Martin Armstrong
@martinarmstrongae
Martin Armstrong is one of the most controversial yet accurate voices in economic forecasting. Founder of Armstrong Economics, the Economic Confidence Model, and Socrates (the only full-functioning AI computer with a trackrecord of 40+ year). Armstrong called the 1987 Black Monday crash to the exact day, predicted the Nikkei peak in December 1989 before its devastating collapse (earning him the title of top North American economist), and forecasted the Russian bond default in 1998 that nearly destroyed the global financial system and broke the hedge fund Long-Term Capital Management. His model also identified July 20, 1998, as the S&P high before the crisis, and more recently predicted the 2008 financial crisis and the start of the Ukraine and Russian War. The CIA approached him after the Russian Bond collapse prediction. They wanted the source code to his computer. Under a legal persecution to get the code he spent 11 years in prison. Today Martin continues stronger than ever.
53K
Subscribers
4.91M
Total views
930
Videos
2.2K
Avg views (recent)
2-video sample · 2026-07-26
Estimated rate · Integrated video
Avg views · niche CPM · High confidenceLow
$99
Typical
$121
High
$165
- Niche CPM
- $45–75anchor $55
- Avg views
- 2,202per recent video
- Format mult
- 1.00×
- Exclusivity
- No exclusivitybaseline
Estimated · benchmark only · not claimed by creator. How this works →
Influence profile
Display only · not a price input- Size tier
- Micro (50K–100K)
- Activity
- Below averageratio 0.042 · n=2
- Niche
- Finance
- Country · language
- —· en
- US equities coverage
- Tracked
- SEC §17(b) disclosure
- Not assessed
About Martin Armstrong
Martin Armstrong, operating under the handle @martinarmstrongae on YouTube, is a finance creator known for his focus on economic forecasting and market analysis. As the founder of Armstrong Economics, he is associated with the Economic Confidence Model and Socrates, an AI-driven analytical system. The channel features 839 videos, which have collectively accumulated 4,518,726 views. With a subscriber base of 51,900, Armstrong's content delves into complex financial topics, often drawing on historical economic patterns and predictive models. His work is presented as a data-driven approach to understanding global financial markets and trends. The creator's biography highlights specific past predictions, including the 1987 Black Monday crash, the 1989 Nikkei peak, and the 1998 Russian bond default, positioning him as a long-standing figure in economic commentary.
Martin Armstrong's estimated sponsorship rates
InfluencerUnion estimates Martin Armstrong's rate for a YouTube video integration to range from $327 to $817, with a typical rate around $545. This pricing falls within the 'low' audience tier, reflecting his current subscriber count of 51,900. The estimated rate is influenced by several factors, including the creator's primary category of finance, which often commands higher CPMs due to its appeal to financially engaged audiences. While the specific country and primary language are not available, the niche focus on economic forecasting and market analysis contributes to the valuation. The rate is algorithmically derived, taking into account the platform (YouTube), content format (video integration), and the creator's engagement metrics. For a detailed breakdown of how these estimates are calculated, including the specific multipliers applied for niche and format, readers are encouraged to consult the /methodology page. The current estimate is based on a metric-driven model, as community data points for calibration are not yet available.
Who should partner with Martin Armstrong?
The audience for Martin Armstrong's channel, @martinarmstrongae, primarily consists of individuals interested in finance, economic forecasting, and market analysis. With 51,900 subscribers, the channel attracts viewers seeking in-depth commentary on global financial systems and predictive economic models. This demographic is typically engaged with content related to investment strategies, macroeconomic trends, and financial history. Brands targeting audiences interested in fintech applications, online brokerage services, economic research platforms, and personal finance education would find this creator's viewership relevant. The channel's focus on complex economic topics suggests an audience that values detailed analysis and data-driven insights. In terms of audience size, Martin Armstrong is positioned similarly to peers such as SundayBoyInvest (@sundayboyinvest) with 51,800 subscribers, and Steve Miller (@askslimteam) with 52,200 subscribers, indicating a consistent reach within this specific finance niche.
Martin Armstrong's growth and performance
Martin Armstrong's subscriber growth data is currently limited to a single snapshot. The channel was first tracked on 2026-05-19 with 51,900 subscribers. The latest available snapshot, also from 2026-05-19, shows the same subscriber count. Over this one-day span, no change in subscriber count was recorded. We do not yet have historical data to analyze long-term subscriber trends or significant growth periods. Information regarding average views over the last 30 days is also not available at this time. Future data collection will provide a more comprehensive view of the channel's growth trajectory and audience engagement patterns.
How our pricing estimate works for Martin Armstrong
The pricing estimate for Martin Armstrong's content is generated through a proprietary algorithmic model developed by InfluencerUnion. This model calculates an estimated rate for a YouTube video integration by considering several key factors, including the creator's subscriber count, the specific niche (finance in this case), and platform-specific multipliers for YouTube. The algorithm also accounts for content format, applying adjustments for integrated video content. The estimate provided—ranging from $327 to $817, with a typical rate of $545—is a data-driven projection of market value. It is important to note that this estimate is based on a metric-driven calibration, as community-sourced pricing data points for this creator are not yet available. For a comprehensive explanation of the variables, formulas, and data sources utilized in our algorithmic pricing methodology, please refer to the detailed documentation available on our /methodology page.