The Effects of Timing your Investments

Below are some visualizations that help to give a sense of to what degree does timing affect the final returns for a particular asset (in this case, index).

A larger and/or more uniform area of colour indicate that timing is not that important, whereas a very small area of different colour (especially blues) indicate that you could only have achieved the highest returns if you invested in the right time.

For the SPY, the areas of highest returns are only achievable if you invested during either of the 2 recent crises (2000 and 2008), and held it until today (end Aug 2020). The similar shades of dark blue show that the 2008 crisis wiped out most of the gains achieved investing in 2000-2008, such that investing in 2008 yields similar returns as investing in 2000.

A dark vertical bar of red at the 2008 area also show that any investment prior to 2008 would have achieved pretty much nothing if exited at that time.

The difference in QQQ is much more pronounced - the highest returns could only have been achieved if you invested in the trough of the 2000 crash.

For the below ETFs representing China and Singapore, investing early was the key to achieving the best returns - see how the blues are in horizontal swathes near the top instead of localized ones as per the SPY and QQQ.

Finally, Bitcoin. Because of the wider range of returns, I've truncated returns above 400% - here all represented by the darkest blue. Early adopters have it the best, but note the red patch near the top left, indicating how they had to suffer severe drawdowns too.

I am not recommending that you should or should not time the market - this simply shows that for some investments, timing plays a huge role in determining whether you make average returns or not. It may be seen one way as "since the odds are against me, I might as well settle for average returns", or "since the rewards of beating the market are so great, I should strive to get the timing right".

*ETF prices and returns are dividend-adjusted

Decomposition of Returns by Hour

Where  do stocks make their greatest moves?

For the SPY ETF, it is the overnight session (closing times + extended hours).

Here we look at the SPY ETF and decompose the movements into overnight and intraday by the hour.

Hour 9 captures the  overnight gap, while hour 10 is captures the price change from 0900 to 1000 local exchange time. Hour 16 captures the action in the last hour of trading from 1500-1600.

Table 1 - Cumulative points gained by the SPY and the contribution of points by the hour

05 May 2008 to 31 Dec 2019

Date 9 10 11 12 13 14 15 16 Total
31 Dec 2019 117.4739 22.9358 -4.3967 8.6097 15.5789 -3.2806 19.6799 4.1291 180.73

Two thirds of the gains from May 2008 are from the extended session + overnight moves! This means that if you pursued a strategy of simply buying at the close and then exiting at the next day's open, you would have captured the majority of the price returns for this whole bull run.

 

Table 2 - Statistical description of percent changes in each segment

Values in % terms i.e. 0.0171 means 0.0171% not 1.71%, normalized by their price at the top of the hour

The largest changes are seen in the overnight gaps - 9.2% and +10.1% and the hour with the least variation in percent returns is the 1200-1300 time period.

Update 2020-08-11

Some general updates regarding how I'm positioning myself.

I'm still remaining long Calls in some depressed stock - it rallied for a burst in May but it was very short-lived. The risks still remain and the play remains viable in the long term (to recap, I'm looking at next Jan). The virus itself may be more long-drawn than initially expected, but it's ok because the market seems ever ready to reward any good news. Keep riding on the disconnect.

The newer idea is OTM Verticals on pharma stocks, since there's nothing else much to do now.

The curve steepener idea initiated some time ago is now expiring for what is likely a small gain or loss. The curve never steepened fast enough for the index to capture it. The only thing wasted on this trade was the tied up capital but that's easily managed.

In the FX space, it seems like the dollar has formed a mid term bottom and I would go long the USD for a bit here. The EUR rally is no doubt warranted so I won't contest that.

I remain underweight SG stocks - it's quite clear that it's an asymmetric play to the downside - has been since the financial crisis.

I think the next 5-10 years or so, we will have to be more active in managing our portfolios - it's hard to see where returns are going to be attractive in the passive space.

The Way Forward

The rally is firmly in place now despite "common sense" that we should be seeing a 2nd wave down. I've mentioned in previous posts that we should find ways we can position ourselves should the rally happen and I've mainly done that through Calls which I believe give the best risk-reward in this lower volatility environment. VIX was then about 38.

Now that some of the Calls are ITM, I have to increasingly take care of the downside. The risks of another downturn are still there - I am still suspicious of the strength of the rally. Some say it's priced for perfection, and so the surprises are to the downside. Given the lower volatility environment we still find ourselves in, with VIX being at 25, we can consider a few things: (a) buying Puts, and (b) rolling the Calls to further OTM ones. I'm using strategy (b) for individual counters which have seen their prices severely depressed e.g. cruise liners for which I've already bought Calls for, so I don't want to be paying double premiums. Strategy (a) is more for general downside protection on indices.

These are just nascent ideas as of now, spurred by Friday's strong rally. Subject to correction in the future when I've thought through them more.

Price prediction using historical trajectories

Was curious what it would look like if I simply scanned through all of history, look for price movements within the same 60-day window that are similar to current movements, and then see what happened to them 30 days later. The results are above.

Each line is a 90-day price trajectory. I used the first 60 days to fit the pattern to current price movements, and selected the top 14 where the patterns matched the closest. The decimal in the parenthesis is the range of the movement, denominated by the starting price (on day 0). The patterns are filtered such that only ranges of >15% are selected.

Some current caveats: is not a quant study - nothing very rigorous about this nor will I place any bets on it. Another caveat: I simply normalized the frame by min and max values - and simply filtered them to be > 15%, judge for yourselves how representative each move is with respect to the current one (35%).