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.
Managing a portfolio to a risk model can tilt the portfolio toward weaknesses of the model. As a result, the optimized portfolio acquires downside exposure to uncertainty in the model itself, what we call "second order risk." We propose a risk measure that accounts for this bias. Studies of real portfolios, in asset-by…
We developed a strategic of optimal portfolio based on information theory and Tsallis statistics. The growth rate of a stock market is defined by using -deformed functions and we find that the wealth after n days with the optimal portfolio is given by a -exponential function. In this context, the asymptotic optim…
Study market-to-book ratios using Stochastic Portfolio Theory.
A discrete time probabilistic model, for optimal equity allocation and portfolio selection, is formulated so as to apply to (at least) reinsurance. In the context of a company with several portfolios (or subsidiaries), representing both liabilities and assets, it is proved that the model has solutions respecting constr…
This paper considers the mean variance portfolio management problem. We examine portfolios which contain both primary and derivative securities. The challenge in this context is due to portfolio's nonlinearities. The delta-gamma approximation is employed to overcome it. Thus, the optimization problem is reduced to a we…
A new portfolio method uses NMF for risk budgeting, outperforming classical methods.
We employ perturbation analysis technique to study multi-asset portfolio optimisation with transaction cost. We allow for correlations in risky assets and obtain optimal trading methods for general utility functions. Our analytical results are supported by numerical simulations in the context of the Long Term Growth Mo…
A new approach to continuous-time universal portfolios using pathwise Itô calculus.
Determining contributions by sub-portfolios or single exposures to portfolio-wide economic capital for credit risk is an important risk measurement task. Often economic capital is measured as Value-at-Risk (VaR) of the portfolio loss distribution. For many of the credit portfolio risk models used in practice, the VaR c…
We consider the problem of portfolio optimization in the presence of market impact, and derive optimal liquidation strategies. We discuss in detail the problem of finding the optimal portfolio under Expected Shortfall (ES) in the case of linear market impact. We show that, once market impact is taken into account, a re…
Optimal option portfolios under Sharpe Ratio maximization with skew-elliptical t-distributed returns
SAA method solves insurance portfolio optimization with CVaR constraints.
In financial asset management, choosing a portfolio requires balancing returns, risk, exposure, liquidity, volatility and other factors. These concerns are difficult to compare explicitly, with many asset managers using an intuitive or implicit sense of their interaction. We propose a mechanism for learning someone's s…
Optimizes cryptocurrency portfolios using MNTS GARCH model.
Develops BPDS for better financial portfolio decisions.
Study shows short exposure and systematic risk exposure affect disposition effect asymmetries.
Study finds physical momentum portfolios in Indian stock market yield higher returns than benchmarks.
Performance analysis, from the external point of view of a client who would only have access to returns and holdings of a fund, evolved towards exact attribution made in the context of portfolio optimisation, which is the internal point of view of a manager controlling all the parameters of this optimisation. Attributi…
A technique from stochastic portfolio theory [Fernholz, 1998] is applied to analyse equity returns of Small, Mid and Large cap portfolios in an emerging market through periods of growth and regional crises, up to the onset of the global financial crisis. In particular, we factorize portfolios in the South African marke…
Develops FGL for better portfolio allocation under common factor influence.
Signature portfolios approximate optimal wealth in non-Markovian markets.
Enhances currency strategy Sharpe ratio by 30% using context-aware Learning to Rank.
Optimal portfolios for fat-tailed risks using a new tail risk measure.
New method finds profitable investment opportunities by considering additional financial variables.
Paper integrates LLMs into portfolio optimization to improve decision quality.
We present an online approach to portfolio selection. The motivation is within the context of algorithmic trading, which demands fast and recursive updates of portfolio allocations, as new data arrives. In particular, we look at two online algorithms: Robust-Exponentially Weighted Least Squares (R-EWRLS) and a regulari…
The main contribution of the paper is to employ the financial market network as a useful tool to improve the portfolio selection process, where nodes indicate securities and edges capture the dependence structure of the system. Three different methods are proposed in order to extract the dependence structure between as…
The study examines how limited liability and haircut affect a bank's loan portfolio's liquidity risk.
Study uses AI agents to improve equity portfolio management.
Dynamic risk constraints help limit risky behavior in financial portfolios.
A new method for calculating risk budgeting portfolios is proposed.
Unified model combines shrinkage, views, and factor models for better portfolio selection.
Spectral portfolio theory links neural networks to wealth dynamics via SGD weight matrices.
Recent studies inspired by results from random matrix theory [1,2,3] found that covariance matrices determined from empirical financial time series appear to contain such a high amount of noise that their structure can essentially be regarded as random. This seems, however, to be in contradiction with the fundamental r…
In this paper, motivated by the celebrated work of Kelly, we consider the problem of portfolio weight selection to maximize expected logarithmic growth. Going beyond existing literature, our focal point here is the rebalancing frequency which we include as an additional parameter in our analysis. The problem is first s…
We analyze linear factor models for asset pricing panels.
New model uses Half-Full/Half-Empty approach for better portfolio selection.
New approach avoids restrictive assumptions for optimal portfolio in default risk scenarios.
Study applies HRP to Latin American markets, showing smoother risk-return profile.
Study uses CSIE to estimate portfolio volatility relative to market.
In this paper we propose and discuss different 0-1 linear models in order to solve the cardinality constrained portfolio problem by using factor models. Factor models are used to build portfolios to track indexes, together with other objectives, also need a smaller number of parameters to estimate than the classical Ma…
Study benchmarks LLMs in portfolio optimization tasks.
Double descent in portfolio optimization shows improved performance with complexity, then declines, due to overfitting.
In the context of jump-diffusion market models we construct examples that satisfy the weaker no-arbitrage condition of NA1 (NUPBR), but not NFLVR. We show that in these examples the only candidate for the density process of an equivalent local martingale measure is a supermartingale that is not a martingale, not even a…
We treat a fairly broad class of financial models which includes markets with proportional transaction costs. We consider an investor with cumulative prospect theory preferences and a non-negativity constraint on portfolio wealth. The existence of an optimal strategy is shown in this context in a class of generalized s…
Paper presents a DRL framework for detecting and anticipating financial crises.
TRR detects stock portfolio crashes by simulating human reasoning.
We propose a Markov chain model for credit rating changes. We do not use any distributional assumptions on the asset values of the rated companies but directly model the rating transitions process. The parameters of the model are estimated by a maximum likelihood approach using historical rating transitions and heurist…