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.
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.
Mean reversion has been the cornerstone in differentiating the random from the non-random behavior. Stationarity is used to identify mean reversion in a time series.
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.
In finance, arbitrage is an essential framework to understand asset pricing. However, the study of anomalies also called as premiums, which are not arbitrageable has led to a debate regarding whether markets are efficient in correcting price imbalances or is inefficiency a reality.
Information is an assumption for modern finance. The Efficient Market Hypothesis uses information to back its case for efficiency. The EMH case is weak, but as Martin Swell (2011) explains until a flawed hypothesis is replaced by better hypothesis, criticism is of limited value.
Momentum and Reversion have always been seen as independent of each other and never as a composite. This study explains how the two behaviors are not only connected but also get transformed into each other.
Natural systems witness reversion and divergence simultaneously across different periods of time. This paper tests the performance proxy as mentioned in a previous paper on the ‘Mean Reversion Framework’ for Markov’s transition probabilities.
The original work by Galton on mean reversion in 1886 emphasized relative before absolute, talked about the relation of the variable with the sample average, pointed out the balance between convergence and divergence and showcased cross-domain expression of mean reversion.
Searching for alpha is searching through inefficiency because there are no supernormal returns in efficiency. This seems intuitive and correct because there is a cost to searching through inefficiency. And agents who spend that cost have to be rewarded for that effort. The case for inefficiency is unassailable.
I really don’t know why Richard Thaler chose this headline for a research paper. Many other behavioral finance academic papers also capture attention. “Can the markets add and subtract?”; “The winner’s curse”; “The gambler’s fallacy”, “Does the stock market overreact?” While the popularity of the subject has increased and behavioral biases have got so pervasive that everybody seems to be biased, the question is whether the behavioral finance experts are bias-free?
Style box for the investor is a visual representation of investment characteristics for stocks, fixed income and mutual funds offered as a comparative tool to investors around the world for helping them determine asset allocations based on their risk preference.
Over the post-event dinner at the Princeton – UChicago quant conference, the conversation veered to the definition of an intelligent system. My co-speaker (a physicist) from the conference defined an intelligent system as a system that is entropy reducing.
Quantum physics may not seem much to have to do with risk and investing, but with subjects like psychophysics explaining the statistical behavior in psychological behavior, it’s just a matter of time that science could explain more of markets. John Gribbin’s biographic work on Schrödinger has a lot of jargon relevant for understanding universal behavior.
Disruption is happening all the time. And it will happen in the active investing business too. What is active investing? Anything which starts from intraday trading to mutual funds to hedge funds can be bundled as active investing. Anything which is designed to conserve capital (even if it does not) and deliver absolute returns is Active. Anything non-standardized using fundamental analysis, quantitative, technical, behavioral or based on interdisciplinary studies also would come under the same classification. Anything not Passive can also be called Active. Anything held for a periodic rebalancing of 3 months and higher (say up to 60 months) would be more towards Passive.
Is there was a way to build a portfolio of non-leveraged short positions? This is new territory because industry’s focus on building models for a long-only focused market, but also because short, and long-short enter the alternative or hedge territory, an underrepresented segment globally. The idea of a short index does not exist while the short ETFs are in their nascency. As global money managers still are predominantly ‘buy and close’ rather than buy with a short hedge. As markets become harder to understand, the lines between trading and investing have finally started to blur.
Building my case from last time (the fortune index), where I suggested databases should talk to themselves and if natural systems express universal laws like patterns, divergences, seasonality, and constants then the data generated or derived from these natural systems should also express this universality. And if the data also express this universality then the question to be asked here is whether the universality character lies in something common to data rather than to a natural system.
Somehow my Interdisciplinary mind registered Eugene faster than Fama. After all Eugene Stanley, the father of Econophysics could also get a Nobel. If Psychologists could get the biggest award for Economics, a physicist could have been there too. But then the surprise became bigger, not because it was Fama, not Stanley but because Robert Shiller shared the award.
Data models should work across regions, across nature, across data sets. This means “Data Universality”. If natural phenomenon exhibit universal patterns like geometry, outliers, 80-20 principle, mean reversion, fat tails etc. then the data these natural phenomena generate should also express a similar behavior; actually they do. But we still consider data sets as religious, the stock market data is useful for the financial analyst, while the subatomic data is useful for physicists, the social network data is for marketers.
Why is the stock market not a science? Today many elements of our life have a degree of predictability, consumption patterns, social behavior, earthquakes, etc. However, the predictive measure is lacking when it comes to stock markets. Behavioral finance highlighted this lacking measure and accountability. Even statisticians limit themselves to the prediction of stock market volatility rather than stock market direction.