The rich do not always get richer. Explaining this probabilistically resolves a 100-year-old puzzle, opening up opportunities for smart beta portfolios, an architecture of complexity and eventually an architecture of data that could become Web 4.0.
Ok! advisors might be inefficient but is it enough reason for the media wave against them. Agreed that evolution has to happen, and after the industry restructured broking, sized up researchers, and the equity sales, maybe it’s time to disrupt advising. One may argue that somebody has to take the responsibility for underperformance netted for a fee, so maybe it’s time for retribution for advisors. But before we choose the robots over the advisors, and see passive investing as a solution for all investing, maybe we need another perspective.
Despite its popularity, the power law has not been without its failures and has rather come under criticism. In the paper ‘Scale-dependent price fluctuations for the Indian stock market’, Matia K, Pal M, Salunkay H, Stanley HE (2004), the authors explained how Indian stock market may belong to a universality of class different than that observed in developed markets.
Adaptive Market Hypothesis (AMH) embraces Efficient Market Hypothesis (EMH) as an idealization that is economically unrealizable, but which serves as a useful benchmark for measuring relative efficiency. AMH’s adaptability to changing dynamics of the market suggests that investors are potentially capable of an optimal dynamic allocation. There is nothing wrong here in the direction pointed by Andrew Lo. However the assumption that human innovation driven adaptability is the way ahead is an open-ended solution. This leaves little room for system thinking and overrules the possibility that natural systems could explain human behavior rather than vice versa. AMH just like EMH is based on a set of assumptions, which are good for illustrating market idealizations but lack in terms of addressing contradictions. This makes both AMH and EMH a system philosophy rather than a system framework. Reversion Diversion Hypothesis (RDH) (Pal, 2015) reconciles the contradictory assumptions into a statistical framework that addresses the limitations of EMH, AMH and extends the idea of a natural system functioning to markets.
I was in Mumbai recently, meeting fund managers to understand where India was on the smart beta road. How fast was investor education evolving? What was the appetite for ETF’s? And what should be done from the regulation point of view to take the Indian markets to the next stage?
Jules Augustin Frédéric Regnault was a French stock broker’s assistant who first suggested a modern theory of stock price changes in Calcul des Chances et Philosophie de la Bourse (1863) and used a random walk model. He is also one of the first authors who tried to create a “stock exchange science” based on a statistical and probabilistic analysis. His hypotheses were used by Louis Bachelier, who is considered a pioneer in the study of financial mathematics. Bachelier had anticipated many of the mathematical results developed in Albert Einstein’s 1905 paper.
John Rae’s inter-temporal choices explained the statistical nature of human behavior in 1834. However, despite the subject’s insight in the objectiveness of behavior, inter-temporal choices remains a peripheral science. This paper takes a sequential approach to question how inter-temporal choices could be behind human behavior, behavioral anomalies and even market anomalies. If these inter-temporal anomalies were consistent, unarbitrageable and explain asset returns better than the Fama and French Factor model, this could further our understanding of asset pricing models by establishing a new duration factor which could subsume both size and value factors.
There is no disagreement regarding the statistics of mean reversion. What goes up comes down and vice versa. Campbell and Shiller (1988) said that the simple theory of mean reversion was basically right. Fama and French (1989) also suggest that valuation ratios forecast five-year returns with quantifiable accuracy. It is the failure of reversion (divergence) that has not been reconciled with the expression of reversion. Why is the society so keen to accept reversion as statistical, but its failure as behavioral? John Bogle’s (2001), Star, Comets and the Sun analogy to the idea of reversion around investing styles reinforces the idea, that all failure of reversion is an error while reversion is a statistical reality. The reason Bogle suggests not to focus on investing styles, as markets are anyway going to recoil back to the statistical mean. Thaler (1999), goes a step further explaining reversion failure as driven by behavioral errors. How strong is the behavioral case? Should reversion and diversion both be statistical?
It was in 1968 that Ball and Brown considered the content of accounting information, the flow of information, the relevance of information, its predictive powers and its continuity and time dependence. The observed reversion was used as a validation of predictive content in earnings. This relevance was later shown to cause a drift, an anomaly, un unexplainable behavior of a market. Bernard and Thomas (1989) showcased a seasonal positive autocorrelation in partial periods connected to the news followed by a seasonal negative autocorrelation.
The modern city I knew as a child keeps changing. There used to be more roads and fewer cars, now there are only cars. If you want to see roads and have driving pleasure you have to skip two-thirds of the day. Ok, this may be more valid coming from an emerging market like India or China, but the emerging market outlook for auto has been assumed to be significant globally.