Income+ Fund for Retirees and Ultra-Conservative Investors (like me)

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1-year ahead probabilistic returns forecast using Ridge Regression and Monte Carlo Simulation

Acknowledgement: Data kindly provided by https://EODHD.com, the innovative data source company with the widest range of financial markets data from over 60 global Exchanges.  

 

Introduction
As a Singaporean retiree, I need to make my money work for me to earn some side income. But my risk appetite is very low, and so in addition to money market funds, I am invested in Trust Bank Singapore’s Income+ Fund. This is a Fund that distributes monthly dividends and its mandated portfolio structure is: a) 34% Emerging markets and Asia fixed income (b) 33% Developed markets fixed income (c) 33% High dividend yield global equities.
In this post, we take a look at a model that simulates the performance of Income+ Fund and projects its probabilistic returns 1 year ahead. The model uses established US-listed ETFs  as proxies, and your assets and dividends will be in Singapore Dollars. The Fund Manager is Aberdeen Investments a well-known international investment advisory managing over USD430 billion in assets.  

Executive Summary
The 1-week forecast horizon is converted to a 1-year horizon by iterating a weekly Ridge regression forward for 52 weeks. We look at past weekly moves of the ETFs together and randomly pick real weeks from history. Doing it together keeps the natural relationships between them (for example, when bonds went up while stocks went down in a given week, we keep that pairing).
Then we add a bit of extra noise based on how much the model has been wrong in the past. This mimics real‑world uncertainty, so the forecasts aren’t unrealistically precise. Finally, I did a 20,000 trials Monte Carlo Simulation to obtain the Probability Distribution Functions, and from there we mark of the probabilities for the main quantiles. 

The Results in Plain Words
• The Median forecast is 6.15 % per annum which is only slightly less than the Income+ Fund’s projection of 6.5% 
• The shape of the Monte Carlo Simulation’s Probability Distribution Function (PDF) shows a long right tail which means the Average is greater than the Median.  The positive implication of this are as follows: 
• Average > median: The expected (average) return is pulled up by a few big winners, so it will be higher than the typical (median) outcome.
• Upside surprises: There’s a small but meaningful chance of outsized gains. Don’t be surprised by rare, very good years.
• The expected magnitude of losses (-9.31% at 5th quantile and -0.5% at 25th quantile) is smaller than the expected magnitude of gains (6.15% at 50th quantile, 13.05% at 75th quantile and 24.32% at 95th quantile)
• In short: expect many ordinary years, some weak ones, and a small chance of very strong years that lift the long-run average.

Data Frequency Choice

Weekly data is recommended for a 1-year horizon. It materially reduces daily noise yet retains enough observations for estimation and multi-step simulation. Daily data could over-emphasize short-term volatility and microstructure effects, while monthly data is too sparse for supervised 12-step targets with the current history.

Methodology

We use ETFs that are proxies of Income+ Fund mandated portfolio structure of (a)34% Emerging markets and Asia fixed income (b) 33% Developed markets fixed income (c) 33% High dividend yield global equities

Data: End -of day historical daily prices are aggregated to Friday week-end; weekly returns for EMLC, IGOV, EFA.  fund constructed by mandate-aligned weights.

Full Name of proxy ETFs

EMLC: VanEck J. P. Morgan EM Local Currency Bond

IGOV: iShares International Treasury Bond ET

EFA: iShares MSCI EAFE ETF

Model: Ridge regression with standardized lagged ETF weekly returns (1 and 2 weeks) and iterated for 52 weeks for 1-year ahead forecast.  

Simulation: 52-step forward iteration with empirical ETF row-resampling and residual bootstrapping; Normal and Student-t fits compared via AIC.

Output metrics

Test MAE 0.0092, RMSE 0.0109, R2 0.0702. Best-fit distribution Student-t. 1Y forecast quantiles: 5th -0.0931, 25th -0.0051, median 0.0615, 75th 0.1305, 95th 0.2432.

Visuals







 

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