Category: Investing Insight

Investing insight to make you a better investor.

What if: abki baar NO modi sarkar?

What if Modi fails to become the prime minister of India? Some are expecting the Nifty to crack by 1000 points in such a scenario. Although not a perfect hedge, a bear spread makes sense – think of it as insuring your portfolio against the adverse outcome.

NIFTY May 6600/6750 Long Put Spread

NIFTY May 6600-6750 Long Put Spread

The Nifty will have to expire below 6695.00 for the trade to be profitable. The max profit is Rs. 4750.00 and the cost to enter the trade (and max loss) is Rs. 2750.00.

NIFTY May 6600-6750 Long Put Spread payoff
NIFTY May 6600-6750 Long Put Spread PL

Thought process

This trade can be best described as buying a limited form of insurance. You are assuming that the Nifty will not fall too far below 6600 and losses are not going to be catastrophic. You could go farther down the option chain if you are feeling too nervous, but then your δs will get smaller so you will have to buy more spreads to cover your portfolio.

For example, if you did a NIFTY May 6500/6600 Long Put Spread instead, you will be moving the break-even to 6569.70, pay less (Rs. 1515.00) for a max profit of Rs. 3485.00. But the delta of this spread is -0.08 vs. -0.15 for spread described above.

Exiting the trade

The result of this election is expected to be declared on 16 May (Friday). Exit soon after election results are announced or right before it if the trade is profitable.

Read more about options: Options Trading Guide

Of whatsnexters, horoscopes and personal experiences

Can Stock Market Forecasters Forecast?

It’s time we stopped listening to the “whatsnexters.” These folks are everywhere in the financial media pontificating confidently about what they can’t possibly know — what’s next for the economy or the stock market.

Read: Don’t let market pundits lead you astray

Good to Great

The story of success swarms statistics. And there’s always enough random success to justify almost anything to someone who wants to believe.

Read: Stories triumph Statistics

A case for rules-based investment methodology

Our personal experiences disproportionately impact our investing behavior. By simply repeating investing behaviors that resulted in good outcomes for us in the past, and avoiding those that resulted in poor outcomes, we’re potentially eliminating important information that could help future investment performance.

Read: Bad Investor Behavior: Overemphasizing Experience

Long term ≠ Always


Previously, we had highlighted the difference between ‘Long-Term Investing vs. Buy And Hold Forever’ (src) Economies shift, competition catches up, moats wither away. So it is essential to have a periodic review of your portfolio.

Recently, in a post titled ‘The answer is that there is no answer’ (src) we saw the inherent contradictions that exist in asset management.

James Osborne over at Bason Asset Management brings it together brilliantly:

The world is a complex place and how each generation of investors experiences these long term truths may be very different. This is simply a reminder that in some periods, we may have a different experience than these long-term facts.
Most importantly we should be aware that “over the long term” doesn’t mean “always” in advance so that we aren’t surprised when we experience the opposite result of our long-term beliefs.

The whole thing is worth a read.


Understanding Nifty Volatility


Volatility (σ) is a measure for variation of price of a financial instrument over time. Historic volatility is derived from time series of past market prices. There are different ways of calculating volatility. At StockViz, we use Yang Zhang Volatility.

σ is one of the biggest contributor of option premiums. Understanding its true nature will help you trade it better.

Volatility spikes

Observe the volatility spikes since 2005. Even though the average is around 0.3, its not uncommon to have huge swings.


Fat tails abound





Trading strategy

Always try to be on the long-side of volatility. It might be tempting, while trading options, to try and clip the carry on θ-decay. But you should always be aware of the fat-tails of volatility that can crush many months of carry P&L overnight.

The most important assumption

Prices and Returns

Prices don’t follow a statistical distribution (they are not ‘stationary’.) There is no obvious mean price and it doesn’t make sense to talk about the standard deviation of the price. Working with such non-stationary timeseries is a hassle.

NIFTY 2005-2014

But returns, on the other hand, are distributed somewhat like a normal (Gaussian) distribution.


And there doesn’t seem to be any auto-correlation between consecutive returns.


If returns are normally distributed, then how are prices distributed? It turns out that the logarithm of the price is normally distributed. Why? Because

returns(t) = log(price(t)/price(t-1))

Now statisticians can magically transform a random time-series (prices) into something that is normally distributed (returns) and work with that instead. Almost all asset pricing models that you will come across in literature has this basic assumption at heart.

Fat tails

The assumption that returns are normally distributed allow mathematically precise models to be constructed. However, they are not very accurate.

In the normal distribution, events that deviate from the mean by five or more standard deviations (“5-sigma events”) have lower probability, thus meaning that in the normal distribution rare events can happen but are likely to be more mild in comparison to fat-tailed distributions. On the other hand, fat-tailed distributions have “undefined sigma” (more technically, the variance is not bounded).

For example, the Black–Scholes model of option pricing is based on a normal distribution. If the distribution is actually a fat-tailed one, then the model will under-price options that are far out of the money, since a 5- or 7-sigma event is much more likely than the normal distribution would predict.

Precision vs Accuracy

When you build models, the precision that they provide may lull you into a false sense of security. You maybe able to compute risk right down to the 8th decimal point. However, it is important to remember that the assumptions on which these models are build don’t led themselves to accuracy. At best, these models are guides to good behavior, and nothing more.

accuracy vs precision

Fat-tailed distribution