Clustering Digital Assets Using Path Signatures: Application to Portfolio Construction
- URL: http://arxiv.org/abs/2410.23297v1
- Date: Tue, 15 Oct 2024 17:17:02 GMT
- Title: Clustering Digital Assets Using Path Signatures: Application to Portfolio Construction
- Abstract summary: We propose a new way of building portfolios of cryptocurrencies that provide good diversification properties to investors.
First, we seek to filter these digital assets by creating some clusters based on their path signature.
The goal is to identify similar patterns in the behavior of these highly volatile assets.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: We propose a new way of building portfolios of cryptocurrencies that provide good diversification properties to investors. First, we seek to filter these digital assets by creating some clusters based on their path signature. The goal is to identify similar patterns in the behavior of these highly volatile assets. Once such clusters have been built, we propose "optimal" portfolios by comparing the performances of such portfolios to a universe of unfiltered digital assets. Our intuition is that clustering based on path signatures will make it easier to capture the main trends and features of a group of cryptocurrencies, and allow parsimonious portfolios that reduce excessive transaction fees. Empirically, our assumptions seem to be satisfied.
Related papers
- Resolving Action Bottleneck: Agentic Reinforcement Learning Informed by Token-Level Energy [64.72195169444738]
Policy-gradient methods treat each token in a trajectory equally, leading to uniform credit assignment.<n>We show that such uniform credit assignment largely misallocates token-level training signals.<n>We propose an embarrassingly simple token reweighting approach, ActFocus, that downweights gradients on reasoning tokens.
arXiv Detail & Related papers (2026-05-14T08:33:02Z) - Designing Agentic AI-Based Screening for Portfolio Investment [1.7499351967216341]
We introduce a new agentic artificial intelligence (AI) platform for portfolio management.<n>Our architecture consists of three layers. First, two large language model (LLM) agents are assigned specialized tasks.<n>Second, these agents deliberate to generate and agree upon buy and sell signals from a large portfolio, substantially narrowing the pool of candidate assets.
arXiv Detail & Related papers (2026-03-24T15:03:40Z) - Learning to Manage Investment Portfolios beyond Simple Utility Functions [0.9999629695552193]
We propose a generative framework that learns latent representations of fund manager strategies without requiring explicit utility specification.<n>We validate our framework on a dataset of 1436 U.S. equity mutual funds.<n>Our framework provides a data-driven approach for characterizing investment strategies for applications in market simulation, strategy attribution, and regulatory oversight.
arXiv Detail & Related papers (2025-10-30T06:01:20Z) - Building crypto portfolios with agentic AI [46.348283638884425]
The rapid growth of crypto markets has opened new opportunities for investors, but at the same time exposed them to high volatility.<n>This paper presents a practical application of a multi-agent system designed to autonomously construct and evaluate crypto-asset allocations.
arXiv Detail & Related papers (2025-07-11T18:03:51Z) - IGD: Token Decisiveness Modeling via Information Gain in LLMs for Personalized Recommendation [70.2753541780788]
We introduce an Information Gain-based Decisiveness-aware Token handling (IGD) strategy that integrates token decisiveness into both tuning and decoding.<n>IGD consistently improves recommendation accuracy, achieving significant gains on widely used ranking metrics compared to strong baselines.
arXiv Detail & Related papers (2025-06-16T08:28:19Z) - Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation [63.89280381800457]
We propose TokenBridge, which maintains the strong representation capacity of continuous tokens while preserving the modeling simplicity of discrete tokens.
We introduce a dimension-wise quantization strategy that independently discretizes each feature dimension, paired with a lightweight autoregressive prediction mechanism.
Our approach achieves reconstruction and generation quality on par with continuous methods while using standard categorical prediction.
arXiv Detail & Related papers (2025-03-20T17:59:59Z) - Large-scale Time-Varying Portfolio Optimisation using Graph Attention Networks [4.2056926734482065]
This is the first study to incorporate risky firms and use all the firms in portfolio optimisation.
We propose and empirically test a novel method that leverages Graph Attention networks (GATs)
GATs are deep learning-based models that exploit network data to uncover nonlinear relationships.
arXiv Detail & Related papers (2024-07-22T10:50:47Z) - Semantic Equitable Clustering: A Simple and Effective Strategy for Clustering Vision Tokens [57.37893387775829]
We introduce a fast and balanced clustering method, named textbfSemantic textbfEquitable textbfClustering (SEC)
SEC clusters tokens based on their global semantic relevance in an efficient, straightforward manner.
We propose a versatile vision backbone, SECViT, to serve as a vision language connector.
arXiv Detail & Related papers (2024-05-22T04:49:00Z) - Attribute-Aware Deep Hashing with Self-Consistency for Large-Scale
Fine-Grained Image Retrieval [65.43522019468976]
We propose attribute-aware hashing networks with self-consistency for generating attribute-aware hash codes.
We develop an encoder-decoder structure network of a reconstruction task to unsupervisedly distill high-level attribute-specific vectors.
Our models are equipped with a feature decorrelation constraint upon these attribute vectors to strengthen their representative abilities.
arXiv Detail & Related papers (2023-11-21T08:20:38Z) - Uncovering Prototypical Knowledge for Weakly Open-Vocabulary Semantic
Segmentation [59.37587762543934]
This paper studies the problem of weakly open-vocabulary semantic segmentation (WOVSS)
Existing methods suffer from a granularity inconsistency regarding the usage of group tokens.
We propose the prototypical guidance network (PGSeg) that incorporates multi-modal regularization.
arXiv Detail & Related papers (2023-10-29T13:18:00Z) - Unlocking the Transferability of Tokens in Deep Models for Tabular Data [67.11727608815636]
Fine-tuning a pre-trained deep neural network has become a successful paradigm in various machine learning tasks.
In this paper, we propose TabToken, a method aims at enhancing the quality of feature tokens.
We introduce a contrastive objective that regularizes the tokens, capturing the semantics within and across features.
arXiv Detail & Related papers (2023-10-23T17:53:09Z) - Cryptocurrency Portfolio Optimization by Neural Networks [81.20955733184398]
This paper proposes an effective algorithm based on neural networks to take advantage of these investment products.
A deep neural network, which outputs the allocation weight of each asset at a time interval, is trained to maximize the Sharpe ratio.
A novel loss term is proposed to regulate the network's bias towards a specific asset, thus enforcing the network to learn an allocation strategy that is close to a minimum variance strategy.
arXiv Detail & Related papers (2023-10-02T12:33:28Z) - Sparse Index Tracking: Simultaneous Asset Selection and Capital Allocation via $\ell_0$-Constrained Portfolio [7.5684339230894135]
A sparse portfolio is preferable to a full portfolio in terms of reducing transaction costs and avoiding illiquid assets.
We propose a new problem formulation of sparse index tracking using an $ell_p$-norm constraint.
Our approach offers a choice between constraints on portfolio and turnover sparsity, further reducing transaction costs by limiting asset updates at each rebalancing interval.
arXiv Detail & Related papers (2023-07-22T04:47:30Z) - Discriminative Co-Saliency and Background Mining Transformer for
Co-Salient Object Detection [111.04994415248736]
We propose a Discriminative co-saliency and background Mining Transformer framework (DMT)
We use two types of pre-defined tokens to mine co-saliency and background information via our proposed contrast-induced pixel-to-token correlation and co-saliency token-to-token correlation modules.
Experimental results on three benchmark datasets demonstrate the effectiveness of our proposed method.
arXiv Detail & Related papers (2023-04-30T15:56:47Z) - Digital Asset Valuation: A Study on Domain Names, Email Addresses, and
NFTs [4.16271611433618]
DASH is the first Digital Asset Sales History dataset that features multiple digital asset classes spanning from classical to blockchain-based ones.
We build strong conventional feature-based models as the baselines for DASH.
We explore deep learning models based on fine-tuning pre-trained language models, which have not yet been explored in the previous literature.
arXiv Detail & Related papers (2022-10-06T12:59:06Z) - Fund2Vec: Mutual Funds Similarity using Graph Learning [0.966840768820136]
We propose a radically new approach to identify similar funds based on the weighted bipartite network representation of funds and their underlying assets data.
Ours is the first ever study of the weighted bipartite network representation of the funds-assets network in its original form.
arXiv Detail & Related papers (2021-06-24T17:35:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.