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.
Paper measures cognitive bias in positive feedback trading using diffusion process estimates.
The starting point of this paper is the so-called Robust Positive Expectation (RPE) Theorem, a result which appears in literature in the context of Simultaneous Long-Short stock trading. This theorem states that using a combination of two specially-constructed linear feedback trading controllers, one long and one short…
LLM trading agents show risk feedback can improve alignment without fine-tuning.
We consider in a market model the cooperative emergence of value due to a positive feedback between perception of needs and demand. Here we consider also a negative feedback from production of the traded products, and find that this cooperativity is robust, provided that the production rate is slow. Cooperativity is fo…
New method learns from either positive or negative feedback alone.
Whenever a social media user decides to share a story, she is typically pleased to receive likes, comments, shares, or, more generally, feedback from her followers. As a result, she may feel compelled to use the feedback she receives to (re-)estimate her followers' preferences and decides which stories to share next to…
This paper extends stock trading results to include stop-loss orders.
A microeconomic approach is proposed to derive the fluctuations of risky asset price, where the market participants are modeled as prospect trading agents. As asset price is generated by the temporary equilibrium between demand and supply, the agents' trading behaviors can affect the price process in turn, which is cal…
The paper analyzes regret in bilateral trade mechanisms without prior valuations.
ATLAS uses LLMs to adaptively trade by optimizing prompts and coordinating agents.
We describe a simple model for speculative trading based on adaptive behavior of economic agents.The adaptive behavior is expressed through a feedback mechanism for changing agents' stock-to-bond ratios, depending on the past performance of their portfolios.The stock price is set according to the demand-supply for the …
Maximizing trading volume in online learning framework between traders.
Recommender systems widely use implicit feedback such as click data because of its general availability. Although the presence of clicks signals the users' preference to some extent, the lack of such clicks does not necessarily indicate a negative response from the users, as it is possible that the users were not expos…
Study analyzes broker's gain from trade in repeated context-based trading.
This paper extends a Kyle model to include price-responsive traders, revealing new dynamics and equilibria.
Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process and make recommendations following a fixed strategy. In this paper…
We seek a discussion about the most suitable feedback control structure for stock trading under the consideration of proportional transaction costs. Suitability refers to robustness and performance capability. Both are tested by considering different one-step ahead prediction qualities, including the ideal case, correc…
Pairs trading is a market-neutral strategy that exploits historical correlation between stocks to achieve statistical arbitrage. Existing pairs-trading algorithms in the literature require rather restrictive assumptions on the underlying stochastic stock-price processes and the so-called spread function. In contrast to…
FinRLlama wins FinRL Challenge 2024 by fine-tuning LLMs with market data.
New method detects when models influence their own drift in real-time data streams.
Framework uses human feedback to safely set OOD detection thresholds, reducing false positives.
We present a simple agent-based model to study the development of a bubble and the consequential crash and investigate how their proximate triggering factor might relate to their fundamental mechanism, and vice versa. Our agents invest according to their opinion on future price movements, which is based on three source…
We present a generalization of the Simultaneous Long-Short (SLS) trading strategy described in recent control literature wherein we allow for different parameters across the short and long sides of the controller; we refer to this new strategy as Generalized SLS (GSLS). Furthermore, we investigate the conditions under …
Modeling HFT interactions reveals market instability.
Adaptive sampler improves recommendation for implicit feedback data.
Study how communication and feedback graphs affect learning outcomes.
We consider a discrete-time, linear state equation with delay which arises as a model for a trader's account value when buying and selling a risky asset in a financial market. The state equation includes a nonnegative feedback gain and a sequence which models asset returns which are within known bounds but o…
A trading system uses LLMs to adapt to volatile crypto markets.
Anomaly detectors are often used to produce a ranked list of statistical anomalies, which are examined by human analysts in order to extract the actual anomalies of interest. Unfortunately, in realworld applications, this process can be exceedingly difficult for the analyst since a large fraction of high-ranking anomal…
Social media systems rely on user feedback and rating mechanisms for personalization, ranking, and content filtering. However, when users evaluate content contributed by fellow users (e.g., by liking a post or voting on a comment), these evaluations create complex social feedback effects. This paper investigates how ra…
Generative model solves financial market equilibria with stable reinforcement learning.
A new high-frequency market making strategy using Deep Hawkes process.
Study shows observing order book can significantly improve online market making performance.
Unified framework for expert selection with bandit and lower-bound feedback.
Agents learn to outperform in trading by using past and current prices.
We analyze a controlled price formation experiment in the laboratory that shows evidence for bubbles. We calibrate two models that demonstrate with high statistical significance that these laboratory bubbles have a tendency to grow faster than exponential due to positive feedback. We show that the positive feedback ope…
The paper explores a Multi-Objective RL approach for trading that generalizes reward functions.
We apply the potential force estimation method to artificial time series of market price produced by a deterministic dealer model. We find that dealers' feedback of linear prediction of market price based on the latest mean price changes plays the central role in the market's potential force. When markets are dominated…
Not all types of supervision signals are created equal: Different types of feedback have different costs and effects on learning. We show how self-regulation strategies that decide when to ask for which kind of feedback from a teacher (or from oneself) can be cast as a learning-to-learn problem leading to improved cost…
The author seeks to develop a model to alter the bid-offer spread, currently quoted by market makers, that varies with the market and trading conditions. The dynamic nature of financial markets and trading, as with the rest of social sciences, where changes can be observed and decisions can be made by participants to i…
Model analyzes OTC market making with reputation feedback.
Model analyzes how reputation feedback affects OTC market making.
Meta-learning approach to learn interpretable models from human feedback.
New oracle uses uncertainty for active classification with noisy feedback.
Proposes a method to select fair performance metrics through metric elicitation.
Paper finds optimal selling rule for pairs trading with stock constraints.
Proposes a method to train classifiers with delayed feedback using a time window.
A dealer manages quotes and rejection rules to control slippage risk in FX markets.