Paper introduces lexical ratio to measure portfolio diversification.
problem Traditional diversification metrics overlook non-numerical relationships.
method Uses textual data to capture diversification dimensions through entropy-based insights.
result Lexical ratio (LR) outperforms traditional metrics in optimizing portfolio returns.
Diversification return is an incremental return earned by a rebalanced portfolio of assets. The diversification return of a rebalanced portfolio is often incorrectly ascribed to a reduction in variance. We argue that the underlying source of the diversification return is the rebalancing, which forces the investor to se…
In the market place, diversification reduces risk and provides protection against extreme events by ensuring that one is not overly exposed to individual occurrences. We argue that diversification is best measured by characteristics of the combined portfolio of assets and introduce a measure based on the information en…
The study revisits portfolio diversification by relaxing assumptions for skewed, multi-regime, and leptokurtic asset returns.
problem Underestimation of risk in portfolio diversification due to assumptions that are inconsistent with real-world asset returns.
method Calibrated a Markov-modulated Levy process model to equity market data to demonstrate the merits of the approach.
result The calibrated models effectively match empirical moments and show the importance of relaxing assumptions in portfolio diversification.
The conventional wisdom of mean-variance (MV) portfolio theory asserts that the nature of the relationship between risk and diversification is a decreasing asymptotic function, with the asymptote approximating the level of portfolio systematic risk or undiversifiable risk. This literature assumes that investors hold an…
Clusters of crypto assets by path signature improve diversification and reduce fees.
problem Building diversified portfolios of volatile cryptocurrencies.
method Clustering digital assets using path signatures to identify similar behavior patterns.
result Optimal portfolios outperform unfiltered ones, reducing transaction fees.
New methods show sparse portfolios offer no advantage over mean-variance in diversification.
problem Investment diversification and risk management with sparse portfolios.
method Developed and implemented a new estimation procedure for sparse second-order stochastic spanning using a greedy algorithm and Linear Programming.
result No benefit from expanding a sparse opportunity set beyond 45 assets; optimal sparse portfolio reduces tail risk.
We extend to the multi-asset case the framework of a discrete time model of a single asset financial market developed in Ghoulmie et al (2005). In particular, we focus on adaptive agents with threshold behavior allocating their resources among two assets. We explore numerically the effect of this diversification as an …
The paper proposes a new approach to portfolio selection that maximizes diversification and return.
problem Maximizing diversification and return in portfolio selection.
method A bi-objective model that maximizes a diversification measure and portfolio expected return.
result The return-diversification approach outperforms strategies based on diversification or classical risk-return approaches.
A new portfolio method using quantum mechanics improves risk diversification.
problem Improving risk-based portfolio construction methods for multi-asset portfolios.
method Schrödinger principal component analysis applied to extract common factors from asset fluctuations.
result The proposed method outperforms conventional risk parity and other risk diversification methods.
Clusters of highly correlated stocks are identified for better asset selection.
problem Identifying a small set of stocks to approximate the diversification of the whole stock universe.
method Data-driven correlation blockmodel clustering approach.
result The algorithm effectively detects clusters of highly correlated stocks.
This paper focuses on a dynamic multi-asset mean-variance portfolio selection problem under model uncertainty. We develop a continuous time framework for taking into account ambiguity aversion about both expected return rates and correlation matrix of the assets, and for studying the join effects on portfolio diversifi…
New risk measures improve portfolio diversification and stability.
problem Concentration risk in traditional portfolio optimization methods.
method Equal-correlation portfolio strategy with mathematical optimization.
result Improved risk diversification and stable returns.
Unified framework for portfolio optimization using multiple hypotheses.
problem Risk diversification in portfolio allocation.
method Structured ensemble learning approach with diversity control.
result Structured ensembles link predictor diversity to risk diversification.
When assets are correlated, benefits of investment diversification are reduced. To measure the influence of correlations on investment performance, a new quantity - the effective portfolio size - is proposed and investigated in both artificial and real situations. We show that in most cases, the effective portfolio siz…
This research introduces dynamic portfolio cuts using a spectral approach for graph-theoretic diversification.
problem Traditional methods for estimating asset-return covariance assume statistical time-invariance, failing to capture the nonstationary nature of asset price movements.
method Introduces graph spectral estimators that account for nonstationarity, partitioning the market graph into time-evolving clusters for dynamic portfolio cuts.
result Demonstrates the advantages of the proposed framework over traditional methods through numerical case studies using real-world price data.
RPS uses graph-based representation learning for better portfolio optimization.
problem Improving portfolio optimization with better returns and lower risks.
method RPS redefines the distance matrix of financial assets using Representation Learning and Clustering algorithms.
result RPS proposes a heuristic to select closer to the optimal subset of assets.
This study diversifies stock and crypto portfolios using network analysis.
problem Balancing returns and volatility in diversified portfolios.
method Community detection in network representations of assets, using Louvain and Affinity propagation algorithms.
result Opposite trends in crypto and traditional asset markets.
Investing in cryptocurrencies can improve portfolio risk-return profile, especially with diversification strategies.
problem Investing in cryptocurrencies and evaluating their potential for portfolio allocation strategies.
method Investigated different types of investors, various portfolio construction rules, and incorporated liquidity constraints.
result Cryptocurrencies can improve the risk-return profile of portfolios, especially with diversification strategies.
This paper challenges the conventional wisdom of trend-following by showing that the medium-term horizon adds little value once short- and long-term components are included.
problem The conventional wisdom that more horizons improve diversification and performance is challenged.
method A Bayesian optimization framework reallocates exposure dynamically across horizons, optimizing horizon-level weights at the asset level and applying sparsity and turnover control for dynamic allocation across assets.
result The medium-term horizon contributes little incremental performance or diversification once short- and long-term components are included.
Investment diversification affects financial stability, depending on network connectivity.
problem Analyzing stability of financial networks with diversified portfolios.
method Random matrix dynamical model with portfolio rebalancing, considering heterogeneity and diversification effects.
result Stability/instability transition depends on the largest eigenvalue of the random matrix.
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…
New framework tests mean-variance spanning in high dimensions.
problem Testing mean-variance spanning in high-dimensional asset spaces.
method Robust Student-t statistic based on batch-mean method, combined using Cauchy combination test.
result Advantages of diversification vary by economic conditions and cross-country.
Paper uses news data to model asset correlations without market data.
problem Traditional risk models rely on market data; this paper offers an alternative.
method Uses encoder-only language models to embed news data, then calculates asset return distributions and covariance through Energy Distance.
result Established connections between distributional differences and excess returns co-movements using Energy Distance.
Given a new candidate asset represented as a time series of returns, how should a quantitative investment manager be thinking about assessing its usefulness? This is a key qualitative question inherent to the investment process which we aim to make precise. We argue that the usefulness of an asset can only be determine…
This paper explores portfolio management strategies to maximize alpha and minimize beta.
problem Maximizing returns while minimizing risk in investment portfolios.
method Examines asset allocation, diversification, active management, and risk management strategies.
result Combining these strategies optimizes portfolio performance.
When the available statistical information is imperfect, it is dangerous to follow standard optimisation procedures to construct an optimal portfolio, which usually leads to a strong concentration of the weights on very few assets. We propose a new way, based on generalised entropies, to ensure a minimal degree of dive…
Oil is perceived as a good diversification tool for stock markets. To fully understand this potential, we propose a new empirical methodology that combines generalized autoregressive score copula functions with high frequency data and allows us to capture and forecast the conditional time-varying joint distribution of …
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…
A new framework for portfolio diversification is introduced which goes beyond the classical mean-variance approach and portfolio allocation strategies such as risk parity. It is based on a novel concept called portfolio dimensionality that connects diversification to the non-Gaussianity of portfolio returns and can typ…
This paper examines cryptocurrency integration with traditional markets, showing how network structure and turbulence influence cross-asset spillovers.
problem Understanding how cryptocurrencies integrate with traditional financial markets and the impact of market stress on cross-asset spillovers.
method Combining rolling correlation networks, community structure, market-specific and system-wide Turbulence Indices, and VAR-based connectedness analysis.
result Cross-asset integration is episodic, with network structure and turbulence playing a role in transmission during stress periods.
A new portfolio method uses NMF for risk budgeting, outperforming classical methods.
problem Portfolio diversification and risk management in crypto and traditional assets.
method Risk factor budgeting using convex Non-negative Matrix Factorization (NMF).
result Our method outperforms classical portfolio allocations in diversification and risk profile.
Power-law portfolios improve diversification by scaling weights sub-linearly.
problem Optimization methods struggle with unstable pair correlations and non-Gaussian risk measures.
method Construct portfolios with penalty proportional to arbitrary order moment of returns, leading to sub-linear weight scaling.
result Infinite order power-law portfolios are perfectly diversified, improving diversification over Kelly portfolios.
The paper proposes using network science to improve portfolio optimization by reducing noise in covariance estimation.
problem Noise in covariance estimation leads to suboptimal portfolio performance.
method The paper introduces SR-IFN, a network-based method to filter out noise from empirical covariance, enhancing portfolio optimization.
result The SR-IFN network improves portfolio performance by selecting peripheral, diversified assets and inversely weighting them based on centrality.
A new approach to risk allocation balances asset and factor risks.
problem Challenges in estimating expected returns for portfolio optimization.
method Risk Budgeting framework that allocates risk at the factor level.
result Effective portfolios can be constructed by balancing asset and factor risks.
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…
Paper introduces Arte-Blue Chip Index for diversifying portfolios with art investments.
problem Evaluating blue-chip art as a viable asset class for diversification.
method Developed Arte-Blue Chip Index tracking top-performing artists over 24 years.
result 20% allocation of blue-chip art in a diversified portfolio increases risk-adjusted returns by 20%.
Study examines financial contagion at community level, finding increased contagion density and widespread transmission.
problem Understanding and managing financial contagion in interconnected markets.
method High-frequency data, Louvain community detection, Vector Autoregression, Tracy-Widom random matrix theory.
result Contagion density increases over time, and there is no significant difference between intra- and inter-community contagion.
As financial instruments grow in complexity more and more information is neglected by risk optimization practices. This brings down a curtain of opacity on the origination of risk, that has been one of the main culprits in the 2007-2008 global financial crisis. We discuss how the loss of transparency may be quantified …
Optimizes portfolios using neural network approximations of asset sensitivities to common drivers.
problem Optimizing portfolios with complex asset dynamics and common drivers.
method Model asset dynamics with PDEs, approximate sensitivities with neural networks, and use hierarchical clustering on sensitivity matrix for optimization.
result Achieves over-performance in portfolio optimization across various markets and datasets.
Cryptocurrency markets exhibit violent, synchronised drawdowns, challenging diversification claims.
problem Cryptocurrency markets' violent drawdowns challenge diversification claims.
method Dynamic conditional tail dependence analysis
result Near-complete and stable lower-tail graph, upper tail that thins over time, dissolution of token categories into a core.
Optimizes portfolios by identifying causal drivers of diversification.
problem Achieving efficient portfolio optimization based on asset and diversification dynamics.
method Commonality Principle, Reichenbach Common Cause Principle, conformal maps, Bayesian networks, correlation-based algorithms, neural networks, SDEs.
result Optimal portfolio diversification achieved through causal methodologies and sensitivity forecasting.
BPASGM uses sparse graphical models to optimize portfolio selection.
problem Portfolio optimization in high-dimensional settings with estimation error.
method BPASGM extends BPA to a sparse graphical model, screening assets for diversification.
result BPASGM portfolios outperform standard mean-variance portfolios in risk-adjusted performance.
We consider a portfolio allocation problem for trend following (TF) strategies on multiple correlated assets. Under simplifying assumptions of a Gaussian market and linear TF strategies, we derive analytical formulas for the mean and variance of the portfolio return. We construct then the optimal portfolio that maximiz…
Study minimizes market inefficiency in systemic economies.
problem Minimizing deviations of market prices from fundamental values.
method Characterized market inefficiency and developed a matrix of holdings to minimize it.
result Portfolio holdings should deviate more from diversification if banks have similar systemic significance.
Paper proposes novel hedging strategies using LSTM models for diversified investment portfolios.
problem Hedging risky asset portfolios in turbulent financial markets.
method Four diverse models (LSTM, ARIMA-GARCH, momentum, contrarian) generate price forecasts for diversified AIS.
result LSTM-based strategies outperform other models, with Bitcoin being the best diversifier for S&P 500 index.
In the standard equilibrium and/or arbitrage pricing framework, the value of any asset is uniquely specified from the belief that only the systematic risks need to be remunerated by the market. Here, we show that, even for arbitrary large economies when the distribution of the capitalization of firms is sufficiently he…
Study compares optimal vs. naive diversification in crypto markets, finds time-varying moments improve performance.
problem Optimizing portfolio construction in volatile crypto markets.
method Examines time-varying moments and transaction costs, incorporates turnover penalty.
result Time-varying moment estimators outperform conventional estimators in practical portfolio construction.