Do Expected Stock Returns Wear a CAPE? (EP.146)

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Manage episode 290565042 series 2428102
By Benjamin Felix & Cameron Passmore, Benjamin Felix, and Cameron Passmore. Discovered by Player FM and our community — copyright is owned by the publisher, not Player FM, and audio is streamed directly from their servers. Hit the Subscribe button to track updates in Player FM, or paste the feed URL into other podcast apps.

As many of you already know, we have been working hard to figure out the best way to model expected stock returns for financial planning and asset allocation. It has a lot of history in financial literature, which is to be expected, given the importance of the figure. In today’s episode, we’re looking all the way back to 1985, when Rajnish Mehra and Edward C.Prescott called the equity premium a puzzle, through to the present day, when the equity risk premium has only gotten larger. We dive into some of the theories for resolving the equity premium puzzle, explain why US stock market data isn’t the best way to estimate future premiums, thanks to its survivorship bias, and some of the general issues with interpreting past returns. Benjamin also gets into predictability, which is not as obvious as it seems, and highlights some of the information from the simulation he performed, and the big breakthroughs from running the numbers. All this and more in today’s episode on expected stock returns, so make sure to tune in today!

Key Points From This Episode:

  • Kicking off with the fallout from the collapse of Archegos Capital, the death of Bernie Madoff, and the story of the $100 million New Jersey deli. [0:06:35]
  • Reflecting on the recent article, ‘Could Index Funds be ‘Worse Than Marxism’?’. [0:11:05]
  • On to today’s topic: do expected stock returns wear a cape? [0:13:05]
  • Theories for resolving the equity premium puzzle; either the model is wrong or the historical premium was higher than it will be in the future. [0:14:14]
  • Hear John H. Cochrane’s theory from his 1997 paper, ‘Where is the Market Going?’ [0:14:42]
  • Why we can’t use historic US stock market data to approximate future premiums. [0:14:57]
  • Other issues with looking to past returns, like no proof that the equity premium was stationary. [0:15:23]
  • Why time periods characterized by decreasing risk should effectively see decreased discount rates too. [0:16:04]
  • Dimson, Marsh, and Staunton (DMS) on expected stock returns using out of sample data. [0:16:40]
  • Hear some of the equity risk premium stats from their world index versus the US. [0:19:38]
  • How annual returns have been relatively unaffected by global financial crises. [0:21:15]
  • From looking back, to what to expect going forward: the issues with interpreting past returns. [0:22:10]
  • Why, according to DMS, expected returns equal the growth rate in dividends plus the dividend yield. [0:25:26]
  • Hear the actual figures, which reflect the minor contribution of multiple expansion. [0:26:49]
  • What a company is worth if it doesn’t distribute capital to shareholders. [0:29:03]
  • Find out why the expected geometric equity risk premium works out to 3.5 percent. [0:30:13]
  • While the DMS approach is reasonable, it still doesn’t account for whether expected returns are constant through time or if they vary. [0:32:21]
  • Predictable stock returns dictate that changing risk aversion over time measurably affects risk premiums after good and bad events. [0:34:45]
  • Diving into the vast literature on return predictability, including a paper by Goyal and Welch. [0:35:12]
  • Why predictability is not as obvious as it seems, thanks to our sample data. [0:36:15]
  • What we can learn from ‘Long Horizon Predictability’ by Boudoukh, Israel, and Richardson. [0:39:30]
  • R-squared and market timing decisions; why it would need to be higher than it was historically. [0:40:32]
  • Hear about the world index analysis Benjamin performed and what it proves about risk premiums over 30 and 60 year periods. [0:42:31]
  • Bootstrap simulations and why they are criticized; because they ignore mean relationship, you get a much wider distribution of outcomes. [0:44:50]
  • Big breakthroughs from running through these numbers, like noting the upward bias and tighter distribution in long-run historical data. [0:50:34]
  • How to apply this on your own, using the 3.5 percent risk premium in the long run. [0:52:23]
  • Some of the other interesting things we noted during these simulations. [0:53:10]
  • We pull two cards: choosing between a holiday and a pet, and borrowing money with interest. [0:53:56]
  • Bad advice of the week: a free lunch-esque article on investing in private credit. [0:55:53]

180 episodes