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Economic Rockstar

Connecting Brilliant Minds in Economics and Finance

125: Eugene Fama on the Efficient Market Hypothesis, the Feds Fund Rate, Bitcoin and Daily Routines

January 25, 2018 by Frank

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125: Eugene Fama on the Efficient Market Hypothesis, the Feds Fund Rate, Bitcoin and Daily Routines

Eugene Fama Economic Rockstar

“I went into academics because I didn’t want to go into anything that would affect my sports life.” – Professor Eugene Fama

Eugene F. Fama is Professor of Finance at the University of Chicago Booth School of Business. Professor Fama was awarded the 2013 Nobel laureate in economic sciences and is widely recognized as the “father of modern finance.”

Professor Fama’s research is well known in both the academic and investment communities. He is strongly identified with research on markets, particularly the efficient markets hypothesis. He focuses much of his research on the relation between risk and expected return and its implications for portfolio management. His work has transformed the way finance is viewed and conducted.

Eugene is a prolific author, having written two books and published more than 100 articles in academic journals. He is among the most cited researchers in economics.

In addition to the Nobel Prize in Economic Sciences, Professor Fama was the first elected fellow of the American Finance Association in 2001. He is also a fellow of the Econometric Society and the American Academy of Arts and Sciences. He was the first recipient of three major prizes in finance: the Deutsche Bank Prize in Financial Economics (2005), the Morgan Stanley American Finance Association Award for Excellence in Finance (2007), and the Onassis Prize in finance (2009).

Professor Fama was awarded doctor of law degrees by the University of Rochester and DePaul University, a doctor honoris causa by the Catholic University of Leuven, Belgium, and a doctor of science honoris causa by Tufts University.

Eugene is chairman of the Center for Research in Security Prices at Chicago Booth, which was founded 40 years ago to create the finest tools for tracking, measuring, and analyzing securities data. He is also an advisory editor of the Journal of Financial Economics.

Professor Fama earned a bachelor’s degree from Tufts University in 1960, followed by an MBA and PhD from the University of Chicago Graduate School of Business (now the Booth School) in 1964. He joined the GSB faculty in 1963.

Economists:

In this episode, Professor Fama mentions: Gary Becker, Vernon Smith, John Cochrane,Robert Shiller, Campbell Harvey,  John Campbell, Narasimhan Jagadeesh, Sheridan Titman, Cliff Asness, Louis Bachlier, Paul Samuelson, Benoit Mandlebrot, Robert C. Merton, Fischer Black, Myron Scholes, Merton Miller, Harry Roberts and Kenneth French.

Economics:

In this episode, Professor Fama mentions: EMH, anomalies, Momentum Effect, January Effect, Options Pricing Model, Price Earnings Ratio, Federal Reserve, Fed Funds Rate, reserves, reserve requirements, lending mechanism, quantitative easing, economic activity, bitcoin, speculation, medium of exchange, Ripple and blockchain.

Professor Fama’s Mentors:

  • Merton Miller, Harry Roberts and Benoit Mandelbrot.

Individuals were very important to me especially Merton Miller and Harry Roberts. And Benoit too. – Professor Fama

Find out:

  • How studying economics in the 1960s differs to present day.
  • What is EMH and how it relates to the random walk and the submartingale process.
  • The beginning of mathematics in economics in the 1960s.
  • Independent, identically distributed  – a more restrictive view of EMH.
  • How prices and returns are so noisy that it is difficult to identify stock-picking skills.
  • About stock market anomalies.
  • What is the problem in academics?
  • About the Federal Funds Rate.
  • Does the Federal Reserve Bank or the market control the Fed Funds Rate?
  • If there is a lending channel.
  • Do we need a Federal Reserve bank?
  • About Professor Fama’s views on Quantitative Easing (QE).
  • About Professor Fama’s hobbies and how he uses them to regain balance in his life as an economist.
  • Why Eugene Fama went into academics.
  • Find out about Eugene’s daily routines.
  • About Bitcoin.

On the Problem in Academics:

“There is a problem in academics. Everybody wants to publish papers. That’s the way they advance and get tenure and get higher salaries. They also get noticed on Wall Street for doing it. So there’s an incentive to dredge the data and come with things that will be attention-grabbing but won’t necessarily be there in new data and aren’t the basis for new strategies.” – Professor Fama

Eugene Fama Economic Rockstar

On Theoretical Models

“Robustness is the name of the game. All scientific theories have anomalies otherwise they’re not theories, they’re reality.”

“All science is you propose models, you test them and you come up with some stuff that says that says this works pretty well and then you come with other stuff that says well it doesn’t work very well on this particular so called anomaly. And so you either tweak the model to incorporate that or you just accept it as one of the shortcomings of the model. That’s why you called them models.”

“You have to be careful. It has to be systematic empirical work. You can’t just go work with anecdotes. Anecdotes are not empirical work.”

On the Fed:

“What goes on when you go to work for the Fed or you get onto the Board or whatever, it’s the invasion of the Body Snatchers. Whatever you thought before becomes irrelevant and you buy the party line or you buy the line that says they have a lot of power.”

“I don’t there ever was a lending channel but there certainly isn’t one now.”

“The main job of the Fed is to control inflation. Unfortunately, in the current regime they can’t do that.”

On Bitcoin:

  • I’m suspicious about it as a unit of account because it has such an uncertain value. Monetary theory basically says that you want a unit of account that has a certain value.
  • It’s just like paper currency. If no body is willing to use it, it becomes valueless.

Thanks to Conor Murray for the question on Bitcoin!

On Writing:

  1. There’s no easy way to do it. I do a lot of writing with Kenneth French. We always re-write these papers that we put out at least twenty or thirty times front to back. And you struggle over every word and you try to say stuff as simply as possible because by saying it simply you reach more people than saying it in a more complex way.
  2. Work on it. Really read it. Get other people to read it and get their reactions.
  3. Organize how you present stuff. You want a brief introduction. Most papers tend to have long introductions. Get right into the guts and keep it as simple as possible for as long as possible so that you lose the fewest number of people.

Movies:

  • Invasion of the Body Snatchers
  • Equilibrium

 

Patreon

If you’re a fan of the podcast and would like to show your support in anyway, please check out my Patreon page at patreon.com/economicrockstar where you can sign up for any of the awards for as little as $1 a month or you can simply follow me on the Economic Rockstar Facebook page or on Twitter or simply recommend the show to a friend, especially if they have never had the opportunity to study economics.

 

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056: Campbell Harvey on Improving Significance Tests, the Importance of Positive Skew and the Future of Blockchain

October 28, 2015 by Frank

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056: Campbell Harvey on Improving Significance Tests, the Importance of Positive Skew and the Future of Blockchain

Campbell R. Harvey is Professor of Finance at the Fuqua School of Business at Duke University and a Research Campbell HarveyAssociate of the National Bureau of Economic Research in Cambridge, Massachusetts. He served as Editor of The Journal of Finance from 2006-2012 and is President-elect of the American Finance Association.

Professor Harvey obtained his doctorate at the University of Chicago in business finance. He has served on the faculties of the Stockholm School of Economics, the Helsinki School of Economics, and the Booth School of Business at the University of Chicago. He has also been a visiting scholar at the Board of Governors of the Federal Reserve System.

Campbell received the 2014 Reader’s Choice Award for the best paper published in the Financial Analysts Journal and the 2015 prize for the best paper published in the Journal of Portfolio Management. His recent work on evaluating trading strategies has won best paper awards.

Campbell’s research interests include statistical methods, risk management, asset allocation, real assets and cryptocurrencies. He is the Investment Strategy Advisor to the Man Group plc, the world’s largest, publicly listed, global hedge fund.

Economics:

In this interview, Campbell mentions: t-statistics, significance tests, trading strategies, investment premium, beta, correlation, standard deviation, confidence interval, P-value, Bonferroni multiple testing method, Type I error, Type II error, probability, normal distribution, optimal portfolio, volatility, expected returns, portfolio, pay-off, skew, over-fitting, regularisation, Efficient Market Hypothesis, Fractal Markets, stock market anomalies, Straw Man Model, momentum effect, mis-pricing and outliers.

Economists:

In this interview, Campbell mentions: Nassim Taleb, Benoit Mandlebrot, Peter Edgar, Yan Liu and Eugene Fama.

In this episode you will learn:

  • why it’s important to use t-statistics and significance tests and how it can be improved.
  • about the very simple idea Professor Campbell Harvey applies to his statistical modelling to improve the robustness of his tests.
  • why it’s wrong to use 2 standard deviations to have 95% confidence when running many tests.
  • about ‘Significant’, the XKCD cartoon that illustrates the vulnerability of statistical significance testing.
  • do green jelly beans really cause acne? How significance tests can mislead with a fluke.
  • how a trading strategy based upon picking a portfolio of shares based upon the first letter of a ticker symbol showed that those tickers that began with the letter A outperformed other stocks.
  • how testing multiple times is effectively data mining and what should be done about it.
  • about the meaning of 95% confidence and 5% level of significance.
  • what a p-value is and why we ant it to be as small as possible.
  • if it’s important for the finance and economics profession to look at how other sciences are applying testing methods?
  • whether we need a tougher standard to lower the possibility of false discoveries?
  • if there is a chance of a fluke finding and why we should apply the Bonferroni multiple testing method solve this?
  • about the decay signature of the Higgs Boson and whether it is just background noise.
  • whether the findings of many published academic peer-reviewed papers are wrong.
  • about Type I and Type II errors and their trade-off.
  • about All Trials’ mission to make all randomised control trials made public.
  • the problems when measuring and using volatility in asset returns.
  • why the level of skew in a distribution must play more of an important role in risk management and portfolio selection.
  • why Taleb’s Black Swan only looks at one side of the distribution – the negative side, and why we must also look at the positive side.
  • how applying ‘regularization’ to portfolio selection avoids ‘over-fitting’ the data so that unexpected future outcomes can be considered.
  • about the efficient market hypothesis and the 316 anomalies that have been published to refute this hypothesis.
  • why the best traders are in Asia and how insider activity makes them so.
  • about the rise of crypto currencies and Bitcoin and why schools across US universities are introducing modules on it.
  • what is blockchain and why its is safe.
  • about the bank’s idea of creating a permission blockchain.

The Problem with Significance Testing and How to Solve It

If you’re trying to see if a variable Y is associated with a variable in a significant way, we usually think of looking at that correlation and determining whether you’re 95% confident that you’ve got it right. Usually what that means is that you’re 2 standard deviations away from zero. So, zero would be there’s no relation.

It turns out that that is perfectly acceptable if we’re looking at one correlation between Y and X. However, if it’s not X, it’s X1 you try. You try X2. You try X3, you try … X100. You try 100 different things. Then the criteria of using 2 standard deviations to have 95% confidence is just plain wrong.

The reason why this is wrong, is that when you’re running 100 tests, there is going to be a high probability that something will turn up that’s 2 standard deviations from zero just by chance.

The ‘Jelly Bean’ cartoon by XKCD called ‘Significant’ illustrates how testing a hypothesis can become misleading when conducting a significance test. The hypothesis being tested here is whether jelly beans causes acne.

A randomised control trial is ‘conducted’ by scientists. This is done where, say we have 50 people with jelly beans and 50 people with no jelly beans and we count the acne. And what basically happens is that there is no significance. So the scientists don’t achieve the 95% and conclude that there is no relation between jelly beans and acne.

However, the cartoon further illustrates what happens when the color of each jelly bean is tested to see if a particular color causes acne. 20 additional randomised control trials are conducted. The cartoon shows that the link between the Red Jelly Bean and acne is insignificant. Blue Jelly Bean – insignificant. Until you get to the last jelly bean, the 20th, which is the Green Jelly Bean. They find that there is a significant relation between Green Jelly Beans and acne. The final frame in the cartoon is a headline saying ‘Green Jelly Beans Linked to Acne’.

So, if you do 20 trials, one of those is likely to show up as significant using the standard criteria and it’s a fluke.

“The idea of my research is that we need to raise the bar that 2 standard deviations is no longer – that 2 sigma is no longer – something that should be considered. We need to go much higher.” – Professor Campbell Harvey

http://imgs.xkcd.com/comics/significant.png

The Bonferroni Multiple Testing Method

When we say that there is 95% confidence, we are saying that there is a 5% chance that the finding is a fluke. The 5% is called the p-value. What you would like is for that p-value to be as small as possible. You want as small as possible probability that the finding is a fluke. So the usual p-value for a single test with just X and Y for 5%, would imply 2 standard deviations. When you do multiple tests, you need more than 2 standard deviations from zero. If there is a chance of a fluke finding, then we should apply the Bonferroni multiple testing method solve this.

What the Bonferroni does is a simple correction. What it says is ‘you discover a p-value which is, say, 0.004 and you multiply by the number of things or X’s you’ve tried, which is, say, X1 to X100. All of a sudden, your p-value transforms to 0.4 or 40%. That means there is a 40% chance that in repeated trials that this thing you’ve identified, say X57, is a fluke. So when you use this adjustment, you discard that variable.

Quotes by Professor Campbell Harvey in Episode 56 of the Economic Rockstar Podcast:

In the practice of finance, some investment manager goes to a client and shows a great strategy and looks amazing. But they don’t tell the client or potential client that they tried 499 other possibilities and this is the only one out of 500 that worked – Professor Campbell Harvey. 

“Over half of what’s published in empirical asset pricing is probably incorrect” – Professor Campbell Harvey

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“The problem with volatility is that it is a symmetric measure, that if you’re way above the average that contributes to the same volatility as if you’re way below the average” – Professor Campbell Harvey

“I’ve being pushing for the last 15 years to reform the way that we do our portfolio analysis, our standard models, to have the skew play a role.” – Professor Campbell Harvey

“It’s also a fact that it’s really hard to find any asset return that adheres to a normal distribution. If it does, it is very unusual.” – Professor Campbell Harvey

“What we want in economics and finance is repeatability.” – Professor Campbell Harvey

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“I believe, just as Gene Fama believes, that markets are inefficient.” – Professor Campbell Harvey

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“Blockchain provides a way to give unprecedented security. You’re immune effectively from this hacking.”

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Books:

  • The New York Times Dictionary of Money and Investing: The Essential A-to-Z Guide to the Language of the New Market by Campbell Harvey and Gretchen Morgenson
  • The Black Swan by Nassim Taleb
  • The Ascent of Money by Neil Ferguson

Papers:

  • Evaluating Trading Strategies. by Campbell Harvey and Yan Lui
  • Where are the World’s Best Analysts? Campbell Harvey, Sam Radnor, Khalil Mohammed and William Ferreira
  • Conditional Skewness in Asset Pricing Tests. Campbell Harvey and Akhtar Siddique, Journal of Finance 55, (2000): 1263-1295. (P56)

Other Resources:

  • Garden of Econ podcast
  • Hypertextual Finance Glossary – Over 8,000 Entries and 18,000 Hyperlinks: The largest financial glossary on the Internet
  • The New York Times Dictionary of Money and Investing: The Essential A-to-Z Guide to the Language of the New Market by Campbell Harvey and Gretchen Morgenson

Websites:

  • www.alltrials.net

Where to Find Campbell: 

Website: Duke University

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Frank Conway

Frank Conway is founder of Economic Rockstar and lecturer of economics, finance and statistics. Read More…

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