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1. 发明授权

US11205231B2 Performance attribution for portfolios with composite investments 有权
公开(公告)号：US11205231B2
公开(公告)日：2021-12-21
申请号：US16522620
申请日：2019-07-25
申请人： Axioma, Inc.
发明人： Vishv Jeet , Vishal Shekhar
IPC分类号： G06Q40/06 , G06Q40/02
摘要： In existing performance attribution, composite investments are resolved into simple assets, and the performance attribution provides results only for the resolved, net investment in the simple assets. As a result, the individual investment in the composite investment in isolation is lost, and it is impossible to determine if the investment in the composite investment in isolation helped or hurt performance. Approaches are described to determine attribution in a manner in which the attribution hierarchy is altered so that, after reporting on the performance of the full portfolio, a further level of attribution reports on a set of sub-portfolios. The first sub-portfolio represents the original investments in simple assets only while the other sub-portfolios represent investments in each composite investment. This composite-first performance attribution determines the individual contribution to performance of each composite investment, resulting in more detailed, practical, and intuitive results.

2. 发明申请

US20130041848A1 Identifying and Compensating for Model Mis-Specification in Factor Risk Models 审中-公开
标题翻译：在因子风险模型中识别和补偿模型误差规范
公开(公告)号：US20130041848A1
公开(公告)日：2013-02-14
申请号：US13654797
申请日：2012-10-18
申请人： Robert A. Stubbs , Stefan Hans Schmieta
发明人： Robert A. Stubbs , Stefan Hans Schmieta
IPC分类号： G06Q40/06
CPC分类号： G06Q40/06 , G06Q40/00 , G06Q40/04
摘要： Techniques for using factor risk models to more accurately estimate the risk or active risk of an investment portfolio are disclosed. Inherent “modeling error” in factor risk models is identified and compensated for. One or more factors are added to compensate for factors that are unspecified or unattributed in the original factor risk model and which lead to modeling error. The approach can be used with a variety of different factor risk models, and for a variety of securities. Knowledge of the risk associated with modeling error can be utilized when estimating risk or active risk using factor risk models or when constructing optimal portfolios by mean-variance optimization or other portfolio construction strategies using factor risk models.
摘要翻译：披露了使用因子风险模型更准确地估计投资组合的风险或主动风险的技术。确定并补偿因素风险模型的固有建模误差。添加一个或多个因素来补偿原始因子风险模型中未指定或未归因的因素，并导致建模误差。该方法可用于各种不同因素风险模型，以及各种证券。在使用因子风险模型估计风险或主动风险时，或通过均值方差优化或使用因子风险模型的其他投资组合构建策略构建最优投资组合时，可以利用与建模误差相关的风险的知识。

3. 发明授权

US11195232B2 Methods and apparatus employing hierarchical conditional value at risk to minimize downside risk of a multi-asset class portfolio and improved graphical user interface 有权
公开(公告)号：US11195232B2
公开(公告)日：2021-12-07
申请号：US17131914
申请日：2020-12-23
申请人： Axioma, Inc.
发明人： Kartik Sivaramakrishnan
IPC分类号： G06Q40/06
摘要： The traditional Markowitz mean-variance-optimization (MVO) framework that uses the standard deviation of the possible portfolio returns as a measure of risk does not accurately measure the risk of multi-asset class portfolios whose return distributions are non-Gaussian and asymmetric. A scenario-based conditional value-at-risk (CVaR) approach for minimizing the downside risk of a multi-asset class portfolio is addressed that uses Monte-Carlo simulations to generate the asset return scenarios. These return scenarios are incorporated into a modified Rockafellar-Uryasev based convex programming formulation to generate an optimized hedge. One example addresses hedging in an equity portfolio with options. Testing shows that a hierarchical CVaR approach generates portfolios with better predicted worst case loss, downside risk, standard deviation, and skew.

4. 发明申请

US20160110811A1 Methods and Apparatus for Implementing Improved Notional-free Asset Liquidity Rules 审中-公开
标题翻译：实施无条件资产流动性规则的方法与手段
公开(公告)号：US20160110811A1
公开(公告)日：2016-04-21
申请号：US14519991
申请日：2014-10-21
申请人： Frank Pak-Ho Siu , Anthony A. Renshaw
发明人： Frank Pak-Ho Siu , Anthony A. Renshaw
IPC分类号： G06Q40/06 , G06Q40/04
CPC分类号： G06Q40/06 , G06Q40/04
摘要： On the basis of simulated backtests, many portfolios including so-called smart beta and other factor products often boast impressive track records. However, given the additional trading that occurs, can such advertised performance truly be realized once transaction costs are taken into account? One might expect these products to make a concerted effort to manage liquidity. However, explicit efforts to manage liquidity in existing smart beta and factor ETF and index products have been relatively modest. A new set of rules are provided that are independent of the notional value of the portfolio that can be used to better manage the liquidity of investment portfolio.
摘要翻译：在模拟反向测试的基础上，包括所谓的智能测试版和其他因素产品的许多组合通常都拥有令人印象深刻的记录。但是，考虑到发生额外的交易，一旦考虑到交易成本，这种广告的表现能否真正实现？人们可能希望这些产品能够共同努力来管理流动性。然而，在现有智能beta和因子ETF和指数产品中明确管理流动性的努力相对较小。提供了一套新的规则，独立于投资组合的名义价值，可用于更好地管理投资组合的流动性。

5. 发明申请

US20130332391A1 Methodology and Process For Constructing Factor Indexes 审中-公开
标题翻译：构建因子指标的方法与过程
公开(公告)号：US20130332391A1
公开(公告)日：2013-12-12
申请号：US13965621
申请日：2013-08-13
申请人： Anthony A. Renshaw
发明人： Anthony A. Renshaw
IPC分类号： G06Q40/06
CPC分类号： G06Q40/06
摘要： Approaches to the construction of indexes are addressed wherein a portfolio of securities such as stocks, bonds, or the like and their associated investment weights or shares is generated. Indexes can be used as investment tools in various ways. For instance, indexes comprising a plurality of securities can often be bought and sold more cheaply than buying and selling the individual constituents of the index. This pricing differential allows investment with reduced transaction costs. Alternatively, in passive and enhanced indexing, investments are made with reference to an index. Performance statistics such as return and risk are reported with respect to the reference index. Factor indexes can serve as active manager benchmarks or the underlyers for investable products such as exchange traded funds and mutual funds. Computer based systems, methods and software are addressed for constructing indexes that replicate the returns of a quantitative factor such as medium term momentum or value. Further, processes and methodology are described by which the index can have the best possible replication of the underlying factor returns as well as other desirable characteristics. The methodology provides an approach to determine the index even when all desirable characteristics of the index are not simultaneously achievable.
摘要翻译：解决了构建指标的方法，其中生成股票，债券等证券组合及其相关投资权重或股份。索引可以以各种方式用作投资工具。例如，包括多个证券的指数通常比购买和出售指数的个体成本更便宜地买卖。这种定价差异允许投资减少交易成本。或者，在被动和增强的索引中，投资是参考一个指数。关于参考指数报告绩效统计，如回报和风险。因素指标可以作为主要经理人基准，也可以作为交易所交易基金和共同基金等可投资产品的基础。基于计算机的系统，方法和软件被解决用于构建复制定量因素（例如中期动量或价值）的回报的指数。此外，描述了过程和方法，通过该过程和方法，索引可以对潜在的因素返回以及其它期望的特征进行最佳的复制。即使在索引的所有期望特征不能同时实现的情况下，该方法也提供了确定索引的方法。

6. 发明授权

US08315936B2 Identifying and compensating for model mis-specification in factor risk models 有权
标题翻译：在因子风险模型中识别和补偿模型错误规范
公开(公告)号：US08315936B2
公开(公告)日：2012-11-20
申请号：US12711554
申请日：2010-02-24
申请人： Robert A. Stubbs , Stefan Hans Schmieta
发明人： Robert A. Stubbs , Stefan Hans Schmieta
IPC分类号： G06Q40/00
CPC分类号： G06Q40/06 , G06Q40/00 , G06Q40/04
摘要： Techniques for more accurately estimating the risk, or active risk, of an investment portfolio when using factor risk models are disclosed. This improved accuracy is achieved by identifying and compensating for the inherent “modeling error” present when risk is represented using a factor risk model. The approach adds one or more factors that depend on the investment portfolio and that explicitly compensate for factors that are unspecified or unattributed in the original factor risk model. These unspecified factors of the original factor risk model lead to modeling error in the original factor risk model. The approach can be used with a variety of different factor risk models, such as, fundamental, statistical and macro risk models, for example, and for a variety of securities, such as equities, international equities, composites, exchange traded funds (ETFs), or the like, currencies, and fixed-income, for example. The risk associated with modeling error in a factor risk model relative to a particular portfolio is identified and quantified. Knowledge of this risk associated with modeling error can be utilized when estimating risk, or active risk, using factor risk models or when constructing optimal portfolios by mean-variance optimization or other portfolio construction strategies and procedures that make use of factor risk models.
摘要翻译：披露了在使用因子风险模型时更准确地估计投资组合的风险或主动风险的技术。当使用因子风险模型表示风险时，通过识别和补偿存在的固有建模误差来实现提高的准确度。该方法增加了一个或多个依赖于投资组合的因素，并明确补偿了原始因素风险模型中未指定或未归因的因素。原始因素风险模型的这些未明确因素导致原始因素风险模型的建模误差。该方法可用于各种不同因素风险模型，如基本面，统计学和宏观风险模型，以及各种证券，如股票，国际股票，复合材料，交易所交易基金（ETF）等。，等等，货币和固定收益。与特定投资组合相关的因素风险模型中的建模误差相关风险被识别和量化。使用因子风险模型估计风险或主动风险时，可以利用与建模误差相关的风险的知识，或者通过均值方差优化或利用因子风险模型的其他投资组合构建策略和程序来构建最优投资组合。

7. 发明申请

US20100153307A1 Identifying and Compensating for Model Mis-Specification in Factor Risk Models 有权
标题翻译：在因子风险模型中识别和补偿模型误差规范
公开(公告)号：US20100153307A1
公开(公告)日：2010-06-17
申请号：US12711554
申请日：2010-02-24
申请人： Robert A. Stubbs , Stefan H. Schmieta
发明人： Robert A. Stubbs , Stefan H. Schmieta
IPC分类号： G06Q40/00
CPC分类号： G06Q40/06 , G06Q40/00 , G06Q40/04
摘要： Techniques for more accurately estimating the risk, or active risk, of an investment portfolio when using factor risk models are disclosed. This improved accuracy is achieved by identifying and compensating for the inherent “modeling error” present when risk is represented using a factor risk model. The approach adds one or more factors that depend on the investment portfolio and that explicitly compensate for factors that are unspecified or unattributed in the original factor risk model. These unspecified factors of the original factor risk model lead to modeling error in the original factor risk model. The approach can be used with a variety of different factor risk models, such as, fundamental, statistical and macro risk models, for example, and for a variety of securities, such as equities, international equities, composites, exchange traded funds (ETFs), or the like, currencies, and fixed-income, for example. The risk associated with modeling error in a factor risk model relative to a particular portfolio is identified and quantified. Knowledge of this risk associated with modeling error can be utilized when estimating risk, or active risk, using factor risk models or when constructing optimal portfolios by mean-variance optimization or other portfolio construction strategies and procedures that make use of factor risk models.
摘要翻译：披露了在使用因子风险模型时更准确地估计投资组合的风险或主动风险的技术。当使用因子风险模型表示风险时，通过识别和补偿存在的固有“建模误差”来实现这种改进的准确性。该方法增加了一个或多个依赖于投资组合的因素，并明确补偿了原始因素风险模型中未指定或未归因的因素。原始因素风险模型的这些未明确因素导致原始因素风险模型的建模误差。该方法可用于各种不同因素风险模型，如基本面，统计学和宏观风险模型，以及各种证券，如股票，国际股票，复合材料，交易所交易基金（ETF）等。，等等，货币和固定收益。与特定投资组合相关的因素风险模型中的建模误差相关风险被识别和量化。使用因子风险模型估计风险或主动风险时，可以利用与建模误差相关的风险的知识，或者通过均值方差优化或利用因子风险模型的其他投资组合构建策略和程序来构建最优投资组合。

8. 发明授权

US10915962B2 Identifying and compensating for model mis-specification in factor risk models 有权
公开(公告)号：US10915962B2
公开(公告)日：2021-02-09
申请号：US16010059
申请日：2018-06-15
申请人： Axioma, Inc.
发明人： Robert A. Stubbs , Stefan Hans Schmieta
IPC分类号： G06Q40/06 , G06Q40/00 , G06Q40/04 , G06F17/16
摘要： Techniques for using factor risk models to more accurately estimate the risk or active risk of an investment portfolio are disclosed. Inherent “modeling error” in factor risk models is identified and compensated for. One or more factors are added to compensate for factors that are unspecified or unattributed in the original factor risk model and which lead to modeling error. The approach can be used with a variety of different factor risk models, and for a variety of securities. Knowledge of the risk associated with modeling error can be utilized when estimating risk or active risk using factor risk models or when constructing optimal portfolios by mean-variance optimization or other portfolio construction strategies using factor risk models.

9. 发明申请

US20160098796A1 Performance Attribution for Portfolios with Composite Investments 审中-公开
标题翻译：综合投资组合的绩效归因
公开(公告)号：US20160098796A1
公开(公告)日：2016-04-07
申请号：US14505258
申请日：2014-10-02
申请人： Vishv Jeet , Vishal Shekhar
发明人： Vishv Jeet , Vishal Shekhar
IPC分类号： G06Q40/06
CPC分类号： G06Q40/06
摘要： In existing performance attribution, composite investments are resolved into simple assets, and the performance attribution provides results only for the resolved, net investment in the simple assets. As a result, the individual investment in the composite investment in isolation is lost, and it is impossible to determine if the investment in the composite investment in isolation helped or hurt performance. Approaches are described to determine attribution in a manner in which the attribution hierarchy is altered so that, after reporting on the performance of the full portfolio, a further level of attribution reports on a set of sub-portfolios. The first sub-portfolio represents the original investments in simple assets only while the other sub-portfolios represent investments in each composite investment. This composite-first performance attribution determines the individual contribution to performance of each composite investment, resulting in more detailed, practical, and intuitive results.
摘要翻译：在现有绩效归因中，复合投资被解决为简单资产，绩效归因只为解决的简单资产的净投资提供了结果。因此，孤立投资复合投资的个人投资损失，无法确定孤立投资对投资的影响是否有助于或损害业绩。描述方法来确定属性层次结构被改变的方式，以便在报告完整投资组合的表现后，对一组子投资组合进行进一步的归因报告。第一个子投资组合仅代表简单资产的原始投资，而其他子投资组合则代表每个复合投资的投资。这种综合优先性能归因决定了每个复合投资对绩效的个人贡献，从而导致更详细，实用和直观的结果。

10. 发明申请

US20130304671A1 Factor Risk Models with Multiple Specific Risk Estimates 审中-公开
标题翻译：具有多重特定风险估算的因子风险模型
公开(公告)号：US20130304671A1
公开(公告)日：2013-11-14
申请号：US13892644
申请日：2013-05-13
申请人： Anthony A. Renshaw
发明人： Anthony A. Renshaw
IPC分类号： G06Q40/06
CPC分类号： G06Q40/06
摘要： Construction of factor risk models that more advantageously predict the future volatility of returns of a portfolio of securities such as stocks, bonds, or the like is addressed. More specifically, factor risk models with more than one estimate of specific risk or, alternatively an original specific risk estimate together with a set of specific risk differences derived from more than one estimate of specific risk.
摘要翻译：构建更有利地预测股票，债券等证券组合的未来波动性的因素风险模型被解决。更具体地说，具有多于一个具体风险估计值的因子风险模型，或替代原始特定风险估计值以及来自多于一个特定风险估计值的一组特定风险差异。

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