Optimizes social interactions for profit, people, and planet using mathematical models.
problem Determining the most effective social configurations for mutual objectives.
method Formulated as a mathematical optimization problem, using (meta)relational models theory.
result Identifies the most suitable combination of sociality forms for mutual objectives.
Enhances traditional MV model for socially responsible investors.
problem Traditional MV models ignore ESG scores relevant to socially responsible investors.
method Implemented an amended MV model considering ESG scores.
result SR investors can achieve competitive SR portfolios with a trade-off between Sharpe Ratio and ESG scores.
We consider a group of mean-variance investors with mimicking desire such that each investor is willing to penalize deviations of his portfolio composition from compositions of other group members. Penalizing norm constraints are already applied for statistical improvement of Markowitz portfolio procedure in order to c…
We will look at the entire cycle of the investment process relating to all aspects of, formulating an investment hypothesis, constructing a portfolio based on that, executing the trades to implement it, on-going risk management, periodically measuring the performance of the portfolio, and rebalancing the portfolio eith…
Paper tackles ESG rating disagreement in sustainable investing portfolios.
problem Lack of alignment between ESG ratings from different agencies affects investment decisions.
method Proposes a nonlinear optimization model reformulated as a convex quadratic program to address ESG rating disagreement.
result The proposed model can effectively manage ESG rating disagreement and improve investment decisions.
Two approaches integrate qualitative views into portfolio optimization, showing aggregation methods outperform robust optimization.
problem Incorporating qualitative views into portfolio optimization models.
method Robust optimization and order aggregation methods.
result Aggregation methods outperform robust optimization in portfolio performance analysis.
Quantum computing optimizes ESG portfolios efficiently.
problem Optimizing investment portfolios with risk, return, and ESG considerations.
method Formulated discrete Markowitz portfolio theory (DMPT) for quantum annealers, incorporating ESG ratings.
result Discrete portfolios converge to continuous solutions as budgets increase, outperforming traditional methods.
AI investors signal higher debt in ESG firms, boosting portfolio management.
problem Determining the value of ESG investing amid AI investment trends.
method Cross-sectional regressions of ESG scores and debt ratios of S&P 500 firms.
result ESG scores signal higher debt in firms, supporting ESG investing.
Enhances social spam detection using multi-level dependency of relational sequences.
problem Social spam detection in multi-relation social networks.
method Developed the Multi-level Dependency Model (MDM) to exploit long-term and short-term dependencies in user relational sequences.
result MDM improves social spam detection accuracy on a real-world multi-relational social network.
Unified framework for ESG-inclusive portfolio optimization and pricing.
problem Incorporating ESG ratings into dynamic asset pricing theory.
method Introducing ESG-valued return as a linear transformation of financial and ESG scores, preserving traditional risk aversion with an ESG affinity parameter.
result Developed a more complex portfolio optimization problem in a space governed by reward, risk, and ESG score.
A new model selects low-carbon mutual funds considering ESG criteria, risk, and investor preferences.
problem Aligning financial investments with a low-carbon economy.
method Tri-criterion portfolio selection model using a preference-based multi-objective genetic algorithm (ev-MOGA).
result The model successfully incorporates carbon risk exposure and loss-adverse attitudes into portfolio construction.
This paper optimizes stock portfolios considering ESG criteria using Bayesian optimization.
problem Optimizing financial investments while incorporating ESG criteria.
method Bayesian optimization to maximize stock portfolio performance under ESG constraints.
result A scalable approach to optimize stock portfolios that balance financial performance and ESG compliance.
In the last few years, the financial advisory industry has been impacted by the emergence of digitalization and robo-advisors. This phenomenon affects major financial services, including wealth management, employee savings plans, asset managers, etc. Since the robo-advisory model is in its early stages, we estimate tha…
The paper analyzes frameworks for integrating sustainability into investment decisions.
problem Understanding how ESG factors influence investment choices.
method Examined and analyzed various theoretical frameworks including Behavioral Finance, Modern Portfolio, and Risk Management.
result Investors increasingly integrate ESG factors to optimize financial outcomes and societal goals.
Investors adjust spending based on a social norm, spending less during losses and more during gains.
problem Managing spending and portfolio decisions while adhering to a social norm.
method Formulated a preference ordering with two CRRA preference orderings, solved analytically and numerically.
result Annual spending should be lower than expected financial return and procyclical, with spending cuts following losses.
The rise in online social networking has brought about a revolution in social relations. However, its effects on offline interactions and its implications for collective well-being are still not clear and are under-investigated. We study the ecology of online and offline interaction in an evolutionary game framework wh…
Algorithms optimize fair portfolios for diverse risk-tolerant consumers.
problem Designing fair portfolios for consumers with varying risk tolerances.
method Two-player zero-sum game-based algorithms for optimal and near-optimal portfolio design.
result Efficient algorithms for fair portfolio design with and without group structure assumptions.
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.
Study shows social media impacts shareholder returns on ESG risks.
problem Investor sentiment and public opinion on ESG risks.
method Event study design using social media data.
result Statistically significant reduction in abnormal returns after ESG-risk events.
Optimizes stock portfolios with profit, risk, and sustainability.
problem Balancing profit, risk, and sustainability in stock portfolio management.
method Developed a novel utility function combining Sharpe ratio and ESG scores; used genetic algorithm for optimization.
result System outperforms traditional reinforcement learning methods and improves on risk and sustainability metrics.
Investors prioritize ESG in crypto-assets, showing higher exposure than traditional assets.
problem Understanding ESG preferences in crypto-assets and their investment behavior.
method A representative household finance survey in Austria to examine ESG preferences and crypto-investment exposure.
result ESG-conscious investors have higher exposure to crypto-assets compared to traditional asset classes.
I-AID categorizes disaster tweets into useful information types.
problem Filtering useful information from social media during disasters.
method Multimodel approach using BERT, GAT, and Relation Network.
result I-AID outperforms state-of-the-art approaches in F1 scores.
Online social networks offer a new way to investigate financial markets' dynamics by enabling the large-scale analysis of investors' collective behavior. We provide empirical evidence that suggests social media and stock markets have a nonlinear causal relationship. We take advantage of an extensive data set composed o…
A new method uses preference relations to reconcile contradictory trading signals from multiple securities.
problem Difficulty in exploiting multiple pairs trading signals due to contradictions.
method Proposes a portfolio construction method based on preference relation graphs to reconcile contradictory signals.
result Portfolios based on preference relations exhibit robust returns even with high transaction costs and improve with more securities considered.
TRR detects stock portfolio crashes by simulating human reasoning.
problem Detecting stock portfolio crashes with limited historical data.
method Temporal Relational Reasoning (TRR) framework.
result TRR outperforms state-of-the-art techniques in detecting stock portfolio crashes.
A two-stage decision support system optimizes long-short portfolios under ESG considerations.
problem Optimizing long-short portfolios under environmental, social, and governance (ESG) considerations.
method First stage: Multi-criteria evaluation using TODIMSort and MEREC. Second stage: Non-convex portfolio optimization with Omega ratio.
result ESG-enhanced long-short portfolios outperform non-ESG and market-value-weighted benchmarks.
Predicting event attendance using social influence from social networks.
problem Predicting people's participation in real-world events.
method Modeling social influence, using non-geotagged posts and social group structures, applying graph embedding techniques, and training a neural network.
result The proposed classifier achieves 89% accuracy on the VFestival dataset, outperforming state-of-the-art methods.
SLIM model predicts social network polarization using signed links.
problem Polarization and filter bubbles in social networks.
method Signed relational Latent dIstance Model (SLIM) using Skellam distribution.
result SLIM model accurately predicts friendships and animosity in social networks.
A variety of approaches have been proposed to automatically infer the profiles of users from their digital footprint in social media. Most of the proposed approaches focus on mining a single type of information, while ignoring other sources of available user-generated content (UGC). In this paper, we propose a mechanis…
The paper examines how ESG constraints affect portfolio optimization in large datasets.
problem Investment optimization with ESG constraints in large portfolios.
method Asymptotic analysis of out-of-sample Sharpe ratio, regularization matrix estimation, and adaptive portfolio selection.
result The proposed adaptive ESG-constrained portfolio yields a high out-of-sample Sharpe ratio while meeting ESG requirements.
In this paper we apply evolutionary optimization techniques to compute optimal rule-based trading strategies based on financial sentiment data. The sentiment data was extracted from the social media service StockTwits to accommodate the level of bullishness or bearishness of the online trading community towards certain…
This study analyzes dynamic connectedness in global supply chain infrastructure portfolios, identifying key risk factors and extreme events.
problem Understanding dynamic connectedness in global supply chain infrastructure portfolios under various risk factors and extreme events.
method Time-varying parameter vector autoregression (TVP-VAR) model to study spillover and interconnectedness of risk factors.
result Risk shocks influence dynamic connectedness between portfolios and risk factors, and extreme events affect investment outcomes.
In this paper we consider an interval portfolio selection problem with uncertain returns and introduce an inclusive concept of satisfaction index for interval inequality relation. Based on the satisfaction index, we propose an approach to reduce the interval programming problem with uncertain objective and constraints …
Study optimizes stock portfolios using network analysis and forecasting.
problem Optimizing stock portfolios with network analysis and forecasting.
method Constructs dependency networks using VAR and FEVD, applies MST algorithm, and incorporates ARIMA and NNAR forecasts.
result MST-based strategies outperform buy-and-hold benchmarks, achieving higher returns.
Social interactions and personal tastes shape our consumption behavior of cultural products. In this study, we present a computational model of a cultural market and we aim to analyze the behavior of the consumer population as an emergent phenomena. Our results suggest that the final market shares of cultural products …
Method constructs hedging portfolio for carbon risk but not ESG risk.
problem Hedging carbon risk with ESG risk.
method Triangulated Maximally Filtered Graph and node2vec algorithms.
result Efficient hedging portfolio strategy for carbon risk but not ESG risk.
DRL improves ESG financial portfolio management by regulating returns based on ESG scores.
problem Improving ESG financial portfolio management through market regulation.
method Used Advantage Actor-Critic (A2C) agent and adapted OpenAI Gym environments for comparative analysis.
result DRL agent outperforms standard market conditions in ESG-regulated market.
This paper studies the concept of instantaneous arbitrage in continuous time and its relation to the instantaneous CAPM. Absence of instantaneous arbitrage is equivalent to the existence of a trading strategy which satisfies the CAPM beta pricing relation in place of the market. Thus the difference between the arbitrag…
Paper uses inverse optimization to measure risk preference from investment portfolios.
problem Measuring subjective risk preference in investment portfolios.
method Inverse optimization on mean-variance framework.
result Quantified risk preference parameters validated with existing measures.
Social activities play an important role in people's daily life since they interact. For recommendations based on social activities, it is vital to have not only the activity information but also individuals' social relations. Thanks to the geo-social networks and widespread use of location-aware mobile devices, massiv…
Algorithm improves learning by integrating diverse agents' behaviors.
problem Lack of social learning in reinforcement learning algorithms.
method Free energy approach for social bandit learning.
result Algorithm converges to optimal policy and enhances learning.
Proposes using diffusion models for probabilistic stock market predictions.
problem Uncertainties in financial data make deterministic models ineffective for stock market predictions.
method Utilizes Denoising Diffusion Probabilistic Models (DDPM) and Masked Relational Transformer (MRT).
result Achieves state-of-the-art performance in stock movement prediction and portfolio management.
The study examines how social biases are reinforced in machine learning models used for credit scoring.
problem Reinforcement of societal biases in machine learning algorithms for credit scoring.
method Analysis of machine learning models predicting gender or ethnicity based on loan applications data.
result Machine learning models can reflect and reinforce social biases present in the data.
MILLION framework optimizes portfolio risk and return efficiently.
problem Optimizing risk and return in AI for FinTech portfolio management.
method Two phases: return maximization with auxiliary objectives and risk control with portfolio interpolation and improvement.
result Framework achieves fine-grained risk control and improved return rates.
Domestic Violence (DV) is considered as big social issue and there exists a strong relationship between DV and health impacts of the public. Existing research studies have focused on social media to track and analyse real world events like emerging trends, natural disasters, user sentiment analysis, political opinions,…
What predicts the evolution over time of subjective well-being? We correlate the trends of subjective well-being with the trends of social capital and/or GDP. We find that in the long and medium run social capital largely predicts the trends of subjective wellbeing in our sample of countries. In the short-term this rel…
A method for trust evaluation of devices in human-device coexistence systems.
problem Efficient trust evaluation of devices in systems with diverse physical and social attributes.
method Canonical correlation analysis-enhanced hypergraph self-supervised learning (HSLCCA).
result The proposed HSLCCA method significantly outperforms baseline algorithms in identifying trusted devices.
Paper optimizes trend-following portfolios using autocorrelation models.
problem Developing an optimal trend-following portfolio strategy.
method Introduces a unifying theoretical setting with autocorrelation models for covariance matrices of trends and risk premia. Specifies practical models for covariance matrices. Decomposes optimal portfolio into four basic components.
result Empirical backtests confirm overperformance of the proposed optimal portfolio.