A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Machine learning outperforms crowd investors in predicting loan defaults and investment returns.
In spite of the many advantages of aerial imagery for crowd monitoring and management at mass events, datasets of aerial images of crowds are still lacking in the field. As a remedy, in this work we introduce a novel crowd dataset, the DLR Aerial Crowd Dataset (DLR-ACD), which is composed of 33 large aerial images acqu…
It is common for CCTV operators to overlook inter- esting events taking place within the crowd due to large number of people in the crowded scene (i.e. marathon, rally). Thus, there is a dire need to automate the detection of salient crowd regions acquiring immediate attention for a more effective and proactive surveil…
Eliciting labels from crowds is a potential way to obtain large labeled data. Despite a variety of methods developed for learning from crowds, a key challenge remains unsolved: \emph{learning from crowds without knowing the information structure among the crowds a priori, when some people of the crowds make highly corr…
We found that factors decay over time, with momentum fitting best.
Wisdom of the crowd, the collective intelligence derived from responses of multiple human or machine individuals to the same questions, can be more accurate than each individual, and improve social decision-making and prediction accuracy. This can also integrate multiple programs or datasets, each as an individual, for…
Crowding is most likely an important factor in the deterioration of strategy performance, the increase of trading costs and the development of systemic risk. We study the imprints of \emph{crowding} on both anonymous market data and a large database of metaorders from institutional investors in the U.S. equity market. …
This research simplifies lending pools in decentralized finance for better understanding and security.
We propose a Multi-Task Learning (MTL) paradigm based deep neural network architecture, called MTCNet (Multi-Task Crowd Network) for crowd density and count estimation. Crowd count estimation is challenging due to the non-uniform scale variations and the arbitrary perspective of an individual image. The proposed model …
The paper interprets financial markets as crowds during booms and busts.
A model-free hedging method using stock crowding scores.
Crowdsourced wisdom improves causal learning.
Sentiment Analysis of microblog feeds has attracted considerable interest in recent times. Most of the current work focuses on tweet sentiment classification. But not much work has been done to explore how reliable the opinions of the mass (crowd wisdom) in social network microblogs such as twitter are in predicting ou…
Traffic flow prediction is crucial for urban traffic management and public safety. Its key challenges lie in how to adaptively integrate the various factors that affect the flow changes. In this paper, we propose a unified neural network module to address this problem, called Attentive Crowd Flow Machine~(ACFM), which …
Remembering our day-to-day social interactions is challenging even if you aren't a blue memory challenged fish. The ability to automatically detect and remember these types of interactions is not only beneficial for individuals interested in their behavior in crowded situations, but also of interest to those who analyz…
The average portfolio structure of institutional investors is shown to have properties which account for transaction costs in an optimal way. This implies that financial institutions unknowingly display collective rationality, or Wisdom of the Crowd. Individual deviations from the rational benchmark are ample, which il…
Investment herding can reduce household consumption, a phenomenon called crowding-out effect.
Gatherings of thousands to millions of people frequently occur for an enormous variety of events, and automated counting of these high-density crowds is useful for safety, management, and measuring significance of an event. In this work, we show that the regularly accepted labeling scheme of crowd density maps for trai…
This study measures liquidity risks in Aave, a blockchain lending protocol.
This paper proposes a general model for synchronized crowding behavior. An order parameter is introduced to quantify the level of synchronization which is shown a function of percentage of agents in reactive state. Further, synchronization is shown to be driven by the most active agents with the highest volatility. A t…
Evidence shows that in a significant number of cases the current methods of research do not allow for reproducible and falsifiable procedures of scientific investigation. As a consequence, the majority of critical decisions at all levels, from personal investment choices to overreaching global policies, rely on some va…
Contextual bandit framework improves revenue optimization in securities lending market.
Enhances crowd safety through AI and data-driven models.
Relationship lending is broadly interpreted as a strong partnership between a lender and a borrower. Nevertheless, we still lack consensus regarding how to quantify the strength of a lending relationship, while simple statistics such as the frequency and volume of loans have been used as proxies in previous studies. He…
Allowing members of the crowd to propose novel microtasks for one another is an effective way to combine the efficiencies of traditional microtask work with the inventiveness and hypothesis generation potential of human workers. However, microtask proposal leads to a growing set of tasks that may overwhelm limited crow…
Crowded trades by similarly trading peers influence the dynamics of asset prices, possibly creating systemic risk. We propose a market clustering measure using granular trading data. For each stock the clustering measure captures the degree of trading overlap among any two investors in that stock. We investigate the ef…
Dynamic pricing improves DeFi lending efficiency by reducing regret to logarithmic levels.
Low dimensional embeddings that capture the main variations of interest in collections of data are important for many applications. One way to construct these embeddings is to acquire estimates of similarity from the crowd. However, similarity is a multi-dimensional concept that varies from individual to individual. Ex…
The study examines cross-border lending behavior from G7 countries, showing changes in driving factors after the 2008 financial crisis.
Study improves forecasting of ED crowding using advanced ML models.
Market crowd trading behavior and volume impact stock prices in China.
Study analyzes risk management in Aave and Compound lending protocols, finding v3 better than v2.
The study calculates securities lending haircuts and indemnification costs.
Study proposes optimal risk-aware interest rates for crypto lending protocols.
The Wisdom of Crowds is a phenomenon described in social science that suggests four criteria applicable to groups of people. It is claimed that, if these criteria are satisfied, then the aggregate decisions made by a group will often be better than those of its individual members. Inspired by this concept, we present a…
Improved AMM protocol supports diverse loan maturities in DeFi.
Paper assesses risks of stablecoins, from lending to business-to-business.
Reviews six finance topics, including 'radical complexity'.
New method accurately reconstructs Russell 3000 index, revealing crowded portfolios.
The paper analyzes Lending Club's loan applicants to predict default risk.
Bayesian deep learning counts crowds robustly despite occlusions and scale variations.
New measure predicts Dutch housing market downturns.
Debt-financed collateral in DeFi increases stability risks.
Lower bounds show OLS outperforms basis pursuit in overparameterized linear regression.
The study improves credit evaluation in peer-to-peer lending using machine learning.
A new framework integrates credit scoring into profit scoring for better P2P lending investments.
Private credit markets have expanded significantly, offering unique lending technology to private equity firms.
The study analyzes how cross-chain interoperability affects decentralized lending protocols' performance.