sPortfolio visualizes stock portfolios and factor data for better investment analysis.
problem Insufficient intuitive visual analytics for multi-factor stock portfolios.
method Develops a holistic visualization system for risk-factor, multiple-portfolios, and single-portfolios.
result Facilitates actionable insights and market trend understanding through intuitive visual analytics.
We introduce simple cost and risk proxy metrics that can be attached to Treasury issuance strategy to complement analysis of the resulting portfolio weighted-average maturity (WAM). These metrics are based on mapping issuance fractions to their long-term, asymptotic portfolio implications for cost and risk under mechan…
New machine learning method classifies companies effectively.
problem Classifying companies for financial analysis.
method Unsupervised machine learning with t-SNE and spectral clustering.
result Improved portfolio performance through better company classification.
This paper presents a novel application of a clustering algorithm developed for constructing a phylogenetic network to the correlation matrix for 126 stocks listed on the Shanghai A Stock Market. We show that by visualizing the correlation matrix using a Neighbor-Net network and using the circular ordering produced dur…
The main contribution of the paper is to employ the financial market network as a useful tool to improve the portfolio selection process, where nodes indicate securities and edges capture the dependence structure of the system. Three different methods are proposed in order to extract the dependence structure between as…
Software helps finance students construct optimal portfolios using VBA.
problem Finding the best portfolio of assets considering risk and return.
method Two methods: Markowitz and El-Khatib-Hatemi-J, both optimizing risk-adjusted return.
result Software constructs all possible portfolios and helps investors choose the best one.
Unified framework for portfolio optimization using gain PDF.
problem Optimizing portfolios with control over high profits.
method Unified approach incorporating various PO methods using gain PDF.
result Directly matching target PDF for maximal control over PO.
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 European Union and Eurozone present an inquisitive case of strongly interconnected network with high degree of dependence among nodes. This research focused on investment network of European Union and its major trading partners for specific time period 2001 to 2014. The changing investment patterns within Eurozone …
Paper develops a model-based RL framework for portfolio optimization in financial markets.
problem Complex, non-Gaussian environment dynamics in financial markets.
method Heavy-tailed preserving normalizing flows for environment simulation; model-based reinforcement learning framework.
result Proposed method outperforms in various financial markets, especially during the pandemic.
Study on hedging CVA in jump-diffusion setting using Monte Carlo simulations.
problem Hedging Credit Valuation Adjustment (CVA) in financial portfolios.
method Monte Carlo simulation in Black-Scholes and Merton jump-diffusion settings.
result Hedging CVA is crucial for stable trading strategies, especially in jump-diffusion settings.
Identifying customer segments in retail banking portfolios with different risk profiles can improve the accuracy of credit scoring. The Variational Autoencoder (VAE) has shown promising results in different research domains, and it has been documented the powerful information embedded in the latent space of the VAE. We…
Auto insurers improve risk assessment using t-SNE.
problem Accurate risk estimation for auto insurance policyholders.
method Combining neural network with t-SNE for dimensionality reduction.
result Visual representation of risk as a 2D surface, revealing high vs low risk policyholders.
regvis.net offers a visual survey of regulatory visualization.
problem Lack of a comprehensive resource for regulatory visualization.
method Collection and manual tagging of 80+ publications, creation of a searchable webpage.
result First publication set tailored for regulatory visualization.
We study how to leverage off-the-shelf visual and linguistic data to cope with out-of-vocabulary answers in visual question answering task. Existing large-scale visual datasets with annotations such as image class labels, bounding boxes and region descriptions are good sources for learning rich and diverse visual conce…
Proposes a spectral method to assess and combine multiple data visualizations.
problem Evaluating and combining the strengths of different data visualization algorithms.
method Spectral method for assessing and combining multiple visualizations.
result Proposes a visualization eigenscore to quantify relative performance and a consensus visualization.
To convey neural network architectures in publications, appropriate visualizations are of great importance. While most current deep learning papers contain such visualizations, these are usually handcrafted just before publication, which results in a lack of a common visual grammar, significant time investment, errors,…
AV-CPL uses continuous pseudo-labels for AVSR combining labeled and unlabeled data.
problem Improving AVSR performance with labeled and unlabeled data.
method Semi-supervised method using continuous pseudo-labels generated by the same AVSR model.
result Significant improvements in VSR performance on LRS3 dataset.
Fundamental portfolio beats market portfolio under certain conditions.
problem Empirical evidence of fundamental portfolio outperformance.
method Theoretical foundation based on stock price reversion to fundamental values.
result Fundamental portfolio outperforms market portfolio under strong reversion conditions.
This study analyzes cryptocurrency market dynamics using a novel q-dependent detrended cross-correlation method.
problem Capturing correlations at varying fluctuation amplitudes and time scales in complex systems.
method Extends traditional metrics with q-dependent detrended cross-correlation coefficient ρ(q,s) and qMSTs. result Significant shifts in network structures during major disruptions, leading to decentralized correlations.
Visualizes futures markets using particle physics tools.
problem Understanding high-velocity data in futures markets.
method Uses ROOT, an open-source data-analysis tool, to reconstruct and visualize message-based data.
result Allows stakeholders to gain a better understanding of markets and monitor effectively.
New report on machine learning visualization techniques and trends.
problem Improving trust in machine learning models through visualization.
method Analysis of peer-reviewed articles on machine learning visualization techniques.
result Rapid growth in machine learning visualization techniques over the past three years.
The abstract discusses how humans use visualizations in machine learning.
problem The reliance on human involvement in AI systems and analytics.
method Review of seven steps in the ML process and different visualization techniques.
result Different visualizations are used at various stages of the ML process.
IANN visualizes all input variables effects simultaneously.
problem Inability to visualize all input variables effects simultaneously in black-box functions.
method Interpretable Architecture Neural Network (IANN) approach.
result Visualization of all input variables effects directly and simultaneously.
Consensus dimension reduction combines multiple visualizations to identify shared patterns.
problem Conflicting visualizations from different dimension reduction methods.
method Multi-view learning to identify stable patterns across multiple views.
result Consensus visualization effectively identifies shared low-dimensional data structure.
Visualizes deep generative models for drug design.
problem Limited visualization tools for deep generative models in drug discovery.
method Proposes a visualization framework for deep graph generative models.
result Interactive visualization and molecular optimization tools.
SEP framework teaches LLMs to generate explainable stock predictions.
problem Challenging task of generating human-readable explanations for stock predictions.
method Self-reflective agent and Proximal Policy Optimization (PPO) for autonomous learning.
result Fine-tuned LLM outperforms traditional methods in prediction accuracy and Matthews correlation coefficient.
This paper discusses the role of risk communication in macroprudential oversight and of visualization in risk communication. Beyond the soar in data availability and precision, the transition from firm-centric to system-wide supervision imposes vast data needs. Moreover, except for internal communication as in any orga…
Paper establishes baselines for offline RL from visual observations.
problem Challenges in offline reinforcement learning from visual observations with continuous action spaces.
method Simple baselines and benchmarking tasks for offline RL from visual observations.
result Simple modifications to existing online RL algorithms outperform existing offline RL methods.
Reverse-weighted portfolios outperform in commodity futures markets.
problem Efficiency of commodity futures markets.
method Permutation-weighted portfolios, rank-based methods.
result Reverse-weighted portfolio outperforms price-weighted portfolio.
This paper compares three portfolio designs for Indian stocks.
problem Designing an optimum portfolio that balances return and risk.
method Three approaches: minimum risk, optimum risk, and Eigen portfolios.
result Optimum risk portfolios and Eigen portfolios identified for each sector.
WAPPO optimizes feature distributions for better visual transfer in RL.
problem Improving visual transfer in reinforcement learning.
method WAPPO uses Wasserstein Confusion to minimize feature distribution distance.
result WAPPO outperforms previous methods in visual transfer across different environments.
Project predicts stock prices for robust portfolio design in Indian sectors.
problem Precise stock price prediction for robust portfolio design.
method Minimum variance and optimal risk portfolio optimization using past stock prices.
result Backtesting shows improved performance of optimized portfolios over equal weight portfolio.
We introduce the notion of the visual core of a hyperbolic 3-manifold N and explore its basic properties. The visual core can be thought of as a harmonic analysis analogue of the convex core. We investigate circumstances in which the visual core of a cover N' of N embeds under the covering map from N' to N. We apply th…
A new factor analysis method using ICA reduces portfolio concentration and diversifies excess kurtosis.
problem Standard factor analysis suffers from issues with pairwise correlations of asset returns.
method Identifies factors based on non-Gaussianity instead of variance, using ICA.
result Fat-tailed portfolios significantly reduce portfolio concentration and winner-takes-all problem.
A novel method visualizes higher-dimensional spaces using hyperbolic geometry.
problem Challenges in visualizing higher-dimensional spaces.
method Interactive visualization of higher-dimensional grids based on hyperbolic geometry.
result Our method shows the whole higher-dimensional space at once and avoids disadvantages of previous methods.
Paper uses neural networks to compress large portfolios of options, reducing risk and capital requirements.
problem Managing risk and capital requirements for large portfolios of financial options.
method Artificial neural network framework for portfolio compression, static hedging, and risk management.
result The compressed portfolio's risk profiles align closely with the target portfolio's, reducing capital requirements.
Consider a family of portfolio strategies with the aim of achieving the asymptotic growth rate of the best one. The idea behind Cover's universal portfolio is to build a wealth-weighted average which can be viewed as a buy-and-hold portfolio of portfolios. When an optimal portfolio exists, the wealth-weighted average c…
This study compares three portfolio design approaches for stock selection.
problem Designing a profitable portfolio with precise stock returns and risks.
method Three portfolio design approaches: mean-variance portfolio, hierarchical risk parity, and autoencoder-based portfolio.
result Autoencoder portfolios outperform MVP on annual returns, but MVP is best on risk-adjusted returns.
In this paper Portfolio Optimization techniques were used to determine the most favorable investment portfolio. In particular, stock indices of three companies, namely Microsoft Corporation, Christian Dior Fashion House and Shevron Corporation were evaluated. Using this data the amounts invested in each asset when a po…
Ray marching method visualizes flat surfaces efficiently.
problem Efficient visualization of flat surfaces.
method Ray marching approach for intuitive exploration.
result Effective visualization of translation surfaces and polyhedra.
New portfolios outperform traditional methods by using factor weights.
problem Improving portfolio allocation in markets driven by factors.
method Factor-weighted Dirichlet portfolios outperform uniform Dirichlet portfolios.
result Factor-weighted portfolios outperform uniformly sampled portfolios in market returns.
This study compares two portfolio optimization methods on Indian stocks.
problem Designing an optimal portfolio considering stock returns and risks.
method Hierarchical Risk Parity and Eigen Portfolio approaches on NIFTY 50 sectors.
result Hierarchical Risk Parity portfolio outperforms Eigen portfolio in most sectors tested.
Signature portfolios approximate optimal wealth in non-Markovian markets.
problem Approximating optimal wealth in non-Markovian markets.
method Linear path-functional portfolios based on signatures of market weights.
result Signature portfolios can uniformly approximate any continuous portfolio function.
Pretrained model improves visual dialog performance.
problem Improving performance in visual dialog tasks.
method Pretrained ViLBERT model on vision-language datasets, fine-tuned on VisDial.
result Best model outperforms prior work by more than 1% on NDCG and MRR.
We present GraphTSNE, a novel visualization technique for graph-structured data based on t-SNE. The growing interest in graph-structured data increases the importance of gaining human insight into such datasets by means of visualization. Among the most popular visualization techniques, classical t-SNE is not suitable o…
The effect of proportional transaction costs on systematically generated portfolios is studied empirically. The performance of several portfolios (the index tracking portfolio, the equally-weighted portfolio, the entropy-weighted portfolio, and the diversity-weighted portfolio) in the presence of dividends and transact…
Adversarial attacks can manipulate ML-aided visualizations, tricking analysts.
problem Adversarial attacks on ML-aided visualizations.
method Identifying attack surface and exemplifying five adversarial attacks.
result Adversaries can induce various attacks, like creating arbitrary and deceptive visualizations.