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Here are five unique books that will will completely change the way you think about stock market risk and reward. They have certainly shaped mine and my first ever financial calculator was based on one of them. The books will bust established myths about the market - its so-called efficiency, mean reversion, and the way we measure risk and reward - not with opinions, but with data.

1 Unveiling The Retirement Myth - Jim Otar

Tag line: Advanced Retirement Planning Based On Market History

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I discuss ways in which we can visualise and interpret the annualised return, also known as the compounded annualised growth rate or the CAGR and volatility of returns. Franklin India Prima Fund had a NAV of 201.75 on April 3rd, 2006 and a NAV of 878.5 on 12th April 2017. What is the annualised return?

To compute this, we first realise 11 years is the intervening time period - 11.033 to be precise. The annualized return is given by,

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Concerned that the stock market is overheated? Then here is why it makes sense to focus on the risk associated with the money already invested and not worry about when to invest more. Suppose, I have a long-term goal and I can only spare Rs. 500 towards an equity SIP. I choose a nondescript mid-cap mutual fund and start a SIP for that amount in Aug 2009, switched to direct sometime in 2013 and kept SIPing. Today, the net annualised return (regular + direct)  would be about 23-24% and the fund will have a value of almost 1.1 Lakh.

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While discussing the distribution of wealth in India, I had referred to the idea of self-similarity with the example of the Pareto principle. Today I would like to discuss how the stock market behaves in a similar way!

The first part of this series is here: Fat Tails: The True Nature of Stock Market Returns – Part 1

To recap, we say that 20% of the population hold 80% wealth. If we zoom in on the 20%, then: 20% of 20% population hold 80% of 80% wealth.

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In a two-part post, I discuss the nature of stocks market returns, first by pointing out the influence of extreme market events and then by considering their fractal or self-similar nature. Readers may recall that last week, we considered self-similarity in wealth distribution where I had mentioned their universal nature.

Both posts in this series shall deal with the same data set: daily, weekly and monthly returns of the S&P 500 from Jan 3rd, 1950 to Jan 21st, 2017 obtained from Yahoo Finance.

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