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Campbell Harvey (Duke): Fund managers will lose their jobs to robots!

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When? This feed was archived on January 23, 2019 02:36 (5y ago). Last successful fetch was on November 16, 2018 13:46 (5+ y ago)

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Manage episode 190088580 series 1681187
Content provided by Zhijian Wu. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Zhijian Wu or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

In August 2017, I was very lucky to have interviewedProfessor Campbell Harvey of Duke University.

Prof Harvey is one of the most qualified experts in the field of financial econometrics, behavioral finance and computer science. Our conversation covers a wide range of topics.

For the convenience of the listeners, I have split our conversation into 2 parts. The current episode is the second part, in which we mainly talked about the difference between discretionary and systematic strategies, and the adoption of machine learning in financial investment.

We have gone through some very interesting questions, such as:

1) Overall we know that it is very rare to find discretionary managers who can time the market with consistent accuracy. Do you see it different for systematic strategies? Is there any reason to believe that a systematic strategy would be better than a discretionary manager in timing the market?

2) Is it correct to say that in essence, quant strategy investors are in a competitive zero sum game, and the only way for them to make money is to take it from someone who is less good?

3) When the market goes into extended period, or heightened level of “irrationality”, is it rational for us to continue to have faith in a systematic strategy? Would we look foolish to blindly follow some quant strategy when the ship looks to be sinking?

I hope you enjoy the conversation. Our contact detail: info@woodsfordcapital.com

Reference:

Evaluating Trading Strategies (https://ssrn.com/abstract=2474755)

Backtesting (https://ssrn.com/abstract=2345489)

The Cross-Section of Expected Returns (https://ssrn.com/abstract=2249314)

Lucky Factors (https://ssrn.com/abstract=2528780)

Detecting Repeatable Performance (https://ssrn.com/abstract=2691658)

Decreasing Returns to Scale, Fund Flows, and Performance (https://ssrn.com/abstract=2990737)

The Scientific Outlook in Financial Economics (https://ssrn.com/abstract=2893930)

  continue reading

21 episodes

Artwork
iconShare
 

Archived series ("Inactive feed" status)

When? This feed was archived on January 23, 2019 02:36 (5y ago). Last successful fetch was on November 16, 2018 13:46 (5+ y ago)

Why? Inactive feed status. Our servers were unable to retrieve a valid podcast feed for a sustained period.

What now? You might be able to find a more up-to-date version using the search function. This series will no longer be checked for updates. If you believe this to be in error, please check if the publisher's feed link below is valid and contact support to request the feed be restored or if you have any other concerns about this.

Manage episode 190088580 series 1681187
Content provided by Zhijian Wu. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Zhijian Wu or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

In August 2017, I was very lucky to have interviewedProfessor Campbell Harvey of Duke University.

Prof Harvey is one of the most qualified experts in the field of financial econometrics, behavioral finance and computer science. Our conversation covers a wide range of topics.

For the convenience of the listeners, I have split our conversation into 2 parts. The current episode is the second part, in which we mainly talked about the difference between discretionary and systematic strategies, and the adoption of machine learning in financial investment.

We have gone through some very interesting questions, such as:

1) Overall we know that it is very rare to find discretionary managers who can time the market with consistent accuracy. Do you see it different for systematic strategies? Is there any reason to believe that a systematic strategy would be better than a discretionary manager in timing the market?

2) Is it correct to say that in essence, quant strategy investors are in a competitive zero sum game, and the only way for them to make money is to take it from someone who is less good?

3) When the market goes into extended period, or heightened level of “irrationality”, is it rational for us to continue to have faith in a systematic strategy? Would we look foolish to blindly follow some quant strategy when the ship looks to be sinking?

I hope you enjoy the conversation. Our contact detail: info@woodsfordcapital.com

Reference:

Evaluating Trading Strategies (https://ssrn.com/abstract=2474755)

Backtesting (https://ssrn.com/abstract=2345489)

The Cross-Section of Expected Returns (https://ssrn.com/abstract=2249314)

Lucky Factors (https://ssrn.com/abstract=2528780)

Detecting Repeatable Performance (https://ssrn.com/abstract=2691658)

Decreasing Returns to Scale, Fund Flows, and Performance (https://ssrn.com/abstract=2990737)

The Scientific Outlook in Financial Economics (https://ssrn.com/abstract=2893930)

  continue reading

21 episodes

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