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arXiv research

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.

168,742 papers · 148 categories

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9182736 · Feb 202519922001200920172026
48 results for strategic investment

The paper analyzes strategic irreversible investments with novel dynamic strategies.

problem Tradeoff between preemption incentives and option value of waiting in oligopolistic markets.
method Developed novel Markov perfect equilibrium to handle singular control of optimal investment.
result Simpler strategies lead to a 'preemption trap' with zero net present values.

Algorithms are often used to produce decision-making rules that classify or evaluate individuals. When these individuals have incentives to be classified a certain way, they may behave strategically to influence their outcomes. We develop a model for how strategic agents can invest effort in order to change the outcome…

2018-07-13abs ↗pdf ↗

Game theory models storage investment to balance market competition and profits.

problem Strategic storage investment impacts electricity market prices and revenues.
method Formulated a non-cooperative game between investors to model strategic storage decisions.
result Increasing storage capacity reduces individual profits but increases total investment.

Strategic feature manipulation helps learners identify meaningful variables in online regression settings.

problem Strategic feature manipulation poses a challenge for learners in online regression settings.
method Investigates how strategic feature manipulation by individuals can help learners recover meaningful features.
result Simple learner behavior can help accurately recover meaningful features and incentivize feature improvement.

This study improves valuation of post-revenue biopharmaceutical assets using Pfizer's data.

problem Accurate valuation of post-revenue drug assets in biotech and pharma.
method Historical sales data analysis to forecast future sales and calculate Net Present Value.
result Demonstrates a method for more informed investment decisions in biotech and pharma.

Investors can enhance their portfolios by strategically using LETFs, especially with dynamic strategies.

problem Unsuitability of passive or static approaches to LETFs leads to undesirable risk-return profiles.
method Demonstrated the effectiveness of simple dynamic strategies in exploiting favorable Omega ratio dynamics.
result Dynamic strategies can exploit the compounding effect of LETFs, improving risk-return profiles.

We are witnessing an increasing use of data-driven predictive models to inform decisions. As decisions have implications for individuals and society, there is increasing pressure on decision makers to be transparent about their decision policies. At the same time, individuals may use knowledge, gained by transparency, …

2019-05-22abs ↗pdf ↗

The paper presents a practical method for evaluating investment projects using real options.

problem Evaluating investment projects under uncertainty and strategic risk management.
method Binomial trees and real options techniques for evaluating investment projects.
result The method can be used for most real options and introduces Project Value at Risk for feasibility.

Paper presents a new method for better financial market forecasting.

problem Traditional investment strategies fail to capture market nuances and risks.
method Combines deep learning, factor integration, and correlated stock analysis.
result Enhanced diversification and performance capture in financial markets.

This paper analyzes the dynamic incentives for technology adoption under a transferable permits system, which allows for strategic trading on the permit market. Initially, firms can invest both in low-emitting production technologies and trade permits. In the model, technology adoption and allowance price are generated…

2011-03-15abs ↗pdf ↗

Investors can achieve optimal risk-reward trade-offs with bonds and stocks under mean-reverting stock returns.

problem Optimizing investment strategies with mean-reverting stock returns.
method Calculus of variations to derive the entire family of extremal strategies, not just the optimal ones.
result The value of the portfolio is effectively bounded from below, providing a 'guarantee' on the horizon.

The paper analyzes how mutable blockchain protocols affect miner behavior and strategic stability.

problem The mutability of blockchain protocols undermines long-term planning and cooperative equilibria.
method Integrates Austrian capital theory with repeated game theory to examine miner behavior under different institutional conditions.
result Effective time preference increases when protocol rules are mutable, leading to political rent-seeking and undermining strategic coherence.

This research tackles backdoor attacks on audio data using a stochastic investment approach.

problem The threat of backdoor attacks on audio data, especially in voice-activated systems.
method A Stochastic investment-based backdoor attack (MarketBack) approach.
result MarketBack can achieve an average attack success rate close to 100% with less than 1% of poisoned data.

This paper evaluates investment risks in LATAM AI startups using DCF method.

problem Unique challenges and risks faced by LATAM tech startups.
method Total Addressable Market (TAM), Serviceable Available Market (SAM), and Serviceable Obtainable Market (SOM) metrics; Discounted Cash Flow (DCF) method.
result Developed a ranking of emerging powers in Latin America for tech startup investment.

The study improves Monte Carlo simulations for long-term investments using advanced financial models.

problem Improving the accuracy of long-term investment simulations.
method Developed a multivariate process incorporating recent financial models and probabilistic forecasts.
result Increased accuracy in predicting portfolio values over decades.

The paper analyzes strategic interactions in a multi-agent reinsurance chain using game theory.

problem Strategic behavior and competition among insurers and reinsurers in a multi-layer reinsurance chain.
method Employed Stackelberg differential games and non-zero-sum game models to characterize strategic interactions. Used dynamic programming and game theory to derive equilibrium strategies for investment and reinsurance.
result Intensified competition leads to reduced safety loadings in reinsurance contracts.

Dynamic tracking error framework shows similar performance but varying volatility across different constraints.

problem Differences in governance parameters between Total Portfolio Approach and Strategic Asset Allocation.
method Portfolio simulations using U.S. equity and bond data from 2000 to 2026, spanning 2004 to 2026.
result Realized tracking error volatility varies 12-fold across different constraints, with costs highest during crises.

Investor and firm optimize sustainable investment and emission reduction through a dynamic game.

problem Optimal sustainable investment and emission reduction in a dynamic game setting.
method Formulated as a nonzero-sum dynamic game, solved via variational inequalities and verified in a diffusive setup.
result Nash equilibria show moving boundaries increasing with emission abatement, triggered by both investor and firm actions.

AI agents manage portfolios, improving on human oversight.

problem Improving strategic asset allocation for institutional investors.
method 50 specialized agents produce capital market assumptions, construct portfolios, critique, and vote on each other's output.
result Meta-agent compares forecasts with realized returns and improves agent performance.

We devise an optimal allocation strategy for the execution of a predefined number of stocks in a given time frame using the technique of discrete-time Stochastic Control Theory for a defined market model. This market structure allows an instant execution of the market orders and has been analyzed based on the assumptio…

2019-09-24abs ↗pdf ↗

The paper tackles strategic behavior in decision-making with counterfactual explanations.

problem Finding optimal counterfactual explanations and policies in a strategic setting.
method NP-hard problem, greedy algorithm, submodularity, randomized algorithm, matroid constraint.
result Optimal counterfactual explanations and policies increase utility.

We investigate a randomization procedure undertaken in real option games which can serve as a basic model of regulation in a duopoly model of preemptive investment. We recall the rigorous framework of [M. Grasselli, V. Leclère and M. Ludkovsky, Priority Option: the value of being a leader, International Journal of Theo…

2013-09-07abs ↗pdf ↗

Study explores optimal strategies in games with multiple players and mean-field interactions.

problem Optimal strategies in games with multiple players and mean-field interactions.
method Exploration of three different notions of optimality, including mean-field control solution, mean-field coarse correlated equilibria, and mean-field Nash equilibria.
result Approximation of cooperative and competitive equilibria in large NN-player games by mean-field control and mean-field equilibria.

Research evaluates three risk models for portfolio construction during market downturns.

problem Challenges in constructing quantitative portfolios using statistical risk models.
method Three statistical risk models tested on 1,000 stocks across four periods.
result Models consistently outperform market returns in various crises.

Study optimal incentives for cleaner energy production.

problem Accelerate transition to cleaner technologies in energy market.
method Stochastic control models for three scenarios: single firm, two firms, and two firms without incentives.
result Optimal strategies for investment and production emerge, highlighting firm interactions and incentive effects.

FinDKG uses LLMs to detect financial trends from news articles.

problem Detecting global financial trends from unstructured text data.
method Fine-tuned LLMs for generating DKGs, KGTransformer for analysis.
result KGTransformer outperforms existing thematic ETFs in financial thematic investing.

Introduces PIT-plot for prioritizing projects based on their impact.

problem Optimizing R&D investments in project portfolios.
method Develops a new tool (PIT-plot) focusing on project impact rather than project properties.
result Identifies projects with the largest impact for risk mitigation or value-adding.

Deep RL algorithm trades high-dimensional stock portfolios.

problem Trading high-dimensional stock portfolios with data gaps and non-unique history lengths.
method Deep Q-learning algorithm, sequentially setting up environments, rewarding based on asset returns and cash reservation.
result Algorithm outperforms all passive and active benchmarks by a large margin.

Paper generalizes strategic classification framework and introduces SVC for PAC-learning.

problem Strategic manipulation of testing data to fool classifiers.
method Unified framework for strategic classification, strategic VC-dimension (SVC).
result Characterizes the learnability and computational tractability of linear classifiers.

Study strategic dynamic pricing for buyers with unknown manipulation costs.

problem Strategic buyers manipulate their features to get lower prices, hindering profit maximization.
method Proposes a strategic dynamic pricing policy that incorporates strategic behavior and binary response data.
result Achieves sublinear regret bound of O(T)O(\sqrt{T}) compared to linear Ω(T)Ω(T) regret of non-strategic policies.

Enhanced financial forecasting with supervised autoencoders for S&P 500 and cryptocurrencies.

problem Improving investment strategy performance in financial markets.
method Supervised autoencoders with noise augmentation and triple barrier labeling.
result Supervised autoencoders with balanced parameters significantly boost strategy effectiveness.

New framework reduces strategic manipulation cost for minority groups in fair classification.

problem Strategic manipulation disparities in fair classification.
method Constrained optimization framework that constructs classifiers to reduce strategic manipulation cost for minority groups.
result Empirically, the approach reduces strategic manipulation cost for minority groups over multiple real-world datasets.

Study compares employers with and without anticipating strategic labor force responses.

problem Understanding and optimizing strategic interactions in labor markets.
method Formulation of causal strategic classification, theory, and experiments.
result Performatively optimal hiring policies improve employer and labor outcomes, but can also harm labor force utility.

Study examines how traders with asymmetric information and adaptive learning strategies affect market efficiency.

problem Effect of traders' strategic behavior on market efficiency and informational asymmetry.
method Examines a market with boundedly rational, asymmetrically informed traders using multiarmed bandit algorithms.
result Strategically acting traders can lead to more efficient markets than purely competitive ones under certain conditions.