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Dr. Andrew W. Lo - Adaptive Markets: Financial Evolution and the Speed of Thought

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Credit: MIT Sloan School of Management

Dr. Andrew W. Lo - Adaptive Markets: Financial Evolution and the Speed of Thought

  1. 1. Adaptive Markets: Financial Evolution at theFinancial Evolution at the Speed of ThoughtSpeed of Thought Andrew W. Lo, MIT Museum of American Finance January 24, 2018January 24, 2018
  2. 2. Markets are People behaveare efficient behave irrationally © 2018 by Andrew W. Lo All Rights Reserved24 Jan 2018 Slide 2
  3. 3. Personal JourneyPersonal Journey Efficient Markets Rational Expectations Efficient Markets Rational Expectations Behavioral Finance Psychology Behavioral Finance Psychology Cognitive NeurosciencesCognitive NeurosciencesArtificial Intelligence Bounded Rationality Artificial Intelligence Bounded RationalityBounded RationalityBounded Rationality Adaptive MarketsAdaptive MarketsEvolutionary BiologyEvolutionary Biology HypothesisHypothesisEcologyEcology Slide 424 Jan 2018
  4. 4. © 2018 by Andrew W. Lo All Rights Reserved24 Jan 2018 Slide 5
  5. 5. SummarySummary  Traditional investment framework is flawed  Not wrong, but incomplete (physics envy)  Stable environment  stable investment policies (EMH)  Dynamic environment  dynamic investment policies (AMH)  The current environment is highly dynamic  We must adapt to changing market conditions i t  “it’s the economy, stupid”  The Adaptive Markets Hypothesis provides a framework for environment © 2018 by Andrew W. Lo All Rights Reserved24 Jan 2018 Slide 6 investing, risk management, financial regulation, and more
  6. 6. The Traditional Investment ParadigmThe Traditional Investment Paradigm In the beginning… Implications:  Correlation matters; diversification  Benchmarks, performance attribution  Passive investing I d ti h d i t bl l h Indexation, hedging, portable alpha  Risk budgeting  Framework for fiduciary duties © 2018 by Andrew W. Lo All Rights Reserved Framework for fiduciary duties Slide 724 Jan 2018
  7. 7. The Traditional Investment ParadigmThe Traditional Investment Paradigm But This Framework Requires Several Key Assumptions:  Relationship is linear  Relationship is static across time and circumstances  Parameters can be accurately estimatedy  Investors behave rationally  Markets are stationary (static probability laws)y ( p y )  Markets are efficient What If Some of These Assumptions Don’t Hold? © 2018 by Andrew W. Lo All Rights Reserved24 Jan 2018 Slide 8 What If Some of These Assumptions Don t Hold?
  8. 8. The Traditional Investment ParadigmThe Traditional Investment Paradigm Cumulative Return of S&P 500 (log scale) 10000 But Do They Still Hold Cumulative Return of S&P 500 (log scale) January 1926 to December 2015 1000 Today?? 10 100 1 © 2018 by Andrew W. Lo All Rights Reserved 0.1 Source: CRSP and author’s calculations. Slide 924 Jan 2018
  9. 9. Nikk i 225 (l l ) The Traditional Investment ParadigmThe Traditional Investment Paradigm Nikkei 225 (log scale) May 16, 1950 to May 13, 2016 50,000 5,000 500 50 © 2018 by Andrew W. Lo All Rights Reserved 50 Slide 1024 Jan 2018
  10. 10. The Adaptive Markets HypothesisThe Adaptive Markets Hypothesis “Nothing makes sense in biology except in the light of evolution,” Dobzhansky (1973) “Nothing makes sense in the hedge fund industry except in the light of the Adaptive Markets Hypothesis,” Lo (2017) 1. Individuals act in their own self-interest 2. Individuals make mistakes (“satisfice”) 3 I di id l l d d t (h i ti )3. Individuals learn and adapt (heuristics) 4. Competition drives adaptation and innovation 5 Evolution determines market dynamics © 2018 by Andrew W. Lo All Rights Reserved 5. Evolution determines market dynamics 24 Jan 2018 Slide 11
  11. 11. What Do Investors Want?What Do Investors Want? $20 $16 $18 $20 U.S. Treasury Bills Stock Market Pfizer F i fi ld S $10 $12 $14 veReturn Fairfield Sentry $6 $8 $10 Cumulativ $2 $4 © 2018 by Andrew W. Lo All Rights Reserved24 Jan 2018 Slide 12 $0 19901130 19941130 19981130 20021129 20061130 20101130
  12. 12. Risk Perception and Adaptive BehaviorRisk Perception and Adaptive Behavior © 2018 by Andrew W. Lo All Rights Reserved24 Jan 2018 Slide 13 Journal of Political Economy 83(1975), 677–726.
  13. 13. Risk Perception and Adaptive BehaviorRisk Perception and Adaptive Behavior Southern Economic Journal 74(2007) 71–84 © 2018 by Andrew W. Lo All Rights Reserved24 Jan 2018 Slide 14 Southern Economic Journal 74(2007), 71–84.
  14. 14. Implications for the Current EcosystemImplications for the Current Ecosystem 22 Jan 2018 © 2018 by Andrew W. Lo All Rights Reserved Slide 1524 Jan 2018
  15. 15. A New Investment Paradigm Is EmergingA New Investment Paradigm Is Emerging Efficient Markets  Long-only constraint  Diversify across stocks and Adaptive Markets  Long/short strategies  Diversify across more asset classes andDiversify across stocks and bonds  Market-cap-weighted indexes  Manage risk via asset Diversify across more asset classes and strategies  Passive transparent indexes  Manage risk via active volatility scaling Manage risk via asset allocation  Alpha vs. market beta M k ffi i  Manage risk via active volatility scaling algorithms  Alphas  multiple betas M k d i Markets are efficient  Equities in the long run  Markets are adaptive  “In the long run we’re all dead,” but make sure the short run doesn’t kill you © 2018 by Andrew W. Lo All Rights Reserved24 Jan 2018 Slide 16 first
  16. 16. A New Investment Paradigm Is EmergingA New Investment Paradigm Is Emerging Efficient Markets  Long-only constraint  Diversify across stocks and Adaptive Markets  Long/short strategies  Diversify across more asset classes andDiversify across stocks and bonds  Market-cap-weighted indexes  Manage risk via asset Diversify across more asset classes and strategies  Passive transparent indexes  Manage risk via active volatility scaling Manage risk via asset allocation  Alpha vs. market beta M k ffi i  Manage risk via active volatility scaling algorithms  Alphas  multiple betas M k d i Markets are efficient  Equities in the long run  Markets are adaptive  “In the long run we’re all dead,” but make sure the short run doesn’t kill you © 2018 by Andrew W. Lo All Rights Reserved24 Jan 2018 Slide 17 first
  17. 17. What Is An Index??What Is An Index??  Market-cap-weighted portfolio? Jack Bogle (1997) on the Origins of the Vanguard Index Trust:Jack Bogle (1997) on the Origins of the Vanguard Index Trust: The basic ideas go back a few years earlier. In 1969–1971, Wells Fargo Bank had worked from academic models to develop the principles and techniques leading to index investing. John A. McQuown and William L. Fouseleading to index investing. John A. McQuown and William L. Fouse pioneered the effort, which led to the construction of a $6 million index account for the pension fund of Samsonite Corporation. With a strategy based on an equal-weighted index of New York Stock Exchange equities, its ti d ib d “ i ht ” Th t t b d d iexecution was described as “a nightmare”. The strategy was abandoned in 1976, replaced with a market-weighted strategy using the Standard & Poor's 500 Composite Stock Price Index. The first such models were accounts run by Wells Fargo for its own pension fund and for Illinois Bell. © 2018 by Andrew W. Lo All Rights Reserved24 Jan 2018 Slide 18 by Wells Fargo for its own pension fund and for Illinois Bell.
  18. 18. What Is An Index??What Is An Index??  Market-cap weighting requires little trading  “Buy-and-hold” portfolio  What if trading were cheaper, faster, and automatable? In·dex ˈin-ˌdeks noun A i d i tf li t t ti f i thAn index is any portfolio strategy satisfying three properties: (1) it is completely transparent; (2) it is i t bl d (3) it i t t ll t ti © 2018 by Andrew W. Lo All Rights Reserved24 Jan 2018 Slide 19 investable; and (3) it is totally systematic.
  19. 19. What Is An Index??What Is An Index?? Value-weighted average? Yes No Maybe  Equal-weighted average? Target-date fund?   FHFA House Price Index? Hedge Fund Index?  g Trend-following futures? Risk-managed large-cap core?   © 2018 by Andrew W. Lo All Rights Reserved24 Jan 2018 Slide 20 g g p
  20. 20. What Is An Index??What Is An Index?? © 2018 by Andrew W. Lo All Rights Reserved24 Jan 2018 Slide 21
  21. 21. What Is An Index??What Is An Index?? Active ActiveHedge Fund Passive PassiveIndex F d © 2018 by Andrew W. Lo All Rights Reserved24 Jan 2018 Slide 22 alpha risk controlFund
  22. 22. Active FullFull--Spectrum InvestingSpectrum Investing Active High High High High High High HighHedge Active Active High High High High High High HighHedge Fund Untapped Investment Opportunities Passive Passive Low Low Low Low Low Low Low alph risk con liqu turn cred curr Sha max skew Index Fund © 2018 by Andrew W. Lo All Rights Reserved24 Jan 2018 Slide 23 ha trol uidity nover dit rency rpe xDD w Fund
  23. 23. The Opportunity: Precision IndexesThe Opportunity: Precision Indexes  Instead of the DowJones30, FTSE100, or S&P500, imagine investing in the: J D 30 Ri h dR 100 <Y N H >500– JaneDoe30, RichardRoe100, or <YourNameHere>500  Imagine if such portfolios took into account income, expenses, age health taxes and behavior  really smart beta!age, health, taxes, and behavior  really smart beta!  Imagine if such portfolios were automated  We have the hardware and software; we need the algorithms We have the hardware and software; we need the algorithms © 2018 by Andrew W. Lo All Rights Reserved24 Jan 2018 Slide 24
  24. 24. This Idea Is Not NewThis Idea Is Not New “Artificial intelligence and active management are not at odds with i d i b i d i lindexation, but instead imply a more sophisticated set of indexes and portfolio management policies for the typicalmanagement policies for the typical investor, something each of us can look forward to, perhaps within the next decade.” – Andrew W. Lo, Journal of Indexes Q2, 2001 © 2018 by Andrew W. Lo All Rights Reserved24 Jan 2018 Slide 25
  25. 25. So What’s Missing?So What’s Missing? …Not Artificial Intelligence Artificial StupidityHumanityp yy  We need an algorithm for investor behavior so we can b l l d i i ( lcounterbalance our least productive actions (e.g., loss aversion, overconfidence, overreaction, etc.) © 2018 by Andrew W. Lo All Rights Reserved24 Jan 2018 Slide 26
  26. 26. Artificial vs. Natural IntelligenceArtificial vs. Natural Intelligence  Expert systems vs. machine-learning techniques E i t ll d t l d Expensive storage  small data, complex code  Cheap storage  big data, simpler code Thi i l l i lli ! N i f © 2018 by Andrew W. Lo All Rights Reserved  This is closer to natural intelligence! Narrative vs. facts Slide 2724 Jan 2018
  27. 27. Friend or Foe?Friend or Foe? © 2018 by Andrew W. Lo All Rights Reserved24 Jan 2018 Slide 28
  28. 28. Friend or Foe?Friend or Foe? J é S  Gender and sex orientation (4) GM HF  Race/ethnicity (4) Latino White José Susan  Race/ethnicity (4) Latino White  Age (4) Young Prof. Mid. Age  Current home state (50) CA TX  Religious affiliation (4) None Christian  Political party (3) Democrat Republican ( ) Economic status (3) Mid. Class Affluent  Education (3) MBA BA 345,600 Possible Types! © 2018 by Andrew W. Lo All Rights Reserved24 Jan 2018 Slide 29 , yp But Beware of Learning With Sparse Data
  29. 29. Evolution at the Speed of ThoughtEvolution at the Speed of Thought Aron Lee Ralston, 4/26/03 © 2018 by Andrew W. Lo All Rights Reserved24 Jan 2018 Slide 32
  30. 30. Evolution at the Speed of ThoughtEvolution at the Speed of Thought A blond three-year-old boy in a red polo shirt comes running across a sunlit hardwood floor in what I somehow know is my future home. By the same intuitive perception, I know the boy is my own. I bend to scoop him into my left arm, using my handless right arm to balance him, and we laugh together as I swing him up to my shoulder… Then, with a shock, the vision blinks out. I’m back in the canyon, echoes of his joyful sounds resonating in my mind, creatingbac t e ca yo , ec oes o s joy u sou ds eso at g y d, c eat g a subconscious reassurance that somehow I will survive this entrapment. Despite having already come to accept that I will die where I stand before help arrives now I believe I will livearrives, now I believe I will live. That belief, that boy, changes everything for me. – Aron Lee Ralston (2005) © 2018 by Andrew W. Lo All Rights Reserved24 Jan 2018 Slide 33
  31. 31. Evolution at the Speed of ThoughtEvolution at the Speed of Thought We Need New Narratives In Finance! © 2018 by Andrew W. Lo All Rights Reserved24 Jan 2018 Slide 34 We Need New Narratives In Finance!
  32. 32. Th k Y !Th k Y !Thank You!Thank You! d i kFor more on Adaptive Markets:  http://bit ly/2t3Sre6 (MIT Sloan Lecture) http://bit.ly/2t3Sre6 (MIT Sloan Lecture)  http://bit.ly/2ty6Rqp (Clarendon Lectures)  http://alo.mit.edu (website)  @AndrewWLo (Twitter) @AndrewWLo (Twitter) Slide 3524 Jan 2018
  33. 33.  Abbe E Khandani A and A Lo 2012 “Privacy-Preserving Methods for Sharing Financial Risk Exposures” American Economic Review: Papers Additional ReferencesAdditional References Abbe, E., Khandani, A. and A. Lo, 2012, Privacy Preserving Methods for Sharing Financial Risk Exposures , American Economic Review: Papers and Proceedings 102, 65–70.  Bisias, D., Flood, M., Lo, A., and S. Valavanis, 2012, “A Survey of Systemic Risk Analytics”, Annual Review of Financial Economics 4, 255–296.  Brennan, T. and A. Lo, 2012, “Do Labyrinthine Legal Limits on Leverage Lessen the Likelihood of Losses? An Analytical Framework”, Texas Law Review 90, 1775–1810.  Brennan T and A Lo 2014 “Dynamic Loss Probabilities and Implications for Financial Regulation” to appear in Yale Journal on Regulation Brennan, T. and A. Lo, 2014, Dynamic Loss Probabilities and Implications for Financial Regulation , to appear in Yale Journal on Regulation.  Butaru, F., Chen, Q., Clark, B., Das, S., Lo, A., and A. Siddique, 2015, “Risk and Risk Management in the Credit Card Industry” (June 14, 2015). Available at SSRN: http://ssrn.com/abstract=2618746 or http://dx.doi.org/10.2139/ssrn.2618746  Campbell, J., Lo, A. and C. MacKinlay, 1997, The Econometrics of Financial Markets. Princeton, NJ: Princeton University Press.  Chan, N., Getmansky, M., Haas, S. and A. Lo, 2007, “Systemic Risk and Hedge Funds”, in M. Carey and R. Stulz, eds., The Risks of Financial Institutions and the Financial Sector Chicago IL: University of Chicago PressInstitutions and the Financial Sector. Chicago, IL: University of Chicago Press.  Getmansky, M., Lo, A. and I. Makarov, 2004, “An Econometric Analysis of Serial Correlation and Illiquidity in Hedge-Fund Returns”, Journal of Financial Economics 74, 529–609.  Getmansky, M., Lo, A. and S. Mei, 2004, “Sifting Through the Wreckage: Lessons from Recent Hedge-Fund Liquidations”, Journal of Investment Management 2, 6–38.  Kh d i A Ki A d A L 2010 “C C dit Ri k M d l i M hi L i Al ith ” J l f B ki & Fi 34 2767 Khandani, A., Kim, A. and A. Lo, 2010, “Consumer Credit Risk Models via Machine-Learning Algorithms”, Journal of Banking & Finance 34, 2767– 2787.  Khandani, A. and A. Lo, 2007, “What Happened to the Quants In August 2007?”, Journal of Investment Management 5, 29–78.  Khandani, A. and A. Lo, 2009, “What Happened to the Quants in August 2007?: Evidence from Factors and Transactions Data”, to appear in Journal of Financial Markets. Kh d i A L A d R M 2013 “S i Ri k d h R fi i R h Eff ” J l f Fi i l E i 108 29 45 24 Jan 2018 Slide 36  Khandani, A., Lo, A., and R. Merton, 2013, “Systemic Risk and the Refinancing Ratchet Effect”, Journal of Financial Economics 108, 29–45.
  34. 34.  Kirilenko A and A Lo 2013 “Moore's Law vs Murphy's Law: Algorithmic Trading and Its Discontents” Journal of Economic Perspectives 27 Additional ReferencesAdditional References  Kirilenko, A., and A. Lo, 2013, Moore s Law vs. Murphy s Law: Algorithmic Trading and Its Discontents , Journal of Economic Perspectives 27, 51–72.  Li, W., Azar, Larochelle, D., Hill, P. and A. Lo, 2015, “Law Is Code: A Software Engineering Approach to Analyzing the United States Code”, Journal of Business & Technology Law 10, 297–374.  Lo, A., 2001, “Risk Management for Hedge Funds: Introduction and Overview”, Financial Analysts Journal 57, 16–33 Lo, A., 2002, “The Statistics of Sharpe Ratios”, Financial Analysts Journal 58, 36–50.of Sharpe Ratios , Financial Analysts Journal 58, 36 50.  Lo, A., 2002, “The Statistics of Sharpe Ratios”, Financial Analysts Journal 58, 36–50.  Lo, A., 2004, “The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective”, Journal of Portfolio Management 30, 15– 29.  Lo, A., 2005, The Dynamics of the Hedge Fund Industry. Charlotte, NC: CFA Institute. L A 2005 “R ili Effi i M k i h B h i l Fi Th Ad i M k H h i ” J l f I C l i 7 21 Lo, A., 2005, “Reconciling Efficient Markets with Behavioral Finance: The Adaptive Markets Hypothesis”, Journal of Investment Consulting 7, 21– 44.  Lo, A., 2007, “Where Do Alphas Come From?: Measuring the Value of Active Investment Management”, to appear in Journal of Investment Management.  Lo, A., 2012, “Reading About the Financial Crisis: A 21-Book Review”, Journal of Economic Literature 50, 151–178.  Lo, A., 2013, “Fear, Greed, and Financial Crises: A Cognitive Neurosciences Perspective”, in J.P. Fouque and J. Langsam, eds., Handbook of Systemic Risk, Cambridge University Press.  Lo, A. and C. MacKinlay, 1999, A Non-Random Walk Down Wall Street. Princeton, NJ: Princeton University Press.  Lo, A., Petrov, C. and M. Wierzbicki, 2003, “It's 11pm—Do You Know Where Your Liquidity Is? The Mean-Variance-Liquidity Frontier”, Journal of Investment Management 1, 55–93. 24 Jan 2018 Slide 37

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