An Investment “Compass” For Our Volatile Times

Understanding the Market

With the market rising and falling like a roller-coaster over the past year, we thought that it would be timely and useful to put out an article that could act as your compass in these times.

Many of us heard of the age-long investment adage of “buying low and selling high”. Basically, the lower you pay for something, the more you profit when you sell it off at a higher price. Sounds easy right? We would like to think so, however investors often do the opposite – “buy high, sell low” because of behavioural biases.

Even before we start to “buy low”, we would have to understand the following,

  • How low is ‘low’?
  • How frequent do ‘low’ occurs?
  • And how much returns could we expect from buying at low levels?

To answer the above questions, we have included some interesting graphs (below) on the US and Singapore indexes to get our point across1. We find that pictures tend to be more convincing than words!

In essence, the way to interpret the graphs is that a point on the horizontal axis (Change from 52 week high) corresponds to a point on the vertical-axis (Returns in 1,2 and 3 Years). This means that for x% drop, we are able to arrive at an average return of y%.

STI 1,2,3Y Returns

Exhibit 1: STI returns 1,2,3 years later vs % Change from 52 week high (as of 15 April 2016)

 S&P500 1,2,3Y Returns

Exhibit 2: SnP 500 returns 1,2,3 years later vs % Change from 52 week high (as of 15 April 2016)
Source: Raw data is obtained from Yahoo finance and graphs are generated using R programme
Note: The best fit line (straight line) represents the average relationship between x and y variable. The curved line (loess regressions) could also be interpreted as the average relationship, but the difference is that it’s locally weighted by the points around the region.

Here are THE 2 key insights from the above plots:

1) The best-fit line is sloping downward. This suggests that on average, as the market falls, you would expect higher gains 1, 2 and 3 years later.

2) Hence, for most parts of the graph, the lower our purchase price, we should reasonably expect higher returns.

With that out of the way, our next questions would be:

– When is the price low enough to call a bottom?

– Assuming nothing fundamental serious has gone wrong with the economies, how low is low? When the index falls by: 30%, 40% or 50%?

This is pretty much a trick question on 2 counts:

1) “Cheapness” is heavily dependent on your personal opinion.

2) Without a time machine, how do you even know the bottom? You would only know of the ‘bottom’ many months down the road. And by then if you’re still waiting by the side-lines, you may even miss out the huge rally usually associated with a recovery.’

And right here we have one of the key takeaways of our article. If you only remember ONE thing from our article, remember this:

You don’t have to catch the “bottom” to come out decent in your investment journey.

Sometimes to come out ahead in the investment world, we don’t have to wait for market to fall by a ‘huge %’. Of course if you can consistently do it, then by all means. But for the rest of us without the ability to call “the bottom”, don’t worry. Why so?

Markets, historically, doesn’t always fall by a huge %. In fact, it’s pretty rare for indexes to fall by more than 20%. Here are more graphs to convince you 🙂

STI lower_52weekhigh_histS&P lower_52weekhigh_hist

Exhibit 3: Frequency of Change (Fall) from 52 week high
Source: Raw data is obtained from Yahoo finance and graphs are generated using R programme

So, in future when the market dips by >20%, instead of rushing to press the ‘sell’ button, it may be good to pause and ponder if it is a rare opportunity to bolster your future investment returns. As of right now (as of 15 April 2016), the % change from 52 week high for STI is ~-17%. From the graphs in Exhibit 1, this translates to an average cumulative returns of ~12%, 18%, 21% 1,2 and 3 years later.

As emphasized throughout our article, the above only show average returns. Even with a -17% drop from 52 week high (x-axis), it may still be possible for you to experience returns of -50% (y-axis). A case of improbable but not impossible -and this happened in the early 2000s – as seen with the green dots in our earlier scatter plot of the US and SG Indexes).

On the other hand, for US S&P 500, the market has already rebounded strongly, the % change from 52 week high is a mere -3%. But the average returns is still a respectable ~8%, 16%, 27% 1,2 and 3 years later – albeit with a broader possibilities of positive and negative returns.

As usual, for all type of analysis, there are always limitations and caveats. Here are ours:

1) This downward sloping relationship seems to hold better for the more developed countries. But of course, this may be due to small sample bias. Points that cluster on the LHS seems to be due to few events.

2) Our analysis is not done in the most statistically robust way, as the more statistically inclined readers (the pros) would identify. More sophisticated time series techniques are required to study it in a robust manner. What we hope to lay out here is a simple road map in this increasingly volatile investment environment and we feel that our analysis provides a decent starting point for further work to be done.

3) % fall from 52 week high should not be a sole determinant to your investment decision. We should also keep an eye other factors like the P/E ratio.  For those familiar with valuation techniques, you would know that the true value of an investment is dependent on fundamentals like cash flows/earnings/assets. For example if the P/E ratio is “unreasonable” priced, something has to give and unsustainable valuations will more often than not fall back to fair value (similar to gravitational force) or in finance parlance – “reversion to mean”. A good source for these ratios could be found in iFAST publications.

4) Our article does not delve into portfolio management. We leave that up to you J


In conclusion, we leave you with 2 key points:

1) It is important to understand the probability distribution of your investment decisions, and what we try to show here is that the odds of higher returns increases with a greater fall in market prices. Warren Buffet once commented: “Risk comes from not knowing what you’re doing”, and we hope that this article has illustrated the possible risk-return possibilities.

2) Markets do not always crash, in fact it is not very frequent. So, we may not have to wait for a huge market dip of around 30-50% before investing.

In parting, we hope that you bear in mind that this just serves as an investment compass – a tool to help in your investment journeys. At the end of the day, a tool is only useful if you know your destination. In the event that you do not know your destination, it wold end up like what the Cheshire cat in Alice in Wonderland says, “Then it doesn’t matter which way you go“.

Jirong’s comments:
I’m interested in mixing analytics with areas that I’m interested in. In this article, I’m able to combine my interests in both analytics and finance. For those who are interested in the codes used to generate the graphs, you can visit the following link

1 In this article, our focus is on mature indexes, and less so on emerging economies and individual companies. This is simply because, The following strategy better suits indexes, where the company-specific risks are removed. And stable economies (eg. United States of America, Hong Kong and Singapore, etc.) where the prices are more transparent and does not deviate much from the valuations.

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Disclosure: Jirong is a unitholder of the STI ETF.

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