In "How AI Resolved Your Wealth Problem! And the S&P500 Myth," the presenter takes the participants on an enlightening journey through the realms of wealth management, the history of science, mechanism thinking, and artificial intelligence. This five part lecture series boldly challenges traditional investment practices, presenting an in-depth exploration of how AI can effectively address the intricate challenges and inefficiencies inherent in conventional approaches, particularly within the context of the S&P 500 and other benchmark indices. The book is structured into 15 sections, including Difficulty, Legacy, Dependency, Inaccuracy, Recovery, Activity, Mediocrity, Aristocracy, Inadequacy, (Ir)relevancy, Peculiarity, Probability, Entropy, Universality, and Transcendency.
Part 1: Beginning of the Information Age to Financial Rot
This part introduces the wealth problem and sets the stage for exploring the challenges faced by investors. It highlights issues like high fees, concentration risks, unethical practices, and the pension crisis, while also questioning the myth of beating the S&P 500.
In the "Difficulty" section, the author serves a compelling introduction, shedding light on the numerous obstacles faced by investors in today's complex financial landscape. High fees, concentration risks, herding behavior, unethical practices, and the looming pension crisis are eloquently outlined, forming the backdrop of the wealth problem connected with the unsolvable problem of beating the S&P 500. The author unveils a thought-provoking paradox inherent in traditional investment philosophies, setting the stage for a fresh perspective to explain why the idea of an unbeatable S&P 500 was a myth.
The "Legacy" section gracefully pays tribute to the intellectual giants of the past who have laid the groundwork for contemporary finance. From the revolutionary invention of the printing press to the profound contributions from Aristotle to Varnum Poor, the author draws inspiration from historical achievements to illuminate the path forward in wealth management. This section underscores the significance of acknowledging the legacy of past pioneers while building upon their wisdom.
In "Dependency," the author meticulously examines the limitations entrenched in conventional investment metrics, particularly market capitalization (MCAP), and the biased investment solutions that operate on winner-take-all mathematics. The discussion masterfully underscores the risks embedded in the inflated valuations inherent in traditional investment approaches and how it creates an addiction that is in the long term harmful for investors and the integrity of markets.
Part 2: Propagation of the Myth
This part delves into the historical development of modern finance, the launch of passive index funds, and the current state of non-normal markets. It explores the role of key figures like Cowles and Fisher in shaping investment philosophies and index methodologies.
In the "Inaccuracy" section, the author delves into the realization of bias and the attempts to correct it, along with the role of key figures like Cowles in amplifying bias. This part also explores what AI was doing during this period and how it was maturing.
In "Recovery," the author unveils the exceptional contributions of Fisher, an instrumental figure in reshaping conventional thoughts on indexing and hence investments. The concepts of momentum crash, convergence, and divergence are masterfully explored, illustrating how a faster recovery of a strategy from a period of crash or slowdown is the hallmark of a great strategy. The section highlights how current investing solutions, because of their archaic design, are slow to recover and hence deliver subnormal returns compared to what can be generated by machine investing based on new-age science. Fisher's pioneering work becomes the cornerstone for redefining the mathematics of indexing that powers today's passive investing.
"Activity" takes readers on the futility of beating the market by active selection of stocks. By ingeniously employing the analogy of an urn, the author artfully portrays the unpredictable nature of financial markets, highlighting the limitations of human forecasting and the distinct advantages mathematics can hold over discretionary decision-making.
Part 3: Statistical Laws and Their Inadequacy
Part 3 examines the concept of mean reversion and its inadequacies in explaining stock market returns. It discusses the historical context and introduces figures like Galton, Bienaymé, and Heisenberg to shed light on the limitations of traditional statistical approaches in financial systems.
In "Mediocrity," the author delves into the history of the statistical idea of mean reversion and how mean reversion is one of the many expressions of nature. The author takes the height experiment of the father of statistics, Francis Galton, and explains how the expression of mean reversion was a part of a mechanism and how the statistical law could be transformed into machine thinking.
"Aristocracy" explains the chronology of the rich-get-richer phenomenon in 1845 with the ‘Branching Process.’ Irénée-Jules Bienaymé was the first to explain mathematically the observed phenomenon that family names, both among the aristocracy and among the bourgeoisie, tend to endure over time. The ‘Bienaymé-Tchebichev Inequality’ explained the concentration of wealth. The author meticulously illustrates the limitations of the Aristotelian thinking of thinking about nature as statistics.
"Inadequacy" questions the adequacy of mean reversion in explaining stock market returns and introduces the concept of autocorrelation and how it has been used to explain stock market returns. The section introduces readers to Ludwig Boltzmann and his kinetic theory of gases, illustrating the inadequacy of traditional statistical approaches in modeling complex systems like financial markets.
Part 4: (Ir)relevancy and the Genesis of the Machine Thinking
Part 4 delves into the limitations of the Efficient Market Hypothesis (EMH) and explores the development of machine thinking in financial markets. It introduces the work of Shannon, Turing, and Kolmogorov, highlighting their influence on AI and finance.
In "(Ir)relevancy" the author delves into the concept of information and its relevance, highlighting that information is not static and can fluctuate between relevance and irrelevance. This section explores the challenges AI faces in dealing with the unpredictable nature of information.
"Peculiarity" explores the tenets of the Efficient Market Hypothesis (EMH) and its implications on market efficiency. The author eloquently challenges the EMH by introducing the concept of algorithmic thinking and how it fundamentally reshapes the understanding of market behavior. The section touches upon the pioneering work of Claude Shannon and Alan Turing, whose groundbreaking contributions paved the way for machine thinking in finance.
"Probability" takes a deep dive into the evolution of probability theory and its application in finance. The author masterfully navigates the historical context, featuring prominent figures like Pierre-Simon Laplace and Andrey Kolmogorov, to shed light on the complexities of probability within financial systems. It underscores how the probabilistic nature of financial markets necessitates a shift towards machine thinking to decipher underlying patterns.
Part 5: Entropy, Universality, and Transcendency
In the final part, the author delves into entropy and its significance in financial markets, explores the universality of machine thinking across various domains, and culminates with a vision of transcending traditional investment paradigms.
The "Entropy" section brilliantly dissects the concept of entropy and its profound implications in understanding the intricacies of financial markets. The author artfully connects the dots between entropy and information theory, unraveling how this concept can serve as a powerful lens to view market dynamics. It reiterates the importance of machine thinking in unraveling the mysteries of financial entropy.
"Universality" expands on the universality of machine thinking by illustrating its applicability beyond finance. Drawing parallels with nature's universal laws, the author underscores the far-reaching impact of machine thinking in diverse domains. This section presents compelling arguments for the adoption of AI-driven solutions in addressing complex problems across industries.
The "Transcendency" section serves as a fitting conclusion to the lecture series, offering a visionary outlook on the future of wealth management and investment. The author invites readers to transcend conventional investment paradigms and embrace the transformative potential of AI. It inspires a shift towards data-driven, machine-powered solutions that hold the promise of resolving the wealth problem once and for all.
Overall, "How AI Resolved Your Wealth Problem! And the S&P500 Myth" is a meticulously crafted lecture series that skillfully dismantles traditional investment dogmas while illuminating the path to a future where AI-driven machine thinking revolutionizes wealth management. Through an engaging narrative that weaves together history, mathematics, science, and finance, the author delivers a compelling message that challenges the status quo and empowers readers to embrace the limitless possibilities offered by AI in reshaping the world of finance.
Difficulty
1. [Heroin of the story- Beverley Schottenstein] [Explain it to mom]
2. Why pay a fee for what you can do yourself?
3. 9/10 Fail Logic [SPIVA's Geographical pride]
4. Fees compound too! Image [Simulations]
5. Warren, SPY, and the concentration risk!
6. Are Index funds herding mechanisms?
7. Benchmark Heist [Fraudulent practice to illustrate skill]
8. Closet Indexing [highest case in Canada]
9. S&P’s Google Paradox [Are Index manufacturers poorly governed?]
10. S&P500 isn’t Passive
11. How Passive S&P500 is harmful to your health?
12. S&P’s oil bias
13. Disco Door ETFs
14. Death by concentration
15. Poor governance in Active management
16. [All things that had to go wrong, went wrong]
17. Why are pension funds in a crisis?
18. [MIT Fintech - Alpha is going to be absent]
19. [Can new age science extract alpha?]
20. [1 % more on 100 trillion USD is a $ trillion more each year]
21. Informational paradoxes [All the information and intelligence in the world and.]
22. [Something bigger than the Millennium Prize]
23. 18 Social media resistance [LinkedIn Comments]
24. Is Index Concentration an Inevitable Consequence of MCAP Weighting?
Legacy
25. Aristotle's Bathtub/ Indexing [Shoulder of giants]
26. [Disproportional design of nature] [275 BC]
27. The printing presses
28. Rice Vaughan, “Coins and Coinage”, 1675
29. William Fleetwood, “Chronicon Preciosum” 1706
30. Adam Smith, The Wealth of Nations”. 1776 [Missed opportunity]
31. Nicolas Dutot's Library, 1738
32. Gian Rinaldo Carli, 1764 [Beginning of the upward bias]
33. Joseph Lowe, 1774 [Tabular standard]
34. George Evelyn, 1820
35. John and Henry Poor, 1849-1862
36. Dry Goods Shop [Hero of the story is Varnum Poor]
37. History of railroads
38. Standard Statistics Bureau
39. Axe and Houghton, 1854
40. Candy Store
41. Dow Jones Average, 1897
42. New York Times Average, 1911
43. Herald Tribune Stock Averages, 1925
44. New York Stock Exchange Averages, 1925
45. Standard Statistics, 1926
46. Stock Index 1935
47. Associated Press Average, 1936
48. The Annalist Average, 1957
49. Standard & Poor 500
Dependency
50. The true dangers of AI [MIT Technology - William Issac] - Bias
51. What is a bias?
52. What is a winner's bias?
53. What's wrong with a winner's bias?
54. Why is a winner's bias addictive?
55. The journey to market cap!
56. What are the other names of a winner's bias? [Rich get richer bias]
57. Laspeyres [Hero of the story] [No mathematical logic]
58. What’s Laspeyres Index?
59. Why is it biased? (Overstates Value)
60. What is Laspeyresian convenience?
61. How convenience lead to the popularity of the method
62. Who was Paasche?
63. Why is he the villain of the story? [The lower boundary] [forgot him]
64. Why is Laspeyres bias a complex issue?
65. Why is the index number problem a longstanding challenge in economics?
66. How did the Laspeyres (L) method lead to Market cap weighting?
67. Post L, the world got stuck and dependent on an upward bias?
Inaccuracy
68. Indexing methods and tests
69. Chain Indexing [Path dependent]
70. How did the Market cap weighting got the Investors herding
71. How herding led to amplification of the bias
72. Francis Ysidro Edgeworth, 1887
73. Alfred Marshall, 1925
74. Frederick Macaulay, 1910
75. John Maynard Keynes, 1930
76. Arthur Cecil Pigou, 1932
77. Leo Törnqvist, 1936
78. Walter Erwin Diewert, 1972
79. Alfred Cowles III [The hero or villain of the story]
80. [His Influence] on Paul Samuelson
81. Samuelson’s Influence on Fama
82. Samuelson’s influence on John Bogle
83. John Bogle’s influence on Index Funds
84. Launch of Index Funds
85. Index Fund’s influence on the passive thought
86. Herding Mechanisms and Systematic Risk
87. Passive overtaking active [Stats]
88. Inefficient Universes [Duplicated, multiplicand…everyone buying copies of c]
89. Skewed Incentives and Vicious Design
90. Premiums, Discounts and market integrity
91. Negroponte Digital
92. Electronic Investment Funds
Recovery
93. Fisher [The Hero of the story]
94. His illustration of bias
95. His highlighting of the failures of Laspeyres
96. His solution [Fisher’s Index']
97. Red Sack and Blue Sack Analogy
98. On the mountain
99. On the Sea
100. Resilience, Physics, [Mechanism] — why do things bounce back or sink
101. The art of keeping things afloat
102. Nifty 50 Case Study - Inertia
103. S&P500 Case Study
104. Japanese Case Study
105. Stoxx 50 Case Study
106. Indonesian 30 Case Study
107. Metals Case Study
108. Agro Case Study
109. Commodities Case Study
110. Assumption of excess returns, less risk, better design
111. Rule of 72
112. Generational wealth [destruction to creation]
113. Doubling wealth in half time
114. The Achilles Heel
115. LTCM Notes – Anticipation of spreads
116. Depression, Inflation, Double digit rates – lead stagnation is the testing time
Activity
117. Polya [The hero of the story]
118. Urn of tomorrow
119. The rigged game
120. Futility of Active
121. Speculation and Human nature
122. Lottery ticket investing
123. Thrill seeking
124. Need for selection and overconfidence bias
125. Paradox - beat mediocrity
126. Analogy - rocket, car, horse, electric bike (donkey*) – recognised race
127. Not about Skill vs. Luck But about Skill vs. Bias
128. Machines vs. humans
129. Simple rules beat complex rules
130. Paradox of fees
131. [No apples to apples]
132. Forced to select because fees are for selection which does not work
133. Thematic Funds
134. Repackaging
135. Marketing
136. Story telling [This time it’s different]
137. Hunting tickers [Stealing Tickers] [HACK legal case]
138. Turtle beating the hare all the time
139. EVA Scorecards India
140. EVA Scorecards USA
141. EVA Scorecards Canada
142. EVA Scorecards Europe
143. Death of mutual funds
144. ETFs are new repackaging
145. Active ETFs [ Don’t solve the real problem]
146. Human nature – digging holes – Keynes – discretion is a lost cause
147. Machines vs. humans – “Difficulty Section” poses and obvious conclusion
148. Inactive mind – activity is a must for human brain
149. Why Weak AI is destined to fail because the game is rigged
150. Sophistical AI has to still beat the donkey [electric bike]
151. And specially if futility of Active is not understood
Mediocrity
152. Life of an average
153. Beginnings from - Francis Galton [Hero]
154. Height Experiment
155. History of Central Limit Theorem
156. Cases in Nature and in Financial Markets
157. What came first Equilibrium or mean reversion?
158. What’s the connection of Equilibrium, mean reversion and predictability?
159. Dual and singular perspective of mean reversion among economists
160. Lack of Science/ Conflict
161. Connection of mean reversion with Investing
162. EMH school and Investing
163. Paradoxes of mediocrity [ non-scientific/ concentrated/ flawed]
164. Francis Galton [hero]
165. Height Experiment
166. Gauss
167. Other thinkers
168. Normal distribution
169. Life of Mediocrity
170. Bonds and Thaler
171. Kahneman’s idea on air flights and reversion
172. John Bogle’s [Everything reverts to mean why waste time]
173. Economic cycles are built on the idea of reversion
174. Mean reversion’s equation
175. Normality inside Black and Scholes pricing model
Aristocracy
176. Bienaymé [Hero of the story]
177. Aristocracy
178. [Non-Normality]
179. Paper Clips
180. Pareto
181. Willis
182. Simon
183. Yule
184. Psychophysical Law
185. Flory
186. Earthquakes
187. Zipf’s law
188. Estoup
189. Citations
190. Power law expressions in nature
191. The snowball effect
Inadequacy
192. Hero of the story [Heisenberg]
193. Turbulence and Heisenberg [Why relativity and why turbulence?]
194. Turbulence and Heisenberg [Why relativity and why turbulence?]
195. Revisiting Willis
196. Goyal and Wahal
197. Power law failures
198. Zipf’s Model of learning language
199. Ising’s Law [ Had dual expression]
200. The Monkey can write Shakespeare
201. Champernowne numbers
202. Complexity [Fox, Mitchell]
203. 80% of Economic Nobel have touched the law of Pareto efficiency
204. Noise generation between the interaction of the two laws
205. Blind men and the elephant
Information
206. Hero of the story [Shanon] [indicate for a need for meta layer]
207. Information age/ Information business/ Reuter’s and the pigeons
208. Sentiment information
209. Social mood information
210. alternative information
211. Trillions of pictures are taken every year [Learn faster with lesser]
212. Garbage in garbage out
213. Things converge and diverge
214. convexity and concavity
215. Information is not an orange
216. The conceptual age
217. Information is not an orange
218. [Info can’t be understood or predicted] [extension of complexity]
219. [balls thrown into a twister] [Helen Hunt]
Peculiarity
220. Sunspots and economic cycles
221. Length of the skirts
222. Hero of the story Kenneth Boulding [Information flits]
223. Tulip, Mania and the Informational realms
224. Breaking News [17% of headlines fail]
225. Campbell and Goodhart [Indicator failure is a reality]
226. Inverted yield curve
227. How wrong we are
228. Before BB68
229. After BB68
230. Shiller’s exuberance
231. Shiller’s 10 Year P/E - negative slope
232. Reversion - Diversion Hypothesis
233. Information relevance and irrelevance
234. Inefficient efficient markets / efficiently inefficient markets
235. Modern Finance is broken
236. Redundancy of modern finance
237. CAPM is CRAP
238. Slant Finance
239. Physics is dangerous to your wealth
240. Slant Finance
241. Unethical Insider
242. Idiosyncraticity/ What is peculiarity? Why is it always ascending?
243. Why is it a dilemma? [Logic]
244. Nobel’s interdisciplinary connections
245. Failed causality
Probability
246. Hero of the story [Bernoulli]
247. Probability as a chance of a chance
248. History of Probability
249. Probability as a mechanism
250. Can AI Anticipate
251. AI’s peculiarity dilemma
252. Mean Reversion Framework [Early indication of mechanisms]
253. Pizza pie
254. Idealized case study
255. Markov’s butterflies
256. Estimates over precision
257. Why do animals herd?
258. The sum is more than its parts
259. Group supersedes components
260. Chaos is a 3
261. Crows/ Bees/ Animals/ Flock of birds
262. Observer Effect - Effect can influence cause
263. Context over content
264. Planck - Science progresses with a funeral
265. Economics is not a Science with a capital S - Solow
266. Everything is relative
267. Patterns of randomness
268. How nature uses randomness
269. Pearson and randomness
Entropy
270. Hero of the story [Schrodinger]
271. What is life?
272. The 2nd law of thermodynamics
273. Perpetual motion machines
274. No patent for PMM
275. 3N model of life
276. 3N Method
277. Physics is not dangerous to your wealth
278. Maxwell’s demon
279. Mechanisms Climate
280. Mechanisms Cancer
281. Mechanisms of Social Behavior
282. Mechanisms of Psychology
283. Mechanisms everywhere
284. Intelligence as a noise manipulating mechanism
285. The White Swan
Universality
286. Firefighter from Idaho
287. Universality
288. Enrichment
289. Universal Indexing
290. Human AI
291. Artificial General Intelligence
292. Generalized Machine Learning
293. Artificial Neural Networks
294. Future of AI
295. Architecture of information
296. Data universality
297. Universal systems - random - order - herding
298. Meta machine data
Transcendency
299. Factor Zoo
300. Factor Timing
301. Naïve Investing
302. Cash Cow and Broken Clocks
303. Exceptional & Rich
304. 100 bps and top percentile
305. Beyond 1800 bps
306. Robo Asset Manager [Alphie investing]
307. Perpetual Bull AI
308. Altas of AI [ ESG Alpha/ ESG AI]
309. The Intelligence Web
310. Conscious Web