Aviation Related Stocks

According to World Bank’s data (Read more here), the air transport passenger grew almost five times in the past thirty years.  The total passenger in 1986 and 2016 were 0.8 billion and 3.7 billion respectively.  The compounded annual growth rate was 5.1%.  See below chart for details.

This was underpinned by world stronger economy growth and cheaper air travelling cost.  People are willing to spend more on vacation travelling around the world.  The international world tourist arrival hit 1.24 billion in 2016 (Read more here), see below chart for details.

Moving forward, the air travel outlook is forecasted to be growing at 2.7% to 5.7% annually upto year 2036, as depicted by the following chart.

Industries that are benefited from the travel boom include tourism, airlines, airport and aerospace engineering.  Although tourism operators and airlines markets are growing, competition among them is stiff (Read more here).  The market will undergo consolidation via merger and acquisition before it could achieve new equilibrium for growth.  For airport operators, they are regulated heavily by authority.  As such, the income stream will be difficult to predict given the recent changes of political landscape in many countries.  Hence, aerospace engineering companies are relatively less volatile as it is dominated by two large international players.

In Malaysia, there are few companies that involved in aerospace engineering such as SAM, Kobay, Texchem, T7 Global, JHM and others.  Among the above list, only SAM derives its major chunk of revenue from the aerospace engineering segment, which contributed about 60% to its total revenue in FY2017.   Meanwhile Kobay’s aerospace engineering segment contributed about 20% to its revenue; Texchem, T7 Global and JHM have less than 1% of aerospace engineering contribution to their revenue, but they are actively expanding their aerospace engineering capacity lately.  As such, this study will focus only on SAM.

SAM’s Profile

SAM Engineering & Equipment (M) Berhad (“SAM Malaysia”) is a key player in precision machining, equipment integration and automation solutions, primarily for the aerospace and equipment industries.

Listed on the Main Board of Bursa Malaysia, SAM Malaysia is a subsidiary of Singapore Aerospace Manufacturing (SAM) Pte Ltd, a leading manufacturer of critical aero-engine components whose clientele includes some of the world’s major aviation players.

The following table shows the major shareholders of SAM.

The AlphaIndicator (Read more here) gave SAM an average score of 9, a rather good rating but since AlphaIndicator is useful only when comparing among various companies, thus the score may not have any significant meaning.  Anyway, it will be stated here for convention purpose.

The next step is to screen for potential financial manipulation by using Beneish M-Score (Read more here).  The following table shows the Beneish M-Score of SAM.

Ratio Component	Beneish M-Score
	2018*	2017
DSRI	1.286507061	1.078304673
GMI	0.854792636	0.912826519
AQI	2.321155929	3.334740491
SGI	1.113062895	0.866704298
DEPI	1.26083027	1.29914754
SGAI	1.079260482	1.242004052
TATA	-0.052041742	0.008710172
LVGI	1.389254511	1.038488428
M-Score (fail if > -1.78)	-2.01	-1.61

* Based on 4^th Quarter Results Annoucement

SAM failed the Beneish M-Score test on 2017.  A closer look into the M-Score components revealed that the main culprit could be AQI (Asset Quality Index) score.  The cash level of SAM is deteriorating since 2017.  It was due to higher dividend payout despite lower revenue on 2017.  The reason behind the higher dividend payout was the conversion of 39,567,728 ICULS into ordinary share.  As such, although the dividend per share of 2017 was lower than 2016, the total amount of payout was higher.  One explanation for this move could be the low gearing position of SAM has taken, thus to attract additional capital funding via equity, SAM has to maintain a decent dividend per share to stay competitive.  For now, the risk of potential financial manipulation could be low.  The next section will look into SAM’s liquidity position and its capability to fund future growth. 

	2018*	2017	2016	2015
Debt to Equity Ratio **	0.04	0.00	0.01	0.03
Cash and Cash Equivalent (RM’000)	21,556	99,001	173,644	103,585
Return on Assets **	9.55%	7.51%	11.36%	7.29%
Return on Capital Employed (ROCE) **	15.27%	11.99%	15.54%	10.37%
Free Cash Flow to Total Capital	n/a	(9.84%)	16.89%	(1.66%)
CAPEX (RM’000)	134,307	82,641	24,425	5,401
Cash Flow from Operating Activities (RM’000)	97,160	37,822	99,430	(983)

Source: Dynaquest Sdn. Bhd. STOCKBASE platform except * and **.  See “Notes” at the end of this article for Copyrights details.

* and ** are from Bursa Market Place

The above table indicates that the cash position of SAM is deteriorating.  The cash generated from operating activity is not sufficient to fund CAPEX and pay dividend.  As such, the company is obtaining additional funding via debt in 2018.  Fortunately, SAM still has ample of room to raise fund given that its low gearing position.  However, looking at the ROA and ROCE, although it is pretty decent compare to other contract manufacturers, it does not show any advantage for SAM to be in aerospace engineering sector.  Thus, additional funding via debt could eat into its profit, thus the management shall look into ways to improve their margin if heavy CAPEX is required to continue growing in aerospace engineering industry.

Given that the Free Cash Flow to Total Capital is negative, the dividend payout for the foreseeable future could be stagnant.  Hence, the Monte Carlo valuation model based on P/E ratio, using three years projection is used for this analysis.  The following assumptions are needed as input into the Monte Carlo simulation model.  Total 3000 samples were used for this analysis.

Company	Avg. 5Y PE range*	Forecasted lowest EPS from 2019 – 21 (sen)	Highest EPS from 2019 -21	Average EPS growth (%)
SAM	9 – 17	45	CAGR 5%	3.9%

* Dynaquest Sdn. Bhd. STOCKBASE platform.  See “Notes” at the end of this article for Copyrights details.

The following charts are the forecasted price range of SAM based on the Monte Carlo method.

As at 8^th June 2018 noon, SAM was trading at RM7.52, which is close to the maximum forecasted price.  The risk of adding SAM into investment portfolio is relatively high.

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.

Notes:  The data are the property of Dynaquest Sdn. Bhd.  It is subject to Intellectual Property Rights and T&C.  Do not reproduce without the consent from Dynaquest Sdn. Bhd.  (The author has signed a “Data Sharing Agreement” with Dynaquest Sdn. Bhd., based on a “Data Sharing Fee”, to use the data from Dynaquest Sdn. Bhd.’s STOCKBASE platform in this blog.  The content of this blog in no way represents the views or opinions of Dynaquest Sdn. Bhd.)

Bitcoin Block Hashing Algorithm (Part III)

In previous two articles, Part (I) and Part (II) of the Bitcoin Block Hashing Algorithm [(Read more here) & (Read more here)], some basic hashing algorithm and mining process were illustrated.  This article will demonstrate the hash verification steps by using Python script.

Three examples were selected from Bitcoin block explorer (Read more here).  The first example was the genesis block, and the other two were randomly selected.  The last example included a loop code to mimic the actual nonce solving process.  It took about 7 hours to find the nonce, by using a normal day-to-day laptop.  As such, one really needs a powerful system in order to be a miner!

Block #0 (Read more here)

Block #125989 (Read more here)

Block #522885 (Read more here)

Bitcoin Block Hashing Algorithm (Part II)

In previous article (Read more here), the basic core hashing algorithm of Bitcoin using SHA256 was illustrated.  SHA256 hashing algorithm can cryptograph input messages of any size into a unique 64-character code.  Thus, contracts or transactions could be hashed and distributed on the Blockchain network.

This article will provide an overview of the Bitcoin hashing process using real-world example.  Each Bitcoin transaction info is publicly available on the internet.  One can use the Bitcoin Block Explorer (Read more here) to view the transaction info of every block (ledger) that is registered onto the Blockchain.  For example, the first Bitcoin block was created by the Bitcoin inventor, Sathoshi.  The block info is accessible on the internet and it is commonly regarded as the Genesis Block (Read more here).

As you may have already learnt from the previous article, input message is required to generate the unique hash for this transaction block.  The input message (block header) of Bitcoin transaction comprises of

• Version info
• Hash of Previous Block
• Hash of Merkle Root
• Time
• Bits
• Nonce

For the Genesis block,

• the version info is 1;
• the hash of previous block is 0 because there was no previous block;
• hash of merkle root is ‘4a5e1e4baab89f3a32518a88c31bc87f618f76673e2cc77ab2127b7afdeda33b’, which is the hashing results of the transaction details such as someone sending some bitcoins to another party. (Read more here);
• the time when the transaction was accepted, which was 2009-01-03 18:15:05;
• the bits that was provided by the algorithm to adjust the difficulty of the “mining” task, depending on the number of “miners” participating in the process, for this case was 486604799; and
• finally the nonce, which is a guessing number that was “mined” by the miners.  The nonce is a moving target and depends on time, bits and merkle root, that will be updated roughly every ten minutes.  The successful miner is the one who could guess the nonce that will produce the output hash of certain length of leading zeros.   For the Genesis block, the nonce is 2083236893.

The following diagram shows the flow of the hashing and mining process.

The hash for Genesis block is ‘000000000019d6689c085ae165831e934ff763ae46a2a6c172b3f1b60a8ce26f’.  It has 10 leading zeros.  At the time of this article was written, the target leading zero for new hash was 18.

As we can see from the above diagram, every new block incorporates the info from the previous block.  Thus, changing a small info from a block that sits in the middle of the blockchain without impacting the entire chain is impossible.  As such, once the transaction is registered on the blockchain, it is theoretically irreversible.

Part III of this series will illustrate the Python script for the above real-world hashing algorithm.  Stay online!

Bitcoin Block Hashing Algorithm (Part I)

By now, most people may have already heard about Bitcoin and Blockchain, knowing that it could be described as “distributed ledger”, with the characteristic of irreversible, pseudonymous, global and secure.  Another interesting characteristic of Bitcoin or Blockchain is the “mining” process.  The reason behind the irreversible and secure characteristics of Bitcoin and Blockchain is that for each transaction block to become valid, it requires huge computing power to solve the hash algorithm.

 

The “miner”, could be anyone whom has the computing power to solve the algorithm.  They are competing among each other to solve the hash.  The winner (the first one who solves the hash) will be rewarded with some amount of new Bitcoin, generated automatically by the Bitcoin Blockchain algorithm, and also transaction fees.

The core hash algorithm is based on SHA256.  Secure Hash Algorithm, is a set of cryptographic hash functions designed by the United States National Security Agency (NSA) (Read more here).  It is designed in a way that the input messages of almost any size (over 18 quintillion different values) are converted into a unique 64-character long code in hexadecimal format.  And the one-way process means it is impossible to obtain the input message by knowing the unique 64-character long code.  A simple analogy of this process is colour mixing.  One can produce a new unique colour by mixing various colours, but one cannot “unmix” the new unique colour back to their individual component.

Let’s look at some simple examples of SHA256 hashing using either the following Python script (a programming language) or an online hashing tool (Read more here)

The input message “abc” is hashed using SHA256 and the output of the hash is ‘ba7816bf8f01cfea414140de5dae2223b00361a396177a9cb410ff61f20015ad’.

A miniscule change to the input message will significantly alter the output hash.  For example, changing the ‘c’ of the input message to capital ‘C’ will produce an entirely new hash output '0a2432a1e349d8fdb9bfca91bba9e9f2836990fe937193d84deef26c6f3b8f76'.

Using the same approach, the entire Universal Declaration of Human Rights (Read more here) can be hashed into ‘c08345541d77c256c914d0ada1ea02497c7525efe0e5699d6ef6126a66b4ee59’.

.

.

.

As such, any contracts, agreements or transactions could be cryptographed and distributed on the Blockchain network with its unique tampered proof hashing algorithm.  The next article will illustrate more real-world examples of Bitcoin transaction algorithm.  Stay online!

Now here’s the quiz, what is the input message of the following hash?

1d0807cab52364ee6536ac5880e522765e66de90cab618797492afeb80fb89b2

Hint: “V*** *** **t*** M*****i*”