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.
Cryptocurrency markets show higher spreads during extreme fear and greed phases.
We study an economic model where agents trade a variety of products by using one of three competing rules: "need", "greed" and "noise". We find that the optimal strategy for any agent depends on both product composition in the overall market and composition of strategies in the market. In particular, a strategy that do…
We explain the main concepts of Prospect Theory and Cumulative Prospect Theory within the framework of rational dynamic asset pricing theory. We derive option pricing formulas when asset returns are altered with a generalized Prospect Theory value function or a modified Prelec weighting probability function and introdu…
Study uses AI to simulate stock market behavior, revealing how trader psychology affects market stability.
In retail, there are predictable yet dramatic time-dependent patterns in customer behavior, such as periodic changes in the number of visitors, or increases in customers just before major holidays. The current paradigm of multi-armed bandit analysis does not take these known patterns into account. This means that for a…
Improves bandit algorithms to stop exploring arms about to disappear.
Estimates crypto risk premia using hidden factors and finds significant integration with traditional markets.
The paper integrates behavioral finance into asset pricing using subordinated models.
In this paper, we consider the classic measurement error regression scenario in which our independent, or design, variables are observed with several sources of additive noise. We will show that our motivating example's replicated measurements on both the design and dependent variables may be leveraged to enhance a spa…
The paper integrates behavioral distortions into portfolio optimization using implied probability weighting functions.
Investors in stock market are usually greedy during bull markets and scared during bear markets. The greed or fear spreads across investors quickly. This is known as the herding effect, and often leads to a fast movement of stock prices. During such market regimes, stock prices change at a super-exponential rate and ar…
We present a new recommendation setting for picking out two items from a given set to be highlighted to a user, based on contextual input. These two items are presented to a user who chooses one of them, possibly stochastically, with a bias that favours the item with the higher value. We propose a second-order algorith…
Unified approach to guided generation techniques.
New greedy algorithms improve Bayesian optimisation performance.
Neural network tackles continual learning with neuromodulation and local error signals.
Crypto simulations show HODL strategy loads risk onto most investors, with macro-sentiment affecting returns.