arXiv - QuantFin - Portfolio Management最新文献

Optimal Investment with Costly Expert Opinions 用昂贵的专家意见优化投资

arXiv - QuantFin - Portfolio Management Pub Date : 2024-09-17 DOI: arxiv-2409.11569

Christoph Knochenhauer, Alexander Merkel, Yufei Zhang

引用次数: 0

Anatomy of Machines for Markowitz: Decision-Focused Learning for Mean-Variance Portfolio Optimization 马科维茨的机器解剖学：均值-方差投资组合优化的决策学习

arXiv - QuantFin - Portfolio Management Pub Date : 2024-09-15 DOI: arxiv-2409.09684

Junhyeong Lee, Inwoo Tae, Yongjae Lee

{"title":"Anatomy of Machines for Markowitz: Decision-Focused Learning for Mean-Variance Portfolio Optimization","authors":"Junhyeong Lee, Inwoo Tae, Yongjae Lee","doi":"arxiv-2409.09684","DOIUrl":"https://doi.org/arxiv-2409.09684","url":null,"abstract":"Markowitz laid the foundation of portfolio theory through the mean-variance\u0000optimization (MVO) framework. However, the effectiveness of MVO is contingent\u0000on the precise estimation of expected returns, variances, and covariances of\u0000asset returns, which are typically uncertain. Machine learning models are\u0000becoming useful in estimating uncertain parameters, and such models are trained\u0000to minimize prediction errors, such as mean squared errors (MSE), which treat\u0000prediction errors uniformly across assets. Recent studies have pointed out that\u0000this approach would lead to suboptimal decisions and proposed Decision-Focused\u0000Learning (DFL) as a solution, integrating prediction and optimization to\u0000improve decision-making outcomes. While studies have shown DFL's potential to\u0000enhance portfolio performance, the detailed mechanisms of how DFL modifies\u0000prediction models for MVO remain unexplored. This study aims to investigate how\u0000DFL adjusts stock return prediction models to optimize decisions in MVO,\u0000addressing the question: \"MSE treats the errors of all assets equally, but how\u0000does DFL reduce errors of different assets differently?\" Answering this will\u0000provide crucial insights into optimal stock return prediction for constructing\u0000efficient portfolios.","PeriodicalId":501045,"journal":{"name":"arXiv - QuantFin - Portfolio Management","volume":"188 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142260130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Disentangling the sources of cyber risk premia 厘清网络风险溢价的来源

arXiv - QuantFin - Portfolio Management Pub Date : 2024-09-13 DOI: arxiv-2409.08728

Loïc Maréchal, Nathan Monnet

引用次数: 0

A Deep Reinforcement Learning Framework For Financial Portfolio Management 金融投资组合管理的深度强化学习框架

arXiv - QuantFin - Portfolio Management Pub Date : 2024-09-03 DOI: arxiv-2409.08426

Jinyang Li

{"title":"A Deep Reinforcement Learning Framework For Financial Portfolio Management","authors":"Jinyang Li","doi":"arxiv-2409.08426","DOIUrl":"https://doi.org/arxiv-2409.08426","url":null,"abstract":"In this research paper, we investigate into a paper named \"A Deep\u0000Reinforcement Learning Framework for the Financial Portfolio Management\u0000Problem\" [arXiv:1706.10059]. It is a portfolio management problem which is\u0000solved by deep learning techniques. The original paper proposes a\u0000financial-model-free reinforcement learning framework, which consists of the\u0000Ensemble of Identical Independent Evaluators (EIIE) topology, a\u0000Portfolio-Vector Memory (PVM), an Online Stochastic Batch Learning (OSBL)\u0000scheme, and a fully exploiting and explicit reward function. Three different\u0000instants are used to realize this framework, namely a Convolutional Neural\u0000Network (CNN), a basic Recurrent Neural Network (RNN), and a Long Short-Term\u0000Memory (LSTM). The performance is then examined by comparing to a number of\u0000recently reviewed or published portfolio-selection strategies. We have\u0000successfully replicated their implementations and evaluations. Besides, we\u0000further apply this framework in the stock market, instead of the cryptocurrency\u0000market that the original paper uses. The experiment in the cryptocurrency\u0000market is consistent with the original paper, which achieve superior returns.\u0000But it doesn't perform as well when applied in the stock market.","PeriodicalId":501045,"journal":{"name":"arXiv - QuantFin - Portfolio Management","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142260132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Betting Against (Bad) Beta 与（糟糕的）贝塔值对赌

arXiv - QuantFin - Portfolio Management Pub Date : 2024-08-31 DOI: arxiv-2409.00416

Miguel C. Herculano

引用次数: 0

Hedging in Jump Diffusion Model with Transaction Costs 有交易成本的跳跃扩散模型中的套期保值

arXiv - QuantFin - Portfolio Management Pub Date : 2024-08-20 DOI: arxiv-2408.10785

Hamidreza Maleki Almani, Foad Shokrollahi, Tommi Sottinen

引用次数: 0

Portfolio and reinsurance optimization under unknown market price of risk 未知市场风险价格下的投资组合和再保险优化

arXiv - QuantFin - Portfolio Management Pub Date : 2024-08-14 DOI: arxiv-2408.07432

Claudia Ceci, Katia Colaneri

引用次数: 0

Predicting the distributions of stock returns around the globe in the era of big data and learning 预测大数据和学习时代的全球股票收益分布

arXiv - QuantFin - Portfolio Management Pub Date : 2024-08-14 DOI: arxiv-2408.07497

Jozef Barunik, Martin Hronec, Ondrej Tobek

引用次数: 0

Optimizing Portfolio with Two-Sided Transactions and Lending: A Reinforcement Learning Framework 优化双面交易和借贷的投资组合：强化学习框架

arXiv - QuantFin - Portfolio Management Pub Date : 2024-08-09 DOI: arxiv-2408.05382

Ali Habibnia, Mahdi Soltanzadeh

{"title":"Optimizing Portfolio with Two-Sided Transactions and Lending: A Reinforcement Learning Framework","authors":"Ali Habibnia, Mahdi Soltanzadeh","doi":"arxiv-2408.05382","DOIUrl":"https://doi.org/arxiv-2408.05382","url":null,"abstract":"This study presents a Reinforcement Learning (RL)-based portfolio management\u0000model tailored for high-risk environments, addressing the limitations of\u0000traditional RL models and exploiting market opportunities through two-sided\u0000transactions and lending. Our approach integrates a new environmental\u0000formulation with a Profit and Loss (PnL)-based reward function, enhancing the\u0000RL agent's ability in downside risk management and capital optimization. We\u0000implemented the model using the Soft Actor-Critic (SAC) agent with a\u0000Convolutional Neural Network with Multi-Head Attention (CNN-MHA). This setup\u0000effectively manages a diversified 12-crypto asset portfolio in the Binance\u0000perpetual futures market, leveraging USDT for both granting and receiving loans\u0000and rebalancing every 4 hours, utilizing market data from the preceding 48\u0000hours. Tested over two 16-month periods of varying market volatility, the model\u0000significantly outperformed benchmarks, particularly in high-volatility\u0000scenarios, achieving higher return-to-risk ratios and demonstrating robust\u0000profitability. These results confirm the model's effectiveness in leveraging\u0000market dynamics and managing risks in volatile environments like the\u0000cryptocurrency market.","PeriodicalId":501045,"journal":{"name":"arXiv - QuantFin - Portfolio Management","volume":"177 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142217131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hedge Fund Portfolio Construction Using PolyModel Theory and iTransformer 利用 PolyModel 理论和 iTransformer 构建对冲基金投资组合

arXiv - QuantFin - Portfolio Management Pub Date : 2024-08-06 DOI: arxiv-2408.03320

Siqiao Zhao, Zhikang Dong, Zeyu Cao, Raphael Douady

{"title":"Hedge Fund Portfolio Construction Using PolyModel Theory and iTransformer","authors":"Siqiao Zhao, Zhikang Dong, Zeyu Cao, Raphael Douady","doi":"arxiv-2408.03320","DOIUrl":"https://doi.org/arxiv-2408.03320","url":null,"abstract":"When constructing portfolios, a key problem is that a lot of financial time\u0000series data are sparse, making it challenging to apply machine learning\u0000methods. Polymodel theory can solve this issue and demonstrate superiority in\u0000portfolio construction from various aspects. To implement the PolyModel theory\u0000for constructing a hedge fund portfolio, we begin by identifying an asset pool,\u0000utilizing over 10,000 hedge funds for the past 29 years' data. PolyModel theory\u0000also involves choosing a wide-ranging set of risk factors, which includes\u0000various financial indices, currencies, and commodity prices. This comprehensive\u0000selection mirrors the complexities of the real-world environment. Leveraging on\u0000the PolyModel theory, we create quantitative measures such as Long-term Alpha,\u0000Long-term Ratio, and SVaR. We also use more classical measures like the Sharpe\u0000ratio or Morningstar's MRAR. To enhance the performance of the constructed\u0000portfolio, we also employ the latest deep learning techniques (iTransformer) to\u0000capture the upward trend, while efficiently controlling the downside, using all\u0000the features. The iTransformer model is specifically designed to address the\u0000challenges in high-dimensional time series forecasting and could largely\u0000improve our strategies. More precisely, our strategies achieve better Sharpe\u0000ratio and annualized return. The above process enables us to create multiple\u0000portfolio strategies aiming for high returns and low risks when compared to\u0000various benchmarks.","PeriodicalId":501045,"journal":{"name":"arXiv - QuantFin - Portfolio Management","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0