Asset Allocation

Correlation structure analysis of the global agricultural futures market ArXiv ID: 2310.16849 “View on arXiv” Authors: Unknown Abstract This paper adopts the random matrix theory (RMT) to analyze the correlation structure of the global agricultural futures market from 2000 to 2020. It is found that the distribution of correlation coefficients is asymmetric and right skewed, and many eigenvalues of the correlation matrix deviate from the RMT prediction. The largest eigenvalue reflects a collective market effect common to all agricultural futures, the other largest deviating eigenvalues can be implemented to identify futures groups, and there are modular structures based on regional properties or agricultural commodities among the significant participants of their corresponding eigenvectors. Except for the smallest eigenvalue, other smallest deviating eigenvalues represent the agricultural futures pairs with highest correlations. This paper can be of reference and significance for using agricultural futures to manage risk and optimize asset allocation. ...

Black-Litterman Asset Allocation under Hidden Truncation Distribution ArXiv ID: 2310.12333 “View on arXiv” Authors: Unknown Abstract In this paper, we study the Black-Litterman (BL) asset allocation model (Black and Litterman, 1990) under the hidden truncation skew-normal distribution (Arnold and Beaver, 2000). In particular, when returns are assumed to follow this skew normal distribution, we show that the posterior returns, after incorporating views, are also skew normal. By using Simaan three moments risk model (Simaan, 1993), we could then obtain the optimal portfolio. Empirical data show that the optimal portfolio obtained this way has less risk compared to an optimal portfolio of the classical BL model and that they become more negatively skewed as the expected returns of portfolios increase, which suggests that the investors trade a negative skewness for a higher expected return. We also observe a negative relation between portfolio volatility and portfolio skewness. This observation suggests that investors may be making a trade-off, opting for lower volatility in exchange for higher skewness, or vice versa. This trade-off indicates that stocks with significant price declines tend to exhibit increased volatility. ...

Arguably Adequate Aqueduct Algorithm: Crossing A Bridge-Less Block-Chain Chasm ArXiv ID: 2311.10717 “View on arXiv” Authors: Unknown Abstract We consider the problem of being a cross-chain wealth management platform with deposits, redemptions and investment assets across multiple networks. We discuss the need for blockchain bridges to facilitates fund flows across platforms. We point out several issues with existing bridges. We develop an algorithm - tailored to overcome current constraints - that dynamically changes the utilization of bridge capacities and hence the amounts to be transferred across networks. We illustrate several scenarios using numerical simulations. ...

Analysis of Optimal Portfolio Management Using Hierarchical Clustering ArXiv ID: 2308.11202 “View on arXiv” Authors: Unknown Abstract Portfolio optimization is a task that investors use to determine the best allocations for their investments, and fund managers implement computational models to help guide their decisions. While one of the most common portfolio optimization models in the industry is the Markowitz Model, practitioners recognize limitations in its framework that lead to suboptimal out-of-sample performance and unrealistic allocations. In this study, I refine the Markowitz Model by incorporating machine learning to improve portfolio performance. By using a hierarchical clustering-based approach, I am able to enhance portfolio performance on a risk-adjusted basis compared to the Markowitz Model, across various market factors. ...

Transfer Learning for Portfolio Optimization ArXiv ID: 2307.13546 “View on arXiv” Authors: Unknown Abstract In this work, we explore the possibility of utilizing transfer learning techniques to address the financial portfolio optimization problem. We introduce a novel concept called “transfer risk”, within the optimization framework of transfer learning. A series of numerical experiments are conducted from three categories: cross-continent transfer, cross-sector transfer, and cross-frequency transfer. In particular, 1. a strong correlation between the transfer risk and the overall performance of transfer learning methods is established, underscoring the significance of transfer risk as a viable indicator of “transferability”; 2. transfer risk is shown to provide a computationally efficient way to identify appropriate source tasks in transfer learning, enhancing the efficiency and effectiveness of the transfer learning approach; 3. additionally, the numerical experiments offer valuable new insights for portfolio management across these different settings. ...

AlphaPortfolio: Direct Construction Through Deep Reinforcement Learning and Interpretable AI ArXiv ID: ssrn-3554486 “View on arXiv” Authors: Unknown Abstract We directly optimize the objectives of portfolio management via deep reinforcement learning—an alternative to conventional supervised-learning paradigms that Keywords: Deep Reinforcement Learning, Portfolio Optimization, Artificial Intelligence, Asset Allocation, Portfolio Management Complexity vs Empirical Score Math Complexity: 8.5/10 Empirical Rigor: 9.0/10 Quadrant: Holy Grail Why: The paper employs advanced deep reinforcement learning (RL) with attention-based neural networks (Transformers/LSTMs) and polynomial sensitivity analysis, which involves high mathematical complexity; it also provides out-of-sample performance metrics (Sharpe ratios, alphas) and robustness checks across market conditions, indicating strong empirical backing for implementation. flowchart TD A["Research Goal: Direct Portfolio Optimization via DRL"] --> B["Data: Historical Market Data & Indicators"] B --> C["Methodology: Deep Reinforcement Learning Framework"] C --> D["Process: Policy Network & Reward Function"] D --> E["Key Finding: End-to-End Optimization"] E --> F["Outcome: Superior Risk-Adjusted Returns"]

Strategic Rebalancing ArXiv ID: ssrn-3330134 “View on arXiv” Authors: Unknown Abstract A mechanical rebalancing strategy, such as a monthly or quarterly reallocation towards fixed portfolio weights, is an active strategy. Winning asset classes are Keywords: rebalancing, portfolio weights, momentum, risk-adjusted returns, asset allocation, Multi-Asset Complexity vs Empirical Score Math Complexity: 5.5/10 Empirical Rigor: 7.0/10 Quadrant: Holy Grail Why: The paper presents several analytical derivations, including a two-period model and convexity/concavity arguments, which indicate moderate mathematical density. It also includes extensive empirical backtesting on long historical datasets (1927-2017) with specific drawdown analysis and risk metrics, demonstrating strong implementation and data reliance. flowchart TD A["Research Goal"] --> B["Rebalancing<br>vs. Buy-and-Hold"] B --> C["Data Inputs<br>Multi-Asset Classes"] C --> D["Methodology<br>Strategic Rebalancing<br>Monthly/Quarterly"] D --> E["Computational Process<br>Calculate Returns &<br>Risk-Adjusted Metrics"] E --> F["Key Findings<br>Active Strategy<br>Better Risk-Adjusted Returns"]

Advances in Financial Machine Learning: Lecture 4/10 (seminar slides) ArXiv ID: ssrn-3257420 “View on arXiv” Authors: Unknown Abstract Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform Keywords: Machine learning, Algorithmic trading, Asset allocation, Multi-Asset Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 4.0/10 Quadrant: Philosophers Why: The content is conceptual and tutorial-like, explaining ensemble methods and financial CV issues with moderate formulas, but lacks implementation details, code, or backtest results. flowchart TD A["Research Goal:<br>ML for Financial Markets?"] --> B["Methodology:<br>Labeling & Fractional Differentiation"] B --> C["Data Inputs:<br>Multi-Asset Time Series"] C --> D["Computational Process:<br>Portfolio Optimization & ML Algorithms"] D --> E{"Evaluation"} E -->|Success| F["Key Outcomes:<br>Algorithmic Trading & Asset Allocation"] E -->|Failure| B

The Market for Financial Adviser Misconduct ArXiv ID: ssrn-2739590 “View on arXiv” Authors: Unknown Abstract We construct a novel database containing the universe of financial advisers in the United States from 2005 to 2015, representing approximately 10% of employment Keywords: Financial Advisers, Wealth Management, Labor Market, Investment Advisory, Asset Allocation, Asset Management Services Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 8.5/10 Quadrant: Street Traders Why: The paper’s mathematics is primarily statistical and econometric (e.g., comparisons of proportions, regression analysis on job turnover), scoring a moderate 3.5. The empirical rigor is extremely high, driven by the construction of a novel, large-scale database covering the universe of U.S. financial advisers over 10 years and the use of detailed, implementable data on employment history, misconduct disclosures, and settlements. flowchart TD A["Research Goal: How does adviser misconduct affect<br>the market for financial advice?"] --> B subgraph B["Methodology & Data"] B1["(Novel Database: 2005-2015,<br>~10% of US Advisers)"] B2["Match to BrokerCheck & CRD<br>Regulatory Disclosures"] B3["Link to Employment History<br>& Asset Allocation Data"] end B --> C{"Computational Analysis"} C --> D["Estimate Impact on<br>Employment, Wages, & Assets"] C --> E["Test Market Segmentation<br>by Firm Type & Geography"] D --> F["Key Findings: Advisers with<br>misconduct face severe penalties"] E --> F

The Market for Financial Adviser Misconduct ArXiv ID: ssrn-2739170 “View on arXiv” Authors: Unknown Abstract We construct a novel database containing the universe of financial advisers in the United States from 2005 to 2015, representing approximately 10% of employment Keywords: Financial Advisers, Wealth Management, Labor Market, Investment Advisory, Asset Allocation, Asset Management Services Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 9.0/10 Quadrant: Street Traders Why: The paper relies primarily on descriptive statistics and econometric analysis of a large administrative dataset rather than complex mathematical modeling, and its core contribution is the construction and exhaustive analysis of a novel, comprehensive database ready for empirical validation. flowchart TD A["Research Goal: How does adviser misconduct<br>shape the market for financial advice?"] --> B subgraph B["Methodology & Data"] direction LR B1["Novel Database:<br>US Financial Advisers 2005-2015"] B2["Data Source: Form ADV<br>Investment Adviser Public Disclosure"] B1 --> B2 end B --> C{"Key Method: Difference-in-Differences"} C --> D["Computational Process:<br>Estimate Treatment Effects"] D --> E subgraph E["Key Findings/Outcomes"] direction LR E1["Misconduct Advisers<br>Switch Firms More Often"] E2["Sanctions Reduce<br>Client Assets by 12%"] E3["Market Segments by<br>Adviser Quality"] end