Based on the analysis in part I (Read
more here), US housing market is performing better than other region. Thus, companies that have high wooden
furniture export to US market may continue to do well. However, this assumption requires further analysis
to identify the company earnings growth quality, whether it was due to foreign
exchange rate or business growth. Secondly,
as the timber business is cyclical, companies that have higher cash reserved
could weather the downturn better and the ability to pay dividend is higher
too. Finally, either single stage or
multistage DDM model may not be appropriate to evaluate the stocks as the
perpetual value is hard to estimate.
Thus, the Monte Carlo valuation model based on P/E ratio, using three
years projection is recommended.
A quick search on Bursa Market
Place yielded the following stocks (Table 1). The five columns of ratings are AlphaFactor
developed by S&P Capital IQ (Read
more here). It is a free and easy
tool to help investor to screen the stocks in Bursa Malaysia. Most stocks that have exposure to US market have
good rating except Sern Kou. Thus, Sern
Kou is removed from the subsequent screening.
Table 1
Stocks
|
Momentum
|
Valuation
|
Growth
|
Efficiency
|
Quality
|
Average
|
SHH
|
1
|
1
|
1
|
1
|
1
|
1
|
LiiHen
|
2
|
1
|
1
|
1
|
1
|
1.2
|
Latitude Tree
|
3
|
1
|
1
|
1
|
1
|
1.4
|
Jaycorp
|
3
|
1
|
1
|
1
|
1
|
1.4
|
HomeRiz
|
2
|
2
|
1
|
1
|
1
|
1.4
|
PoHuat
|
3
|
2
|
2
|
2
|
1
|
2
|
Sern Kou
|
2
|
5
|
4
|
4
|
5
|
4
|
Table 2 is the percentage of revenue from US market. Only 4 companies derived more than 50%
revenue from US market. Thus, HomeRiz
and Jaycorp are excluded in subsequent screening as these companies may require
different valuation method.
Table 2
Stocks
|
% of revenue from US
(2015)
|
SHH
|
Only stated all products
mainly exported to US. No numbers were
given in AR
|
Latitude
|
92%
|
PoHuat
|
79%
|
LiiHen
|
79%
|
HomeRiz
|
40%
|
Jaycorp
|
28%
|
The next step is to use Beneish M-Score (Read
more here) to scan for any potential financial manipulation (Table 3). Most of the companies passed the Beneish
M-Score test except SHH on year 2015. It
is due to the high receivable level on 2015, which was doubled to RM12,714,843
compared to 2014 level RM6,353,626. The company acknowledged the credit risk in 2015 annual
report. The receivable level has subsequently
improved to RM8,323,000 in the Q4’2016 balance sheet, tallied with cash flow
from operating adjustment figures.
Table 3
Beneish M-Score
|
2015
|
2014
|
Benchmark (lower better)
|
Comment
|
SHH*
|
-2.85377
|
-1.57566
|
< -1.78
|
* 2016 and 2015.
|
LiiHen
|
-2.82747
|
-1.96595
|
< -1.78
|
|
Latitud*
|
-2.53439
|
-2.03448
|
< -1.78
|
* 2016 and 2015.
|
PoHuat
|
-2.06839
|
-2.26697
|
< -1.78
|
Table 4 is the growth comparison for US housing start, Malaysia
wooden furniture export, companies’ revenue and profit after tax attributable
to owners. From 2013 to 2015, the US
housing start is growing at CAGR 12.24%.
Malaysia wooden furniture export CAGR is 3.36% due to the negative
growth in 2013. All four companies
exhibited similar trend in 2013 at a different degree. Most of the companies are doing well in
recent years except SHH. Even though its
US market exposure is the highest among the companies, its earnings trend is
very volatile. As such, combining with
the Beneish M-Score results, SHH will be excluded in the subsequent analysis.
From 2014 to 2015, assuming that USD appreciated against RM
at the rate of 20%, thus, either the revenue or profit has to gain more than
20% in order to be classified as business growth. Among the remaining three companies, only
LiiHen and PoHuat meet these criteria.
Latitud Q4’16 PAT attributable to owner was at -6.8%. In fact, Latitud stated in the Q4’16
unaudited report that US sales was declining due to strong competition. As such, Latitud will be excluded in the
subsequent analysis.
Table 4
2013
|
2014
|
2015
|
CAGR
|
Comment
|
|
US Housing Start
|
18.44%
|
7.85%
|
10.70%
|
12.24%
|
|
Wooden Furniture Export
|
-12.41%
|
10.30%
|
15.13%
|
3.61%
|
|
SHH revenue
|
-3.84%
|
15.09%
|
-0.57%
|
3.24%
|
Q4'16 growth 6.7%
|
LiiHen revenue
|
-8.83%
|
25.98%
|
37.43%
|
16.43%
|
Q2'16 + growth intact
|
Latitud revenue
|
-4.67%
|
31.87%
|
9.06%
|
11.09%
|
Q4'16 growth 8.5%
|
PoHuat revenue
|
-8.69%
|
5.36%
|
20.35%
|
5.01%
|
Q3'16 + growth intact
|
SHH PAT attributable to
owner
|
n/a
|
432.19%
|
-20.03%
|
106.30%
|
Q4'16 growth 79%.
|
LiiHen PAT attributable
to owner
|
-17.25%
|
59.45%
|
103.79%
|
39.06%
|
Q2'16 + growth intact
|
Latitud PAT attributable
to owner
|
117.22%
|
100.75%
|
21.64%
|
74.40%
|
Q4'16 growth -6.8%
|
PoHuat PAT attributable
to owner
|
10.59%
|
41.90%
|
64.62%
|
37.21%
|
Q3'16 + growth intact
|
Table 5 is the financial strength of the remaining two
companies based on their most recent financial results. Both companies have decent cash level and able
to serve the debt without additional funding.
Table 5
LiiHen
|
PoHuat
|
|
D/E Ratio
|
0.12
|
0.17
|
Cash/Share
|
0.69
|
0.27
|
Debt/Share
|
0.17
|
0.17
|
NTA/Share
|
1.33
|
0.97
|
Price 30/9/2016
|
3.10
|
1.53
|
P/Cash
|
4.51
|
5.62
|
P/Book
|
2.33
|
1.58
|
Finally, the valuation model is based on Monte Carlo
simulation with the following assumption
1.
PE ranges from 7 – 10. Based on average historical range.
2. The lowest EPS for any particular forecasted
year are fixed at 10% lower than 2015 value.
The intention is to capture any unforeseen impact such currency
fluctuation or market slowdown.
3. The highest EPS for any forecasted year are
maxed at 15% or lesser than the previous forecast year. The intention is to capture any possible
market share expansion.
4.
The average EPS for any forecasted year are
maxed at 8.5% or lesser than the previous forecast year. The intention is to capture the average
growth below the CAGR of US housing start.
5.
3000 samples are used in the simulation.
Table 6a and Table 6b are the forecasted prices using Monte
Carlo simulation for LiiHen and the potential return scenario based on the
closing price as at 30-Sep-16 (LiiHen: RM3.1).
Table 7a and 7b are the forecasted prices using Monte Carlo simulation
for PoHuat and the potential return scenario based on the PoHuat closing price
as at 30-Sep-16 (PoHuat: RM1.53).
Table 6a
LiiHen
|
2016F
|
2017F
|
2018F
|
Average
|
2.88
|
3.11
|
3.36
|
Min
|
1.96
|
1.96
|
1.96
|
Max
|
4.00
|
4.50
|
5.10
|
Median
|
2.80
|
3.04
|
3.30
|
Table 6b
LiiHen
|
2016F
|
2017F
|
2018F
|
Average
|
-7.10%
|
0.32%
|
8.39%
|
Min
|
-36.77%
|
-36.77%
|
-36.77%
|
Max
|
29.03%
|
45.16%
|
64.52%
|
Median
|
-9.68%
|
-1.94%
|
6.45%
|
Table 7a
PoHuat
|
2016F
|
2017F
|
2018F
|
Average
|
1.49
|
1.61
|
1.74
|
Min
|
1.12
|
1.12
|
1.12
|
Max
|
1.90
|
2.20
|
2.50
|
Median
|
1.52
|
1.60
|
1.71
|
Table 7b
PoHuat
|
2016F
|
2017F
|
2018F
|
Average
|
-2.61%
|
5.23%
|
13.73%
|
Min
|
-26.80%
|
-26.80%
|
-26.80%
|
Max
|
24.18%
|
43.79%
|
63.40%
|
Median
|
-0.65%
|
4.58%
|
11.76%
|
Based on the simulation results, both LiiHen and PoHuat are
trading around the average value as at 30-Sep-2016, with LiiHen slightly on the
high side. PoHuat valuation seems fairer
might due to potential EPS dilution of outstanding warrant that expired on
2020. The valuation model in this
article only factored in certain percentage of dilution up to 2018.
This part II analysis is based on publicly available data,
using mainly quantitative approach.
Qualitative method such as supply chain analysis, product mix, and new
product development quality and capability are not covered.
Part III will focus on conventional timber companies that
have logging, plywood manufacturing and other timber harvesting business.
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.
