Ranking Global Indices

Elliott has come under attack as being subjective when it comes to long term price projections. And any long range projection either takes up a mass fancy like the Jim O’Neill, BRIC 2027 (Simplifying BRICS, Dec 31, 2007) or sound ridiculous like Prechter’s DOW 400 forecast. Though the ridiculous has a better chance of working out, markets and history seek objectivity and backtest, or as James Simon will put it, geometry. The long range forecasting just becomes a potential perspective, which has a chance of becoming an alternate low probability perspective.

Long range forecasting was also considered a challenge because Time was and is misunderstood. While speaking at the MTA Global Webcast I realised a strange conflict among behavioural finance experts. De Bondt and Thaler used mean reversion to challenge classical economics, suggesting that because there was a long term seasonal pattern, the randomness assumption of classical theorists was wrong (Does market overreact? 1981). While on the other hand Robert Shiller illustrated that too much fluctuations was a reason why efficient market hypothesis was deficient. If you look at his 1981 American Review paper you will see markets oscillating (mean reverting) around fundamental value. So on one side behavioural finance uses an oscillating behaviour to disprove randomness and on the other hand Shiller uses large fluctuations in an otherwise mean reverting behaviour to call the phenomenon unexplained. Strangely both Thaler and Shiller don’t refer to the determinants of mean reversion in their body of work.
The answer to long term forecasting lies in mean reversion and it’s determinants. We redefined mean reversion as extreme reversion and connected outliers with it, explaining how outliers were happening across time frames. The larger the time frame associated with an outlier, the larger the reversion expressed by the outlier. Even technically larger the previous price structure, larger the breakout, and larger the investment opportunity that outlier presents. Somewhere this outlier approach fits in with the dynamical systems (chaos, The strange Attractor) well because we just talk about reversion, we don’t talk about the pattern of reversion, whether it’s going to be in a zigzag or an impulse. Whether an outlier is going to outperform and become the best or whether an outlier is going to outperform, deliver average performance and stagnate.
The answer to longer term forecasting also lies in considering asset performance inter-connected with every holding period in a heirarichical structure. This means that active and passive investing styles were also connected. This also meant that performances of global indices were connected as a part of the same group. And any outliers among this group too were destined too revert. This is what we did. We took a large group of global indices and ranked them on quarterly performance.
Our universe contained 1000 Global Sector and blue chip Indices. We ranked the following Indices sub group S&P500, German DAX, UK FTSE, Indian Sensex, NIKKEI, French CAC40, Hang Seng, DJ STOXX Euro, Brazlian Bovespa, Chinese SSEC among the universe. For us on a multi year performance…


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Mukul Pal, is a Chartered Market Technician, MBA Finance and a member of the reputed Market Technicians Association (MTA). He has more than a decade of Capital Market experience dealing with derivatives and global assets. He has worked for Bombay Stock  Exchange, multinational Banks and brokerage houses in leading research positions before starting on his own in 2005. He is the President of the MTA Central and Eastern European Chapter.