英文标题：
《Learning zero-cost portfolio selection with pattern matching》
---
作者：
Tim Gebbie and Fayyaaz Loonat
---
最新提交年份：
2016
---
英文摘要：
  We consider and extend the adversarial agent-based learning approach of Gy{\\\"o}rfi {\\it et al} to the situation of zero-cost portfolio selection implemented with a quadratic approximation derived from the mutual fund separation theorems. The algorithm is applied to daily sampled sequential Open-High-Low-Close data and sequential intraday 5-minute bar-data from the Johannesburg Stock Exchange (JSE). Statistical tests of the algorithms are considered. The algorithms are directly compared to standard NYSE test cases from prior literature. The learning algorithm is used to select parameters for agents (or experts) generated by pattern matching past dynamics using a simple nearest-neighbour search algorithm. It is shown that there is a speed advantage associated with using an analytic solution of the mutual fund separation theorems. It is argued that the expected loss in performance does not undermine the potential application to intraday quantitative trading and that when transactions costs and slippage are considered the strategies can still remain profitable when unleveraged. The paper demonstrates that patterns in financial time-series on the JSE can be systematically exploited in collective but that this does not imply predictability of the individual asset time-series themselves.
---
中文摘要：
我们考虑并扩展了Gy{“o}rfi{\\it et al}的基于代理的对抗式学习方法，以零成本投资组合选择的情况下实现，该方法是基于共同基金分离定理的二次近似。该算法应用于约翰内斯堡证券交易所（JSE）每日抽样的顺序开盘-高低收盘数据和每日5分钟的顺序盘中条形数据.对算法进行统计测试。这些算法直接与先前文献中的标准纽约证券交易所测试用例进行比较。该学习算法用于使用简单的最近邻搜索算法，通过模式匹配过去的动态来为代理（或专家）选择参数。结果表明，使用共同基金分离定理的解析解具有速度优势。有人认为，预期的业绩损失不会削弱日内定量交易的潜在应用，如果考虑交易成本和下滑，在无杠杆情况下，策略仍然可以盈利。本文证明，JSE上的金融时间序列模式可以集体系统地利用，但这并不意味着单个资产时间序列本身的可预测性。
---
分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
--
一级分类：Quantitative Finance        数量金融学
二级分类：Portfolio Management        项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
--
一级分类：Quantitative Finance        数量金融学
二级分类：Trading and Market Microstructure        交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
--

---
PDF下载：
-->