arXiv - QuantFin - Portfolio Management最新文献_第4页

Dynamic Asset Allocation with Asset-Specific Regime Forecasts 利用特定资产制度预测进行动态资产配置

arXiv - QuantFin - Portfolio Management Pub Date : 2024-06-13 DOI: arxiv-2406.09578

Yizhan Shu, Chenyu Yu, John M. Mulvey

{"title":"Dynamic Asset Allocation with Asset-Specific Regime Forecasts","authors":"Yizhan Shu, Chenyu Yu, John M. Mulvey","doi":"arxiv-2406.09578","DOIUrl":"https://doi.org/arxiv-2406.09578","url":null,"abstract":"This article introduces a novel hybrid regime identification-forecasting\u0000framework designed to enhance multi-asset portfolio construction by integrating\u0000asset-specific regime forecasts. Unlike traditional approaches that focus on\u0000broad economic regimes affecting the entire asset universe, our framework\u0000leverages both unsunpervised and supervised learning to generate tailored\u0000regime forecasts for individual assets. Initially, we use the statistical jump\u0000model, a robust unsupervised regime identification model, to derive regime\u0000labels for historical periods, classifying them into bullish or bearish states\u0000based on features extracted from an asset return series. Following this, a\u0000supervised gradient-boosted decision tree classifier is trained to predict\u0000these regimes using a combination of asset-specific return features and\u0000cross-asset macro-features. We apply this framework individually to each asset\u0000in our universe. Subsequently, return and risk forecasts which incorporate\u0000these regime predictions are input into Markowitz mean-variance optimization to\u0000determine optimal asset allocation weights. We demonstrate the efficacy of our\u0000approach through an empirical study on a multi-asset portfolio comprising\u0000twelve risky assets, including global equity, bond, real estate, and commodity\u0000indexes spanning from 1991 to 2023. The results consistently show\u0000outperformance across various portfolio models, including minimum-variance,\u0000mean-variance, and naive-diversified portfolios, highlighting the advantages of\u0000integrating asset-specific regime forecasts into dynamic asset allocation.","PeriodicalId":501045,"journal":{"name":"arXiv - QuantFin - Portfolio Management","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141502270","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

Gas Fees on the Ethereum Blockchain: From Foundations to Derivatives Valuations 以太坊区块链上的天然气费：从基础到衍生品估值

arXiv - QuantFin - Portfolio Management Pub Date : 2024-06-10 DOI: arxiv-2406.06524

Bernhard K Meister, Henry CW Price

引用次数: 0

Mean-variance portfolio selection in jump-diffusion model under no-shorting constraint: A viscosity solution approach 无做空约束下跳跃扩散模型中的均方差投资组合选择：粘性求解方法

arXiv - QuantFin - Portfolio Management Pub Date : 2024-06-06 DOI: arxiv-2406.03709

Xiaomin Shi, Zuo Quan Xu

引用次数: 0

Ensembling Portfolio Strategies for Long-Term Investments: A Distribution-Free Preference Framework for Decision-Making and Algorithms 长期投资的组合策略：决策和算法的无分布偏好框架

arXiv - QuantFin - Portfolio Management Pub Date : 2024-06-05 DOI: arxiv-2406.03652

Duy Khanh Lam

{"title":"Ensembling Portfolio Strategies for Long-Term Investments: A Distribution-Free Preference Framework for Decision-Making and Algorithms","authors":"Duy Khanh Lam","doi":"arxiv-2406.03652","DOIUrl":"https://doi.org/arxiv-2406.03652","url":null,"abstract":"This paper investigates the problem of ensembling multiple strategies for\u0000sequential portfolios to outperform individual strategies in terms of long-term\u0000wealth. Due to the uncertainty of strategies' performances in the future\u0000market, which are often based on specific models and statistical assumptions,\u0000investors often mitigate risk and enhance robustness by combining multiple\u0000strategies, akin to common approaches in collective learning prediction.\u0000However, the absence of a distribution-free and consistent preference framework\u0000complicates decisions of combination due to the ambiguous objective. To address\u0000this gap, we introduce a novel framework for decision-making in combining\u0000strategies, irrespective of market conditions, by establishing the investor's\u0000preference between decisions and then forming a clear objective. Through this\u0000framework, we propose a combinatorial strategy construction, free from\u0000statistical assumptions, for any scale of component strategies, even infinite,\u0000such that it meets the determined criterion. Finally, we test the proposed\u0000strategy along with its accelerated variant and some other multi-strategies.\u0000The numerical experiments show results in favor of the proposed strategies,\u0000albeit with small tradeoffs in their Sharpe ratios, in which their cumulative\u0000wealths eventually exceed those of the best component strategies while the\u0000accelerated strategy significantly improves performance.","PeriodicalId":501045,"journal":{"name":"arXiv - QuantFin - Portfolio Management","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552679","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

Simulation-based approach for Multiproject Scheduling based on composite priority rules 基于复合优先规则的多项目调度模拟方法

arXiv - QuantFin - Portfolio Management Pub Date : 2024-06-04 DOI: arxiv-2406.02102

Pablo Alvarez-Campana, Felix Villafanez, Fernando Acebes, David Poza

引用次数: 0

A Geometric Approach To Asset Allocation With Investor Views 资产配置的几何方法与投资者观点

arXiv - QuantFin - Portfolio Management Pub Date : 2024-06-03 DOI: arxiv-2406.01199

Alexandre V. Antonov, Koushik Balasubramanian, Alexander Lipton, Marcos Lopez de Prado

引用次数: 0

A Tick-by-Tick Solution for Concentrated Liquidity Provisioning 逐点集中流动性拨备解决方案

arXiv - QuantFin - Portfolio Management Pub Date : 2024-05-29 DOI: arxiv-2405.18728

Corinne Powers

引用次数: 0

Exploring Sectoral Profitability in the Indian Stock Market Using Deep Learning 利用深度学习探索印度股市的行业盈利能力

arXiv - QuantFin - Portfolio Management Pub Date : 2024-05-28 DOI: arxiv-2407.01572

Jaydip Sen, Hetvi Waghela, Sneha Rakshit

{"title":"Exploring Sectoral Profitability in the Indian Stock Market Using Deep Learning","authors":"Jaydip Sen, Hetvi Waghela, Sneha Rakshit","doi":"arxiv-2407.01572","DOIUrl":"https://doi.org/arxiv-2407.01572","url":null,"abstract":"This paper explores using a deep learning Long Short-Term Memory (LSTM) model\u0000for accurate stock price prediction and its implications for portfolio design.\u0000Despite the efficient market hypothesis suggesting that predicting stock prices\u0000is impossible, recent research has shown the potential of advanced algorithms\u0000and predictive models. The study builds upon existing literature on stock price\u0000prediction methods, emphasizing the shift toward machine learning and deep\u0000learning approaches. Using historical stock prices of 180 stocks across 18\u0000sectors listed on the NSE, India, the LSTM model predicts future prices. These\u0000predictions guide buy/sell decisions for each stock and analyze sector\u0000profitability. The study's main contributions are threefold: introducing an\u0000optimized LSTM model for robust portfolio design, utilizing LSTM predictions\u0000for buy/sell transactions, and insights into sector profitability and\u0000volatility. Results demonstrate the efficacy of the LSTM model in accurately\u0000predicting stock prices and informing investment decisions. By comparing sector\u0000profitability and prediction accuracy, the work provides valuable insights into\u0000the dynamics of the current financial markets in India.","PeriodicalId":501045,"journal":{"name":"arXiv - QuantFin - Portfolio Management","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141523135","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

Optimizing Sharpe Ratio: Risk-Adjusted Decision-Making in Multi-Armed Bandits 优化夏普比率：多臂强盗中的风险调整决策

arXiv - QuantFin - Portfolio Management Pub Date : 2024-05-28 DOI: arxiv-2406.06552

Sabrina Khurshid, Mohammed Shahid Abdulla, Gourab Ghatak

{"title":"Optimizing Sharpe Ratio: Risk-Adjusted Decision-Making in Multi-Armed Bandits","authors":"Sabrina Khurshid, Mohammed Shahid Abdulla, Gourab Ghatak","doi":"arxiv-2406.06552","DOIUrl":"https://doi.org/arxiv-2406.06552","url":null,"abstract":"Sharpe Ratio (SR) is a critical parameter in characterizing financial time\u0000series as it jointly considers the reward and the volatility of any\u0000stock/portfolio through its variance. Deriving online algorithms for optimizing\u0000the SR is particularly challenging since even offline policies experience\u0000constant regret with respect to the best expert Even-Dar et al (2006). Thus,\u0000instead of optimizing the usual definition of SR, we optimize regularized\u0000square SR (RSSR). We consider two settings for the RSSR, Regret Minimization\u0000(RM) and Best Arm Identification (BAI). In this regard, we propose a novel\u0000multi-armed bandit (MAB) algorithm for RM called UCB-RSSR for RSSR\u0000maximization. We derive a path-dependent concentration bound for the estimate\u0000of the RSSR. Based on that, we derive the regret guarantees of UCB-RSSR and\u0000show that it evolves as O(log n) for the two-armed bandit case played for a\u0000horizon n. We also consider a fixed budget setting for well-known BAI\u0000algorithms, i.e., sequential halving and successive rejects, and propose SHVV,\u0000SHSR, and SuRSR algorithms. We derive the upper bound for the error probability\u0000of all proposed BAI algorithms. We demonstrate that UCB-RSSR outperforms the\u0000only other known SR optimizing bandit algorithm, U-UCB Cassel et al (2023). We\u0000also establish its efficacy with respect to other benchmarks derived from the\u0000GRA-UCB and MVTS algorithms. We further demonstrate the performance of proposed\u0000BAI algorithms for multiple different setups. Our research highlights that our\u0000proposed algorithms will find extensive applications in risk-aware portfolio\u0000management problems. Consequently, our research highlights that our proposed\u0000algorithms will find extensive applications in risk-aware portfolio management\u0000problems.","PeriodicalId":501045,"journal":{"name":"arXiv - QuantFin - Portfolio Management","volume":"204 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141523138","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

Constrained monotone mean--variance investment-reinsurance under the Cramér--Lundberg model with random coefficients 具有随机系数的克拉梅尔--伦德伯格模型下的受约束单调均值--方差投资--再保险

arXiv - QuantFin - Portfolio Management Pub Date : 2024-05-28 DOI: arxiv-2405.17841

Xiaomin Shi, Zuo Quan Xu

引用次数: 0