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.
This paper introduces strategies to maximize arbitrage profits in decentralized exchanges.
Proposes a framework to reconcile policy learning and profit maximization in CATE estimation.
Paper uses Bayesian optimization to find best Supertrend indicator settings.
This paper examines if CTE risk measure aligns with profit-maximizing risk capital allocations.
Optimizes profit in targeted marketing across multiple markets with varying marketing expenditures.
A new framework integrates credit scoring into profit scoring for better P2P lending investments.
LemonadeBench evaluates LLMs' economic intuition through a simulated lemonade stand.
Develops a new bidding system to maximize advertiser profit.
Neoclassical economics has two theories of competition between profit-maximizing firms (Marshallian and Cournot-Nash) that start from different premises about the degree of strategic interaction between firms, yet reach the same result, that market price falls as the number of firms in an industry increases. The Marsha…
High-frequency trading strategy boosts battery storage profits.
RL agent learns to avoid market spoofing.
Optimizes insurance profits under regulatory constraints.
It has been assumed that arbitrage profits are not possible in efficient markets, because future prices are not predictable. Here we show that predictability alone is not a sufficient measure of market efficiency. We instead propose to measure inefficiencies of markets in terms of the maximal profit an ideal trader can…
Optimizes bidding in hourly and quarter-hourly electricity markets to reduce price impact.
Proposes a new method to optimize treatment allocation with budget constraints.
Uplift models support decision-making in marketing campaign planning. Estimating the causal effect of a marketing treatment, an uplift model facilitates targeting communication to responsive customers and efficient allocation of marketing budgets. Research into uplift models focuses on conversion models to maximize inc…
Paper optimizes aquaculture feeding and harvesting strategies for profit maximization.
Extends expected value framework for cost-sensitive causal decision-making.
We consider the problem of optimal investment with intermediate consumption in a general semimartingale model of an incomplete market, with preferences being represented by a utility stochastic field. We show that the key conclusions of the utility maximization theory hold under the assumptions of no unbounded profit w…
StockBot uses LSTM to predict stock prices, outperforming market ETFs.
MACE optimizes stock portfolios for maximal predictability.
The basic financial purpose of a firm is to maximize its value. An inventory management system should also contribute to realization of this basic aim. Many current asset management models currently found in financial management literature were constructed with the assumption of book profit maximization as basic aim. H…
Study models weather index insurance pricing by insurers and farmers, finding flexible pricing kernels boost profits.
An agent-based model for firms' dynamics is developed. The model consists of firm agents with identical characteristic parameters and a bank agent. Dynamics of those agents is described by their balance sheets. Each firm tries to maximize its expected profit with possible risks in market. Infinite growth of a firm dire…
This paper optimizes liquidation strategies in DeFi protocols to prevent MEV attacks.
One of the problems faced by a firm that sells certain commodities is to determine the number of products that it must supply in order to maximize its profit. In this article, the authors give an answer to this problem of economic interest. The proposed problem is a generalization of the results obtained by Stirzaker (…
Customer retention campaigns increasingly rely on predictive models to detect potential churners in a vast customer base. From the perspective of machine learning, the task of predicting customer churn can be presented as a binary classification problem. Using data on historic behavior, classification algorithms are bu…
Optimal dividends strategy in a two-state regime-switching environment.
In an online contract selection problem there is a seller which offers a set of contracts to sequentially arriving buyers whose types are drawn from an unknown distribution. If there exists a profitable contract for the buyer in the offered set, i.e., a contract with payoff higher than the payoff of not accepting any c…
New pricing algorithm learns demand curves and optimizes prices in dynamic markets.
New bid shading algorithm reduces costs by 55%.
We study the relationship between price spread, volatility and trading volume. We find that spread forms as a result of interplay between order liquidity and order impact. When trading volume is small adding more liquidity helps improve price accuracy and reduce spread, but after some point additional liquidity begins …
We consider a finite horizon optimal stopping problem related to trade-off strategies between expected profit and cost cash-flows of an investment under uncertainty. The optimal problem is first formulated in terms of a system of Snell envelopes for the profit and cost yields which act as obstacles to each other. We th…
We study arbitrage opportunities, market viability and utility maximization in market models with an insider. Assuming that an economic agent possesses from the beginning an additional information in the form of a random variable G, which only becomes known to the ordinary agents at date T, we give criteria for the No …
LLMs can collude in market divisions, maximizing profits.
Many online companies sell advertisement space in second-price auctions with reserve. In this paper, we develop a probabilistic method to learn a profitable strategy to set the reserve price. We use historical auction data with features to fit a predictor of the best reserve price. This problem is delicate - the struct…
Study examines how EU's Value at Risk constraints affect insurance oligopolies.
Transformer model forecasts electricity price spread for virtual bidding.
Investor and firm optimize sustainable investment and emission reduction through a dynamic game.
In this paper the problem of optimal derivative design, profit maximization and risk minimization under adverse selection when multiple agencies compete for the business of a continuum of heterogenous agents is studied. The presence of ties in the agents' best-response correspondences yields discontinuous payoff functi…
Systematic review finds reinforcement learning enhances financial tech performance.
This paper introduces a Bayesian framework for optimizing online experiments to maximize profit.
The autonomous trading agent is one of the most actively studied areas of artificial intelligence to solve the capital market portfolio management problem. The two primary goals of the portfolio management problem are maximizing profit and restrainting risk. However, most approaches to this problem solely take account …
RL enhances cryptocurrency trading profits.
We investigate activities that have different periods of duration. We define the profit intensity as a measure of this economic category. The profit intensity in a repeated trading has a unique property of attaining its maximum at a fixed point regardless of the shape of demand curves for a wide class of probability di…
Traders buy and sell financial instruments in hopes of making profit, and brokers are responsible for the transaction. There are several hypotheses and conspiracy theories arguing that in some situations, brokers want their traders to lose money. For instance, a broker may want to protect the positions of a privileged …
Paper analyzes how latency affects optimal order execution in markets.
We present an analytical study of an insurance company. We model the company's performance on a statistical basis and evaluate the predicted annual income of the company in terms of insurance parameters namely the premium, total number of the insured, average loss claims etc. We restrict ourselves to a single insurance…