- Arturo Aguilar
- Xiaochen Lin
- Wen Jiang
©Arturo Aguilar. ©Xiaochen Lin. ©Wen Jiang. All Rights Reserved.
Our Implementation (Feb 2020 - Apr 2020): Quantitative Country ETF Trading Strategy
Our Implementation (Dec 2019 - Feb 2020): Integrated Global Asset Management
Our Presentations (Dec 2019 - Apr 2020): Integrated Global Asset Management
This project is an effort to build a country trading strategy to outperform the MSCI All Country World Index (ACWI). The strategy includes technical, fundamental, economic, market sentiment and alternative factors. There seems to be enough room to build a strategy that generates alpha taking into account each countries’ equity and currency performance. A framework of the trading strategy is shown below.
stores time series data for each factor and each ETF, generated using process raw data into factor data and process price data into factor data.
stores daily ohlcv data for each ETF, generated using download price data.
stores forward 21-trading day return for each ETF, generated using process price data into forward return
stores raw factor data (has been removed).
https://github.com/xlinGithub/Quantitative-Country-ETF-Trading-Strategy/blob/master/modeling.ipynb
S&P 500 Index, representing 500 of the largest U.S. companies. Goal is to closely track the index’s return, which is considered a gauge of overall U.S. stock returns.
Source: https://investor.vanguard.com/etf/profile/VOO
The investment seeks to track the investment results of the MSCI ACWI composed of large- and mid-capitalization developed and emerging market equities.
Source: https://screener.fidelity.com/ftgw/etf/goto/snapshot/snapshot.jhtml?symbols=ACWI
The investment seeks to track the investment results of the MSCI ACWI ex USA Index composed of large- and mid-capitalization non-U.S. equities.
Source: https://screener.fidelity.com/ftgw/etf/goto/snapshot/snapshot.jhtml?symbols=ACWX
AQR is a global investment management firm dedicated to delivering results for our clients.
Source: https://www.aqr.com/Insights/Datasets
FF3_factors[['Mkt-RF','SMB','HML']])
FF4_factors[['AQR_MKTRF','AQR_SMB','AQR_BAB','AQR_QMJ']]
FF5_factors[['Mkt-RF','SMB','HML','RMW','CMA']]
Elaborating our model with loss function optimization scheme, we obtain our Five Factors Models with Ridge, a Tikhonov regularization, outperform other Factors Models in ETF return prediction based on the associated market factor exposures.
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