Friday, 29 December 2017

Is 2018 a Good Year?

There are several predictions for 2018. Some are more ominous than others – the world will end,  nuclear war, earthquakes, tsunamis and many more depressing thoughts.

How about the economy? Quite frankly, if the world is going to end then there is no need for any forecast. Just prepare for the worst! And here I could end this week’s entry.

But we live on hope and it is still the season of joy, peace and hope. In that spirit, I will try to gather the future for 2018. Global GDP is forecast to grow between 3.7% - 4.0%. For Malaysia, the consensus seems to suggest a range of 5.0% - 5.5%. Will that happen? Yes, if the following things hold:

·       domestic demand is strong;
·       inflation is below 3.5%;
·       oil prices remain in the current range;
·       fiscal deficit as a percentage of GDP improves further;
·       household debt will not implode; and
·       external position remains favourable

The bigger risk for Malaysia is political. The GE 14, trade impact with Trump’s “US First” policy, escalation of tensions between U.S. and North Korea and instability in the Middle East. Geopolitical risks far outweigh other considerations. And there is a chance of a recession every 10 years.

But let’s hope for better things and “hope is like the sun – if you only believe in it when you see it, you will never make it through the night” (Holdo, quoting Leia, from “Star Wars: The Last Jedi”).

May God bless Malaysia! And a very Happy New Year!

                                             Picture source:

Friday, 22 December 2017

Household Debt and Financial Stability

Global household debts (consumer and mortgage) have been consistently on the rise with around USD152 trillion in 2015. The top five countries with high household debts to GDP include:

(i)         Switzerland
(ii)        Denmark
(iii)       Australia
(iv)       Netherlands
(v)        Canada

Malaysia’s household debt (% of GDP) from 2002 to 2016 was as follows:

According to the IMF, although finance is generally believed to contribute to long-term economic growth, “recent studies have shown that the growth benefits start declining when aggregate leverage is high”. From experience and past crises, increases in private sector credit, including household debt, may raise the likelihood of a financial crisis and could lead to lower growth. In 2016, on average the household debt to GDP ratio reached 63% in advanced economies and 21% in emerging market economies.

Globally, household debt has continued to grow in the past decade. The IMF had a sample of 80 advanced and emerging market economies to study the relationship between debt, growth and stability.

Findings show there is a trade-off between the short-term benefits of rising household debt to growth and its medium-term costs to macroeconomic and financial stability. In the short term, an increase in the household debt-to-GDP ratio is typically associated with higher economic growth and lower unemployment, but the effects are reversed in three to five years. Moreover, higher growth in household debt is associated with a greater probability of banking crisis. These adverse effects are stronger when household debt is higher and become more pronounced for advanced than for emerging market economies.

The impact of household debt on financial stability from a balance sheet and cash flow point of view was examined by IMF staff. The result of which is shown below:

Figure 1 – First and second round effects of the build-up of household debts on financial stability

The above figure depicts the interactions between household debt, the financial sector, and the real economy. The balance sheet view (panel 1) shows assets and liabilities (debt) at the household level, whereas the cash flow view (panel 2) shows household income and expenses in the form of consumption and debt service. The two main channels through which household debt and consumption interact are deleveraging and debt overhang. Debt overhang may adversely affect aggregate demand through deleveraging or a crowding out of consumption by the debt service burden. Deleveraging can occur through forced or accelerated repayment of debt, reduction in new credit, and increased defaults or personal bankruptcies. From a legal standpoint, default follows from a situation in which assets and income are insufficient to cover debt-servicing costs, and bankruptcy from lack of sufficient assets and income to repay the debt. There may be second-round effects, such as Fisher-type debt-deflation dynamics, that may be caused by downward asset price spirals.

However, country characteristics and institutions can mitigate the risks associated with rising household debt. Even in countries where household debt is high, the growth-stability trade-off can be significantly mitigated through a combination of sound institutions, regulations, and policies. With better financial regulation and supervision, less dependence on external financing, flexible exchange rates and lower income inequality may reduce the impact of rising household debt on risks to growth.
The IMF concludes that overall, policymakers should carefully balance the benefits and risks of household debt over various time horizons while harnessing the benefits of financial inclusion and development.

Sources: Trading Economics, CEIC and IMF (Oct 2017)

Friday, 15 December 2017

Leveraged Buy-Outs – How are Financial Instruments Designed?

The outline structure of a leveraged buy-out may be depicted as follows:

Figure 1:  Outline structure of a leveraged buy-out

To make an offer for a target company, a new company is established (Newco) to raise necessary funds for the acquisition from investors and banks.

In a large buy-out it is usual to see several buyers of debt, mezzanine and equity that carry different risks and rewards (Figure 2).

Figure 2: Types of financial instruments risk and reward

In principle creating financial instruments is similar to painting – there are a fixed number of primary colours and a fixed variety of financial characteristics. However, there are two basic sources of financial returns – yield (or income) and capital gains (or wealth creation).

Table 1: Creating a hierarchy of financial instruments by varying risk and reward

Financial engineering blends together a series of rights and obligations to create a mix of risk, reward and control. The “best” instrument is one that ticks all the boxes in Table 1 above – secured, interest, dividend and share of capital gain. But some are mutually exclusive. In the end, negotiation skills determine instruments that are best subscribed for a buy-out. 

Source: Private Equity Demystified – An Explanatory Guide by John Gilligan and Mike Wright

Friday, 8 December 2017

P2P Lending in Malaysia

Digital Finance is growing fast across the world, and Malaysia is no exception.  During the SCxSC Digital Finance Conference held in November 2017, Securities Commission Malaysia (SC) outlined the progress made in digital markets. The SC strategy aims to enhance access to financing, increase investor participation, augment the institutional market, and develop synergistic ecosystems.  According to SC, the equity crowdfunding (ECF) and Peer-to-Peer (P2P) financing platforms have funded 450 campaigns, raising a total of RM50 million to meet the financing needs of the Micro, Small and Medium Enterprises (MSMEs) (Read more here).
Currently there are six P2P operators approved by SC.  P2P financing is a web-based innovation that broadens the ability of entrepreneurs and small business owners to unlock capital from a pool of individual investors in small amounts and provides a quick turnaround time to obtain financing for their businesses, through an online digital platform.  The P2P framework is part of SC’s on-going effort to provide greater access to market-based financing through the application of innovative technology solutions (Read more here).

Below is the fact sheet for P2P lending by Funding Societies Malaysia, a SC’s approved P2P operator.

Source: Funding Societies MalaysiaThis table has been prepared by Modalku Ventures Sdn Bhd (“Funding Societies”) for information purpose only. It is not intended to be an offer for financing or invitation to subscribe or purchase of securities. Financing application and approval are subject to policies and guidelines (including due diligence and credit assessment) maintained by Funding Societies from time to time. Funding Societies reserves the right to change the information of this table without prior notice and to request for additional documentation or impose additional conditions for financing, if deems fit. 

Friday, 1 December 2017

How to Gain Exposure to Cryptocurrency in Malaysia

Warning: This article is not intended to provide any buy or sell recommendation on any kind of cryptocurrency, it is not an investment advice.  Security Commission (SC) has warned the public that investment in cryptocurrency poses significant risks to investors (Read more here)! 

On 22 November 2017, Bank Negara Malaysia (BNM) Governor Tan Sri Muhammad Ibrahim said in his keynote address, “Readying the Financial Sector Amid the Evolving War on Terrorism Financing”, that the regulators must prepare themselves as digital currencies will become the new norm.  As such, BNM is developing the regulatory structure for digital currencies and from 2018, persons converting crypto currencies into fiat money currencies will come under anti-money laundering law  (Read more here).

There are many tutorials available on the internet about cryptocurrency, but they might be too complicated for the general public to comprehend.  The easiest way to gain exposure to cryptocurrency is to own some of it.  The most popular cryptocurrency now is Bitcoin, which is currently trading at around RM 35,000 per coin.  But not to worry, you can own a fraction of a Bitcoin, in a Satoshi (unit).  One Satoshi is equivalent to 0.00000001 Bitcoin, the smallest unit of Bitcoin currency.  As such, owning 100,000 Satoshi, or 0.001 Bitcoin cost only about RM 35 (as at 23 November 2017).   

Currently in Malaysia, according to the info in the press link (Read more here), there are 11 platforms providing cryptocurrency buy/ sell services in Ringgit Malaysia.  They are not regulated, yet.  Thus, the risk of theft and fraud may be high.  It was reported that a popular Bitcoin exchange was hacked and USD 30 million worth of Bitcoins were stolen by attackers on 21 November 2017 (Read more here).  If you plan to buy cryptocurrency in large quantities, it is advisable to store them in “Cold wallet” rather than “Hot wallet”.  The topic on cold and hot wallet will be covered in another article, soon!

To test one of the platform stated in the press link above is to buy RM50 worth of Bitcoin.  The entire process may take less than 30 minutes.  The tools you need are:
·        Smart phone (either Android or iOS)
·        Hot or Cold wallet.  (Hot wallet normally is attached to the exchange)
·        Online Banking Giro Account

You may follow the steps below:
1.      Open Google Play or App Store on your phone, download and install your desired Bitcoin exchange app.
2.      Follow the instruction to signup an account.  Remember, STRONG password is important!
3.      Verify your identity.
4.      Enable Two-factor Authentication.
5.      Tap the “Deposit” icon in the apps, then follow the instruction to transfer money from your bank account to the exchange’s account.
6.      Wait about 10 minutes to receive the successful confirmation notice of the transfer from the app.
7.      You can now buy Bitcoin by tapping the “Buy” icon.  Viola!  You now own your first Bitcoin currency!
8.      If you already have a “Cold wallet”, now it is time to transfer your Bitcoin to your cold wallet.  (Bitcoin could be transferred to another third-party wallet and thus the original purchase is legit).

The exchange charged about 3 ~ 4% for the buy transaction.  As such, the Bitcoin you may own is not the full RM 50 but about RM48+.  Also, transferring Bitcoin from the wallet provided by exchange to third-parties’ hot or cold wallet is also chargeable. 

Friday, 24 November 2017

A Tale of Two Currencies

In 1965, the Singapore dollar and Ringgit Malaysia were on parity i.e. S$1.00=RM1.00. From 1973, Ringgit began its long slide against the Singapore currency. Why was this so? Especially when resource-rich Malaysia should have an appreciating Ringgit rather than the other way around.

The Strengthening SGD vs RM:

1985 : US$1 = S$2.31
2017 : US$1 = S$1.35 (23 November)

1978 : US$1 = RM2.10
2017 : US$1 = RM4.12 (23 November)

The performance of a currency is very much decided by the country’s monetary policy as much as its fiscal position – GDP growth, fiscal budget deficits, foreign reserves, trade surpluses/deficits, debt level and perceived confidence.

Monetary policy is in respect of three areas:

exchange rate
capital control and funds management
money supply and interest rates

No country can control all three levers together – it’s either one or the other.

Singapore focuses on price stability while Malaysia focuses on interest rates for economic growth and price stability. Monetary Authority of Singapore (MAS) prefers the currency to appreciate while Malaysia has an unstated bias towards a weak currency.

At a glance, Malaysia’s fundamentals remain reasonable, except for:

high household debt;
fiscal deficits for over 20 years;
“fluctuating” oil prices;
perception of low forex reserves;
political scandals like 1MDB;
over 40% of short term bonds held by foreigners – which will impact the exchange rate if there is an exodus;
relative increase in currency in circulation; and
inflationary effect of GST

That may be the case but the real reasons remain speculative and with speculators. Market makers create the volume and banks are at the heart of the matter as much as Governments.

Friday, 17 November 2017

Machine Learning – A Simple Example for Stock Market Prediction

Machine Learning (ML) is an application or methodology to analyse input data and then predict an output value using statistical analysis.  It is getting popular in finance and the investing industry.  The basic idea of ML in investing is feeding all available data to a computer and let the algorithm learn the relationship between the data and stock price movement.

Traditionally, finance and economics data have been analysed statistically to find their relationships with KLCI.  Recently, all other variables which are indirectly or not related to KLCI such as weather, traffic conditions, concert ticket sales, celebrity news and others, have been included in the ML algorithm.

Let’s do a simple experiment, using Google Trend data to predict Kuala Lumpur Composite Index (KLCI).  Three search-terms – “Malaysia”, “1MDB”, and “KLCI” were selected.  The popularity of each search-term over time were plotted together with KLCI.  Chart 1 is search-term “Malaysia” and KLCI; Chart 2 is search-term “1MDB” and KLCI; while Chart 3 is search-term “KLCI” and KLCI.

Based on a cursory inspection, Charts 1 and 2 do not reveal any strong relationship between search-term and KLCI movement.  Although there was a sharp drop in KLCI when the popularity of “1MDB” surged in Aug 2015, the subsequent surge did not move KLCI drastically.  Chart 3, on the other hand, is more interesting as each time the popularity of search-term “KLCI” peaked, the KLCI tend to reverse its downtrend movement.

Next, these data were then analysed using basic machine learning algorithm.  Generally, there are two main types of machine learning used in quantitative finance – Regression, and Classification.  For simplicity purpose, Classification method is chosen for this analysis (Read more here).

The KLCI data was transformed into “Up”, “Down”, “Flat”, and “Dunno” by calculating the weekly closing price changes.  Example, if week 2 closing price is higher than week 1 closing price, week 2 will be classified as “Up”.  The “Down”, and “Flat” were calculated similarly.  Additionally, the “Dunno” category was introduced to eliminate noises for the region where no high search popularity occurred.

A time lag effect was also introduced into the model to “predict” whether KLCI will be “Up”, “Down”, “Flat”, or “Dunno” in the coming week.  As such, current week search-term results will affect following week’s KLCI behaviour.

Several algorithms were tested and k-nearest neighbours (KNN) algorithm was chosen as the accuracy is the highest amongst others.  See Pictures 1 and 2 for details.

Picture 1.

Picture 2.

Now, let’s run a hypothetical test case to predict KLCI movement.  In Test case 1, assuming the search-term popularity for “Malaysia”, “1MDB”, and “KLCI” are 2, 1, and 25 respectively.  This means “Malaysia” and “1MDB” search traffics are almost flat but “KLCI” search traffic increased by 25%.  The KNN algorithm predicted the KLCI will go down in the following week.  In Test case 5, both “Malaysia and “1MDB” are almost flat but “KLCI” retreated from a high peak.  The KNN algorithm predicated the KLCI will go up in the coming week.  The machine learning algorithm is giving similar results as eye-balling observation.  Table 1 shows KLCI movement predicted by KNN algorithm based on various test cases.

Above is just an illustrative example of how Google Trend and machine learning algorithm works.  Actual algorithm trading requires more intensive research and data processing effort! 

Friday, 10 November 2017

Cryptocurrency: 21st Century Bane or Boon?

Satoshi Nakamoto, inventor of Bitcoin, never intended to invent a currency. It was a “peer to peer electronic cash system”. The key part was that he found a way to build a decentralized cash system with every peer in the network having a list of all transactions.

A simple definition would be “limited entries in a database no one can change without fulfilling specific conditions”.

 Source: Blockgeeks

The transaction is known immediately by the whole network and gets confirmed after a specific amount of time. It can’t be reversed and is not forgeable. It is part of an immutable record of historical transactions – the so-called block chain.

Crytocurrencies are built on cryptography. They are not secured by people or by trust but by math. We could describe properties of crytocurrencies into transactional and monetary properties.

Transactional properties will include: irreversible; pseudonymous; fast and global; secure; permissionless.

Source: Blockgeeks

Monetary properties will be controlled by supply and no debt but bearer – it’s a system of IOUs.

It is revolutionary in impact. It is an attack on banks and governments over monetary transactions of their citizens. It is an attack on the scope of monetary policy – i.e. central banks control of inflation or deflation is now irrelevant. Cryptocurrencies are changing the world. Step by step. We can either standby on the sidelines and observe it or become part of history in the making.

Notes:  There is a follow up on previous article "India’s GST of 28%:  Is This A Valid Benchmark?".  Please go to this link (Read more here) for the update. 

Friday, 3 November 2017

War and Capital Markets!

We often read headlines relating stock market declines with regional military tensions.  On 28 Aug 2017, North Korea fired a missile that flew over Japan before falling into the Pacific Ocean, which triggered a regional market sell-off where Asian, European and American markets all opened sharply lower, shed 1% roughly.

Mark Ambruster, CFA, published an article in Enterprising Investor (Read more here), examining the capital market performance during times of war.  His data shows that war does not necessarily imply lacklustre returns for US stocks.  Quite the contrary, stocks have outperformed their long-term averages during wars.  Bonds, which are deemed safe harbour during tumultuous times perform below historical averages during periods of wars  See below table for details.

He is in the opinion that the future direction of capital market is dependent on economic growth, earnings, valuation, interest rates, inflation, and a host of other factors; history suggests that any market decline due to war should be short-lived.

Friday, 27 October 2017

India’s GST of 28%: Is This A Valid Benchmark?

The concept of goods and services tax (“GST”) is not new to the world. About 160 countries have opted to adopt GST as a tax mode. France was the first country to introduce this tax regime in 1954. Some others have dual-GST model, like Brazil and Canada – a structure where both Federal and State have powers to levy and collect taxes. India’s GST is essentially under five brackets: 0%, 5%, 12%, 18% and 28%. Its top bracket (of 28%) is the highest rate and exceeds that of Argentina (27%). On average, it is around 18%. More developed economies have rates set between 19-20%, which they use to support social services and benefits.

In India, majority of tax revenues is indirect. Less than 3% pay income tax. In 2016/17, direct taxes was Rs 8.47 trillion while indirect taxes constituted Rs 8.63 trillion.

The problem of GST worldwide is that it is regressive in nature – the lower income bears a higher tax burden than the higher income. The other problem is that it leads to a growing shadow economy and their structures:


Germany has a shadow economy of about 15% per cent of the real economy for decades.

To evaluate GST’s performance, we should examine five indicators (implications):

·       Consumption (whether consumption is reduced which impacts GDP);
·       Production (should be neutral);
·       Inflationary pressures (could increase cost of living);
·       Compliances (creating a “Big Brother” society?  Oversight/ surveillance which impacts business sentiment); and
·       Tax buoyancy (whether tax revenues increase in the medium to long-term)

The performance of a Government has to be measured by the above indicators and not just whether it is 28% or 6%.

Update (8-Nov-2017):

Recently a friend had the following questions on impact of GST in Malaysia (27 Oct 2017).

“How does Malaysia measure up on the five criteria – consumption, production, inflation, compliances and tax buoyancy?”

Positive effects
Perceived Negative Impact
1. Compliances
-Strong effort on enforcement

1. Production
-A slight drop perceived due to consumption decline
1. Inflation
-higher at 4% or more
2. Tax buoyancy
-collected over RM42b in 2016 compared to RM18b under SST

2. Consumption
-dropped significantly with increase in prices – effect of which include closure of
Giant outlets and others

From a Government perspective therefore, the tax is useful and increased Government revenue substantially in the immediate term. From a consumer and retail perspective it has negatively impacted disposable income and turnover of retailers respectively. From the producers’ point of view, it is somewhat neutral to negative as lower consumption impacts production but hopefully this remains a short-term phenomenon.

Friday, 20 October 2017

Detecting Potential Financial Manipulation

In fundamental analysis, besides looking for the potential earnings growth of a company, one has to ensure that the company’s financial position is healthy.  However, analysing a company’s financial statement is not an easy task; one has to go through many annual reports, read the notes and understand the details of each entry in the financial statement.  Often, the process is time consuming.

In 1999, Dr. Messod Beneish, an accounting professor at Indiana University’s Kelly School of Business published a research paper called “The Detection of Earning Manipulation”.     He introduced a simple analysis method, the Beneish M Score, to detect potential financial manipulation by using information that is readily available in the financial statement.

The Beneish M Score is calculated using eight financial ratios with different weightage.

M-Score= -4.840 + 0.920DSRI + 0.528GMI + 0.404AQI + 0.892SGI + 0.115 DEPI - 0.172SGAI + 4.697TATA - 0.327LVGI


       DSRI            =          Days Sales Receivables Index
       GMI             =          Gross Margin Index
       AQI              =          Asset Quality Index
       SGI              =          Sales Growth Index
       DEPI            =          Depreciation Index
       SGAI            =          Sales, General, Administrative Expenses Index
       TATA           =          Total Accruals to Total Assets
       LVGI            =          Leverage Index

For M-Score that is smaller than -1.78 (more negative) is classified as non-manipulator.  Whereas for M-Score that is larger than -1.78 (moving towards zero or positive) is classified as a possible manipulation.

The table below, shows the M-Score for various companies in Malaysia.

Beneish M-Score
Normal < -1.78 < Cautious
Normal < -1.78 < Cautious
Normal < -1.78 < Cautious
Normal < -1.78 < Cautious

All the companies in the table showed to be normal under the M-Score test except for FGV in 2015.  But in 2016, FGV reverted to normal.  Perhaps, a more detailed analysis of FGV’s financial statement may be required.