sales forecasting in e-commerce.
method Uses Convolutional Neural Network to automatically extract features from raw log data.
result Experimental validation shows the effectiveness of the proposed method.
This paper improves sales forecasting on Tmall using Fourier decomposition and Tweedie distribution optimization.
problem Sales forecasting for retailers on Tmall.
method Fourier decomposition for seasonality extraction and Tweedie distribution optimization.
result Improved sales forecasting results using optimized models.
Models predict fire and other emergencies in Edmonton.
problem Accurate prediction of emergency events for timely response.
method Data collection, descriptive analysis, feature selection, and negative binomial regression.
result Models perform well, with acceptable prediction errors for weekly and monthly periods.
Sales data in a commodity market (supermarket sales to consumers) has been analysed by studying the fluctuation spectrum and noise correlations. Three related products (ketchup, mayonnaise and curry sauce) have been analysed. Most noise in sales is caused by promotions, but here we focus on the fluctuations in baseline…
Dynamic pricing improves sales of low-sale products using online clustering.
problem Low-sale products lack sufficient data for traditional dynamic pricing algorithms.
method Online clustering of product demand and dynamic pricing decisions based on cluster analysis.
result The proposed algorithms significantly outperform traditional single-product pricing policies in increasing revenue.
We analyze a database comprising quarterly sales of 55624 pharmaceutical products commercialized by 3939 pharmaceutical firms in the period 1992--2001. We study the probability density function (PDF) of growth in firms and product sales and find that the width of the PDF of growth decays with the sales as a power law w…
New approach optimizes sales process for B2B businesses.
problem Optimizing the sales process for B2B businesses.
method Causal Predictive Optimization and Generation with three layers: prediction, optimization, and serving.
result Significant wins over legacy systems in LinkedIn implementation.
The paper proposes a method to identify latent factors from sampled and fired graph data.
problem Identifying latent factors from sampled and fired graph data.
method The paper presents a theoretical and practical approach to build an identifier of latent factor activations.
result The method successfully identifies latent factor activations from sampled and fired graph data.
Short sales allow tax deferral by offsetting gains from ordinary sales.
problem Tax deferral opportunity in short sales not regulated in the Philippine tax system.
method Short selling to offset gains from ordinary sales of identical stocks.
result Tax deferral opportunity exists but is unregulated in the Philippine tax system.
Improved sales forecasting for new products using transfer learning.
problem Insufficient training data for new products leads to inaccurate sales forecasts.
method Network-based Transfer Learning approach for deep neural networks.
result Deep neural networks' prediction accuracy for food sales forecasting can be effectively increased.