Comparing current Highstreet's assets model with advanced machine learning models

High Street Asset Management Inc.是一家总部位于安大略省伦敦市的投资管理公司。它提供了 为个人投资者及其家人、基金会、养老金提供的可自由支配的资金管理服务 计划和机构投资者。 随着数据变得更容易访问并呈指数级增长，HighStreet的量化分析师正在寻找 分析股票市场数据的有效方法。目前，HighStreet分析师手动测试和选择因素以 构建多因素线性模型。但是，构建模型的过程可能相当耗时，而且 最终的模型不可能完全没有个人偏见，这在一个快速变化的 市场。High Street花了6个月的时间完成了一个加拿大模型的更新，该模型基于280只股票。 2015年，HighStreet将需要完成基于800只股票的美国模型评估。它涉及到 测试数百个因素，选择合适的因素，构建适合不同市场的模型 动力学。因此，迫切需要一个更高效、更系统的模式构建和测试过程。 我以前的博士生Robert Yan(现在在HighStreet工作)和我提出了几个算法来 分析股票市场数据。为了解决HighStreet的现实投资问题，我们计划应用 260名加拿大人数据的特征选择/特征提取和预测建模技术 在HighStreet的股票和800只美国股票。这些机器学习技术可能会帮助HighStreet 加快模型审查过程，以及改善股市预测。 我们与HighStreet合作，旨在为加拿大公司和 对于个人来说，我们将间接地为加拿大人带来经济和社会效益。

Highstreet Asset Management Inc. is an investment management firm based in London, Ontario. It provides discretionary money management services to individual investors and their families, foundations, pension plans, and institutional investors. As data becomes more readily accessible and grows exponentially, quant analysts at Highstreet are looking for efficient ways to analyze stock market data. Currently Highstreet analysts manually test and select factors to build multi-factor linear models. However, the model building process can be quite time-consuming and the final model cannot be completely free from personal bias, which is extremely dangerous in a fast-changing market. Highstreet spent 6 months to complete a Canadian model updating based on a universe of 280 stocks. In 2015, Highstreet will need to finish the US model review based on a universe of 800 stocks. It involves testing on hundreds of factors and selecting appropriate ones to build a model that fits different market dynamics. Thus, a more efficient and systematic process of mode building and testing is urgently needed. My former PhD student Robert Yan (who works in Highstreet now) and I have proposed several algorithms to analyze stock market data. To address the real-world investment problems at Highstreet, we plan to apply feature selection / feature extraction and predictive modelling techniques to the data from the 260 Canadian stocks and 800 US stocks at Highstreet. These machine learning techniques could potentially help Highstreet speed up model review process, as well as improve stock market forecasting. As we work with Highstreet and aim to provide better investing strategies for Canadian companies and individuals, we will generate indirectly economic and social benefits for Canadians.