Multi-Asset

Machine Learning for Socially Responsible Portfolio Optimisation ArXiv ID: 2305.12364 “View on arXiv” Authors: Unknown Abstract Socially responsible investors build investment portfolios intending to incite social and environmental advancement alongside a financial return. Although Mean-Variance (MV) models successfully generate the highest possible return based on an investor’s risk tolerance, MV models do not make provisions for additional constraints relevant to socially responsible (SR) investors. In response to this problem, the MV model must consider Environmental, Social, and Governance (ESG) scores in optimisation. Based on the prominent MV model, this study implements portfolio optimisation for socially responsible investors. The amended MV model allows SR investors to enter markets with competitive SR portfolios despite facing a trade-off between their investment Sharpe Ratio and the average ESG score of the portfolio. ...

Risk Budgeting Allocation for Dynamic Risk Measures ArXiv ID: 2305.11319 “View on arXiv” Authors: Unknown Abstract We define and develop an approach for risk budgeting allocation - a risk diversification portfolio strategy - where risk is measured using a dynamic time-consistent risk measure. For this, we introduce a notion of dynamic risk contributions that generalise the classical Euler contributions and which allow us to obtain dynamic risk contributions in a recursive manner. We prove that, for the class of coherent dynamic distortion risk measures, the risk allocation problem may be recast as a sequence of strictly convex optimisation problems. Moreover, we show that self-financing dynamic risk budgeting strategies with initial wealth of 1 are scaled versions of the solution of the sequence of convex optimisation problems. Furthermore, we develop an actor-critic approach, leveraging the elicitability of dynamic risk measures, to solve for risk budgeting strategies using deep learning. ...

Portfolio Optimization Rules beyond the Mean-Variance Approach ArXiv ID: 2305.08530 “View on arXiv” Authors: Unknown Abstract In this paper, we revisit the relationship between investors’ utility functions and portfolio allocation rules. We derive portfolio allocation rules for asymmetric Laplace distributed $ALD(μ,σ,κ)$ returns and compare them with the mean-variance approach, which is based on Gaussian returns. We reveal that in the limit of small $\fracμσ$, the Markowitz contribution is accompanied by a skewness term. We also obtain the allocation rules when the expected return is a random normal variable in an average and worst-case scenarios, which allows us to take into account uncertainty of the predicted returns. An optimal worst-case scenario solution smoothly approximates between equal weights and minimum variance portfolio, presenting an attractive convex alternative to the risk parity portfolio. We address the issue of handling singular covariance matrices by imposing conditional independence structure on the precision matrix directly. Finally, utilizing a microscopic portfolio model with random drift and analytical expression for the expected utility function with log-normal distributed cross-sectional returns, we demonstrate the influence of model parameters on portfolio construction. This comprehensive approach enhances allocation weight stability, mitigates instabilities associated with the mean-variance approach, and can prove valuable for both short-term traders and long-term investors. ...

Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models ArXiv ID: 2305.06704 “View on arXiv” Authors: Unknown Abstract In multivariate time series systems, key insights can be obtained by discovering lead-lag relationships inherent in the data, which refer to the dependence between two time series shifted in time relative to one another, and which can be leveraged for the purposes of control, forecasting or clustering. We develop a clustering-driven methodology for robust detection of lead-lag relationships in lagged multi-factor models. Within our framework, the envisioned pipeline takes as input a set of time series, and creates an enlarged universe of extracted subsequence time series from each input time series, via a sliding window approach. This is then followed by an application of various clustering techniques, (such as k-means++ and spectral clustering), employing a variety of pairwise similarity measures, including nonlinear ones. Once the clusters have been extracted, lead-lag estimates across clusters are robustly aggregated to enhance the identification of the consistent relationships in the original universe. We establish connections to the multireference alignment problem for both the homogeneous and heterogeneous settings. Since multivariate time series are ubiquitous in a wide range of domains, we demonstrate that our method is not only able to robustly detect lead-lag relationships in financial markets, but can also yield insightful results when applied to an environmental data set. ...

From Data to Trade: A Machine Learning Approach to Quantitative Trading ArXiv ID: ssrn-4315362 “View on arXiv” Authors: Unknown Abstract “Machine learning has revolutionized the field of quantitative trading, enabling traders to develop and implement sophisticated trading strategies that lev Keywords: Machine Learning, Quantitative Trading, Algorithmic Trading, Time Series Forecasting, Financial Markets, Multi-Asset Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a broad, introductory survey of ML concepts in quantitative trading with minimal advanced mathematics or original derivations, and lacks any code, backtests, or specific empirical results. flowchart TD A["Research Goal"] --> B["Data Collection"] A --> C["ML Model Selection"] B --> D["Feature Engineering"] C --> D D --> E["Model Training"] E --> F["Backtesting"] F --> G["Key Findings"]

Advanced Course in Asset Management (Presentation Slides) ArXiv ID: ssrn-3773484 “View on arXiv” Authors: Unknown Abstract These presentation slides have been written for the Advanced Course in Asset Management (theory and applications) given at the University of Paris-Saclay. They Keywords: Asset Management, Modern Portfolio Theory, Risk Management, Factor Investing, Multi-Asset Complexity vs Empirical Score Math Complexity: 7.5/10 Empirical Rigor: 3.0/10 Quadrant: Lab Rats Why: The slides present advanced mathematical theory including Markowitz optimization, CAPM, and Black-Litterman models with quadratic programming formulations and covariance matrix algebra. While it includes tutorial exercises and practice sections, it lacks empirical backtesting data, code implementations, or statistical performance metrics, remaining primarily theoretical and educational. flowchart TD A["Research Goal<br>Modern Asset Management"] --> B["Key Methodology<br>Portfolio Optimization"] B --> C["Data Inputs<br>Market Factors & Risk"] C --> D["Computational Process<br>Factor Analysis & MPT"] D --> E["Key Outcomes<br>Strategic Asset Allocation"] E --> F["Applications<br>Risk-Adjusted Returns"]

Deep Learning and Financial Stability ArXiv ID: ssrn-3723132 “View on arXiv” Authors: Unknown Abstract The financial sector is entering a new era of rapidly advancing data analytics as deep learning models are adopted into its technology stack. A subset of Artifi Keywords: Deep Learning, Data Analytics, Fintech, Natural Language Processing (NLP), Financial Modeling, Multi-Asset Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a conceptual policy analysis that identifies theoretical transmission pathways (e.g., data aggregation, model design) for systemic risk without presenting mathematical models, statistical metrics, or backtesting results. It focuses on qualitative governance frameworks rather than quantitative implementation. flowchart TD A["Research Goal: Deep Learning in Financial Stability"] --> B["Data Inputs & Methodology"] B --> C["Computational Processes"] C --> D["Key Findings & Outcomes"] B --> B1["Multi-Asset Data"] B --> B2["NLP on Financial Text"] B --> B3["Alternative Data Sources"] C --> C1["Deep Learning Models"] C --> C2["Financial Stability Metrics"] C --> C3["Risk Assessment Algorithms"] D --> D1["Enhanced Risk Prediction"] D --> D2["Systemic Stability Insights"] D --> D3["Fintech Innovation Pathways"] style A fill:#e1f5fe style D fill:#e8f5e8

Does Sustainability Generate Better Financial Performance? Review, Meta-analysis, and Propositions ArXiv ID: ssrn-3708495 “View on arXiv” Authors: Unknown Abstract Sustainability in business and ESG (environmental, social, and governance) in finance have exploded in popularity among researchers and practitioners. We survey Keywords: ESG (Environmental, Social, and Governance), Sustainable Finance, Asset Pricing, Portfolio Management, Literature Review, Multi-Asset Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper relies on large-scale meta-analysis of existing studies rather than novel mathematical modeling, yet demonstrates high empirical rigor through systematic review of 1,141 papers and providing public replication data and methodology. flowchart TD A["Research Goal:<br>Does Sustainability Improve Financial Performance?"] B["Methodology:<br>Systematic Review & Meta-Analysis"] C["Data Inputs:<br>Existing Studies on ESG & Returns"] D["Computational Process:<br>Aggregation & Bias Correction"] E["Outcome 1: Positive<br>ESG-Return Relationship"] F["Outcome 2: Risk-Based<br>Explanations Dominate"] G["Proposition:<br>ESG as Risk Factor in Asset Pricing"] A --> B B --> C C --> D D --> E D --> F E & F --> G

Python Guide to Accompany Introductory Econometrics forFinance ArXiv ID: ssrn-3475303 “View on arXiv” Authors: Unknown Abstract This free software guide for Python with freely downloadable datasets brings the econometric techniques to life, showing readers how to implement the approaches Keywords: Python, econometric techniques, software guide, dataset, data analysis, Multi-Asset Complexity vs Empirical Score Math Complexity: 5.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper is a practical Python guide with downloadable datasets and implementation code, indicating high empirical rigor, while the mathematics is introductory and applied, placing it in the low-to-moderate range. flowchart TD A["Research Goal: <br>Implement Econometrics for Finance"] --> B["Data/Inputs: <br>Freely Downloadable Datasets"] B --> C["Methodology: <br>Apply Econometric Techniques"] C --> D["Computational Process: <br>Python Implementation"] D --> E["Outcome: <br>Multi-Asset Data Analysis"] E --> F["Deliverable: <br>Software Guide & Insights"]

Trends and Applications of Machine Learning in QuantitativeFinance ArXiv ID: ssrn-3397005 “View on arXiv” Authors: Unknown Abstract Recent advances in machine learning are finding commercial applications across many industries, not least the finance industry. This paper focuses on applicatio Keywords: machine learning, algorithmic trading, predictive analytics, quantitative finance, Multi-Asset Complexity vs Empirical Score Math Complexity: 4.5/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper is a broad literature review of ML applications in finance, focusing on conceptual categorization rather than novel mathematical derivations or empirical backtesting. It outlines common algorithms and use cases but lacks implementation details, statistical metrics, or specific experimental results. flowchart TD G["Research Goal: Evaluate ML in Quant Finance"] --> D["Data Sources"] D --> M["Key Methodology"] D --> C["Computational Processes"] M --> F["Key Findings/Outcomes"] C --> F subgraph D ["Data/Inputs"] D1["Multi-Asset Market Data"] D2["Historical Price & Volatility"] end subgraph M ["Methodology Steps"] M1["Algorithmic Trading Strategies"] M2["Predictive Analytics"] end subgraph C ["Computational Processes"] C1["Deep Learning Models"] C2["Reinforcement Learning"] end subgraph F ["Outcomes"] F1["Enhanced Portfolio Optimization"] F2["Improved Risk Management"] F3["Commercial Applications in Finance"] end