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A Swiss National Bank working paper by Severin Joho and Enzo Rossi examines 1,063 speeches by senior SNB officials from 1928 to 2024. It reports that speeches became more frequent, shorter and more accessible, while inflation gained prominence in policy discussion during the flexible exchange-rate era.
Severin Joho and Enzo Rossi have published a Swiss National Bank working paper analyzing 1,063 speeches by senior SNB officials from 1928 through 2024. The study finds that speeches became more frequent, shorter and more accessible over time, and that inflation gained prominence in the bank’s discussion of policy during the flexible exchange-rate era.
The paper, published by the Swiss National Bank, assesses changes in the volume, length, readability and themes of officials’ speeches. Its dataset covers nearly a century of public remarks, allowing the authors to compare communication patterns across different periods in the bank’s history.
The authors report that annual speech frequency rose from the mid-1950s, with a more pronounced acceleration after Switzerland’s move to flexible exchange rates. They also identify a broad shift toward shorter speeches and increasingly accessible language beginning in the 1960s. The report does not give a single overall percentage change for these measures in the material provided.
On subject matter, the paper says inflation moved to the forefront of policy discussion in the flexible-rate period, while cyclical economic developments and unemployment received less emphasis. The authors also compare conventional Python-based natural-language processing with ChatGPT, discussing how the methods can complement each other and where language-model analysis can be vulnerable to algorithmic pitfalls.
How SNB Communication Changed
The findings offer a historical view of how a central bank’s public communication changed alongside the monetary-policy environment. The reported rise in speech frequency and shift in themes may help researchers understand which topics the SNB chose to emphasize in different eras; the study describes these patterns, rather than establishing that a change in communication caused policy outcomes.
The analysis also matters for researchers who use automated tools to study official statements. Comparing deterministic Python methods with a generative model such as ChatGPT highlights that the choice of tool can affect how large historical text collections are categorized and interpreted. The authors frame the approaches as complementary, while warning that model limitations need attention.
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A Century of SNB Speeches
The study’s evidence spans speeches delivered from 1928 to 2024, covering substantial changes in the way Swiss monetary policy was conducted and discussed. The authors single out the transition to flexible exchange rates as a point after which speech frequency accelerated and inflation became more prominent in the thematic record.
The paper is a working paper listed in the SNB’s research publications. Its stated scope includes both historical findings about the speeches and methodological lessons for applying language-processing systems to central-bank communication. The source summary does not describe the authors’ full coding rules, statistical estimates or detailed validation results.
Limits of the Reported Findings
The source summary does not provide the exact annual speech counts, readability scores, speech-length measurements or the size of the thematic shifts. It therefore supports the direction of the reported trends, but not a more precise numerical comparison. The specific years used to define the flexible exchange-rate period are also not stated in the summary.
The paper raises methodological concerns about generative AI, but the supplied material does not detail which ChatGPT version was tested, how prompts were designed, or how its outputs compared with Python-based results for each task. It also does not establish whether changes in speech patterns reflect deliberate communication choices, shifts in institutional practice, or other factors.
Further Tests of Language Models
The authors identify open questions for future research on using large language models to analyze central-bank communication. Further work could clarify how results vary with model versions, prompt design and validation methods, and how best to combine probabilistic model outputs with reproducible Python-based analysis.
The SNB source identifies the publication as a working paper; the supplied information does not state whether a journal review, revised edition or follow-up study is planned. For now, the paper’s conclusions are its reported historical patterns and methodological discussion, with detailed measurements and additional validation left to the full report and future research.
Key Questions
What did the SNB working paper study?
It analyzed 1,063 speeches by high-ranking Swiss National Bank officials delivered from 1928 through 2024, examining frequency, length, readability and thematic content.
What changes in speech patterns did the authors report?
The authors report that annual speech frequency increased from the mid-1950s and accelerated after the move to flexible exchange rates. They also found a shift toward shorter speeches and more accessible language.
How did policy themes change?
The paper says inflation became more prominent in the flexible-exchange-rate era, while cyclical economic development and unemployment received less emphasis.
Why did the authors compare Python tools with ChatGPT?
They benchmarked traditional Python-based text mining against ChatGPT to examine methods for analyzing central-bank communication. Their report describes the approaches as complementary and discusses algorithmic pitfalls.
Does the summary provide exact figures for these changes?
No. The supplied report summary describes the direction of the trends but does not give the specific speech counts, readability scores or percentage changes needed for precise numerical comparisons.
Source: primary
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