However, anybody with any degree of market experience is aware that markets overshoot in both directions. When stocks or more generally, financial assets fall, they can plunge to well below the fair valuation. When they rise, they usually exceed fair value by significant amounts. A long-term investor will exploit the periods of under-valuation by buying. But few investors actually sell during periods of over-valuation.
Technical analysts can adopt many different approaches. The trend followers will not set explicit targets; instead, they set stop-losses. But of course, a stop loss implies a minimum target, since the trader hopes to make more than the maximum he is prepared to lose. Day-traders and chartists often do set explicit targets. So do range-traders.
The markets do have some mechanisms for letting traders know what price levels the big money considers critical. One number is only available on a fleeting basis for day-traders but it is one of those details which high-frequency traders (HFT) pay great attention to. This is market depth - the volume of buy-sell orders at a given price and the chain of buy-sell orders at distances above/below that price. Many HFT algorithms revolve around methods to discover depth and fine-tune strategies around depth discovery. Technical analysts say that such volume patterns eventually show up as congestion points, indicating levels of support/resistance.
Another data set can be studied across many time frames. This is the open interest in option contracts. There will always be outstanding calls and puts at various strike prices in liquid options markets, such as the Nifty index derivatives. A strike price with high open interest is presumably considered significant by many traders. Also, the overall ratio of puts to calls contains information about sentiment. But interpreting this data is not easy.
A trader might take out an option as either a hedge against an opposed cash market position or as a naked position with hopes of potential profit. The sentiments and expectations are obviously different in those two cases. If there are a higher number of overall puts to calls, what does it mean? It could mean that the derivatives market is oversold - there are more bears than bulls.
If those put-buying bears are "naked", they are not holding long positions in the underlying, they are expecting a drop because they think the cash market is overbought. If a large number of those puts are hedges against underlying long positions, it could mean investors are prudently taking out insurance but not expecting the market to drop. A converse logic applies if the overall number of outstanding calls is greater than the number of outstanding puts.
Distinguishing between these situations might be difficult. Most analysts try to tease out historic correlations by crunching large amounts of data. In general, it has been noted that a situation where outstanding puts exceed calls is bullish, while bearishness seems to occur when outstanding calls exceed puts. Correlations can change and specific ratio levels would be required for trades and there, opinions can violently differ.
The long-term outstandings are interesting and less difficult to interpret than short-term put-call ratios. Relatively few "naked option" traders take long-term positions, hence, one can assume only serious long-term investors or big operators are trading the December 2014 Nifty contract.
There's a massive number of calls in the Nifty 8,000c December contract (premium 102 on April4). There's a large number of puts in the December 5,000p contract (premium 43). Also, the number of calls in the 8,000c far exceed the number of outstanding puts in the 5,000p. The overall number of December calls also exceed the overall number of Dec puts. The 8,000 call is roughly 25 per cent above the current price while the put is roughly 20 per cent below. That range of 25 per cent up and 20 per cent down appears to be the limit of expectations in the next nine months. The number of long-term optimists seems to outweigh the number of long-term pessimists.
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