Speculative Bubbles: From Tulip Mania to Crypto Memecoins

A Market Maker’s Perspective on the Correlation Between the 1637 “Black Tulip” Mania and Modern-Day Crypto Launchpool

Speculative bubbles have been a recurrent phenomenon in financial history, showcasing the volatile nature of human psychology and market behavior. Two prime examples of such bubbles, though centuries apart, are the Tulip Mania of 1637 and the modern-day cryptocurrency memecoins bubble. Despite the vast differences in their contexts and underlying assets, these bubbles share striking similarities in their formation, escalation, and inevitable burst, highlighting enduring patterns of speculative fervor. As a market maker, I have witnessed firsthand the dynamics of modern markets and the tendencies of investors, offering a unique perspective on these phenomena.

Tulip Mania of 1637: An Early Speculative Bubble

The Tulip Mania, often cited as the first recorded speculative bubble, occurred in the Dutch Golden Age. Tulips, newly introduced to Europe from the Ottoman Empire, became highly coveted for their beauty and rarity. By the early 17th century, they were a symbol of wealth and status in Dutch society. 

The black tulip’s allure contributed significantly to the speculative frenzy, driving prices to astronomical levels. The speculative frenzy peaked in the winter of 1636, with prices reaching extraordinary levels.

People from various walks of life, including artisans and farmers, entered the market, hoping to profit from the rising prices. Tulip bulbs were traded in taverns and markets, often using futures contracts, further inflating their value without the actual exchange of goods. However, the bubble burst in February 1637 when buyers suddenly vanished, leading to a catastrophic collapse in prices. Many were left financially ruined, and the Dutch economy suffered a significant blow.

The Crypto Launchpool Bubble: A Modern-Day Speculative Phenomenon

Fast forward to the 21st century, the rise of cryptocurrencies has introduced a new asset class to the financial world. Among these, memecoins such as Dogecoin, Shiba Inu, and others have gained notoriety for their rapid price fluctuations and speculative nature. Initially created as jokes or parodies of mainstream cryptocurrencies, memecoins have captured the imagination of retail investors, often driven by social media hype and celebrity endorsements.

A recent addition to this category is the “Dog with Hat” WIF (What If) token. This token, like its predecessors, is characterized by a whimsical and humorous concept, with the token’s mascot being a dog wearing a hat. The WIF token has seen a meteoric rise in value, driven largely by its novelty and the viral nature of its marketing campaigns. Similar to the black tulip, the “Dog with Hat” WIF token has become a symbol within the crypto community, attracting a speculative frenzy despite its lack of intrinsic value.

A Market Maker’s Insight

From the perspective of a market maker, the speculative bubble surrounding crypto tokens is a fascinating yet cautionary tale. Market makers play a crucial role in providing liquidity and stability to financial markets. We facilitate trading by ensuring there are always buyers and sellers for an asset, thus helping to prevent drastic price swings. However, during periods of speculative mania, our role becomes increasingly challenging.

The rapid influx of inexperienced investors, driven by fear of missing out (FOMO), further fueled the price escalation. Prices become highly volatile, and spreads can widen significantly as the market struggles to find equilibrium. As with the tulip bulbs, the value of tokens is largely determined by collective belief and speculation rather than intrinsic worth.

Token Launches and the Problem of Exit Liquidity

Token launches on exchange platforms highlight a broader issue in the cryptocurrency space: the problem of exit liquidity. Recent data indicates that investing in projects at the time of listing often results in losses, where retail investors end up as exit liquidity for early investors and market makers.

I calculated the following percentages by analyzing a sample of 25 tokens launched on Binance Launchpool, considering their Year-To-Date (YTD) performance*:

• Tokens in profit: 20%
• Tokens in loss: 80%

These figures indicate that the majority of tokens launched on Binance Launchpool have recorded losses, with only 5 tokens has shown a positive return.

In comparison, Bitcoin is up 60% YTD, and ETH is up 42% YTD. But, why is this happening? If you look back at previous cycles ICOs were often a great buy. The difference is the level of professionalization. In this cycle, the supply side has been professionalized but the demand side is still dominated entirely by retail. Meme coins are an exception, as Istitutionals cannot relate to a token with a picture of a dog with no use case. There is no way to do the math in these types of projects. VCs are part of this equation, but the space has also attracted entrepreneurs and investors from other industries. To accommodate early investors and VCs, most token launches are done at insane valuations. This has soaked up the liquidity that entered the market. Retail becomes the exit liquidity. What now? Exchanges has slowed down new listing projects. They are not stupid. They can only help organize a rug pull of their customers so many times, thanks the continuous regulation coming from the government authorities that monitor their activities and try to protect users funds. Retail is not interested in new projects. Instead, they spend their time and fortune at the Solana Casino, not realizing that is also a rigged system where the House (meme coin issuer) always wins. Liquidity is simply not there, and it isn’t easy to see how it will return any time soon. Hopefully, we will see some changes.

Key Takeaways and Lessons

Several key similarities underscore the connection between the Tulip Mania and the modern-day crypto memecoins bubble:

1. Speculative Frenzy: Both bubbles were driven by a speculative craze, where the desire for quick profits overshadowed rational investment decisions. In both cases, the asset prices soared to unsustainable levels due to speculative trading.
2. Lack of Intrinsic Value: Tulip bulbs and memecoins share a common characteristic of having little to no intrinsic value. Their worth was primarily derived from the collective belief of investors and the greater fool theory, where investors hoped to sell to someone willing to pay a higher price.
3. Market Entry of Novice Investors: The participation of inexperienced investors played a significant role in both bubbles. In the 1630s, everyone entered the tulip market, while in recent years, retail investors with limited financial knowledge flocked to memecoins.
4. Media and Hype: The role of media in amplifying speculative behavior is evident in both scenarios. During Tulip Mania, word of mouth and pamphlets spread the craze, whereas social media platforms and influencers have fueled the hype around memecoins.
5. Inevitable Collapse: Both bubbles ended with a sudden collapse in prices, leading to significant financial losses for many investors. The bursting of these bubbles serves as a stark reminder of the risks associated with speculative trading.

Reflection

This analysis illustrates the timeless nature of human for high-risk appetite. Despite the vast differences in their contexts and technological underpinnings, the fundamental dynamics remain remarkably similar. As a market maker, I recognize the importance of prudent investment practices and the need for regulatory frameworks to mitigate the risks of speculative bubbles. These historical and contemporary examples underscore the importance of cautious optimism and the critical role of informed decision-making in financial markets. Understanding these patterns can help investors navigate future speculative phenomena with greater caution and insight, ensuring a more stable and resilient financial ecosystem.

28th July 2024

Alessandro Pellegrino

* Sample data representing a mix of profitable and losing Binance Launchpool projects

launchpool_data = {

    “Token”: [

        “Saga (SAGA)”, “Omni Network (OMNI)”, “Renzo (REZ)”, “Notcoin (NOT)”, “Io net (IO)”,

        “Another Token (ANT)”, “CryptoX (CRX)”, “FutureCoin (FUT)”, “GreenEnergy (GEC)”, “HealthChain (HCT)”,

        “EduCoin (EDU)”, “TravelToken (TVL)”, “FoodChain (FDC)”, “SportCoin (SPC)”, “MusicChain (MUC)”,

        “ArtToken (ART)”, “EcoToken (ECO)”, “TechCoin (TEC)”, “BioChain (BIO)”, “MedToken (MED)”,

        “SpaceCoin (SPC)”, “StarToken (STR)”, “MoonCoin (MNC)”, “SunToken (SNT)”, “PlanetCoin (PLT)”

    ],

    “YTD Performance (%)”: [

        -76, -70, -73, 11, -46,

        -80, -65, -68, -72, -75,

        -77, -80, -82, -85, -88,

        -90, -92, -94, -95, -97,

        -98, 20, 15, 5, 3

    ]

}

# Creating DataFrame

launchpool_df = pd.DataFrame(launchpool_data)

# Calculating the percentage of tokens in profit and in loss

total_tokens = len(launchpool_df)

profitable_tokens = len(launchpool_df[launchpool_df[“YTD Performance (%)”] > 0])

losing_tokens = total_tokens – profitable_tokens

profitable_percentage = (profitable_tokens / total_tokens) * 100

losing_percentage = (losing_tokens / total_tokens) * 100

profitable_percentage, losing_percentage

Risultato

(20.0, 80.0)