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.
New heuristic selects fewer assets for efficient portfolios, reducing costs.
Clusters of highly correlated stocks are identified for better asset selection.
Unified pair trading approach using hierarchical reinforcement learning.
RPS uses graph-based representation learning for better portfolio optimization.
We propose a modelling framework for the optimal selection of crypto assets. Crypto assets differ by two essential features: security (technological) and stability (governance). Investors make choices over crypto assets similarly to how they make choices by using a recommender app: the app presents each investor with a…
Study optimal portfolio selection using average and current profitability of risky assets.
Study finds stock selection ability of Chinese mutual funds is better than asset allocation ability.
The paper optimizes asset selection for index trackers and enhanced trackers with varying cardinality constraints.
Estimates true Sharpe ratio of selected assets with various methods.
MDS selects assets by combining daily returns and intraday risk curves, improving portfolio performance.
Given a set of assets and an investment capital, the classical portfolio selection problem consists in determining the amount of capital to be invested in each asset in order to build the most profitable portfolio. The portfolio optimization problem is naturally modeled as a mean-risk bi-criteria optimization problem w…
Paper presents a new framework for optimal asset and signal combination.
Paper uses RL to optimize multi-asset portfolios in fluctuating markets.
New model uses financial news to predict stock returns.
Develops a dynamic latent-factor model for high-dimensional asset characteristics.
A scalable gradient-based framework for sparse portfolio selection.
Study optimizes investment strategies in markets with contagious price jumps.
BPASGM uses sparse graphical models to optimize portfolio selection.
Study proposes DRL for investor-specific portfolio optimization considering asset volatility.
A novel graphical matching approach improves pairs trading by reducing portfolio variance and risk-adjusted returns.
Financial asset markets are sociotechnical systems whose constituent agents are subject to evolutionary pressure as unprofitable agents exit the marketplace and more profitable agents continue to trade assets. Using a population of evolving zero-intelligence agents and a frequent batch auction price-discovery mechanism…
We empirically test predictability on asset price by using stock selection rules based on maximum drawdown and its consecutive recovery. In various equity markets, monthly momentum- and weekly contrarian-style portfolios constructed from these alternative selection criteria are superior not only in forecasting directio…
PS^2 selects assets then weights for high-dimensional investing.
Given a set-valued stochastic process , we say that the martingale selection problem is solvable if there exists an adapted sequence of selectors , admitting an equivalent martingale measure. The aim of this note is to underline the connection between this problem and the problems of asset pr…
Quantum computing speeds up asset pricing models exponentially.
Paper uses Simulated Bifurcation for quick asset allocation optimization.
In this paper, we propose an innovative investment framework incorporating asset allocation and class diversification oriented specifically for the biotechnology industry. With growing interests and capitalization in multiple biotech markets, investors require a more dynamic method of managing their assets within indiv…
Develops a method for stress testing correlations of financial portfolios.
Roy's `Safety First' criterion for selecting one risky asset from many is adapted to the case of non-normal returns, via Cornish Fisher expansion. The resulting investment objective is consistent with first order stochastic dominance, and is equal to the Sharpe ratio for the case of normal returns. An investor selectin…
The aim of this paper is to compare two asset allocation methods for a pension scheme during the decumulation phase in the simplified portfolio selection between a risky asset following a geometric Brownian motion and a riskless asset. The two asset allocation criteria are the ruin probability of the insurance company …
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…
With the advent of Web 2.0, various types of data are being produced every day. This has led to the revolution of big data. Huge amount of structured and unstructured data are produced in financial markets. Processing these data could help an investor to make an informed investment decision. In this paper, a framework …
A new DQN algorithm improves portfolio management and risk assessment in digital assets.
The study identifies assets with local balance deviating from global balance to mitigate financial risk.
Portfolio allocation with gross-exposure constraint is an effective method to increase the efficiency and stability of selected portfolios among a vast pool of assets, as demonstrated in Fan et al (2008). The required high-dimensional volatility matrix can be estimated by using high frequency financial data. This enabl…
The paper uses TDA to select stocks for a sparse portfolio, improving performance across market scenarios.
By monitoring the time evolution of the most liquid Futures contracts traded globally as acquired using the Bloomberg API from 03 January 2000 until 15 December 2014 we were able to forecast the S&P 500 index beating the Buy and Hold trading strategy. Our approach is based on convolution computations of 42 of the most …
Markowitz (1952, 1959) laid down the ground-breaking work on the mean-variance analysis. Under his framework, the theoretical optimal allocation vector can be very different from the estimated one for large portfolios due to the intrinsic difficulty of estimating a vast covariance matrix and return vector. This can res…
The study introduces new liquidity measures and models for assets with extreme liquidity.
The study finds that maximizing median returns is the only viable strategy in portfolio selection.
The paper optimizes stock portfolios with constraints based on performance attribution.
We study investment strategy in different models of financial markets, where the investors cannot reach a perfect knowledge about available assets. The investor spends a certain effort to get information; this allows him to better choose the investment strategy, and puts a selective pressure upon assets. The best strat…
We consider the mean--variance portfolio optimization problem under the game theoretic framework and without risk-free assets. The problem is solved semi-explicitly by applying the extended Hamilton--Jacobi--Bellman equation. Although the coefficient of risk aversion in our model is a constant, the optimal amounts of m…
The paper proposes a new algorithm for the high-dimensional financial data -- the Groupwise Interpretable Basis Selection (GIBS) algorithm, to estimate a new Adaptive Multi-Factor (AMF) asset pricing model, implied by the recently developed Generalized Arbitrage Pricing Theory, which relaxes the convention that the num…
We study an optimization-based approach to con- struct a mean-reverting portfolio of assets. Our objectives are threefold: (1) design a portfolio that is well-represented by an Ornstein-Uhlenbeck process with parameters estimated by maximum likelihood, (2) select portfolios with desirable characteristics of high mean r…
Unified framework for portfolio optimization using multiple hypotheses.
Several portfolio selection models take into account practical limitations on the number of assets to include and on their weights in the portfolio. We present here a study of the Limited Asset Markowitz (LAM), of the Limited Asset Mean Absolute Deviation (LAMAD) and of the Limited Asset Conditional Value-at-Risk (LACV…
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…