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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.

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0.4%0.7%1.1%1.5% · Aug 202219922001200920172026
48 results for short-selling prohibition

Study optimal portfolio strategies with periodic evaluation under short-selling prohibition.

problem Optimal portfolio strategies with periodic evaluation under short-selling prohibition.
method Reformulate the original problem into an auxiliary one-period optimization problem and introduce dual control problem.
result Derive and verify the value function and optimal constrained portfolio for the original problem.

Study examines insider trading in short-selling restricted markets.

problem Analyzing insider trading opportunities in short-selling prohibited markets.
method Introducing minimal supermartingale measure and analyzing its properties in relation to minimal martingale measure.
result Conditions under which both measures fail to exist, indicating insider information affecting market perception.

We propose a continuous-time model of trading with heterogeneous beliefs. Risk-neutral agents face quadratic costs-of-carry on positions and thus their marginal valuations decrease with the size of their position, as it would be the case for risk-averse agents. In the equilibrium models of heterogeneous beliefs that fo…

2017-05-16abs ↗pdf ↗

The paper extends ERP framework to non-monotonic payoffs and short selling bans.

problem Valuation of contingent claims with short selling bans under ERP framework.
method Unified framework for ERP pricing, extending to non-monotonic payoffs, and comparing with Black-Scholes.
result Equal-risk prices differ from Black-Scholes prices under short selling bans.

Study uses RL to optimize investment with financial constraints, showing exploration benefits.

problem Optimal investment with financial constraints in continuous time.
method Reinforcement learning framework, focusing on Gaussian and truncated Gaussian distributions.
result Exploration leads to more dispersed wealth distribution with heavier tails, especially with smaller exploration parameters.

A financial model without short-selling shows deviations from normality.

problem Modeling financial asset prices with constraints on short selling.
method Developed a binomial model with two types of investors (bulls and bears) and a market maker, proving moments and fitting parameters.
result The model can approximate skewness and excess kurtosis, demonstrated with real data.

Enhances portfolio management with RL, considering transaction costs and short selling.

problem Lack of practical aspects in RL for portfolio management.
method Proposes a general RL framework for asset management with continuous weights, short selling, and relevant features. Compares PGAC, PPO, and ES algorithms in a simulated environment with transaction costs.
result Demonstrates advantages of RL algorithms in real-life asset management scenarios.

This paper uses DRL for long-short portfolio optimization, improving risk-adjusted returns.

problem Traditional portfolio optimization limits diversification by excluding short-selling.
method Developed a DRL framework with a short-selling mechanism for continuous trading.
result DRL model with short-selling achieves superior risk-adjusted returns.

We show that a trader, who starts with no initial wealth and is not allowed to borrow money or short sell assets, is theoretically able to attain positive wealth by continuous trading, provided that she has perfect foresight of future asset prices, given by a continuous semimartingale. Such an arbitrage strategy can be…

2016-04-26abs ↗pdf ↗

Short sales are regarded as negative purchases in textbook asset pricing theory. In reality, however, the symmetry between purchases and short sales is broken by a variety of costs and risks peculiar to the latter. We formulate an optimal stopping model in which the decision to cover a short position is affected by two…

2019-03-28abs ↗pdf ↗

Optimal investment and risk control strategies for insurers are derived using a time-consistent approach.

problem Optimal investment and risk control for insurers under mean-variance criterion.
method Introducing a deterministic forward auxiliary process to formulate a time-consistent problem.
result Optimal strategy and value function obtained in closed-form for the new problem.

Reinsurance can help life insurers maintain higher capital guarantees without losing utility.

problem Decreasing capital guarantees in life insurance products.
method Dynamic investment-reinsurance optimization problem with simultaneous Value-at-Risk and no-short-selling constraints. Introduced guarantee-equivalent utility gain for comparison.
result Optimally managed reinsurance allows insurers to offer higher capital guarantees without reducing expected utility.

A new trading model uses deep reinforcement learning to optimize portfolio weights.

problem Optimizing portfolio weights with risk and return considerations.
method Improved deep reinforcement learning with actor-critic architecture, quantile regression, and asset short selling.
result The proposed model outperforms benchmark strategies in backtesting.

We give a rough sketch of the Judaic, Greek, Islamic and Christian positions in the matter of interest prohibition during the last few millennia and discuss the way in which interest prohibition is dealt with in Islamic finance, the problems with authority-based arguments for interest prohibition, and the prospects of …

2011-04-13abs ↗pdf ↗

Interest prohibition theory concerns theoretical aspects of interest prohibition. We attempt to lay down some aspects of interest prohibition theory wrapped in a larger framework of informal logic. The reason for this is that interest prohibition theory has to deal with a variety of arguments which is so wide that a li…

2011-04-02abs ↗pdf ↗

End-to-end portfolio optimization framework bypassing covariance matrix estimation.

problem Optimizing portfolios with large numbers of assets and constraints.
method Deep learning approach that directly optimizes asset distributions without forecasting.
result Framework outperforms classical methods and handles various constraints.

It is argued that arguments for strict prohibition of interests must be based on the use of arguments from authority. This is carried out by first making a survey of so-called dialectical roots for interest prohibition and then demonstrating that for at least one important positive interest bearing financial product, t…

2011-05-14abs ↗pdf ↗

We give an algebraic definition of a Markowitz market and classify markets up to isomorphism. Given this classification, the theory of portfolio optimization in Markowitz markets without short selling constraints becomes trivial. Conversely, this classification shows that, up to isomorphism, there is little that can be…

2016-11-23abs ↗pdf ↗

We consider an insurance company whose surplus is represented by the classical Cramer-Lundberg process. The company can invest its surplus in a risk free asset and in a risky asset, governed by the Black-Scholes equation. There is a constraint that the insurance company can only invest in the risky asset at a limited l…

2011-12-17abs ↗pdf ↗

A large portfolio of independent returns is optimized under the variance risk measure with a ban on short positions. The no-short selling constraint acts as an asymmetric 1\ell_1 regularizer, setting some of the portfolio weights to zero and keeping the out of sample estimator for the variance bounded, avoiding the di…

2016-12-21abs ↗pdf ↗

For utility maximization problems under proportional transaction costs, it has been observed that the original market with transaction costs can sometimes be replaced by a frictionless "shadow market" that yields the same optimal strategy and utility. However, the question of whether or not this indeed holds in general…

2011-11-28abs ↗pdf ↗

The Sharpe ratio is a way to compare the excess returns (over the risk free asset) of portfolios for each unit of volatility that is generated by a portfolio. In this paper we introduce a robust Sharpe ratio portfolio under the assumption that the risk free asset is unknown. We propose a robust portfolio that maximizes…

2016-10-04abs ↗pdf ↗

New explicit constructions of unbalanced Ramanujan bipartite graphs.

problem Constructing bipartite Ramanujan graphs with specified degrees and avoiding certain edges.
method Presented explicit constructions and discussed known methods for Ramanujan graph construction.
result Affirmative answer to constructing unbalanced Ramanujan bipartite graphs under certain conditions.

This research combines DRL with BL model for better portfolio optimization.

problem Lack of dynamic correlation knowledge in DRL for optimal portfolio optimization.
method Hybrid model combining DRL and Black-Litterman model.
result DRL agent significantly outperforms other strategies in terms of return and risk.

Study asset price bubbles in markets with short sales prohibitions and model uncertainty.

problem Investigating asset price bubbles in markets with short sales prohibitions and model uncertainty.
method Introducing a novel definition of the fundamental price and analyzing the types and characterization of bubbles using a new fundamental theorem of asset pricing and superhedging duality.
result Two distinct types of bubbles arise depending on the maturity structure of the asset, and conditions for their existence are provided.

Investors who optimize their portfolios under any of the coherent risk measures are naturally led to regularized portfolio optimization when they take into account the impact their trades make on the market. We show here that the impact function determines which regularizer is used. We also show that any regularizer ba…

2014-04-15abs ↗pdf ↗

Study compares short vs long strategies for equity factors, finds short strategy better.

problem Determining the best market-neutral implementation of equity factors.
method Revisited the relative predictability of short and long legs, diversification, and costs.
result Long-Short implementation yields superior risk-adjusted returns compared to Hedged Long-Only.

Deep learning models improve stock market portfolio returns.

problem Optimizing portfolio returns using deep learning methods.
method Deep neural networks (feedforward and LSTM) applied to stock market excess returns forecasting.
result Deep learning models deliver significant gains in portfolio certainty equivalent returns and Sharpe ratios.

This paper deals with the super-replication of non path-dependent European claims under additional convex constraints on the number of shares held in the portfolio. The corresponding super-replication price of a given claim has been widely studied in the literature and its terminal value, which dominates the claim of i…

2013-07-23abs ↗pdf ↗