arXiv research

Study optimal periodic dividend strategies for risky businesses with transaction costs.

New framework finds periodic policies in reset-free MDPs with sublinear regret.

Develops RL algorithm for non-Markovian, non-stationary reward streams.

PQ-learning improves Q-learning by periodically updating target estimates.

We derive an optimal strategy for minimizing the expected loss in the two-period economy when a pivotal decision needs to be made during the first time period and cannot be subsequently reversed. Our interest in the problem has been motivated by the classical shopper's dilemma during the Black Friday promotion period, …

In this paper, we study the design and analysis of experiments conducted on a set of units over multiple time periods where the starting time of the treatment may vary by unit. The design problem involves selecting an initial treatment time for each unit in order to most precisely estimate both the instantaneous and cu…

Optimizes trading strategy considering alpha decay and transaction costs.

We discuss the role of integrated chance constraints (ICC) as quantitative risk constraints in asset and liability management (ALM) for pension funds. We define two types of ICC: the one period integrated chance constraint (OICC) and the multiperiod integrated chance constraint (MICC). As their names suggest, the OICC …

In this paper, we revisit the optimal periodic dividend problem, in which dividend payments can only be made at the jump times of an independent Poisson process. In the dual (spectrally positive Lévy) model, recent results have shown the optimality of a periodic barrier strategy, which pays dividends at Poissonian divi…

New model optimizes assortment and pricing with dynamic customer arrivals.

Model investor risk preferences to adjust real option valuation.

The paper addresses contextual optimization problems with feedback, aiming to minimize regret.

Evaluating AI investment strategies

Proposes an online model for LLM cascading with adaptive API selection.

The paper tackles non-stationary MAB with periodic rewards.

This paper proposes a formal approach to online learning and planning for agents operating in a priori unknown, time-varying environments. The proposed method computes the maximally likely model of the environment, given the observations about the environment made by an agent earlier in the system run and assuming know…

A new model detects financial bubbles with high accuracy.

We consider Markov Decision Processes (MDPs) where the rewards are unknown and may change in an adversarial manner. We provide an algorithm that achieves state-of-the-art regret bound of $O( \sqrt{τ(\ln|S|+\ln|A|)T}\ln(T))$, where $S$ is the state space, $A$ is the action space, $τ$ is the mixing time of the MDP, and $…

Study optimal portfolio management with periodic evaluations in stochastic models, considering convex constraints.

Study classifies stock price data into stationary and non-stationary periods for mechanical trading.

Richard Bellman's Principle of Optimality, formulated in 1957, is the heart of dynamic programming, the mathematical discipline which studies the optimal solution of multi-period decision problems. In this paper, we look at the main trading principles of Jesse Livermore, the legendary stock operator whose method was pu…

MRC improves credit assignment in multi-agent LLM systems, achieving high returns and transparency.

Payments data and machine learning improve nowcasting accuracy for macroeconomic indicators.

We introduce tools to capture the dynamics of three different pathways, in which the synchronization of human decision-making could lead to turbulent periods and contagion phenomena in financial markets. The first pathway is caused when stock market indices, seen as a set of coupled integrate-and-fire oscillators, sync…

Step-DAD improves BED by periodically updating a design policy during experiments.

Real-world problems of operations research are typically high-dimensional and combinatorial. Linear programs are generally used to formulate and efficiently solve these large decision problems. However, in multi-period decision problems, we must often compute expected downstream values corresponding to current decision…

New policy for non-stationary multi-armed bandits with recovering rewards.

The paper analyzes optimal dividend strategies for risky businesses, considering both periodic and extraordinary payments.

An element in Artin's braid group B_n is said to be periodic if some power of it lies in the center of B_n. In this paper we prove that all previously known algorithms for solving the conjugacy search problem in B_n are exponential in the braid index n for the special case of periodic braids. We overcome this difficult…

Empirical study shows carriers ignore past shippers' behavior, focusing only on current actions.

This study examines the presence of the day-of-the-week effect on daily returns of biotechnology stocks over a 16-year period from January 2002 to December 2015. Using daily returns from the NASDAQ Biotechnology Index (NBI), we find that the stock returns were the lowest on Mondays, and compared to the Mondays the stoc…

This paper introduces an agent-based artificial financial market in which heterogeneous agents trade one single asset through a realistic trading mechanism for price formation. Agents are initially endowed with a finite amount of cash and a given finite portfolio of assets. There is no money-creation process; the total…

MarketSenseAI uses AI to select stocks with 10-30% excess alpha.

New algorithm reduces online decision-making regret with efficient LP re-solving and parallel first-order method.

New method controls false discoveries in real-time data streams.

Improved algorithms solve multi-period multi-class packing problems with bandit feedback.

Optimal algorithms for non-linear ridge bandits reduce burn-in cost.

This paper improves Thompson Sampling for complex decision-making problems.

The algorithmic trading comes from digitalisation of the processing of trading assets on financial markets. Since 1980 the computerization of the stock market offers real time processing of financial information. This technological revolution has offered processes and mathematic methods to identify best return on trans…

Optimistic pricing algorithm handles online dynamic pricing with censored demand.

We introduce a reinforcement learning framework for retail robo-advising. The robo-advisor does not know the investor's risk preference, but learns it over time by observing her portfolio choices in different market environments. We develop an exploration-exploitation algorithm which trades off costly solicitations of …

The paper examines how loss aversion impacts multi-armed bandit decisions over long periods.

The paper tackles resource allocation for arms with unknown and random rewards, achieving optimal regret bounds.

A smart method predicts and optimizes decisions online with resource constraints.

Using a methodology similar to that used the in the worldwide research, the cost performance of Dutch large-scale transport infrastructure projects is determined. In the Netherlands, cost overruns are as common as cost underruns but because cost overruns are larger than cost underruns projects on average have a cost ov…

This study analyzes cryptocurrency market crashes using complex network analysis.

Study shows how repetition affects learning in bandit settings, providing algorithms with sublinear regret.

We study optimal investment problems under the framework of cumulative prospect theory (CPT). A CPT investor makes investment decisions in a single-period financial market with transaction costs. The objective is to seek the optimal investment strategy that maximizes the prospect value of the investor's final wealth. W…