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.
UK hosts 62.89% of all HYIPs, many registered as 'limited company'.
Enhances genetic programming for stock alpha discovery with warm start and structural constraints.
A neural network approach solves dynamic portfolio optimization without dynamic programming.
The paper uses clustering and integer programming to optimize stock selection for investment funds.
The classical optimal investment and consumption problem with infinite horizon is studied in the presence of transaction costs. Both proportional and fixed costs as well as general utility functions are considered. Weak dynamic programming is proved in the general setting and a comparison result for possibly discontinu…
Dynamic rule-based investment strategies outperform static ones in pension schemes.
Study optimal investment strategies for an insurer in two currency markets.
Solves VaR-constrained portfolio optimization in markets with stochastic volatility.
This paper optimizes DC pension plan investments using O-U process and loan.
Model for optimal cybersecurity investment considering clustered cyberattacks.
Study optimizes insurance investment to maximize utility across all capital levels.
The paper fits cash management models to data using stochastic and linear programming.
Study finds similar companies in Dhaka Stock Exchange using technical data.
Study proposes a neural network approach for high inflation investment portfolios with leverage constraints.
New methods evaluate stock market anomalies for prospect investors.
How effective are the most common trading models? The answer may help investors realize upsides to using each model, act as a segue for investors into more complex financial analysis and machine learning, and to increase financial literacy amongst students. Creating original versions of popular models, like linear regr…
Solves Merton's investment-consumption problem with certainty equivalent approach.
Develops a machine-learning framework for optimal share repurchase hedging.
Study optimal investment and consumption strategies with various transaction costs.
Investment and insurance decisions are studied in a model with nonlinear portfolio frictions and background risk.
Study adds investment gains and losses to recursive utility model, proving existence and uniqueness of utility process.
The study finds no evidence of stochastic arbitrage opportunities in S&P 500 index options.
Stan is a probabilistic programming language that is popular in the statistics community, with a high-level syntax for expressing probabilistic models. Stan differs by nature from generative probabilistic programming languages like Church, Anglican, or Pyro. This paper presents a comprehensive compilation scheme to com…
We consider a class of linear-programming based estimators in reconstructing a sparse signal from linear measurements. Specific formulations of the reconstruction problem considered here include Dantzig selector, basis pursuit (for the case in which the measurements contain no errors), and the fused Dantzig selector (f…
AI enhances quantitative investment for better returns and risk control.
We consider a spread financial market defined by the multidimensional Ornstein--Uhlenbeck (OU) process. We study the optimal consumption/investment problem for logarithmic utility functions in the base of stochastic dynamical programming method. We show a special Verification Theorem for this case. We find the solution…
Paper tackles ESG rating disagreement in sustainable investing portfolios.
We consider an optimal investment and consumption problem for a Black-Scholes financial market with stochastic volatility and unknown stock appreciation rate. The volatility parameter is driven by an external economic factor modeled as a diffusion process of Ornstein-Uhlenbeck type with unknown drift. We use the dynami…
We provide an extension of the explicit solution of a mixed optimal stopping-optimal stochastic control problem introduced by Henderson and Hobson. The problem examines wether the optimal investment problem on a local martingale financial market is affected by the optimal liquidation of an independent indivisible asset…
Study uses Perelman and Ricci flow methods to analyze economic inequality.
The p-index improves investment performance for NYSE stocks but not for SSE stocks.
Study optimal consumption and investment strategies with leverage constraints using Epstein-Zin utility.
We consider a high dimensional binary classification problem and construct a classification procedure by minimizing the empirical misclassification risk with a penalty on the number of selected features. We derive non-asymptotic probability bounds on the estimated sparsity as well as on the excess misclassification ris…
Supply chains are the backbone of the global economy. Disruptions to them can be costly. Centrally managed supply chains invest in ensuring their resilience. Decentralized supply chains, however, must rely upon the self-interest of their individual components to maintain the resilience of the entire chain. We examine t…
Ansor generates high-performance tensor programs for deep learning.
This paper aims to make a new contribution to the study of lifetime ruin problem by considering investment in two hedge funds with high-watermark fees and drift uncertainty. Due to multi-dimensional performance fees that are charged whenever each fund profit exceeds its historical maximum, the value function is expecte…
New approach to goal-based investing using hedging and reinforcement learning.
Sharpe et al. proposed the idea of having an expected utility maximizer choose a probability distribution for future wealth as an input to her investment problem instead of a utility function. They developed a computer program, called The Distribution Builder, as one way to elicit such a distribution. In a single-perio…
PS^2 selects assets then weights for high-dimensional investing.
In this paper, we present a probabilistic numerical algorithm combining dynamic programming, Monte Carlo simulations and local basis regressions to solve non-stationary optimal multiple switching problems in infinite horizon. We provide the rate of convergence of the method in terms of the time step used to discretize …
Smart beta, also known as strategic beta or factor investing, is the idea of selecting an investment portfolio in a simple rule-based manner that systematically captures market inefficiencies, thereby enhancing risk-adjusted returns above capitalization-weighted benchmarks. We explore the idea of applying a smart strat…
Gradient estimation techniques applied to programs with randomness in high energy physics.
Enhances robo-advisors with client investment preference inference.
Forward inference techniques such as sequential Monte Carlo and particle Markov chain Monte Carlo for probabilistic programming can be implemented in any programming language by creative use of standardized operating system functionality including processes, forking, mutexes, and shared memory. Exploiting this we have …
We consider an optimal investment and consumption problem for a Black-Scholes financial market with stochastic coefficients driven by a diffusion process. We assume that an agent makes consumption and investment decisions based on CRRA utility functions. The dynamical programming approach leads to an investigation of t…
We consider a model of optimal investment and consumption with both habit formation and partial observations in incomplete Itô processes market. The investor chooses his consumption under the addictive habits constraint while only observing the market stock prices but not the instantaneous rate of return. Applying the …
We revisit the optimal investment and consumption model of Davis and Norman (1990) and Shreve and Soner (1994), following a shadow-price approach similar to that of Kallsen and Muhle-Karbe (2010). Making use of the completeness of the model without transaction costs, we reformulate and reduce the Hamilton-Jacobi-Bellma…
How can graph theory be applied to investing in the stock market? The answer may help investors realize the true risks of their investments, help prevent recessions like that of 2008, and increase financial literacy amongst students. Using several original Python programs, we take a correlation matrix with correlations…