Translate

Saturday, September 13, 2025

The "Certainty System": How I Use Math to Rebalance My CDP Dividend Portfolio | Lin Cangyan



According to data from DBS Bank’s net worth planning tool (digiWealth), my owner-occupied property (a three-room HDB flat) currently accounts for approximately 12.35% of my total assets. Over the years, I have consistently regarded my property as living infrastructure rather than an investment asset. My long-term asset allocation and capital management are conducted primarily through Singapore Real Estate Investment Trusts (S-REITs) and retail unit trusts offered by DBS Bank.

My current asset allocation is as follows:

Owner-occupied property: 12.35%
Singapore Central Provident Fund (CPF): 24.70%

This allocation reflects a deliberate separation between living infrastructure and capital infrastructure within the CYSM framework. It embodies my long-standing financial strategy: housing serves as essential living infrastructure, while the investment portfolio functions as the primary engine for capital growth and cash-flow generation.

I decided to retire early, at age 58, on March 1, 2024, seven years earlier than the official retirement age of 65, which was originally scheduled for 2030. My basic monthly salary before retirement was SGD 3,114. In hindsight, SGD 3,114 was not an income milestone, but a system signal. It marked the point at which my financial and living infrastructure had matured sufficiently to support time sovereignty. CYSM does not interpret numbers as achievements; it interprets numbers as indicators of system state.

My CPF investments have maintained a profitable return on investment so far. On March 16, 2024, having reached the FRS, I decided to close my CPF investments account and withdraw all my CPF investments, allowing me greater flexibility and convenience in managing my retirement assets.

The value of investment funds fluctuates with global economic developments, so the timing of fund redemptions is crucial. Past returns, which can be found online, are for reference only. The following is my CPF Investment portfolio before closing, please feel free to refer to it:

73.56% — FSSA dividend advantage fund
6.84% — Mapletree Pan Asia Cm reit
5.67% — Frasers log & co tr
3.79% — Sasseur reit
3.67% — Lendlease gl co reit
3.53% — Capitaland ascendas reit
2.92% — Aims apac reit

Achieving retirement officially opens the door to my money working for me. I no longer work for money and become fully committed to controlling my finances and my time. The next step is to reduce investment risk, increase sustainable investment returns, and properly manage my retirement assets.

The first formula below estimates the actual amount of cash invested in my CDP fund, using my Amova Singapore Dividend Equity Fund (ASDE) as a benchmark. It provides a standardized way to track relative performance.

Estimated CDP Cash Invested Principal = (ASDE Principal x CDP Market Value) / ASDE Market Value

The second formula below tells me the actual dividend yield of my CDP portfolio, adjusted for invested capital. I regularly update this data in Excel to monitor whether my portfolio remains within my target yield range of 4-8%.

Annual CDP Dividend Return % = (Total Annual CDP Dividends Received x 100) / Estimated CDP Cash Invested Principal

The above formula provides a data-based estimation method to measure the performance of my CDP portfolio by projecting potential paper gains or losses. You can also use the above formula to benchmark your CDP fund against an index fund or ETF. Maintaining no more than 10% of your entire fund in any single stock is generally quite safe.


Generally speaking, I'm not a risk-taker. I tend to choose decisions with clearly defined and controllable risks, carefully designing my life to keep those risks within manageable limits, and ensuring they are clearly visible and repeatable. The systems I build are designed to control and manage risk, making it bearable. 

I’m not trying to predict the market. I rebalance when my cash buffer falls below a set range, gradually reducing positions and simplifying the portfolio. Resilience is not about predicting the future, but about designing a system that survives it. 

I intentionally maintain approximately 15% of my portfolio in cash and Singapore Savings Bonds as a stability buffer. Within CYSM, capital is not designed to maximize wealth indefinitely, but to secure the conditions for long-term autonomy. Beyond a certain point, additional accumulation contributes little to life-system stability, while unnecessary risk may weaken it.

The portfolio is designed as a cash-flow-oriented retirement structure. A significant proportion of its income is ultimately linked to the rental and operating cash flows generated by Asia's real estate and infrastructure assets. In this sense, it resembles a system that collects recurring "tolls" from the region's economic activity—not literally, but as a metaphor for participating in long-term productive assets.

The defining characteristics of my portfolio are stable cash flow, self-regulating risk management, and long-term protection of capital against inflation. As a result, my financial system is designed to remain operational even during periods of severe market stress.

I learned how to invest properly through the CPF Investment Scheme (CPFIS). Without CPFIS, I probably wouldn’t have the portfolio I have today. In many ways, my entire investment system actually grew out of the CPFIS framework.

The CPF Investment Scheme also helped me develop a disciplined investment strategy and, more importantly, kept me away from investment scams.


📖 Financial Transparency as System Validation

To pursue the Advanced Degree in Life Engineering (CYSM), I made public my portfolio of liquid assets and monthly expenses. This is something many people cannot bring themselves to do. Transparency in financial modules is not about showing wealth, but about demonstrating the operational stability of the system. CYSM is not a theory on paper—it is a life protocol validated by real-world constraints.

The true difficulty, however, is not merely in making a portfolio public. The real challenge lies in allowing a theory to be tested against reality. Many are willing to share ideas, philosophies, or methods, but few are willing to disclose actual asset allocation, cash flow structures, spending levels, or risk-bearing strategies. Once such data is revealed, a theory shifts from opinion to verifiable structure.

By publishing key operating parameters—liquid asset allocation, REITs, ETFs, SSBs, cash buffers, living expenses, the SGD 667 low-energy engine, and the 5.61% yield engine—CYSM moved beyond abstract discourse. Readers can now observe not only what CYSM says, but how CYSM runs.

This transparency aligns with CYSM’s guiding principle: Theory may guide, but systems must run. Once data is public, time becomes the strictest examiner. Only long-term stability proves resilience. In this sense, publishing asset configurations is not the “degree” itself, but rather the submission of experimental data. The true test of CYSM lies in whether the system can continue to operate stably under the constraints of Signal × Time × Stability.


Narrative Signal: On the evening I updated this section of the blog describing the deliberate separation between living infrastructure and capital infrastructure within the CYSM framework, a black moth unexpectedly entered my room. After guiding it out, I later discovered that, in traditional folklore and symbolic interpretations, a moth entering the home is often seen as a symbol of transition, fortune, emotional connection, or subconscious awakening. Within the CYSM framework, I interpret this event not as superstition, but as a narrative signal — similar to the sparrow incident in 2025 — a reminder that real-world experiences often echo the system’s ongoing calibration. Just as external signals are filtered, abstracted, and integrated into CYSM, this symbolic encounter reflects the convergence of rational asset allocation with subconscious transformation. A black moth is not a causal omen but a narrative signal. It symbolizes the constraints and transitions that invite recalibration of the life system. Whether interpreted as misfortune or transformation, its meaning lies in how the individual integrates the observation into structural stability.

This comparative chart visualizes two narrative signals — the sparrow of 2025 and the moth of 2026 — illustrating how real‑world encounters can be observed as part of CYSM’s long‑term calibration. Each event occurred about one year apart, forming a natural rhythm of observation rather than prediction, and showing how symbolic experiences can coexist with rational system updates.

Narrative Signals in CYSM In the CYSM framework, certain real-world events are recorded not as proof of theory but as narrative signals — emergent observations that surface during long-term system operation. These signals, such as the sparrow in 2025 and the moth in 2026, are documented as part of ongoing calibration. They represent the overlap between external occurrences and internal system states. While each event is objective in itself, its long-term significance can only be validated through continued operation, feedback, and structural convergence.

Narrative Evidence in CYSM The comparative chart of the sparrow (2025) and the moth (2026) illustrates how narrative signals emerge as part of long‑term calibration. Their one‑year interval filters random events into meaningful observations, aligning external encounters with internal system states. The contexts—retirement transition and infrastructure reflection—map directly onto two stages of system evolution. By juxtaposing quantitative data with symbolic experiences, CYSM demonstrates that stability is achieved not only through numbers but also through the disciplined recognition of signals that surface over time. In CYSM, narrative signals are not decorative metaphors—they are recorded observations that inform calibration, without asserting causality.

 

This framework illustrates the four layers of evidence in CYSM — quantitative, operational, narrative, and structural — showing how the system evolves from data to long-term stability.

Note: Narrative Evidence differs in kind from the Quantitative, Operational, and Structural layers — it is not independently verifiable, reproducible, or causal. It records events surfaced during long-term system operation; its value lies in whether it is consistently incorporated into calibration, not in whether the event itself is objectively true. The four-layer table reflects the completeness of CYSM's recording system, not equal evidentiary weight across layers. Validation here refers to whether signals are consistently integrated into the system, not whether the events themselves are proven true.

CYSM also incorporated the dream—occurring at midnight on July 28, 2026, and representing the subconscious—into the narrative signals. Within CYSM’s semantic framework, "black moths" and "sparrows" constitute external narrative signals—environmental events that serve to observe the system's interaction with the outside world. Dreams, conversely, are internal narrative signals—reflecting the system's internal psychological or cognitive state.

In CYSM, narrative signals are not decorative metaphors—they are recorded observations that inform calibration, without asserting causality. The distinction between the two lies in:



This approach aligns perfectly with the CYSM methodology, clarifying how external events serve as system inputs and internal experiences act as feedback within its long‑term calibration framework.


Conclusion CYSM demonstrates that true stability is not designed once, but sustained through transparent data, disciplined operation, and continuous calibration over time.

Time sovereignty ultimately provides the operational freedom required for long-term system calibration, allowing CYSM to evolve through continuous observation rather than continuous urgency.


All images above provided by Microsoft Copilot. Pie charts were created using VS Code IDE and Google Sheets respectively
The personal information above was analyzed and interpreted by Google Gemini, DeepSeek, ChatGPT and Microsoft Copilot, editorial review by Claude

Regarding naming philosophy →  please refer to"The Use of White Space in Names"


以下是这篇博客使用 Google NotebookLM 制作的视频概览
Below is a video overview of this blog post, created using Google NotebookLM.

 

No comments:

Post a Comment