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20210708_吕漫妮_论文展示1
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2022-08-03
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20210708_吕漫妮_论文展示1
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Machine Learning versus Economic Restrictions:
Evidence from Stock Return Predictability
Doron Avramov, Si Cheng, and Lior Metzker
Working Paper, 2021.4
Lv Manni
2021.7.8
![](https://csdnimg.cn/release/download_crawler_static/86298232/bg2.jpg)
Contents
• Introduction
• Background & Motivation
• Research Problem
• Contribution
• Model Setting
• Empirical Results
• Conclusion
• Extension
2021/7/6
Lv Manni
2
![](https://csdnimg.cn/release/download_crawler_static/86298232/bg3.jpg)
Backgrounds & Motivation
• Recent work has challenged the credibility of the predictable patterns
of a plethora of anomalies. However, anomalous return patterns
characterize expensive-to-buy and difficult-to-arbitrage stocks.
Notably, it is increasingly difficult to exploit anomalies in recent years
due to increased market liquidity and arbitrage activity.
• Counter to this “anomaly challenging” strand of literature, there has
been an emerging body of work that reports phenomenal investment
profitability based on signals generated by ML methods.
Understanding the relevant economic mechanisms is essential.
➢ Whether ML methods clear sensible economic restrictions in
empirical finance and face the same challenges?
➢ Is there any economic grounds of investment decisions advocated by
the seemingly opaque ML methods?
2021/7/6 3
Lv Manni
![](https://csdnimg.cn/release/download_crawler_static/86298232/bg4.jpg)
Research Problem
• Whether machine learning methods sensible economic restrictions in
empirical finance?
➢ We find that machine learning methods often fail to clear
standard economic restrictions, such as VW returns and
excluding microcaps or distressed firms (expensive-to-buy).
➢ The trading strategy is more profitable during periods of
increasing limits to arbitrage, such as high investor sentiment,
high market volatility, and low market liquidity (hard-to-arbitrage).
➢ Given the high turnover and transaction costs, the strategy is
hard to leave alpha on the table.
• Is there any economic grounds of investment decisions advocated by
the seemingly opaque machine learning methods?
➢ We find DL signals identify stocks in line with most anomaly-
based trading strategies.
2021/7/6 4
Lv Manni
![](https://csdnimg.cn/release/download_crawler_static/86298232/bg5.jpg)
Contribution
• Our analysis initiates a protocol for future work to demonstrate the
feasibility of trading profits, such as excluding difficult-to-arbitrage
stocks and high limits-to-arbitrage market states, considering
portfolio turnover and the corresponding transaction costs.
• Our paper provides evidence for the economic constraints and
economic foundations of ML methods, enriches the academic and
policy discussions surrounding the adoption of ML techniques in
asset management, including the effectiveness and sustainability of
new trading signals, the lack of transparency and economic
interpretability in complex machine learning algorithms.
2021/7/7 5
Lv Manni
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