Stellar 1^st Quarter Investment Performance

4 April 2024

Outperformed Benchmarks & Competitors in 1Q2024

We are pleased that our reference SqSave portfolios outperformed both benchmarks and competitors in the first quarter of 2024.

SqSave Reference Portfolios Returns
(SGD terms as at 31 March 2024)*

*Inclusive of ETF expense ratios and net of SqSave management fees. SqSave uses AI to design and manage diversified investment portfolios for each investor. Because SqSave is not an investment fund, there is no single return measure. Instead, every SqSave investor has his/her own investment performance as each investor is managed separately by our SqSave AI. As investors can withdraw and top-up any time, investment returns will be affected by individual investor decisions. Hence, SqSave uses reference portfolios which are actual portfolios managed on an ongoing basis, without any interference with withdrawals or top-ups, to measure investment performance. ** Performance numbers for competitors are estimates. Abbreviation: BMK: Benchmark and Port: Portfolio.

Our Investment Algorithms are Doing Well with Continued Oversight

Our oversight in refining and optimizing our investment algorithms is on the right track. Designed to prioritize risk-adjusted Sharpe ratio (i.e. annualised return in excess of risk free rate divided by annualised volatility) returns whilst minimising maximum drawdowns and volatility, our algorithms are performing most impressively for the reference Balanced (lower risk) portfolio. The reference Balanced portfolio has delivered an impressive average annual return of 6.5% since its inception in 2019, tracking a general 40% equity and 60% bond benchmark mix asset allocation.

Using Artificial Intelligence for Smarter Investing

Our algorithms are data-driven, able to recognise patterns and analyse probable risk-adjusted returns over different time horizons. This simultaneous and instantaneous analytical decision making is humanly impossible, and therefore, artificial intelligence in action enhances probabilities for smarter investing outcomes. Most notably, our algorithms navigated well through major market downturns, such as the Covid-19 pandemic and the Russian-Ukraine war. Our SqSave algorithms protected our lower risk reference portfolios by pivoting into gold - prior to these actual events occurred, thereby shielding our lower risk portfolios from market turbulence.

Optimized Portfolio Strategy

Last year, as inflation risks abated, our algorithms shifted towards the technology sector, anticipating it to rebound as markets recovered. This turned out to be a good decision. Currently, for the lower risk reference portfolios, we are hedged against elevated energy and commodity prices with corporate bond and extended duration oriented ETFs within the fixed-income asset class.

For the higher risk portfolios, our latest quantitative optimization worked well, resulting in our Growth portfolio beating its benchmark as well as that of comparable competitors’ reference portfolios for the past month and 2024 year to date periods . The Aggressive and Very Aggressive portfolios are closing the gap too.

Performance Data Prove the Effectiveness of our SqSave Investment Algorithms

Overall, the investment performance track record demonstrates the effectiveness of our algorithms in adapting to changing market conditions and delivering decent risk-adjusted performance for our clients. Notwithstanding, investment performance over time will depend on being consistently invested or continually investing through market ups and downs.

1-Year Performance Overview

In 2023, SqSave's low-risk portfolios demonstrated resilience amid market fluctuations, while our higher-risk portfolios’ performance closely tracked their benchmarks. Additionally, our performance surpassed many of the competitors’ portfolios shown in the above SqSave Reference Portfolios Returns table.

3-Year Performance Overview

As our algorithms are designed to optimize performance for the long term, we are pleased that our three-year trailing performance has consistently outperformed benchmarks across all risk categories.

Looking Ahead

Moving forward, we foresee a modest market recalibration following a significant asset price surge in recent months. Nonetheless, we are confident that our algorithms can navigate coming market challenges as it has in the past. We look forward to the easing of interest rates globally and the potential for broader market upside. Though the recent hype over generative AI is worrying, we believe there will be a positive earnings impact - reminiscent of other transformative shifts we have witnessed.

Conclusion

1Q2024 marks another notable stride in our algorithms-driven investment portfolios. We will continue to oversee and tune our algorithms to navigate the evolving investment landscape ahead.

Yours sincerely

SqSave Investment Team

Disclaimer

The contents herein are intended for informational purposes only and do not constitute an offer to sell or the solicitation of any offer to buy or sell any securities to any person in any jurisdiction. No reliance should be placed on the information or opinions herein or accuracy or completeness, for any purpose whatsoever. No representation, warranty or undertaking, express or implied, is given as to the information or opinions herein or accuracy or completeness, and no liability is accepted as to the foregoing. Past performance is not necessarily indicative of future results. All investments carry risk and all investment decisions of an individual remain the responsibility of that individual. All investors are advised to fully understand all risks associated with any kind of investing they choose to do. Hypothetical or simulated performance is not indicative of future results. Unless specifically noted otherwise, all return examples provided in our websites and publications are based on hypothetical or simulated investing. We make no representations or warranties that any investor will, or is likely to, achieve profits similar to those shown, because hypothetical or simulated performance is not necessarily indicative of future results.

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