SB2 Price sensitive 23 Aug 2024, 5:38 PM

Appendix 4E and 2024 Annual Report

Salter Brothers Emerging Companies Ltd

Salter Brothers Emerging Companies Ltd reports FY24 results

Key points

  • Investment income up 888.9% to $7.9m
  • Profit up 331.3% to $4.2m
  • Net assets increased by $3.1m to $89.2m

Full summary

Salter Brothers Emerging Companies Ltd completed its third year as a listed investment company during the 2023/24 Financial Year. The fund continued its focus on providing investors with attractive risk adjusted returns with capital growth and income over the long term by investing in a portfolio of typically 20-35 investments in predominantly Australian listed and unlisted securities of emerging companies. The trading result for the past financial year recorded an increase of 8.1% across the portfolio with the pre-tax net tangible assets (NTA) at 94.6c. FY24 saw the Australian economy navigate around interest rate and growth uncertainties, with the company's portfolio companies boasting substantial earnings growth, sustainable business models, and robust balance sheets. During the year, the company made an investment in an unlisted company, Alfabs Group, as part of its Pre-IPO financing. The company also extended its share buyback program, buying back and cancelling 1,935,576 shares at a total cost of $1.083 million. Looking ahead to FY25, the company believes the portfolio is well placed for growth as the Australian economy continues to grow and interest rate uncertainty continues to abate.

Guidance

The company provided the following forward-looking financial guidance for FY25: revenue growth of 18%, EBITDA margin of around 18%, NPAT margin of around 7%, and a P/E multiple of 8.1x.

Outlook

The company is optimistic for a rebound in investor sentiment specifically directed at small caps with non-discretionary earnings streams, as interest rates are likely approaching their cyclical peak and there is a material disconnect between small cap fundamentals and their trading performance.

Summary generated by a large language model. Please interpret with care.

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