Seventy years after Markowitz, portfolio optimization is still an open problem — not because the math is unsettled, but because estimation error eats the theoretical gains. The literature here spans classical mean-variance and its regularized descendants, risk parity and volatility targeting, Black-Litterman-style view blending, and newer machine-learning approaches to covariance estimation and end-to-end allocation.
The recurring finding worth internalizing: naive equal-weight portfolios are shockingly hard to beat out of sample, so a paper’s value usually lies in how honestly it handles estimation risk — shrinkage, robust optimization, turnover penalties — rather than in the elegance of its objective function. The ranking below favors papers that test allocations on real data with realistic constraints.
Related hubs: Factor Investing & Asset Pricing, Risk Management, Machine Learning for Forecasting.
In July 2023, Nasdaq announced a `Special Rebalance’ of the Nasdaq-100 index to reduce the index weights of its large constituents. A rebalance as suggested currently by Nasdaq index methodology may have several undesirable effects. These effects can be avoided by a different, but simple rebalancing
In sharp contrast to the recommendations of Modern Portfolio Theory (MPT), a vast majority of investors are not well diversified. This neglect of diversificatio
Portfolio management is an essential component of investment strategy that aims to maximize returns while minimizing risk. This paper explores several portfolio management strategies, including asset allocation, diversification, active management, and risk management, and their importance in optimiz
Traditionally, assets are selected for inclusion in a portfolio (long or short) by human analysts. Teams of human portfolio managers (PMs) seek to weigh and balance these securities using optimisation methods and other portfolio construction processes. Often, human PMs consider human analyst recomme
The paper reviews historical finance theory (MPT, CAPM, Fama-French) with minimal advanced math, focusing on conceptual discussion rather than new derivations or models. It lacks any backtests, datasets, or implementation details, serving primarily as a theoretical critique and framework proposal wi
Investor behaviour often deviates from logic and reason, and investors display many behaviour biases that influence their investment decision-making processes.
Multi-horizon price forecasting is central to portfolio allocation, risk management, and algorithmic trading, yet deep learning architectures have proliferated faster than rigorous financial benchmarks can evaluate them. This study provides a controlled comparison of nine architectures (Autoformer,
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