In August 2017, I was very lucky to have interviewed Professor 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 the 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: [email protected]:EvaluatingTrading Strategies (https://ssrn.com/abstract=2474755)Backtesting (https://ssrn.com/abstract=2345489)TheCross-Section of Expected Returns (https://ssrn.com/abstract=2249314)LuckyFactors (https://ssrn.com/abstract=2528780)Detecting RepeatablePerformance (https://ssrn.com/abstract=2691658)DecreasingReturns to Scale, Fund Flows, andPerformance (https://ssrn.com/abstract=2990737) TheScientific Outlook in Financial Economics (https://ssrn.com/abstract=2893930)

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“What did the council decide on item 42?” — answered with the speaker and the moment.

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