Machine learning for stock selection
Machine learning algorithms are increasingly used to solve problems in the financial markets. However, applications of the models for investment management and stock selection are still subject to debate with many challenges to overcome, such as model overfitting.
The Financial Analysts Journal explores this controversial topic further. Using the IHS Markit Research Signals factor library as a clean and robust feature set, the piece investigates the use of various machine learning models to forecast stock returns and build systematic portfolios. Options to leverage these tools are considered, while methods to overcome challenges such as overfitting, are also suggested. Will the results show that using a library of proven factors based on economic rationale as inputs to machine learning models produces strategies that generate statistically significant alpha and enhance stock selection?
To find out more read full article here
IHS Markit provides industry-leading data, software and technology platforms and managed services to tackle some of the most difficult challenges in financial markets. We help our customers better understand complicated markets, reduce risk, operate more efficiently and comply with financial regulation.
Follow Financial Services
- Section 871(m) Counterparty Reporting: IRS and Financial Institutions May be Underestimating Work Involved
- High Yield Energy Debt Running out of Gas
- Corporate Bond Pricing Recap for July 2019
- July 2019 Model Performance Report
- iBoxx TRS Comes of Age
- Mid-cycle adjustment to factor returns
- Forecast dividend yield basket outperforms
- How to maximize value from your valuation process
RT @IHSMarkitPMI: U.S business activity growth eased in August, amid a slower rise in service sector output. The Flash U.S PMI slipped to 5…
Anticipation of an economic slowdown and more market volatility has triggered an uptick in US HY energy issues’ ris… https://t.co/YFZFqJclAt