Edinburgh Worldwide Investment Trust : Interim Financial Report

EDINBURGH WORLDWIDE INVESTMENT TRUST plc

Growth companies shaping our tomorrow

Interim Financial Report 30 April 2023

Objective

Edinburgh Worldwide's objective is the achievement of long term capital growth by investing primarily in listed companies throughout the world.

Comparative Index

The index against which performance is compared is the S&P Global Small Cap Index total return (in sterling terms).

Principal Risks and

Uncertainties

The principal risks facing the Company are financial risk, investment strategy risk, climate and governance risk, discount risk, regulatory risk, custody and depositary risk, small company risk, private company (unlisted) investments risk, operational risk, leverage risk, political and associated economic risk, cyber security risk and emerging risks. An explanation of these risks and how they are managed is set out on pages 9 and 10 of the Company's Annual Report and Financial Statements for the year to 31 October 2022 which is available on the Company's website: edinburghworldwide.co.uk. The principal risks and uncertainties have not changed since the date of the Annual Report.

Responsibility Statement

We confirm that to the best of our knowledge:

1. the condensed set of Financial Statements has been prepared in accordance with FRS 104 'Interim Financial Reporting';
2. the Interim Management Report includes a fair review of the information required by Disclosure
   Guidance and Transparency Rule 4.2.7R
   (indication of important events during the first six months, their impact on the Financial
   Statements and a description of the principal risks and uncertainties for the remaining six months of the year); and
3. the Interim Financial Report includes a fair review of the information required by Disclosure
   Guidance and Transparency Rule 4.2.8R (disclosure of related party transactions and changes therein).

On behalf of the Board Henry CT Strutt Chairman

7 June 2023

Summary of Unaudited Results*

30 April 2023 31 October 2022
(audited)	% change

Total assets (before deduction of loans) Borrowings

Shareholders' funds

Net asset value per ordinary share

  (after deducting borrowings at book value)

£808.8m	£879.4m
£97.0m	£103.8m
£711.8m	£775.6m
182.78p	197.70p	(7.5)

Share price	149.20p	172.60p	(13.6)
Comparative index (in sterling terms)#‡			(1.5)
Discount (after deducting borrowings at book value)†	18.4%	12.7%
Active share (relative to S&P Global Small Cap Index)†	99%	99%
Performance since broadening of investment policy	30 April 2023	31 January 2014	% change

9 years 3 months from 31 January 2014

Net asset value per ordinary share

• (after deducting borrowings at book value) Net asset value per ordinary share
• (after deducting borrowings at fair value)†

182.78p87.34p109.3

182.78p87.43p109.1

Share price			149.20p	81.00p	84.2
Comparative index (in sterling terms)#‡					129.5
		Six months to	Six months to
		30 April 2023	30 April 2022
Revenue earnings per share			(0.31p)	(0.23p)
					9 years
		Six months to	Six months to	3 months from
		30 April 2023	30 April 2022 31 January 2014
Total returns (%)†‡
Net asset value per ordinary share
(after deducting borrowings at fair value)			(7.5)	(34.1)	109.2
Share price			(13.6)	(38.5)	84.2
Comparative index (in sterling terms)#‡			(1.5)	(6.8)	129.5
	Six months to 30 April 2023	Year to 31 October 2022
Period's high and low	High	Low	High	Low
Share price	191.00p	147.80p	335.00p	160.40p
Net asset value per ordinary share
(after deducting borrowings at book value)†	223.44p	182.78p	338.36p	188.29p
(Discount)/premium (after deducting
borrowings at book value)†	(5.7%)	(20.1%)	5.5%	(20.1%)

Notes

• For a definition of terms see Glossary of Terms and Alternative Performance Measures on pages 27 to 29

• Alternative Performance Measure see Glossary of Terms and Alternative Performance Measures on pages 27 to 29.

• S&P Global Small Cap Index total return (in sterling terms). ‡Source: Baillie Gifford, Refinitiv and relevant underlying index providers. See disclaimer on page 33.

Past performance is not a guide to future performance.

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Interim Management Report

Over the six months to 30 April 2023, the Company's net asset value per share* decreased by 7.5%, which compares to a fall of 1.5% in the S&P Global Smaller Companies Index, total return in sterling terms, over the same period. The share price

over the six months fell by 13.6% to 149.20p representing a discount of 18.4% to the net asset value at 30 April 2023. This compares to a 12.7% discount at the beginning of the period. The Company buys back its own shares when the discount is substantial in absolute terms and relative to its peers; 2,865,382 shares were bought back in the period and are held in treasury.

Over the five-year period to 30 April 2023, the Company's net asset value per share* increased by 21.8% while the comparative index increased by 33.8%. The share price decreased by 4.8% over this period.

The market environment remains largely as discussed in the 2022 Annual Report: a dynamic post-pandemic adjustment period where companies and stock markets are navigating inflationary and geopolitical challenges. This is sculpting a new investment environment. One where capital is less freely available, the hurdle rate for returns is higher and the tolerance of uncertainty is markedly lower. The immediate manifestation of this is a shortening of the time horizons of many investors, lulling them into a mindset where the near-term resiliency of what they invest in is paramount and the future is approached with a large dose of pessimism.

We are unashamedly long-term investors with our analytical radar tuned towards high-potentialearly-stage growth opportunities. The current myopic environment described above is not conducive to our approach. We are accustomed to having a time horizon and investment style that can be out of sync with broader equity markets. In many ways, this seems an unavoidable aspect of contemporary equity investing. As bruising as this can feel in the near term, it ultimately creates the opportunity.

It shapes the returns available to those willing to postulate how industries might evolve and actively seek out those companies driving that change.

For such companies, it's the fundamental path of progress that ultimately matters most not the prevailing stock market narrative. That progress naturally takes time to manifest but we remain confident that the holdings in the portfolio represent a collection of some of the most exciting and transformational long-term investment opportunities available.

Some Reflections on

Growth and Technology

We have frequently noted how innovation and the application of technology is a structural force that largely sits outside of conventional business cyclicality. But recent headlines on technology sector job losses and retrenchment indicate that many tech-led companies have not been immune from current pressures. In some cases, the reasons for this cyclicality directly relate to end product demand, but in many other areas we suspect it represents a period of adaption to a new normal that we would ultimately expect to see replicated more broadly across the economy.

That 'new normal' will likely favour efficiency in pursuing business growth. In an era of zero-cost money, a surplus of labour and an economic tailwind, the issue of productivity was primarily addressed indirectly through the expansionary pursuit of scale: grow bigger and operational leverage would ultimately drive productivity. Direct investments in productivity tools to drive unit economic efficiencies were generally less popular as they were less likely to yield near-term expansionary growth. Furthermore, they often carried a risk of disrupting an organisation as old processes and workflows are ripped and replaced.

We sense that the broad premise of technology adaptation sitting outside of conventional cyclicality still holds. But we would concede that the dynamics

• Net asset value after deducting borrowings at fair value.

Source: Baillie Gifford, Refinitiv and relevant underlying index providers. See disclaimer on page 33.

For a definition of terms see Glossary of Terms and Alternative Performance Measures on pages 27 to 29. Total return information sourced from Refinitiv/Baillie Gifford and relevant underlying index providers.

Past performance is not a guide to future performance.

02 Interim Financial Report 2023

of growth and business scaling are adapting to the higher direct costs of expansion (e.g. higher borrowing costs and wage inflation). Technology companies are among the first to adjust to this, mainly because they were also the ones at the forefront of pursuing expansionary-based scale.

We should not confuse this as being the end of a technology cycle, far from it. As the focus shifts from the pursuit of scale towards tools of efficiency,

we think companies that offer or exploit deep productivity-enhancing solutions will come to the fore. You might argue that this has long been the case (e.g. the rise of software tools since the 80s) but productivity growth in most major developed market economies has been lacklustre for several decades1. With looming huge improvements in intelligent automation as discussed below, the prospects for meaningful productivity gains look much brighter and we see the role of automation shifting from the current model of assisting humans with mundane tasks towards more value-added assistance or task displacement.

You will have likely heard about some of the recent advances in AI, particularly in the field of generative AI and local language models through tools such as ChatGPT. While AI and machine learning have been in their ascendency in recent years, their relevance has primarily centred upon narrow probabilistic prediction - with the accuracy of that prediction being most influenced by the intrinsic data quality and manual labelling of data used to train a specific algorithm.

Generative AI is focused on building novel content like art, an essay, or lines of code. When challenged, a sophisticated generative AI engine will draw upon the vast breadth of data it has been exposed to, generate an approximate answer and then seek to refine this through critical challenge. Such an iterative process distances generative AI from the narrower predictive AI on several fronts. Strikingly, it can make linkages between discrete observations and deal with areas of ambiguity in what it observes. Moreover, by mimicking mechanisms of natural conscious learning and seeking resolution not

statistical perfection, generative AI outputs instinctively feel much more human-like, and it has proven itself to be especially adept at mastering language and dialogue.

At its core, generative AI advances are about delivering context-relevant, digestible outputs that seek to answer real-world queries. Its power can be pointed in many directions, whether creating novel digital content at a hitherto unimaginable scale or as a user-friendly distiller of complexity. The former could see it garner a role in the production of software code or in-silico screening of vast libraries of compounds for use in areas such as drug discovery or battery technology. The latter uniquely positions it to offer a scalable user interface that could ultimately perform various functions such as knowledge search or fully automated customer service. This is fundamentally different from most current technology interfaces which are about delivering blunt and narrow approximations.

What are the implications of all this? Our initial sense is that these impressive but still nascent advances will lay new foundations for how individuals and businesses engage with technology. Much like the arrival of the internet 30 years ago, we see generative AI as a horizontal technology tool that optically lowers the entry barriers within a range of verticals/industries. Traditionally, such a dynamic would be expected to favour nimble disruptors and disadvantage stale incumbents. Yet to borrow further from the experience of internet-based digitisation, while barriers to entry were initially lowered, we suspect that barriers to scale are likely to prove to be much harder to break down and will likely be better determinants when filtering winners from losers within this technology evolution.

While a clear advantage of generative AI is the ability to train it on vast broad data sources, real-world commercial use cases of this technology will likely have a requirement for domain-relevant digital data with which to hone the algorithms. This proprietary data likely exists within businesses that currently cater to their respective end markets. Furthermore, many incumbents (or at least those that remain/have

1 Since 2005, US labour productivity has grown at a modest 1.4% per annum. In 2022 it dipped to -1.3%, its weakest since 1974.

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