Timing the market will work but not the way we imagined!

We look at the daily returns of the Sensex and S&P 500 and show why market timing does not work the way most investors want it to

image of one hand holding an alarm clock and another hand watching a stock price on a mobile screen representing timing the market

Published: March 10, 2020 at 11:18 am

Last Updated on

You might have heard the chant, “timing the market will not work, time in the market is what matters”. This is industry propaganda to ensure you do not exit your funds while the AMCs themselves have schemes with elaborate strategies to time the market. It is “ok” as long as they do it! In this report let us study daily returns of the Sensex and what we can learn about risk, reward and timing the market.

Timing the market is a method to reduce the impact of market fluctuation on a portfolio. It is a method to lower risk and the only guaranteed way to do that (as we shall see below) is to lower both up (positive) and down (negative) market movements. That is potential returns also gets lowered with potential risk.

Market timing is misunderstood as a way to get more returns. Worse, many people think merely invest on market dips without touching already invested money will work as they are scared of taxes. This has already been rebuffed here: Want to time the market? Then do it right! Buying on dips is not timing!

There is only one way to time – change the total amount exposed to the full heat of the market from time to time regardless of taxes and exit loads. There are multiple methods to do this, each with its own success rates: using market PE, moving averages, double moving averages, Bollinger bands etc. The full archive of market timing backrests is available. A tool to determine market valuation in multiple ways is also available.

Timing the market refers to changing equity allocation in the portfolio as per market conditions. This is the same as tactical asset allocation. 

These backtests already point out that market timing will work best if we try to reduce risk with it. Higher returns from these is pretty much a coin toss. If the sequence of returns are favourable then yes, if not no.

We shall try to understand the underlying reasons for this evidence in this article by looking at the daily returns of the Sensex and the S&P 500.

Let us first look at Sensex price movement (in log) and daily positive returns that are equal to or greater than 3% and daily negative returns that are equal to or less than -3%.

Sensex price movement along with positive and negative returns above and below a threshold value
Sensex price movement along with positive and negative returns above and below a threshold value (3%)

Notice how a big positive return is followed closely by a big negative return or vice-versa. A small positive return by a small negative return or vice-versa. I am much obliged to Siva from AIFW for urging me to plot both positive and negative returns together and for pointing out that this is volatility clustering. Or as pointed out first by Mandelbrot, “large changes tend to be followed by large changes, of either sign, and small changes tend to be followed by small changes.”

This can be viewed as an example of self-similarity or repetition of fluctuation observed over days to those over weeks or months. Regular readers may recall earlier articles on this matter: (1) The 80/20 rule: Making sense of the richest 1% Indians owning 58% wealth! (2) Five Books That Will Redefine Your Understanding of Stock Markets (3) Fractals: The True Nature of Stock Market Returns.

It has been shown that volatility clustering implies that stock returns overtime are correlated. Meaning that the market returns do not exhibit a random walk. This could also be the reason why the sequence of returns risk plays an important role in portfolio management why we need to reduce its impact

This is volatility clustering in the S&P 500 since 1927.

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S and P 500 price movement along with positive and negative returns above and below a threshold value
S and P 500 price movement along with positive and negative returns above and below a threshold value

Now, the key point here is, just like in life, the good is always mixed with the bad. We cannot pick and choose the green dots (positive returns) and or pick and reject the red dots (negative returns).

If we try to reduce the impact of the red dots, we will end up reducing the impact of the green dots as well. If we try to enhance the impact of the green dots then we make the red dots more pronounced.

However, market returns are not symmetric. As shown before, we can visualise the growth of an equity portfolio as a tilted pendulum. That is, it swings on both sides but is a bit tilted to the positive side. This can be exploited but not to beat the market in terms of higher return but in terms of lower risk.

First, let us look at Sensex positive and negative daily returns without the 3% threshold.

Sensex daily returns positive returns are shown in green and negative in red no threshold has been applied
Sensex daily returns positive returns are shown in green and negative in red no threshold has been applied

There are a total of 9431 data points. Out of these 4963 are green and 4468 red. So pretty much an even split! Suppose we take the average of the last 200 days closing price, also known as daily moving average and 200 DMA.  Then find how much this changes each day we get this.

Sensex 200 daily moving average daily returns positive returns are shown in green and negative in red no threshold has been applied
Sensex 200 daily moving average daily returns positive returns are shown in green and negative in red no threshold has been applied

Now there are 9232 data points out of which 6685 are positive. Taking the moving average reduced noise and to some extent reduces the volatility clustering. However, the problem is the extent of the green dots (and red dots) is also significantly lower. That is the price of the smoothening.

Now if we devise a strategy to be invested in equity only if price > 200 DMA and pull out if price falls below 200 DMA (yes, this seems counterintuitive but the idea is to not hold equity when the market has moved up “too much”) then the returns would approximately depend on the daily swings of the 200 DMA shown above.  This is a backtest: Market Timing With Ten-Month Moving Average: Tactical Asset Allocation Backtest (200 DMA is approximately the same as a ten-month moving average).

It is naive to assume that we can beat the market because the smoothening has resulted in more positive movements. The problem is,  we can only drown out both the positive and negative data points.  Also, we have no clue when the red dots would occur in future.

While studying past return sequences, our portfolio might beat the market (= higher return) if we very few red dots or red dots early in the investment window. If the market falls big then we may not be getting more returns than the market.

However, since volatility has been uniformed reduced, a tactical asset allocated portfolio will not swing as much as the market does. It is in this sense and only in this sense we can time the market. Lower volatility, typically more frequent lower drawdown is the consistent benefits of market timing.

Getting more absolute returns is a matter of timing luck. That is it depends on the sequence of returns we would encounter in future. There is little benefit in counting on it “because it feels like commonsense”.

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About the Author Pattabiraman editor freefincalM. Pattabiraman(PhD) is the founder, managing editor and primary author of freefincal. He is an associate professor at the Indian Institute of Technology, Madras. since Aug 2006. Connect with him via Twitter or Linkedin Pattabiraman has co-authored two print-books, You can be rich too with goal-based investing (CNBC TV18) and Gamechanger and seven other free e-books on various topics of money management. He is a patron and co-founder of “Fee-only India” an organisation to promote unbiased, commission-free investment advice. He conducts free money management sessions for corporates and associations on the basis of money management. Previous engagements include World Bank, RBI, BHEL, Asian Paints, Cognizant, Madras Atomic Power Station, Honeywell, Tamil Nadu Investors Association. For speaking engagements write to pattu [at] freefincal [dot] com
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