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1. 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。

2. 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。

3. 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。

4. 发明授权

US08700516B2 Methods and apparatus for improving factor risk model responsiveness 有权
标题翻译：改善因子风险模型反应的方法和装置
公开(公告)号：US08700516B2
公开(公告)日：2014-04-15
申请号：US13503698
申请日：2011-05-19
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 , G06Q40/00 , G06Q40/04 , G06Q40/08
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。

5. 发明申请

US20130297530A1 Methods and Apparatus for Improving Factor Risk Model Responsiveness 有权
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：US20130297530A1
公开(公告)日：2013-11-07
申请号：US13503698
申请日：2011-05-19
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。

6. 发明授权

US08533107B2 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：US08533107B2
公开(公告)日：2013-09-10
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： G06Q40/00
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 effect. 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看起来比真正由于市场滞后效应低。为了解决这些问题，描述了一个稳定的基于日常数据的因素风险模型，其中考虑到不同的市场收盘时间，并相应地纠正模型因素相关性和具体回报。

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