Skip to main content
CenXiv.org
此网站处于试运行阶段,支持我们!
我们衷心感谢所有贡献者的支持。
贡献
赞助
cenxiv logo > q-fin.PM

帮助 | 高级搜索

投资组合管理

  • 新提交
  • 交叉列表
  • 替换

查看 最近的 文章

显示 2025年07月11日, 星期五 新的列表

总共 5 条目
显示最多 2000 每页条目: 较少 | 更多 | 所有

新提交 (展示 2 之 2 条目 )

[1] arXiv:2507.07107 [中文pdf, pdf, html, 其他]
标题: 机器学习增强的多因子量化交易:具有偏差校正的横截面组合优化方法
标题: Machine Learning Enhanced Multi-Factor Quantitative Trading: A Cross-Sectional Portfolio Optimization Approach with Bias Correction
Yimin Du
评论: 9页
主题: 投资组合管理 (q-fin.PM) ; 计算工程、金融与科学 (cs.CE)

本文提出了一种全面的机器学习交易框架,通过系统化的因子工程、实时计算优化和横截面组合构建,实现了优越的风险调整收益。 我们的方法将多因子阿尔法发现与偏差校正技术相结合,利用PyTorch加速的因子计算和先进的组合优化。 该系统处理从开源alpha101扩展和专有市场微观结构信号中得出的500-1000个因子。 关键创新包括基于张量的因子计算加速、几何布朗运动数据增强以及横截面中性化策略。 在中国A股市场(2010-2024年)的实证验证表明,年化收益率为$20\%$,夏普比率超过2.0,显著优于传统方法。 我们的分析揭示了偏差校正在因子构建中的重要性以及横截面组合优化对策略表现的显著影响。 代码和实验实现可在以下地址获取:https://github.com/initial-d/ml-quant-trading

This paper presents a comprehensive machine learning framework for quantitative trading that achieves superior risk-adjusted returns through systematic factor engineering, real-time computation optimization, and cross-sectional portfolio construction. Our approach integrates multi-factor alpha discovery with bias correction techniques, leveraging PyTorch-accelerated factor computation and advanced portfolio optimization. The system processes 500-1000 factors derived from open-source alpha101 extensions and proprietary market microstructure signals. Key innovations include tensor-based factor computation acceleration, geometric Brownian motion data augmentation, and cross-sectional neutralization strategies. Empirical validation on Chinese A-share markets (2010-2024) demonstrates annualized returns of $20\%$ with Sharpe ratios exceeding 2.0, significantly outperforming traditional approaches. Our analysis reveals the critical importance of bias correction in factor construction and the substantial impact of cross-sectional portfolio optimization on strategy performance. Code and experimental implementations are available at: https://github.com/initial-d/ml-quant-trading

[2] arXiv:2507.07358 [中文pdf, pdf, html, 其他]
标题: 变额年金:对爬行保障、混合合同设计和税收的更深入探讨
标题: Variable annuities: A closer look at ratchet guarantees, hybrid contract designs, and taxation
Jennifer Alonso-Garcia, Len Patrick Dominic M. Garces, Jonathan Ziveyi
评论: 33页(包括4页的补充材料),8幅图,5张表
主题: 投资组合管理 (q-fin.PM) ; 证券定价 (q-fin.PR)

本文研究了在包含保证最低提款收益(GMWB)附加条款的可变年金(VA)合同中,政策持有者的最优提款策略和行为,该合同考虑了税收和在合同期内增强收益基础的阶梯机制。 从数学上讲,这是通过解决与优化合同现金流的贴现风险中性期望相关的向后动态规划问题来实现的。 此外,考虑到市场上的交易VA合同,我们考虑了混合产品,为政策持有者提供访问现金基金的途径,该现金基金作为VA收益的中间存储库,并按合同规定的现金利率获得利息。 我们通过揭示税收、现金基金和收益基础更新机制之间的几个重要相互作用,为文献做出了贡献。 当税率较高时,现金基金的避税效应(与VA的普通提款不同征税)在提高整体合同的吸引力方面起着重要作用。 此外,阶梯收益基础更新方案(与文献中普遍存在的返还保费规定相反)倾向于阻止提前退出,因为它提供了增强的下行市场风险保护。 此外,现金基金会抑制积极提款,政策持有者更倾向于将保证提款金额转移到现金基金,以利用现金基金的利率。

This paper investigates optimal withdrawal strategies and behavior of policyholders in a variable annuity (VA) contract with a guaranteed minimum withdrawal benefit (GMWB) rider incorporating taxation and a ratchet mechanism for enhancing the benefit base during the life of the contract. Mathematically, this is accomplished by solving a backward dynamic programming problem associated with optimizing the discounted risk-neutral expectation of cash flows from the contract. Furthermore, reflecting traded VA contracts in the market, we consider hybrid products providing policyholders access to a cash fund which functions as an intermediate repository of earnings from the VA and earns interest at a contractually specified cash rate. We contribute to the literature by revealing several significant interactions among taxation, the cash fund, and the benefit base update mechanism. When tax rates are high, the tax-shielding effect of the cash fund, which is taxed differently from ordinary withdrawals from the VA, plays a significant role in enhancing the attractiveness of the overall contract. Furthermore, the ratchet benefit base update scheme (in contrast to the ubiquitous return-of-premium specification in the literature) tends to discourage early surrender as it provides enhanced downside market risk protection. In addition, the cash fund discourages active withdrawals, with policyholders preferring to transfer the guaranteed withdrawal amount to the cash fund to leverage the cash fund rate.

交叉提交 (展示 1 之 1 条目 )

[3] arXiv:2507.07159 (交叉列表自 cond-mat.dis-nn) [中文pdf, pdf, html, 其他]
标题: 基于变分神经退火的大规模投资组合优化
标题: Large-scale portfolio optimization with variational neural annealing
Nishan Ranabhat, Behnam Javanparast, David Goerz, Estelle Inack
评论: 16页,13图,1表
主题: 无序系统与神经网络 (cond-mat.dis-nn) ; 统计力学 (cond-mat.stat-mech) ; 机器学习 (cs.LG) ; 投资组合管理 (q-fin.PM)

投资组合优化是全球金融机构进行的常规资产管理工作。 然而,在实际约束条件下,如买卖限制和交易成本,其公式化成为一个混合整数非线性规划问题,当前的混合整数优化器通常难以解决。 我们提出将这个问题映射到一个经典的类似伊辛的哈密顿量,并通过使用自回归神经网络实现的经典公式,利用变分神经退火(VNA)来求解。 我们证明,VNA可以识别出包含超过2000种资产的投资组合的近似最优解,并且其性能与最先进的优化器(如Mosek)相当,同时在困难实例上表现出更快的收敛速度。 最后,我们对标准普尔500、Russell 1000和Russell 3000指数进行了动态有限尺寸标度分析,揭示了VNA算法在投资组合优化问题上的普遍行为和多项式退火时间尺度。

Portfolio optimization is a routine asset management operation conducted in financial institutions around the world. However, under real-world constraints such as turnover limits and transaction costs, its formulation becomes a mixed-integer nonlinear program that current mixed-integer optimizers often struggle to solve. We propose mapping this problem onto a classical Ising-like Hamiltonian and solving it with Variational Neural Annealing (VNA), via its classical formulation implemented using autoregressive neural networks. We demonstrate that VNA can identify near-optimal solutions for portfolios comprising more than 2,000 assets and yields performance comparable to that of state-of-the-art optimizers, such as Mosek, while exhibiting faster convergence on hard instances. Finally, we present a dynamical finite-size scaling analysis applied to the S&P 500, Russell 1000, and Russell 3000 indices, revealing universal behavior and polynomial annealing time scaling of the VNA algorithm on portfolio optimization problems.

替换提交 (展示 2 之 2 条目 )

[4] arXiv:2501.12841 (替换) [中文pdf, pdf, html, 其他]
标题: 加密货币市场中的最优分散与简单分散:交易成本和时变矩的作用
标题: Optimal vs naive diversification in cryptocurrencies market: The role of transaction costs and time-varying moments
Heming Chen
主题: 投资组合管理 (q-fin.PM)

这项研究系统地检查了所考虑的几种替代修改如何影响决定投资组合表现的三个方面(总收益、交易成本和投资组合风险)。 我们发现,难以利用加密货币收益的可能可预测性。 然而,资产收益波动性的可预测性在高度相关的加密货币市场中产生了明显的经济价值。

This study systematically examines how several alternative modifications considered affect three aspects that determine portfolio performance (the gross return, the transaction costs, and the portfolio risk). We find that it is difficult to exploit the possible predictability of the returns of cryptocurrencies. However, the predictability of asset return volatility produces obvious economic value, although in a highly correlated cryptocurrencies market.

[5] arXiv:2501.03938 (替换) [中文pdf, pdf, 其他]
标题: 样本内和样本外夏普比率对于线性预测模型
标题: In-Sample and Out-of-Sample Sharpe Ratios for Linear Predictive Models
Antoine Jacquier, Johannes Muhle-Karbe, Joseph Mulligan
评论: 33页,13图
主题: 数学金融 (q-fin.MF) ; 投资组合管理 (q-fin.PM)

我们研究基于线性预测模型的交易策略的样本内表现由于过拟合而在样本外减少的程度。 更具体地说,我们计算了相应的盈亏(PnL)的样本内和样本外均值和方差,并利用这些来推导出相应的夏普比率的闭式近似值。 我们发现,对于基于许多弱信号而非少数强信号的多资产复杂策略,样本外“复制比率”会减小,并且当使用更多训练数据时会增加。 这些效应的显著定量重要性通过一个遵循Gârleanu和Pedersen方法的商品期货模拟案例研究以及一个使用Goyal、Welch和Zafirov编制的数据集的实证案例研究进行了说明。

We study how much the in-sample performance of trading strategies based on linear predictive models is reduced out-of-sample due to overfitting. More specifically, we compute the in- and out-of-sample means and variances of the corresponding PnLs and use these to derive a closed-form approximation for the corresponding Sharpe ratios. We find that the out-of-sample "replication ratio" diminishes for complex strategies with many assets based on many weak rather than a few strong trading signals, and increases when more training data is used. The substantial quantitative importance of these effects is illustrated with a simulation case study for commodity futures following the methodology of G\^arleanu and Pedersen, and an empirical case study using the dataset compiled by Goyal, Welch and Zafirov.

总共 5 条目
显示最多 2000 每页条目: 较少 | 更多 | 所有
  • 关于
  • 帮助
  • contact arXivClick here to contact arXiv 联系
  • 订阅 arXiv 邮件列表点击这里订阅 订阅
  • 版权
  • 隐私政策
  • 网络无障碍帮助
  • arXiv 运营状态
    通过...获取状态通知 email 或者 slack

京ICP备2025123034号