The Sell Down, The Rebound, The Pandemic

885 stocks were grouped based on their performance during Jan – Mar, Mar – Jun, and Jan – Jun 2020.  All data were taken from Bursa Market Place (Read more here).  About 95% of stocks were battered down during the sell down time frame.  50% of them dropped between 30% to 60%.

During the rebound period (Mar – Jun), the group of stocks which were hammered down the most in Jan - Mar period were the best performers.  If you bought these 11 stocks in Mar, your average return is 187%!  But their YTD performance is still -55%.

Generally, the rebound magnitude is directly proportionate to the magnitude of the sell down.  Will these stocks continue to recoup their loses in the next three months?  We shall update the results by the end of RMCO.

Stay Safe!

Table 1

Sell Down Range	Average Sell Down	Average Rebound	Average YTD	No. Stocks
-80% to -90%	-84%	187%	-55%	11
-70% to -80%	-74%	157%	-32%	48
-60% to -70%	-65%	100%	-30%	89
-50% to -60%	-55%	98%	-12%	147
-40% to -50%	-45%	64%	-10%	150
-30% to -40%	-35%	46%	-5%	160
-20% to -30%	-25%	36%	1%	115
-10% to -20%	-16%	26%	6%	85
0% to -10%	-6%	5%	-1%	39
10% to 0%	4%	20%	25%	27
20% to 10%	13%	128%	156%	8
30% to 20%	22%	132%	184%	4
40% to 30%	33%	415%	587%	1
50% to 40%	n/a	n/a	n/a	0
60% to 50%	59%	12%	77%	1
70% to 60%	n/a	n/a	n/a	0
80% to 70%	n/a	n/a	n/a	0
90% to 80%	n/a	n/a	n/a	0
100% to 90%	n/a	n/a	n/a

Worst Performers (-40% to -90%) During Jan - Mar Period

Disclaimer:  The above analysis does not imply any buy or sell recommendation.  The author disclaims all liabilities arising from any use of the information contained in this article.

Disclosure: The author may have interest in the stocks of the companies in this article.

Malaysia Covid-19 Forecast using Generalized Logistic Function (Updated 29-May-2020)

Since the last update on 28 Apr 2020 (Read more here), Malaysia has moved into the Conditional Movement Control Order (CMCO) phase.  Additional easings were introduced, and more economy sectors are allowed to reopen with conditions.

Subsequently, the daily new Covid-19 cases in Malaysia is on the uptrend again.  The original Generalized Logistic Function (GLF) is no longer adequate to predict the outcome as the underlying conditions have changed.

As such, two sets of GLF are now needed to monitor the situation.  The first set of GLF will be spanning from 24 Jan 2020 to 28 Apr 2020 (GLF pre-CMCO), while second set of GLF will be starting from 29 Apr 2020 to the latest date (GLF CMCO).

The GLF pre-CMCO is using the original version GLF but the new data feeding stopped at 28 Apr 2020.  Thus, the subsequent prediction was based on the actual data upto 28 Apr 2020.  The GLF CMCO is forecasted based on the derived CMCO daily data.  The derived CMCO daily data is calculated by subtracting the actual daily data with GLF pre-CMCO predicted data.

For example, on 29 Apr 2020, the actual daily new cases were 94, the GLF pre-CMCO predicted 44 cases on that day.  Hence, the derived CMCO daily data would be 94 – 44 = 50.    Figure 1 shows the derived CMCO daily cases and GLF CMCO daily cases.  Figure 2 shows the 7-days rate of change for the above-mentioned data.

Figure 1: Derived CMCO Daily New Cases

  

Figure 2: 7-Days Rate of Change for derived CMCO Daily New Cases

Figure 3: Actual Daily, GLF pre-CMCO daily & GLF CMCO daily data

 Figure 4: Actual Cumulative Cases vs GLF (pre-CMCO + CMCO)

  

Figure 3 shows the actual daily data, together with GLF pre-CMCO and GLF CMCO prediction.  As the 7-days rate of change has no signs of slowing down, the GLF is predicting the trend will keep moving up for now.  If the conditions do no improve in the next few weeks, the chances of the total cases hitting 10k are high as depicted in Figure 4.

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Malaysia Covid-19 Forecast using Generalized Logistic Function (Updated 03-May-2020)

After some easing measurements were announced at the beginning of Phase 4 MCO, it seems like the daily new cases have been going up again.  Some argued that the higher cases may be due to imported cases.  Table 1 shows the breakdown of local and imported cases since 29 Apr 2020.

Table 1: Local and Import New Covid-19 Cases

Date	Local	Import
29-Apr-2020	22	72
30-Apr-2020	32	25
01-May-2020	57	12
02-May-2020	94	11
03-May-2020	70	52

The local new cases are trending upward since 29-Apr-2020 and slightly retreated on 03-May-2020.  To study the situation, let’s look at the rate of change curve.  Figure 1 shows the daily new cases plot, together with 7-days, 14-days and 21-days rate of change, exclude the imported cases.

Figure 1: Rate Of Change

While it is obvious that daily numbers have gone up, 7-days rate of change has also crept up.  Both the 14-days and 21-days rate of change is at the juncture of turning up again.  To get a clearer picture, let’s look at the 2^nd derivative of these plots.  Figure 2 is the 2^nd derivative of the rate of change.

Figure 2: 2^nd Derivative of Rate Of Change

 

7-days curve has crossed above the zero line.  While both 14-days and 21-days curve are still in the negative territory, they are approaching zero.  Once both cross above the zero line, it could mean that 2^nd wave is coming.

The data imply that something has fundamentally changed.  It could be due to more sectors have resumed work too early or people are lowering down their social distancing guard.

Take care.  Please continue to Stay@Home if possible!

Aviation Related Stock, Revisit (Part III)

In Part II of this series (Read more here), the basic assumptions for post-Covid19 Monte Carlo model were illustrated.  Table 1 shows the summary of the inputs.

Table 1: Summary of Inputs

2020 EPS Estimation
EPS (RM)	0.28	0.18	0.08	-0.03	-0.15
Probability	15%	50%	30%	3%	2%
Recovery Pattern
Shape	V	U	L
Probability	38%	54%	8%
PE Range (2015 – 2020)
PE	10 – 23	7	30	Other
Probability	~ 50%	0.08%	0.08%	~ 50%
Floor Price
RM2.00 *

 *             This is the revised floor price.  In Part II, the floor price was set at RM3.29 (0.8 of tangible book value as at 31 Dec 2019 RM4.11).  The reason of change is that the lowest price to tangible book value of SAM was 0.619 in the past five years period.  By adding 20% additional discount to the lowest price to tangible book value, the revised floor price would be around RM2.00.

These inputs were fed into the Monte Carlo model.  Total 3000 cases were simulated.  The results of the simulation were plotted in Figure 1.  The snapshot (first 30 lines) of the Monte Carlo model could be found in Appendix 1.

Figure 1: Monte Carlo Forecast Heatmap

The average price for 2020 is between RM3.0 – RM3.5, 2021 could be around RM4.0 – RM4.5, 2022 could be around RM5.0 – RM5.5, while 2023 could improve to RM7.0 – RM7.5.

However, there is one significant difference between post-Covid19 and pre-Covid19 Monte Carlo model – the probability of Min value!

In ordinary Monte Carlo model, Min or Max value have very low probability of occurrence, normally less than 1%.  Meanwhile, in the post-Covid19 model, the floor value is fixed at RM2.0.  As such, any results that fall below RM2.0 will be reported as RM2.0.  The heatmap shows that in 2020, there are 50% chance that the price could go down to RM2.0 (or below).  As the model did not limit the Max value, thus there are 1% chance that it could hit RM8.0 – RM8.5 range in 2020.

Conclusion

The post-Covid19 Monte Carlo model shows that the average price for SAM is lower than pre-Covid19 model but could slowly recover back to pre-Covid19 level, depending on the development of Covid19.  The simulation results are based on the assumptions illustrated in this series of article (Part I, II, and III).  The assumptions are not final and must be adjusted frequently in order to reflect the volatile conditions.  Once the situation has become more stable, a final version of the model would be updated.

The probability heatmap of Monte Carlo model is an additional risk assessment tool to investors.  The closing price of SAM on 30-Apr-2020 was RM5.82.  At this level, risk averse investors might stay on the sidelines.  However, on the longer term perspective, the price could be viewed as neutral.

The purpose of this study is to illustrate the concept of constructing a post-Covid19 Monte Carlo model that could incorporate the impact of the pandemic.  It is not meant to provide any buy or sell recommendations.

Stay Safe!

Appendix 1 (First 30 lines of Monte Carlo Model, there are total 3000 lines)

Disclaimer:  The above analysis does not imply any buy or sell recommendation.  The author disclaims all liabilities arising from any use of the information contained in this article.

Disclosure: The author may have interest in the stocks of the companies in this article.