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11. 发明申请

US20130080310A1 Returns-Timing for Multiple Market Factor Risk Models 有权
标题翻译：多个市场因素风险模型的回报 - 时间
公开(公告)号：US20130080310A1
公开(公告)日：2013-03-28
申请号：US13503696
申请日：2011-05-19
申请人： Simon Wannasin Bell , Stefan Hans Schmieta , Frank Pak-Ho Siu
发明人： Simon Wannasin Bell , Stefan Hans Schmieta , Frank Pak-Ho Siu
IPC分类号： G06Q40/04
CPC分类号： G06Q40/06 , G06Q40/08
摘要： Until recently, risk models have been built using low frequency data, such as weekly or monthly data. This approach has resulted in a necessary compromise between model stability for which one needs a long history of data, and model responsiveness, for which, the shorter the history, the better. Stability plus responsiveness can be achieved if one uses daily data, which allows for a large number of observations to be used in model estimation without using long out-of-date data. Daily data have other problems, however, as the differing closing times of markets worldwide may induce spurious relationships across model factors. In particular, correlations between markets may appear lower than they truly are due to a market lag To address such issues, a stable, daily data-based factor risk model is described which takes account of the differing market closing times and corrects the model factor correlations and specific returns accordingly.
摘要翻译：直到最近，已经使用低频数据（例如每周或每月数据）构建风险模型。这种方法导致了模型稳定性之间的必要妥协，人们需要长期的数据历史和模型响应能力，历史越短越好。如果使用日常数据，可以实现稳定性和响应能力，这样可以在不使用长期过期数据的情况下，将大量观测数据用于模型估计。然而，每日数据还有其他问题，因为全球市场的不同关闭时间可能会导致模型因素之间的虚假关系。特别地，市场之间的相关性可能比真正由于市场滞后而显得低。为了解决这些问题，描述了稳定的基于数据的每日因素风险模型，其考虑到不同的市场收盘时间并且校正模型因素相关性并据此具体回报。

12. 发明授权

US07698202B2 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：US07698202B2
公开(公告)日：2010-04-13
申请号：US11668294
申请日：2007-01-29
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。

13. 发明申请

US20210110479A1 METHODS AND APPARATUS EMPLOYING HIERARCHICAL CONDITIONAL VARIANCE TO MINIMIZE DOWNSIDE RISK OF A MULTI-ASSET CLASS PORTFOLIO AND IMPROVED GRAPHICAL USER INTERFACE 有权
公开(公告)号：US20210110479A1
公开(公告)日：2021-04-15
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.

14. 发明申请

US20160086278A1 Risk Factor Splitting 审中-公开
标题翻译：风险因素分解
公开(公告)号：US20160086278A1
公开(公告)日：2016-03-24
申请号：US14495470
申请日：2014-09-24
申请人： Anthony A. Renshaw
发明人： Anthony A. Renshaw
IPC分类号： G06Q40/06 , G06Q40/04
CPC分类号： G06Q40/06 , G06Q40/04
摘要： Factor-based performance attribution results are often used to identify portfolio exposures or bets that either perform well or underperform. By identifying particular exposures or bets that appear to be opportune to be increased or reduced, the overall performance of the portfolio can potentially be improved. However, the factors present in standard factor risk models are often too broad to identify exposures or bets which can be easily altered. Changing exposures based on the original risk model factors can involve trading too many stocks, or can involve trading stocks that a portfolio manager may not want to trade. The present invention allows portfolio managers to split the original risk model factors into more granular factors that cover smaller sub-sets of the assets in the portfolio. The over- and under-performing exposures of split factors are often easier to alter in practice and can be used to improve the performance of the portfolio.
摘要翻译：基于因素的绩效归因结果通常用于识别投资组合风险敞口或表现良好或表现不佳的投注。通过确定看起来有助于增加或减少的特定风险敞口或投注，投资组合的整体绩效可能会有所改善。然而，标准因素风险模型中存在的因素通常太广泛，无法识别易于改变的风险敞口或投注。基于原始风险模型因素改变风险可能涉及交易太多的股票，或者可能涉及投资组合经理不想交易的交易股票。本发明允许投资组合经理将原始风险模型因素分解成更细粒度的因素，以涵盖投资组合中较小的资产子集。分拆因素的过度和不足的风险在实践中往往更容易改变，可用于提高投资组合的表现。

15. 发明申请

US20140201107A1 Methods and Apparatus for Improving Factor Risk Model Responsiveness 审中-公开
标题翻译：改进因子风险模型响应的方法与装置
公开(公告)号：US20140201107A1
公开(公告)日：2014-07-17
申请号：US14203807
申请日：2014-03-11
申请人： Simon Wannasin Bell , Frank Pak-Ho Siu
发明人： Simon Wannasin Bell , Frank Pak-Ho Siu
IPC分类号： G06Q40/06
CPC分类号： G06Q40/06
摘要： Construction of factor risk models that better predict the future volatility of returns of a portfolio of securities such as stocks, bonds, or the like is addressed. More specifically, improved factor-factor covariance estimation is made even when the covariances change rapidly over time. Methods and techniques for achieving better accuracy, responsiveness, and stability of factor risk models are addressed.
摘要翻译：构建更好地预测股票，债券等证券组合的未来波动性的因素风险模型得到解决。更具体地，即使当协方差随时间快速变化时，也进行改进的因子因子协方差估计。解决因素风险模型的更准确度，响应性和稳定性的方法和技术。

16. 发明申请

US20140081889A1 Purifying Portfolios Using Orthogonal Non-Target Factor Constraints 审中-公开
标题翻译：使用正交非目标因子约束来净化投资组合
公开(公告)号：US20140081889A1
公开(公告)日：2014-03-20
申请号：US14025127
申请日：2013-09-12
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： The quantitative construction of investment portfolios of securities such as stocks, bonds, or the like using optimization is addressed. More specifically, during optimization constraints on non-target factor exposures are automatically converted to constraints on the exposure of the projections of the non-target factors that are orthogonal to a specified target factor. Such constraints may be utilized to produce portfolios with superior performance to those produced with traditional factor exposure constraints.
摘要翻译：解决使用优化的股票，债券等证券投资组合的定量构建。更具体地，在优化期间，对非目标因素曝光的约束被自动转换为与指定目标因子正交的非目标因子的投影的曝光的约束。可以利用这些约束来产生具有优于传统因子暴露约束产生的组合的投资组合。

17. 发明授权

US08533089B1 Methodology and process for constructing factor indexes 有权
标题翻译：构建因子指标的方法与过程
公开(公告)号：US08533089B1
公开(公告)日：2013-09-10
申请号：US12958778
申请日：2010-12-02
申请人： Anthony Renshaw
发明人： Anthony Renshaw
IPC分类号： G06Q40/00
CPC分类号： G06Q40/06
摘要： Construction of indexes are addressed wherein a portfolio of securities and their associated investment weights or shares is generated. Indexes comprising a plurality of securities can often be bought and sold more cheaply than buying and selling the individual constituents of the index resulting in reduced transaction costs. In passive and enhanced indexing, investments are made with reference to an index. Factor indexes can serve as active manager benchmarks 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 with the best possible replication of the underlying factor returns. 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：解决指数的构建，其中产生证券组合及其相关投资权重或股份。包括多个证券的指数通常可以比购买和销售指数的各个成分更便宜地购买和出售，导致交易成本降低。在被动和增强的索引中，投资是参考一个指数。因素指标可以作为交易所交易基金和共同基金等可投资产品的主动经理基准。针对基于计算机的系统，方法和软件，构建用于复制定量因子（如中等动量或价值）的回报的索引，并以最佳可能的潜在因素回报率进行复制。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。

18. 发明申请

US20110289017A1 Systems and Methods for Asynchronous Risk Model Return Portfolios 审中-公开
标题翻译：异步风险模型返回组合的系统与方法
公开(公告)号：US20110289017A1
公开(公告)日：2011-11-24
申请号：US12827358
申请日：2010-06-30
申请人： Anthony Renshaw
发明人： Anthony Renshaw
IPC分类号： G06Q40/00
CPC分类号： G06Q40/06
摘要： Portfolio optimization typically involves a risk model to control the level of risk in the portfolio constructed. By creating different portfolios using different risk models (fundamental or statistical; long, medium or short horizon) corresponding to different times or dates (a current or an old risk model), one obtains a large number of low risk (volatility) portfolios. A risk model return portfolio is the difference in the any two of these portfolios, and a risk model return is the return associated with a risk model return portfolio. A number of risk model return portfolios exhibit repeatable returns that can be used to an investor's advantage. Furthermore, these returns exhibit very low correlation with the benchmark returns. As such, they are uncorrelated sources of return. Such returns are considered valuable by investors. The present invention uses risk model return portfolios and their returns to create attractive investments for investors. The risk model return portfolios can be used to analyze market trends and create implied alphas for portfolio construction. They can also be used to provide constituent information that can be further used as the basis for an exchange traded fund (ETF), index or other investment vehicle.
摘要翻译：投资组合优化通常涉及风险模型来控制投资组合中的风险水平。通过使用对应于不同时间或日期（当前或旧风险模型）的不同风险模型（基础或统计学，长期，中期或短期水平）创建不同投资组合，可获得大量低风险（波动）投资组合。风险模型回报组合是这两个投资组合中的两个差异，风险模型回报是与风险模型回报组合相关的回报。一些风险模型回报组合表现出可重复的回报，可用于投资者的优势。此外，这些回报与基准回报的相关性很低。因此，它们是不相关的回报来源。这种回报被投资者认为是有价值的。本发明使用风险模型回报投资组合及其回报，为投资者创造有吸引力的投资。风险模型回报投资组合可用于分析市场趋势，并为投资组合建立创造暗示。它们也可用于提供可进一步用作交易所买卖基金（ETF），指数或其他投资工具的基础的构成信息。

19. 发明申请

US20220058742A1 PERFORMANCE ATTRIBUTION FOR PORTFOLIOS WITH COMPOSITE INVESTMENTS 有权
公开(公告)号：US20220058742A1
公开(公告)日：2022-02-24
申请号：US17517265
申请日：2021-11-02
申请人： Axioma, Inc.
发明人： Vishv Jeet , Vishal Shekhar
IPC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.

20. 发明申请

US20190279305A1 Purifying Portfolios Using Orthogonal Non-Target Factor Constraints 审中-公开
公开(公告)号：US20190279305A1
公开(公告)日：2019-09-12
申请号：US16424043
申请日：2019-05-28
申请人： Axioma, Inc.
发明人： Anthony A. Renshaw
IPC分类号： G06Q40/06
摘要： The quantitative construction of investment portfolios of securities such as stocks, bonds, or the like using optimization is addressed. More specifically, during optimization constraints on non-target factor exposures are automatically converted to constraints on the exposure of the projections of the non-target factors that are orthogonal to a specified target factor. Such constraints may be utilized to produce portfolios with superior performance to those produced with traditional factor exposure constraints.

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