Facebook Series: Dec 2021 Property Cooling Measures

 

We shared out thoughts on the latest property cooling measures here.

Hope you had a great festive break with family and loved one! Here's to a better 2022 ahead!

Facebook Series: leaving a lasting impression

Wrote some tips on my facebook page, do check it out and share your thoughts!

Singapore Property no longer the best retirement investment?

For the purposes of this article, I will be using the definition of retirement as per wikipedia : withdrawal from one's position or occupation or from one's active working life.

I came across a Straits Times Article with headlines “Property no longer the best retirement investment in S'pore: DBS report”. You may read more here 

The release of the research coincided with my recent discussion with a friend on retirement planning, we touched on the following possibilities:

•         Purchasing investment linked insurance policy
•         Investing directly into funds
•         Buying properties.

Most insurance agents will tell you that they can offer you products that can generate higher returns than your current investment portfolio. Most personal financial planners will be able to show you financial products that can generate high returns too and the same can be said for property agents.

As with most things in life, there is no black and white answer (which is great otherwise life would be boring!). We concluded that each has its own merits:

•  Investment linked policies provides an avenue for people with little or no financial background a chance to gain higher returns without the need to actively monitor the portfolio.
• Direct investments into funds are great for those are know what to invest in and have the discipline to constantly monitor the market.
• Properties provides stability and constant stream of rental income.

An important thing is to do proper needs based analysis with a clear investment horizon and not put all your eggs in one basket (diversify).

The DBS report compared returns on buying a second property to various other investments and found that it yields lowest returns amongst eight asset classes. High taxes (e.g. ABSD) was cited as a factor that reduced the returns. One could technically buy a second property as a family unit under another family members name and enjoy a lower tax rate. Hence, do your own research / seek your own advice and interpret the research findings with a pinch of salt. Always think: whose interests does the research seek to serve? (hint: it may not be you, the customer)

How are you planning your retirement? What are your thoughts on purchasing properties in Singapore for investment? Leave your comments below!

Presentation Principles

Continuing the topic from the previous post, it might be useful to consider tactics used by strategy consultants to improve the quality of their presentations.

Horizontal and Vertical Flow

The slide title should generally be the takeaway for the slide. One should be able to determine the "story" of the presentation from reading all the slide titles.

More information here.

Pyramid Principle

This well-known principle bears repeating. This helps one transform complex data into actionable insights. You may see one summary of this here

Part 1

Part 2

MECE Mutually exclusive Completely exhaustive

This is another approach to simplify our complex environment into understandable ideas.

Overall, the above remain ideas on paper until executed. As with most things in life, one can improve your presentation skills via practice, practice, practice.

Any other presentation tips come to mind? Share yours in the comments below!

Data Analytics 101 Part 5 of 5: Presenting findings and recommendations

All that hard work and we are almost at the end. Business (and many parts of life) require effective communication:  your analysis can come to live with the right narratives and visualisations. It is key to be able to  effectively communicate with data, present findings and make recommendations to decision makers.

Data within a business context

If you want to know more about any of the topics explored in the module, you can find further resources here:

• Access the Storytelling with Data resources in the form of a blog and a podcast to find out more about the techniques for data storytelling and for practical examples of the techniques discussed in this module.
• ICAEW presents webinars on various topics including one on data driven decision making.
• TowardsDataScience has an article containing further information on data storytelling.

Data visualisation for presentations

If you want to know more about any of the topics explored in the module, you can find further resources here:

• The Office for National Statistics discusses their findings on their use of visualisations to communicate to the wider public.
• ICAEW has a webinar called ‘The 7 Hats of Data Visualisation Design’ that looks at the skills needed to be a ‘superhero’ in data visualisation.
• This article discusses exactly when a visualisation is a good choice for communicating findings.
• For inspiration when creating visualisations: this Tableau article shares some beautiful examples, and this article on Analytics Vidhya also demonstrates what good visualisation looks like.

And this concludes the five-part series on Data Analytics, hope this has been useful for you, and thanks for reading!

Data Analytics 101 Part 4 of 5: Decision Making

What do you do with all the insights developed in the previous stages of data analytics?
Yes, it is time to decide. Data-driven decision making is a key skill set to acquire and hone in this big data era.

Time series modelling

If you want to know more, you can find further resources here:

• TowardsDataScience demonstrates how to forecast data, including discussing how to identify the best model.
• The statsmodels library is used frequently in this module. Find out more about the time series analysis (tsa) package including a list of commands.
• Learn more from Machine Learning Mastery on autocorrelation and partial autocorrelation.

Anomaly detection

If you want to know more, you can find further resources here.

• DataCamp provides useful cheat sheets and tutorials that detail the various uses and commands in packages. These include a cheat sheet for the sklearn package that is used frequently within this module.
• DataCamp also has tutorials examining the sklearn package to perform KMeans clustering and Principal Component Analysis.
• TowardsDataScience provides an article explaining how to use techniques such as Principal Component Analysis and clustering to detect anomalies.
• Medium has an article explaining unsupervised learning and how it can be used in detail.This includes examining different types of clustering, dimension reduction techniques such as PCA and methods.

Ethical considerations

If you want to know more, you can find further resources here:

• Access this link for the UK Government’s Framework for incorporating Data Ethics into public sector data projects. This includes practical advice to ensure ethical issues are considered throughout.
• Royal Society have examined Data Ethics in depth through a series of seminars, roundtables and in-depth research with professionals in various fields, summarised in this report.
• The Alan Turing Institute is dedicated to using analytics and Artificial Intelligence ethically. To ensure this, they have published an online guide about data ethics.

Any other resources that are relevant to decision making? Please share in the comments below.

Data Analytics 101 Part 3 of 5: Analysing the Data

This is the third part of the series (you may refer to the first and second articles if interested) To analyse data meaningfully, it is necessary to understand the different techniques available (and how/when to use them),  and produce data visualisations to communicate the messages and insights.

a) Data manipulation techniques

If you want to know more about any of the topics explored in the module, you can find further resources here:

• The pandas website provides detailed information on the various uses of functions within the pandas package for Python. Make a start by looking at the user guides under this link.
• Information on how pandas can be used to select data from a dataset and the drop command is available under this link..
• DataCamp provides useful cheat sheets including one covering data wrangling techniques examined in the previous unit and the manipulation techniques examined in this module.
• A useful summary of the different way to manipulate rows and columns is available under this link and covers selecting, adding and deleting.
• Access this link to find out more about aggregating data using the groupby function, including a list of the aggregation functions.

b) Summary statistics in exploratory data analysis

If you want to know more about any of the topics explored in the module, you can find further resources here:

• A deeper look at summary statistics within Python can be found in this article from Real Python.
• TowardsDataScience provides a useful article that explains hypothesis testing in further detail under this link.
• Further documentation on the t-test commands in the SciPy package: scipy.stats.ttest_1samp and scipy.stats.ttest_ind.

c) Basic visualisations in exploratory data analysis

If you want to know more about any of the topics explored in the module, you can find further resources here:

• DataCamp provides a useful cheat sheet that summarises the main visualisation commands in the Seaborn Package.
• TowardsDataScience examines the different data visualisations with the corresponding code and a breakdown of what visualisation to use when in the article under this link.
• Access this TowardDataScience article for a walkthrough of performing exploratory data analysis including summary statistics and visualisations.

Any thoughts on data analysis? Leave your insights in the comments below!

Data Analytics 101 Part 2 of 5: Data Wrangling

This is the next part of the series after the first article. Before diving into analysis, it is often necessary to clean and transform the source data before any meaningful analysis can be done. This can be viewed as six activities: discover, structure, clean, enrich, validate and publish.

a) Importing Data

• DataCamp tutorial and cheat sheets on how to import data using Python and pandas.

b) Data Management

• Towards Data Science article examining the append function in pandas: Access this link.
• DataCamp provides videos examining merges and joins in more detail.
• DataCamp blog post on joining data.
• A breakdown of the different types of joins with further examples.

c) Identifying Data Quality Errors

• Towards Data Science article discussing the how pandas can be used to cleanse data including handling duplicates, missing data and additional information on outliers.
• How a command in Python works and the parameters it takes. Use this link to access information on the duplicated function.

Do you think cleaning data is important? Let me know your thoughts in the comments below.

Data Analytics 101 Part 1 of 5: Business Context

Read a great publication on the concept of Data Governance (meet the author here) and was inspired to share learnings from the ICAEW course on Data Analytics. Before delving into analytics per se, we first need to understand the context within which analytics occurs. There are several useful resources that are available publicly.

a) Data analysis as a concept and process

• TowardsDataScience  article that describes what a data analyst actually does.
• Why analytics is important for accountants: read the ICAEW’s article on the matter.
• DataCamp blog detailing what is data science, why it is important and the skills analysts need.
• News website Medium presents a series of articles that gives a real-world example of an analytics project that demonstrates how to frame a business problem for analytics
  Framing the Business Problem - 1 of 3
  Framing the Business Problem - 2 of 3
  Framing the Business Problem - 3 of 3
• Career website Indeed detailed guide to writing a problem statement.
• Software company SAS on framing a business problem as an analytical solution.
• TowardsDataScience  article on the importance of checking your assumptions in analytical projects.

b) Organisational data and data quality

• DataCamp blog detailing what exactly is a relational database and SQL.

• TowardsDataScience explains everything you need to know about data management.

• Article from news website Medium on why data quality matters.

 

c) Roles and responsibilities around data analytics

• DataCamp has a useful infographic that breaks down the different jobs, roles and skills required in data and the differences between them.
• The blog Everyday Analytics on thinking like an analyst and the questions you need to ask.
• ICAEW article on the skills required for accountants to perform data analysis.
• Information on data governance can be found in this DataCamp blog post.

Hope you found the above resources useful in your continuous learning journey!

If you have more useful resources to share, please add a comment below.

Syfe Cash+ Experience: My Syfe Portfolio is Down. Is It Still 'Safely Growing'?

You open your Syfe app, ready for that small dopamine hit of seeing your investments tick up. Instead, your heart sinks a little. It's red. Maybe it's just by $17, maybe more. Instantly, the promise you bought into—that your money is being "safely grown"—feels a bit hollow.

If you’ve felt this, you're not alone. It’s a completely normal reaction. We're wired to fear loss. But is this small dip a sign of danger, or is it just part of the journey?

In this article, we’ll cut through the confusion. We'll break down what Syfe’s "safely growing" philosophy truly means, why short-term drops are not just normal but expected, and most importantly, what your game plan should be when your portfolio balance isn't going in the direction you want.

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Understanding "Growth" – Why Your Portfolio Isn't a Savings Account

The first mental hurdle is to stop thinking of your investment portfolio like a bank account. A savings account grows predictably, bit by bit. An investment portfolio, on the other hand, is on a long-term journey to climb a mountain. There will be dips, stumbles, and flat trails along the way, but the ultimate goal is a much higher peak.

The Market Doesn't Move in a Straight Line

No stock market in history has ever gone up in an uninterrupted straight line. Daily ups and downs are the market's "breathing." A 0.2% dip over a couple of days feels bad in the moment, but in the grand scheme of your 10, 20, or 30-year investment horizon, it's just noise. Long-term growth is the signal we're listening for; daily price changes are just the static.

A Look Under the Hood: What Are You Actually Invested In?

When you see a single number in your Syfe app, remember that it represents a vast, globally diversified collection of assets. You aren’t just holding one thing; you're invested in Exchange-Traded Funds (ETFs). These ETFs, in turn, hold small pieces of hundreds or even thousands of the world's best companies—think Apple, Microsoft, DBS, and Nestlé—plus a mix of government and corporate bonds.

Your portfolio's value is the sum of all these tiny parts moving every second the global markets are open. A small dip simply means that, on average, the collective value of these global assets went down slightly. This is a sign of a functioning market, not a failing portfolio.

The Power of Perspective: Zoom Out

When you feel anxious about a small drop, the best remedy is to zoom out. Look at the chart below, which shows the performance of a global stock market index over the last 20 years.

See those terrifying drops? They were real, and they felt much worse than a minor dip. Yet, investors who simply held on and stayed the course were rewarded as the market inevitably recovered and climbed to new heights. Your small dip is a tiny blip on this long-term chart.

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Deconstructing "Safety" – What Syfe Really Means

Now for the second part of the phrase: "safely." How can something be "safe" if it can lose money? In the investment world, safety isn't about eliminating risk—that's impossible. It's about intelligently managing it.

Safety Isn't Zero Risk; It's Managed Risk

A fixed deposit in a Singapore bank is safe because its value is guaranteed. An investment is different. Its "safety" comes from strategies designed to prevent catastrophic loss and smooth out the volatile journey. You're trading the certainty of low returns for the high probability of much greater long-term returns, with a calculated safety net in place.

Your Shield and Armour: Diversification

The number one safety feature in your Syfe portfolio is diversification. Your funds aren't just in one company or even one country. They are spread across:

• Asset Classes: A mix of stocks (for growth) and bonds (for stability).

• Geographies: Investments in the US, Europe, China, and other emerging markets.

• Sectors: Exposure to technology, healthcare, finance, consumer goods, and more.

This is the classic "don't put all your eggs in one basket" strategy. When the tech sector is down, perhaps the healthcare sector is up. When the US market is struggling, Asian markets might be thriving. This global balance is your portfolio's built-in shock absorber.

"For retail investors, diversification is the closest thing to a free lunch. It's the most effective tool for managing risk without necessarily sacrificing long-term returns." - Quote from a local financial advisor.

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Your Action Plan: What to Do When Your Portfolio is Red

Okay, so we understand the theory. But what should you do when you see that negative number? The answer is often counter-intuitive.

Rule #1: Do Nothing (Don't Panic and Sell)

Your gut reaction might be to sell to "stop the bleeding." This is almost always the worst thing you can do. A drop in value is only a "paper loss." The moment you sell, you convert that temporary paper loss into a permanent, real loss of capital. You've locked in your losses and forfeited the chance to participate in the recovery.

Frame the Dip as a "Sale" – The Magic of Dollar-Cost Averaging (DCA)

Instead of seeing red as a danger sign, see it as a discount. A market dip means the quality ETFs you believe in are now on sale. If you invest a fixed amount regularly (i.e., Dollar-Cost Averaging), a down market is your friend. Your fixed contribution now buys more units of those ETFs. This lowers your average purchase price and can dramatically accelerate your gains when the market turns around.

Trust the System: Automatic Rebalancing

Remember, you're not just paying Syfe for access to ETFs; you're paying for their algorithm and discipline. A key part of their service is automatic rebalancing. When one part of your portfolio does exceptionally well, they sell a little bit (selling high). When another part dips, they use those funds to buy more of it (buying low). This disciplined process happens in the background, removing emotion from the equation and ensuring your portfolio stays aligned with your long-term goals.

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FAQ Section:

1. Q: How often should I check my Syfe portfolio?

   A: For your own peace of mind, checking once a month or even quarterly is best. Daily monitoring can trigger anxiety and lead to emotional decisions based on normal market noise. Trust your long-term strategy and give it time to work.

2. Q: What if my portfolio is down for a whole year? Does this advice still apply?

   A: Absolutely. Prolonged downturns, or "bear markets," are a natural part of the economic cycle. History shows us that every bear market has been followed by a recovery that leads to new all-time highs. Sticking to your investment plan and continuing to contribute via DCA during these periods is often when the most significant long-term wealth is generated.

3. Q: So is Syfe's "safely growing" promise misleading?

   A: Not at all, once you understand the investment context. The phrase describes a strategy that uses proven safety measures—like global diversification and risk management—to pursue long-term growth. It promises a safer journey toward your financial goals compared to high-risk alternatives, like putting all your money into a single stock. It’s about managing the climb, not pretending the mountain has no dips.

Syfe's reply to the "muted" value decline:
"Cash+ is invested in a combination of money market, enhanced liquidity and short-term bond funds managed by Lion Global Investors. The underlying holdings in these funds are low-risk assets such as Singapore and international government bonds, commercial bills and high-quality corporate bonds. 
The portfolio’s daily value is updated based on the Net Asset Value (NAV) of the three funds, which is driven by the performance of the underlying holdings.
As with all investments, the prices of bonds may fluctuate over time, although you can expect price movements to be more muted due to the low-risk nature of bonds relative to other asset classes.
It is important to realise that although Cash+ is considered a low-risk investment, it is not risk-free. To earn an enhanced return above bank deposit rates in this low interest rate environment, investors have to take on some interest rate and credit risk. Let’s explore what these risks actually mean.
You may wish to find out more here - https://www.syfe.com/magazine/syfe-cash-q1-performance-update"
Guess a "low risk appetite" differs for everyone.