Paper: arXiv 2311.16004

Abstract

We present a novel process for generating synthetic datasets tailored to assess asset allocation methods and construct portfolios within the fixed income universe. Our approach begins by enhancing the CorrGAN model to generate synthetic correlation matrices. Subsequently, we propose an Encoder-Decoder model that samples additional data conditioned on a given correlation matrix. The resulting synthetic dataset facilitates in-depth analyses of asset allocation methods across diverse asset universes. Additionally, we provide a case study that exemplifies the use of the synthetic dataset to improve portfolios constructed within a simulation-based asset allocation process.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 6.0/10 (capped from 8.0: transaction costs not modelled; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced generative neural networks (GANs, Encoder-Decoder architectures) and sophisticated data conditioning, indicating high mathematical complexity. It is highly data- and implementation-heavy, using real-world fixed income datasets, time-series transformations, and a detailed case study with portfolio performance evaluation, showing strong empirical rigor.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Improve Asset Allocation in Fixed Income"] --> B["Methodology: Synthetic Data Generation<br>(CorrGAN + Encoder-Decoder)"]
  B --> C["Input Data:<br>Real Historical Market Data"]
  C --> D["Step 1: Generate Synthetic Correlation Matrices<br>(Enhanced CorrGAN)"]
  D --> E["Step 2: Generate Synthetic Feature Data<br>(Encoder-Decoder conditioned on Correlations)"]
  E --> F["Outcome: Comprehensive Synthetic Dataset"]
  F --> G["Final Result: Enhanced Portfolio Construction<br>via Simulation-Based Allocation"]

Cited References (28)

  1. Corrgan: Sampling realistic financial correlation matrices using generative adversarial networks (2020)
  2. cCorrGAN: Conditional correlation GAN for learning empirical conditional distributions in the elliptope (2021)
  3. Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize (2017) — link
  4. Variational Autoencoders: A Hands-Off Approach to Volatility (2022) — link
  5. Auto-encoding variational bayes (2013) — link
  6. Generative adversarial networks (2020)
  7. Nips 2016 tutorial: Generative adversarial networks (2016) — link
  8. Tactical investment algorithms (2019)
and 20 more
  1. Building diversified portfolios that outperform out-of-sample (2016)
  2. Generative adversarial networks for financial trading strategies fine-tuning and combination (2021)
  3. Alleviating class imbalance in actuarial applications using generative adversarial networks (2021)
  4. Tackling the Exponential Scaling of Signature-Based Generative Adversarial Networks for High-Dimensional Financial Time-Series Generation (2022)
  5. Matrix evolutions: synthetic correlations and explainable machine learning for constructing robust investment portfolios (2021)
  6. Unsupervised representation learning with deep convolutional generative adversarial networks (2015) — link
  7. Autoencoding Conditional GAN for Portfolio Allocation Diversification (2022) — link
  8. Scenario generation for market risk models using generative neural networks (2022)
  9. Validation of machine learning based scenario generators — → 2301.12719
  10. Tail-gan: Nonparametric scenario generation for tail risk estimation (2022) — link
  11. Improved training of wasserstein gans (2017)
  12. Deconvolution and checkerboard artifacts (2016)
  13. Machine learning for asset managers (2020)
  14. A mean/variance analysis of tracking error (1992)
  15. Generative Adversarial Networks in finance: an overview (2021) — link
  16. On variational learning of controllable representations for text without supervision (2020)
  17. A robust estimator of the efficient frontier (2016)
  18. A unified approach to interpreting model predictions (2017)
  19. Pagan: Portfolio analysis with generative adversarial networks (2019) — link
  20. Deep hedging, generative adversarial networks, and beyond (2021) — link

1 of 28 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.