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Portfolio construction for today's markets : a practitioner's guide to the essentials of asset allocation
For most of the past 50 years the simplest asset allocation solution was often the best. A balanced portfolio of stocks and bonds provided the investor with good returns. Unfortunately, this approach is not likely to work as well in the future. Interest rates are close to historic lows, equity valuations and bond prices appear stretched, and global economic growth has slowed. Investors need a new asset allocation solution. In 'Portfolio Construction for Today's Markets,' BlackRock Portfolio Manager and investment expert Russ Koesterich addresses this problem by describing the step-by-step approach to building a portfolio consistent with investor goals and suited to today's market environment.
Book
How digital finance affects the financial asset allocation of brick-and-mortar businesses
The recent integration of digital technology and financial services has given rise to the newly emerging modality of digital finance. However, does digital finance improve the efficiency of financial services while influencing the investment behavior of brick-and-mortar businesses? With the help of the data about Chinese listed companies, this paper uses multiple regression analysis, instrumental variables, and other methods to empirically test whether and how digital finance affects the financial asset allocation decisions of brick-and-mortar enterprises. The findings suggest that digital finance has a galvanizing effect on financial asset allocation. However, this effect mainly stems from the fact that firms allocate more illiquid financial assets and has a dampening effect on liquid financial assets. Path analysis shows that easing financing constraints is a causal pathway through which digital finance dampens firms' liquid financial asset allocation. Moreover, rising risk exposure levels partially mediate the stimulus of digital finance, motivating firms to allocate illiquid financial assets. This paper contributes to the research on the economic consequences of digital finance and provides policy recommendations on how digital finance can better serve the real economy.
Journal Article
Multi-period portfolio selection with drawdown control
In this article, model predictive control is used to dynamically optimize an investment portfolio and control drawdowns. The control is based on multi-period forecasts of the mean and covariance of financial returns from a multivariate hidden Markov model with time-varying parameters. There are computational advantages to using model predictive control when estimates of future returns are updated every time new observations become available, because the optimal control actions are reconsidered anyway. Transaction and holding costs are discussed as a means to address estimation error and regularize the optimization problem. The proposed approach to multi-period portfolio selection is tested out of sample over two decades based on available market indices chosen to mimic the major liquid asset classes typically considered by institutional investors. By adjusting the risk aversion based on realized drawdown, it successfully controls drawdowns with little or no sacrifice of mean–variance efficiency. Using leverage it is possible to further increase the return without increasing the maximum drawdown.
Journal Article
Robustly Strategic Consumption–Portfolio Rules with Informational Frictions
This paper provides a tractable continuous-time, constant absolute risk aversion–Gaussian framework to explore how the interactions of fundamental uncertainty, model uncertainty due to a preference for robustness, and state uncertainty due to information-processing constraints (rational inattention) affect strategic consumption–portfolio rules and precautionary savings in the presence of uninsurable labor income. Specifically, after solving the model explicitly, I compute and compare the elasticities of strategic asset allocation and precautionary savings to risk aversion, robustness, and inattention. Furthermore, for plausibly estimated and calibrated model parameters, I quantitatively analyze how the interactions of model uncertainty and state uncertainty affect the optimal share invested in the risky asset and show that they can provide a potential explanation for the observed stockholding behavior of households with different education and income levels.
This paper was accepted by Neng Wang, finance
.
Journal Article
A machine learning approach to risk based asset allocation in portfolio optimization
This paper introduces a novel machine learning framework for dynamic risk-based asset allocation that addresses fundamental limitations in traditional portfolio optimization methods. The proposed architecture integrates Long Short-Term Memory networks for volatility forecasting with differentiable risk budgeting layers and regime-switching mechanisms, enabling end-to-end training of portfolio weights under adaptive risk constraints. Unlike conventional approaches that rely on static risk budgets and historical covariance estimates, our methodology dynamically adjusts risk targets based on real-time market indicators, including volatility expectations, credit spreads, and yield curve dynamics. The framework achieves three primary research objectives: first, it demonstrates superior risk-adjusted performance with a Sharpe ratio of 1.38 during the out-of-sample period (2017-2022), representing a 55% improvement over traditional risk parity strategies and a 23% enhancement over contemporary deep learning approaches. Second, the architecture maintains computational efficiency through sparse attention mechanisms, scaling linearly with asset count while processing 50-asset portfolios in under 25 milliseconds. Third, the model preserves interpretability via SHAP-based risk attribution, providing transparent insights into allocation decisions across different market regimes. Empirical results reveal particularly strong performance during volatile market conditions, with maximum drawdowns reduced by 41% during stress periods compared to conventional methods. The framework’s proactive risk management capabilities were evidenced during the COVID-19 crisis, where it began reducing equity exposure two weeks before the market trough, demonstrating genuine predictive ability rather than reactive adjustment. Robustness checks confirm performance persistence under varying transaction costs, rebalancing frequencies, and alternative risk measures. These findings establish a new paradigm for portfolio optimization that successfully bridges theoretical finance with practical implementation. The framework’s ability to navigate complex market environments while maintaining computational efficiency and interpretability suggests readiness for widespread institutional adoption. This research contributes to the evolving literature on differentiable finance while providing portfolio managers with a robust tool for constructing adaptive, risk-aware investment strategies.
Journal Article
Derivative applications to asset allocation and multi-asset management
This article provides applications of derivatives to asset allocation and multi-asset management. The four applications include using futures for top-down asset allocation, deploying portable alpha strategies using derivatives to achieve desired convexity in payoff profiles, developing effective hedging strategies, and using derivatives for active speculative views by proprietary traders.
Journal Article