|Chennai||Rs. 27580.00 (0.18%)|
|Mumbai||Rs. 28700.00 (0%)|
|Delhi||Rs. 27700.00 (0.73%)|
|Kolkata||Rs. 28270.00 (0%)|
|Kerala||Rs. 27050.00 (0.74%)|
|Bangalore||Rs. 27350.00 (1.11%)|
|Hyderabad||Rs. 27660.00 (1.21%)|
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 back-test, 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 its 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 heirarichal 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 to revert. This is what we did. We took a large group of global indices and ranked them on quarterly performance.
Our universe contained 1,000 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, Brazilian Bovespa and Chinese SSEC among the universe. For us, on a multi-year performance, barring S&P 500 and German Dax, all the other indices were undervalued i.e. sub 50. What does this mean? This means that global equity is far from expensive and even the most expensive was at 75 percentile, which is far from overextended. Another interesting aspect was that barring S&P 500, German Dax and FTSE all the rest of the global equity indices are below 30 percentile. This also confirms that global equity is not just fairly valued, it is inexpensive. Chinese SSEC and Brazilian Bovespa were the most inexpensive.
|Paris CAC 40||8||+ve||+ve||20.2||57||9.4|
|FTSE 100 Index||5||+ve||-ve||39.7||18||2.8|
|DJ Stoxx 50 Index||4||+ve||+ve||18.6||145||12.2|
|* Percentile Rankings|
In the table we have put two signals, one from price and one from Jiseki performance cycles. When both signals are rising we consider the asset positive. This system has been running positive on the Sensex from the last 81 days and is up six per cent. Nikkei is running for 17 days and is up 10 per cent. Another feature which shows relative strength is the relative ranking (proprietary stock rankings). Here, we rank the sub group with itself. Nikkei is the strongest with a ranking of 10, Hang Seng is nine, CAC is eight and Sensex is seven. This means Asian markets are in an outperforming mode and the idea about a 2013 crash is mere subjective fancy not backed by reasoning or rankings.
The author is CMT and founder, Orpheus CAPITALS, a global alternative research firm