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.
STRAPSim measures ETF portfolio similarity better than existing methods.
A new method identifies similar mutual funds using graph learning.
Although the understanding of and motivation behind individual trading behavior is an important puzzle in finance, little is known about the connection between an investor's portfolio structure and her trading behavior in practice. In this paper, we investigate the relation between what stocks investors hold, and what …
Network theory proved recently to be useful in the quantification of many properties of financial systems. The analysis of the structure of investment portfolios is a major application since their eventual correlation and overlap impact the actual risk diversification by individual investors. We investigate the biparti…
We discuss a weighted estimation of correlation and covariance matrices from historical financial data. To this end, we introduce a weighting scheme that accounts for similarity of previous market conditions to the present one. The resulting estimators are less biased and show lower variance than either unweighted or e…
The paper presents a framework for optimizing crypto-currency portfolios using generative models.
We advocate the use of Agnostic Allocation for the construction of long-only portfolios of stocks. We show that Agnostic Allocation Portfolios (AAPs) are a special member of a family of risk-based portfolios that are able to mitigate certain extreme features (excess concentration, high turnover, strong exposure to low-…
Optimizes a portfolio for an investor preferring accepted securities over a reference security.
New algorithm reduces simultaneous asset shocks in financial portfolios.
Paper classifies economic states and optimizes portfolios for stagflationary environments.
We introduce a bond portfolio management theory based on foundations similar to those of stock portfolio management. A general continuous-time zero-coupon market is considered. The problem of optimal portfolios of zero-coupon bonds is solved for general utility functions, under a condition of no-arbitrage in the zero-c…
We introduce a financial portfolio optimization framework that allows us to automatically select the relevant assets and estimate their weights by relying on a sorted -Norm penalization, henceforth SLOPE. Our approach is able to group constituents with similar correlation properties, and with the same underlyin…
The paper describes a method to infer the signal-to-noise ratio in portfolio optimization.
Growth-optimal portfolios are guaranteed to accumulate higher wealth than any other investment strategy in the long run. However, they tend to be risky in the short term. For serially uncorrelated markets, similar portfolios with more robust guarantees have been recently proposed. This paper extends these robust portfo…
Paper connects two portfolio methods, HRP and Minimum Variance, revealing their underlying similarity.
Optimizes stock portfolios with a constraint on correlation to reduce risk.
We propose a novel investment decision strategy (IDS) based on deep learning. The performance of many IDSs is affected by stock similarity. Most existing stock similarity measurements have the problems: (a) The linear nature of many measurements cannot capture nonlinear stock dynamics; (b) The estimation of many simila…
Introduces PIT-plot for prioritizing projects based on their impact.
Clusters of crypto assets by path signature improve diversification and reduce fees.
New method optimizes insurance portfolios using neural networks.
We introduce a solution scheme for portfolio optimization problems with cardinality constraints. Typical portfolio optimization problems are extensions of the classical Markowitz mean-variance portfolio optimization model. We solve such type of problems using a method similar to column generation. In this scheme, the o…
Optimizes trading trajectories for large portfolios quickly.
The paper extends portfolio theory to include contingent claim functions for option pricing.
Dynamic tracking error framework shows similar performance but varying volatility across different constraints.
Paper proposes a supervised similarity framework for corporate bonds using RF proximities.
Random investment strategies outperform sensible ones, even with forecasts.
A time-varying network reveals community structure in cryptocurrencies.
Study on stock portfolio concentration among Finnish households and investors.
We study the design of portfolios under a minimum risk criterion. The performance of the optimized portfolio relies on the accuracy of the estimated covariance matrix of the portfolio asset returns. For large portfolios, the number of available market returns is often of similar order to the number of assets, so that t…
In mutual fund, an investment adviser gives advice to clients about investing in securities such as stocks, bonds, mutual funds, or exchange traded funds. Some investment advisers manage portfolios of securities. In this paper, we analyze advisor portfolio for each advisor so as to recognize the pattern in each adviser…
The study bounds the utility of empirically optimal portfolios using stock return data.
Paper introduces Arte-Blue Chip Index for diversifying portfolios with art investments.
Optimal portfolio yields a digital option payoff.
This paper studies the empirical tracking performance of leveraged ETFs on gold, and their price relationships with gold spot and futures. For tracking the gold spot, we find that our optimized portfolios with short-term gold futures are highly effective in replicating prices. The market-traded gold ETF (GLD) also exhi…
Heuristic algorithm for portfolio optimization reduces solve times to milliseconds.
In the present work, the optimal portfolio minimizing the investment risk with cost is discussed analytically, where this objective function is constructed in terms of two negative aspects of investment, the risk and cost. We note the mathematical similarity between the Hamiltonian in the mean-variance model and the Ha…
Cryptocurrencies return cross-predictability and technological similarity yield information on risk propagation and market segmentation. To investigate these effects, we build a time-varying network for cryptocurrencies, based on the evolution of return cross-predictability and technological similarities. We develop a …
The problem of estimation error in portfolio optimization is discussed, in the limit where the portfolio size N and the sample size T go to infinity such that their ratio is fixed. The estimation error strongly depends on the ratio N/T and diverges for a critical value of this parameter. This divergence is the manifest…
Investigates cryptocurrency maturity through collective dynamics and diversification.
The paper optimizes portfolios using clustering and Sharpe ratio-based optimization.
The paper uses TDA to select stocks for a sparse portfolio, improving performance across market scenarios.
Paper optimizes demand aggregation for low-level electricity markets.
Proposes a method to incorporate current market conditions in VaR and stress testing.
We consider an investor who seeks to maximize her expected utility derived from her terminal wealth relative to the maximum performance achieved over a fixed time horizon, and under a portfolio drawdown constraint, in a market with local stochastic volatility (LSV). In the absence of closed-form formulas for the value …
The contour maps of the error of historical resp. parametric estimates for large random portfolios optimized under the risk measure Expected Shortfall (ES) are constructed. Similar maps for the sensitivity of the portfolio weights to small changes in the returns as well as the VaR of the ES-optimized portfolio are also…
Study uses MTD model to optimize portfolios by capturing complex financial asset relationships.
Efficiently solves large portfolio optimization problems by reducing and sparsifying covariance matrices.
This study proposes an equal-weight portfolio strategy to reduce risk compared to traditional ETFs.