We empirically show the superiority of the equally weighted S\&P 500 portfolio over Sharpe's market capitalization weighted S\&P 500 portfolio. We proceed to consider the MaxMedian rule, a non-proprietary rule designed for the investor who wishes to do his/her own investing on a laptop with the purchase of only 20 stoc…
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48 results for “equal-weight”
SCS identifies a range of plausible equally weighted portfolios, quantifying selection uncertainty.
problem Uncertainty in selecting the best equally weighted portfolio subset.
method Introduces Selection Confidence Set (SCS) for EWPs, covering plausible portfolios with high probability.
result SCS quantifies selection uncertainty and covers the unknown optimal selection with high probability.
This study proposes an equal-weight portfolio strategy to reduce risk compared to traditional ETFs.
problem Risk of passive ETFs not matching optimal portfolio weights.
method Introduced an equal-weight portfolio strategy to reduce idiosyncratic risk.
result Equal-weight portfolio has lower risk than traditional ETFs, especially during idiosyncratic events.
A new portfolio model DEWSP improves Sharpe ratio by 0.24% to 5.15%.
problem High sensitivity of optimized portfolios to estimation errors.
method Deep learning algorithms predict returns for top-N ranked assets, then equally weight them.
result DEWSPs provide an improvement rate of 0.24% to 5.15% in terms of monthly Sharpe ratio compared to HEWSPs.
A method for optimal Bayesian filtering using progressive particle flow and optimal transport maps.
problem Optimizing Bayesian filtering with deterministic particles to avoid degeneration.
method Progressive flow of particles through a sequence of sub-steps, each using an optimal transport map to replace non-equally weighted particles with equally weighted ones.
result The method avoids particle degeneration and simplifies the filtering process by not requiring inversions or monotonicity constraints.
Performance Evaluation of Equal-Weight Portfolio and Optimum Risk Portfolio on Indian Stocksq-fin.PM
This study evaluates different portfolio designs for Indian stocks.
problem Optimizing portfolio weights for risk and return in volatile stock markets.
method Three portfolio design approaches: risk minimization, risk optimization, and equal weighting. Historical data from 2017-2022 used.
result Equal-weight portfolios outperformed other designs in most sectors.
Diversified risk parity strategies outperform equally-weighted portfolios in various asset universes.
problem Finding optimal portfolio allocations that balance risk and reward.
method Integrates various reward-risk measures and generic allocation rules into diversified risk parity.
result Diversified reward-risk parity strategies exhibit higher average returns, Sharpe ratios, and Calmar ratios compared to equally-weighted risk portfolios.
RL learns to ignore factors in factor investing portfolios.
problem Combining factor investing and reinforcement learning for optimal portfolio allocation.
method RL agent learns through sequential allocations based on firms' characteristics using Dirichlet distributions.
result RL-based portfolios are very close to equally-weighted allocations, indicating agnostic factor learning.
Investigates portfolio optimization with and without gearing constraints.
problem Improving portfolio weights for better alignment with expected returns.
method Extends the alpha-weight angle bound to include gearing constraints and uses theoretical arguments and simulations.
result Equally weighted portfolios are not preferable to mean-variance portfolios even with poor forecast ability and a badly conditioned covariance matrix.
Given a time series of graphs G(t) = (V, E(t)), t = 1, 2, ..., where the fixed vertex set V represents "actors" and an edge between vertex u and vertex v at time t (uv \in E(t)) represents the existence of a communications event between actors u and v during the tth time period, we wish to detect anomalies and/or chang…
Seasonal influenza infects between 10 and 50 million people in the United States every year, overburdening hospitals during weeks of peak incidence. Named by the CDC as an important tool to fight the damaging effects of these epidemics, accurate forecasts of influenza and influenza-like illness (ILI) forewarn public he…
We analyze correlations among stock returns via a series of widely adopted parameters which we refer to as explanatory variables. We subsequently exploit the results to propose a long only quantitative adaptive technique to construct a profitable portfolio of assets which exhibits minor drawdowns and higher recoveries …
Using daily returns of the S&P 500 stocks from 2001 to 2011, we perform a backtesting study of the portfolio optimization strategy based on the extreme risk index (ERI). This method uses multivariate extreme value theory to minimize the probability of large portfolio losses. With more than 400 stocks to choose from, ou…
In this article, the long-term behavior of the stock market index of the New York Stock Exchange is studied, for the period 1950 to 2013. Specifically, the CRSP Value-Weighted and CRSP Equal-Weighted index are analyzed in terms of market efficiency, using the standard ratio variance test, considering over 1600 one week…
Risk reduction and Diversification within Markowitz's Mean-Variance Model: Theoretical Revisitq-fin.PM
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…
Signed network models reduce portfolio risk by considering negative edges in financial markets.
problem Tackles portfolio optimization in financial markets by exploiting negative edges in network representations.
method Proposes a discrete optimization scheme to reduce asset selection, building time series of signed networks from asset returns.
result Empirical results show that signed network portfolios perform similarly to classical mean-variance optimization and equally weighted benchmarks.
Optimal weighted random forests improve prediction accuracy.
problem Unequal prediction performance among random forest trees.
method Proposes 1-step and 2-step optimal weighting algorithms.
result Asymptotically optimal in terms of squared loss and risk.
The paper identifies a mesoscopic market structure and uses it to improve portfolio optimization.
problem The optimal mean-variance allocation differs from the heuristic equally-weighted portfolio.
method Clustering techniques from Random Matrix Theory (RMT) to study mesoscopic market structure.
result A new wealth allocation scheme that attaches equal importance to stocks in the same community improves portfolio reliability.
Hybrid LSTM-PPO optimizes dynamic portfolios with better performance.
problem Dynamic portfolio optimization under non-stationary market conditions.
method Combines LSTM for forecasting and PPO for adaptive portfolio adjustments.
result Hybrid framework outperforms single-model and equal-weight approaches in various metrics.
The effect of proportional transaction costs on systematically generated portfolios is studied empirically. The performance of several portfolios (the index tracking portfolio, the equally-weighted portfolio, the entropy-weighted portfolio, and the diversity-weighted portfolio) in the presence of dividends and transact…
A so called Zipf analysis portofolio management technique is introduced in order to comprehend the risk and returns. Two portofoios are built each from a well known financial index. The portofolio management is based on two approaches: one called the "equally weighted portofolio", the other the "confidence parametrized…
An algorithm was recently introduced by INTECH for the purposes of estimating the trading-profit contribution of systematic rebalancing to the relative return of rules-based investment strategies. We apply this methodology to analyze the size factor through the use of equal-weighted portfolios. These strategies combine…
AI models outperform simple rules in cross-asset futures timing, especially with lower transaction costs.
problem Optimizing cross-asset portfolio weights using traditional forecasting and optimization methods.
method End-to-end AI policies that map market states directly to portfolio weights, trained on CME futures using a differentiable Sharpe ratio loss function.
result Transformer-based AI policies outperform simple rules and equal weighting, trading less and matching or exceeding equal weighting through moderate transaction costs.
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…
Investigates if adding cryptocurrencies to German portfolios diversifies better, finding mixed results.
problem Improving diversification in German investor portfolios using cryptocurrencies.
method Portfolio analysis with descriptive statistics, graphical methods, and econometric spanning tests, using a customized EWCI.
result Cryptocurrencies can improve diversification in some windows but not as a normal case.
In our previous paper, we discussed the hyperbolization of the configuration space of n(> 4) marked points with weights in the projective line up to projective transformations. A variation of the weights induces a deformation. It was shown that this correspondence of the set of the weights to the Teichmüller space when…
Proposes HeteroJIVE for joint subspace estimation in multi-view data with statistical and structural heterogeneity.
problem Joint subspace estimation in multi-view data with varying statistical and structural heterogeneity.
method HeteroJIVE: A weighted two-stage spectral algorithm addressing statistical and structural heterogeneity.
result HeteroJIVE achieves the rate without iterative refinement, validating the oracle-optimal weighting scheme.
A purely combinatorial compactification of the configuration space of n (>4) distinct points with equal weights in the real projective line was introduced by M. Yoshida. We geometrize it so that it will be a real hyperbolic cone-manifold of finite volume with dimension n-3. Then, we vary weights for points. The geometr…
Spectral selectors on lens spaces and applications to the geometry of the group of contactomorphismsmath.SG
Defines spectral selectors on lens spaces for contactomorphisms.
problem Understanding the geometry of contactomorphism groups on lens spaces.
method Using Givental's non-linear Maslov index, defines spectral selectors.
result Standard Reeb flow is a geodesic for specific lens spaces.
The paper predicts an Efficient Market Property for the equity market, where stocks, when denominated in units of the growth optimal portfolio (GP), have zero instantaneous expected returns. Well-diversified equity portfolios are shown to approximate the GP, which explains the well-observed good performance of equally …
Suppose some classifiers are selected from a set of hypothesis classifiers to form an equally-weighted ensemble that selects a member classifier at random for each input example. Then the ensemble has an error bound consisting of the average error bound for the member classifiers, a term for selectivity that varies fro…
Positive weights improve kernel quadrature's accuracy.
problem Improving kernel quadrature weights to be positive and stable.
method Using convex geometry to approximate the kernel mean embedding with positive weights.
result Positive weights lead to improved kernel quadrature bounds with Monte-Carlo-beating rates.
Portfolio Allocation under Asymmetric Dependence in Asset Returns using Local Gaussian Correlationsq-fin.PM
New method improves portfolio allocation using local Gaussian correlation.
problem Asymmetric dependence in asset returns.
method Local Gaussian correlation to extend mean-variance framework.
result New method outperforms existing portfolios for monthly asset returns.
This paper aims at developing a new method by which to build a data-driven portfolio featuring a target risk-return. We first present a comparative study of recurrent neural network models (RNNs), including a simple RNN, long short-term memory (LSTM), and gated recurrent unit (GRU) for selecting the best predictor to u…
It has been widely observed that capitalization-weighted indexes can be beaten by surprisingly simple, systematic investment strategies. Indeed, in the U.S. stock market, equal-weighted portfolios, random-weighted portfolios, and other naive, non- optimized portfolios tend to outperform a capitalization-weighted index …
Proving prediction prudenceq-fin.RM
Tests assess if predictions are prudent by comparing observations and predictions.
problem Assessing the prudence of predictions in samples of observations and predictions.
method Bootstrap and normal approximation algorithms for testing unweighted and weighted means, accounting for randomness.
result Tests reveal whether predictions are prudent by showing significantly negative mean differences.
Ensemble Quantile Classifierstat.ML
Both the median-based classifier and the quantile-based classifier are useful for discriminating high-dimensional data with heavy-tailed or skewed inputs. But these methods are restricted as they assign equal weight to each variable in an unregularized way. The ensemble quantile classifier is a more flexible regularize…
AF improves classification models by adaptively weighting trees.
problem Improving classification model performance.
method AF combines OP2T for input-dependent weights and MIO for dynamic refinement.
result AF consistently outperforms RF, XGBoost, and other weighted RF.
MarketSenseAI system outperforms passive benchmarks by 25.2% on S&P 500, adding value over random selection.
problem Identifying alpha in stock recommendations from multi-agent LLM systems.
method Deployed multi-agent LLM equity system generating live signals, combining four specialist agents into a synthesis agent.
result Strong-buy equal-weight portfolio on S&P 500 earns +2.18%/month, significantly outperforming passive benchmarks.
Bayesian Quadrature improves ensembling for neural networks with dispersed likelihood peaks.
problem Ensembling neural networks struggles with dispersed, narrow peaks in likelihood surfaces.
method Uses Bayesian Quadrature to construct weighted ensembles of architectures.
result Empirically outperforms state-of-the-art baselines in test likelihood, accuracy, and expected calibration error.
Large-scale kernel approximation is an important problem in machine learning research. Approaches using random Fourier features have become increasingly popular [Rahimi and Recht, 2007], where kernel approximation is treated as empirical mean estimation via Monte Carlo (MC) or Quasi-Monte Carlo (QMC) integration [Yang …
Distribution regression has recently attracted much interest as a generic solution to the problem of supervised learning where labels are available at the group level, rather than at the individual level. Current approaches, however, do not propagate the uncertainty in observations due to sampling variability in the gr…
Ensembles of climate models are commonly used to improve climate predictions and assess the uncertainties associated with them. Weighting the models according to their performances holds the promise of further improving their predictions. Here, we use an ensemble of decadal climate predictions to demonstrate the abilit…
Sum-of-norms clustering is a method for assigning points in to clusters, , using convex optimization. Recently, Panahi et al.\ proved that sum-of-norms clustering is guaranteed to recover a mixture of Gaussians under the restriction that the number of samples is not too large. The pu…
Study proposes adaptive RL for dynamic portfolio optimization.
problem Traditional portfolio optimization models fail to adapt to regime shifts.
method Regime-aware reinforcement learning framework with hybrid observations and constrained reward functions.
result Transformer PPO achieves highest risk-adjusted returns, while LSTM variants offer a good balance.
A novel framework combines LLMs and RL for financial portfolio optimization.
problem Optimizing financial portfolios using sentiment analysis and market indicators.
method Hierarchical RL structure with base, meta, and super-agents.
result Achieved a 26% annualized return and Sharpe ratio of 1.2.
Paper presents a hybrid framework combining sentiment analysis and market indicators for financial portfolio optimization.
problem Improving financial portfolio optimization through better integration of sentiment and market data.
method A three-tier hierarchical RL framework integrating LLMs, DRL, and market data.
result Achieved a 26% annualized return and Sharpe ratio of 1.2, outperforming benchmarks.
MAD Risk Parity Portfoliosq-fin.PM
Investigates MAD-RP portfolios for asset allocation.
problem Finding optimal asset allocation strategies.
method Uses MAD as risk measure and proposes computational formulations for MAD-RP portfolios.
result MAD-RP portfolios offer balanced risk and profitability.