• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

HSE Researchers Find Counter-Strike Skins Outperform Bitcoin and Gold as Alternative Investments

HSE Researchers Find Counter-Strike Skins Outperform Bitcoin and Gold as Alternative Investments

Screenshot from CS:GO

Virtual knives, custom-painted machine guns, and gloves are common collectible items in videogames. A new study by scientists from HSE University suggests that digital skins from the popular video game Counter-Strike: Global Offensive (CS:GO) rank among the most profitable types of alternative investments, with average annual returns exceeding 40%. The study has been published in the Social Science Research Network (SSRN), a free-access online repository.

Investors have long sought after assets that are insulated from stock market fluctuations, especially during times of instability. This has fuelled a boom in alternative investments, with people putting their money into art, fine wine, and vintage cars. Videogame skins can now be confidently added to this list, offering not only aesthetic appeal but also impressive financial returns.

Skins are virtual items that alter the appearance of in-game objects, such as weapons or characters. While they don’t usually impact gameplay or a player’s chances of winning, they allow users to express their individuality. Skins range from simple designs to rare collectibles that can be bought, traded, or sold on specialised online platforms. The most sought-after and rare skins have become the foundation of a new investment trend.

The study by Victoria Dobrynskaya, Associate Professor at the Faculty of Economic Sciences of HSE University, and Vladimir Strelnikov, her former student, now a master's student at New Economic School, suggests that digital collectibles from the videogame Counter-Strike: Global Offensive (CS:GO) can yield average annual returns of over 40%.

To assess the investment appeal of CS:GO skins, the researchers analysed data on nearly 5,000 unique in-game items traded on the Steam platform between 2013 and 2024.

The study found that skins are among the top-performing assets in terms of the risk–return ratio. The average annual return on a portfolio of skins was 41.2%, which is several times higher than that of the US stock market (CRSP), real estate (REITs), bonds or gold. While their profitability is comparable to Bitcoin, skins exhibit significantly lower volatility.

According to the analysis, the average skin price was $155, but the distribution was highly skewed, with most items priced significantly lower. The median price was just $11.50, while the most expensive skin in the Steam sample sold for nearly $4,000.

'On average, a skin is traded 800 times per month, with Steam charging a 15% fee on each transaction. As a result, frequent trading is generally unprofitable, making long-term investment in these assets optimal,' explain Dobrynskaya and Strelnikov.

'The Sharpe ratio for skins is 0.34, indicating a favourable return-to-risk profile. For comparison, during the same period, the Sharpe ratios were 0.25 for the CRSP, 0.12 for gold, and 0.21 for Bitcoin. A higher Sharpe ratio signifies better investment performance. While there are no guarantees, we have observed data spanning over a decade, during which most skins have steadily increased in value,' notes Dobrynskaya.

The skin market proved to be largely insensitive to market risks, with the most profitable assets being inexpensive items: common skins of around $2.25 in price generate average monthly returns of nearly 19%. In contrast, rare and expensive skins priced at around $1,700 yield monthly returns below 3%, which is still higher than the average returns of the stock market. As a result, strategies that prioritise marketable, liquid items rather than rare collectibles demonstrate the best performance.

'In-game collectibles are more than just pixels—they are genuine financial instruments that help diversify portfolios and hedge against volatility. At the same time, the market is liquid and transparent, with a capitalisation exceeding $4 billion. This market remains relatively unknown to outside investors, and most skin investments come from actual players. Long-term investment in skins is a common practice among them,' Dobrynskaya and Strelnikov comment.

See also:

Neurolinguists Assist in Awake Surgery on 11-Year-Old Patient with Epilepsy

Researchers at the HSE Centre for Language and Brain took part in a rare awake neurosurgical procedure performed on an 11-year-old patient with drug-resistant epilepsy. Working alongside surgeons at the Voyno-Yasenetsky Centre of Specialised Medical Care for Children in Solntsevo, they monitored the resection of a portion of the left temporal lobe, where the epileptic focus had been identified.

Scientists Explain How Emotions Shape Attitudes Toward Digital Governance

Today, interactions between citizens and government increasingly take place through digital governance platforms, including digital public services, AI-powered systems, and algorithmic decision-making tools. Until now, however, these technologies have largely been viewed as technical instruments, with their effectiveness assessed primarily in terms of efficiency and user-friendliness. The authors of a new study propose a broader perspective, arguing that digital governance should also be understood as an emotional experience that directly shapes citizens' trust in public institutions.

Neural Network Maps as a Method for Constructing Mathematical Models

Scientists from HSE University–Nizhny Novgorod and the Institute of Physics Belgrade, Serbia, are jointly exploring the application of machine learning techniques and neural networks to the study of nonlinear dynamics. Natalya Stankevich, Leading Research Fellow at the Laboratory of Topological Methods in Dynamics of the Faculty of Informatics, Mathematics, and Computer Science at HSE University–Nizhny Novgorod, spoke to the HSE News Service about this international project.

HSE Scientists Develop Method to Compress Large Language Models Without Losing Quality

Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed a new compression method for large language models such as GPT and LLaMA that reduces their size by 25–36% without additional training or significant loss of accuracy. This is the first approach to use mathematical transformations—specifically, rotations of model weights—to make models more amenable to compression with structured matrices. The study results have been published in ACL Findings 2025. The code is available on GitHub.

Machine Learning Models Can Help Reduce Volatility and Boost Stock Market Returns

The use of machine learning models makes it possible to achieve greater accuracy in predicting risks in the Russian stock market compared to classical econometric approaches. The predictive power of these models increases by 23%, while the average investor’s return can reach up to 13% per annum. These conclusions were drawn by Nikita Lysenok from the Department of Financial Market Infrastructure at the HSE Faculty of Economic Sciences. The paper has been published in Fundamental and Applied Mathematics.

Pocket Money, Personal Interest, and Family Practices: What Shapes Students’ Economic Literacy?

University students' economic literacy depends not only on their field of study but also on their interest in economics, the learning environment, and family financial practices. For example, students who received pocket money irregularly tend to perform better on economic literacy tests than their peers who received financial support on a regular basis. These findings come from a study conducted by HSE University involving more than 1,100 students from five Russian universities. The findings have been published in Cakrawala Pendidikan.

HSE Study Reveals Imbalance in the Generative AI Market

Researchers at HSE University analysed how effectively the global generative artificial intelligence market converts investment into real revenue, concluding that AI is currently developing faster than it is paying off. The results have been published in the journal Foresight and STI Governance.

HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors

Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.

The 'Second Shift' Is Not Why Women Avoid News

Women are more likely than men to avoid political and economic news, but the reasons for this behaviour are linked less to structural inequality or family-related stress than to personal attitudes and the emotional perception of news content. This conclusion was reached by HSE researchers after analysing data from a large-scale survey of more than 10,000 residents across 61 regions of Russia. The study findings have been published in Woman in Russian Society.

Resource Race and Green Transition: Three Unexpected Conclusions from Foresight Centre’s Research on Climate and Poverty

Beneath the surface of green energy—which most people associate with solar panels, electric vehicles, and reduced CO2 emissions—lies a complex web of geopolitical interests, international inequality, and resource constraints. Researchers from the Laboratory for Science and Technology Studies (LST) at the HSE ISSEK Foresight Centre have published a series of articles in leading international journals on hidden and overt conflicts surrounding critically important metals and minerals, as well as related processes in the energy sector.