Is meme logic not suitable for Base?

Original Title: “Speculative Swells and the Memecoin Aftermath”

Author: Stanford Blockchain Club

Compiled by: Peisen, BlockBeats

Editor’s Note: This article takes the Base network as an example to delve into how the launch of the meme coin BRETT in early 2024 triggered a significant market event, leading to shifts in user behavior and trading patterns. Through an analysis of supply and demand dynamics, the study reveals the subtle interaction between rising Gas fees and trading activity, showcasing the profound impact of this catalytic event on network users.

The article not only quantifies the impact of the BRETT event on trading behavior through regression models but also further explores how this external shock amplified users' sensitivity to transaction costs, resulting in a sharp decline in demand. This phenomenon reflects the vulnerability of blockchain networks in the face of unexpected events and their rapidly changing nature.

Introduction

For most people, unexpected disruptions in supply chains are rarely beneficial. However, for researchers, these disruptions provide valuable opportunities to understand market dynamics that are difficult to disentangle under normal circumstances. For instance, since prices and quantities are direct manifestations of supply and demand, it is challenging to determine whether supply, demand, or both are influencing the market. This leads to the old adage: "Don't infer solely from price changes." But when one of these factors suddenly changes in a predictable way, you can sometimes draw conclusions from it.

For example, a study from the NBER utilized the supply shock triggered by COVID-19 to understand demand dynamics, demonstrating how such sudden external shocks can become significant redistributive forces affecting employment and sales in the U.S. economy. By analyzing behavioral changes during rare market events, researchers were able to turn crises into opportunities for deep economic insights.

The operation of blockchain networks has inherent capacity limitations similar to those of production lines. Each block has a fixed capacity for transaction data, making space a scarce resource. As transaction demand increases, competition for block space intensifies, potentially leading to network congestion.

In March of this year, Ethereum implemented the EIP-4844 proposal aimed at increasing network capacity and reducing Layer-2 transaction costs, allowing networks like Arbitrum and Optimism to enjoy significant reductions in Gas fees. However, shortly after the implementation of this proposal, Gas prices on the Base network surged, exceeding levels prior to the launch of EIP-4844.

During this period, user activity on Base significantly increased, primarily driven by DeFi trading activities. Given that the Base ecosystem has always leaned towards consumer-oriented applications, this surge was particularly unexpected. Base was initially incubated by the Coinbase team and, thanks to extensive marketing and branding efforts, aims to create a chain that encourages participation from creators, developers, and the community. As a result, the ecosystem is primarily composed of consumer applications, with the most successful applications like Friend.tech being consumer-focused.

The reversal of user activity on Base and the sudden surge in trading volume can be attributed to a supply shock triggered by unexpected external events, which affected the system's supply chain. Such shocks can significantly alter availability and costs, fundamentally changing user behavior and network dynamics.

Catalyst Hunting

To constitute a true supply shock, an event must be exogenous, unexpected, and strong enough to disrupt existing market dynamics.

One of the most significant changes following the implementation of the EIP-4844 proposal was the sudden increase in trading volume on decentralized exchanges (DEX), which extended beyond typical stablecoins and ETH to new token categories. Previously, trading on the Base network was primarily concentrated in these categories, with meme coins averaging less than 15% of the total weekly trading volume across all DEX.

Historically, meme coin booms are often triggered by a "beacon" token that attracts significant market interest and sets new trading benchmarks. This phenomenon is likely driven by factors such as information cascades. On platforms like Crypto Twitter, successful trading stories are amplified while failures are often overlooked, leading to a skewed perception of potential gains. When traders observe and mimic the behavior of others, assuming they possess valuable information, a self-reinforcing cycle is formed. This can rapidly drive up the prices of meme coins and typically results in extreme market volatility.

For instance, at the end of 2023 on Solana, the market cap of the dogwifhat (WIF) token skyrocketed from under $1 million to billions within a few months. The success of WIF triggered a meme coin frenzy on Solana, accompanied by an increase in meme coin issuance and the development of meme coin infrastructure.

Although meme coins had existed since the launch of the Base network, no single meme coin had garnered widespread market attention until March of this year. The initial launch of the Base mainnet was driven by a meme coin trading frenzy. Before the network officially launched, thousands of users flocked to Base for meme coin trading. As new applications were launched, trading activity for these tokens gradually decreased. Inspired by characters from popular books themed around Pepe, the BRETT token launched from late February to early March and quickly emerged on Base, achieving a market cap of $350 million before the large-scale meme trading activity occurred. Its rapid rise not only distanced it from typical market trends but also triggered a broader trading frenzy across the entire network.

The initial success of the BRETT token attracted speculative traders through potential imitation effects, drawing in a new cohort of users more focused on meme trading rather than engaging with the network's applications. While this group's focus is relatively narrow, it is worth exploring the ripple effects of this meme frenzy on the existing user base of the Base ecosystem, particularly how their typical behaviors changed due to this event. Although surface data alone cannot confirm that the observed congestion was directly caused by the BRETT token event, it prompts us to conduct further detailed analyses to accurately assess its direct impact on user behavior and demand.

Experimental Design

The primary objective of this experiment is to analyze the supply and demand dynamics on the Base network, focusing on the interaction between Gas fees (supply) and trading activity (demand) before, during, and after the BRETT event. A key part of this analysis is to isolate the impact of the BRETT launch from general market behavior.

To gain clear insights into market dynamics, we will exclude trading activities directly related to the BRETT token. Our analysis will focus on addresses that were already active before the token's launch at the end of February, allowing us to assess a sustained user base that was not influenced by speculative interest in the new token. This approach ensures that our study of broader user behavior on the Base network is unbiased and not disproportionately affected by users primarily focused on BRETT.

Model Design

In this study, we employed a regression model that includes a core binary variable to analyze the impact of the BRETT launch. The variables and their functional selections in the model are designed to reflect the subtle effects of this market event.

The model is defined as follows:

Where:

• Average Gas Usage (Q): Represents the average Gas usage at a given time, a key indicator of transaction complexity and network load.
• Shock Indicator (D): A binary variable indicating whether the BRETT token event occurred (0 before launch, 1 after launch).
• Gas Fees (P): Represents the Gas price at a given time, measured in gwei.
• Interaction Term (DP): Captures the interactive effect between the BRETT shock and Gas prices.
• Number of Transactions (T): Represents the number of transactions at a given time, used to understand how changes in trading volume affect network congestion and Gas usage.

It is important to note that this model is relatively simple in its current form, primarily aimed at revealing demand changes associated with this specific catalyst. The model does not account for potential endogeneity from baseline conditions or other underlying trends, which may obscure the true causal relationships and demand elasticity prior to the event. For instance, there may be omitted variables, and there may be simultaneous causal relationships between Gas usage and fees, along with other noise, which could affect the accuracy of our initial estimates.

Nevertheless, the model allows us to determine whether the BRETT shock led to significant changes in trading behavior on the Base network, independent of direct trading activities related to BRETT.

Regression Results

Through an hourly analysis of the non-BRETT-related user group from early January to the end of May 2024, we can draw the following conclusions regarding the launch of the BRETT token and its initial surge:

After the launch of the BRETT token, users exhibited significant behavioral changes in response to rising Gas prices. The regression model revealed a significant negative interaction term (₃=−0.333), indicating that the increase in Gas fees following the token's launch likely suppressed user trading behavior.

Specifically, the interaction term indicates that after the meme event, for every standard deviation increase in Gas prices (Δ=1.2×10⁵ gwei), Gas usage (Δ) would decrease by 41,200 gwei, equivalent to 79% of the typical hourly standard deviation. In other words, according to the model's predictions, during high congestion events, for every standard deviation increase in Gas prices, demand would decrease by approximately 0.79 standard deviations.

Overall, the introduction of the meme coin "beacon" BRETT had a negative ripple effect on the initial user base of Base. The congestion triggered by the catalyst heightened this group's sensitivity to rising Gas prices, making them more averse to transaction costs—even when these costs approached levels seen before the implementation of EIP-4844.

Amplifying Perspective

The impact of BRETT on Base illustrates broader vulnerabilities within the crypto ecosystem and users' adaptive behaviors. The event underscores how emerging tokens, particularly unexpected events, can significantly influence trading metrics, user behavior, and network stability, reflecting the rapidity of dynamic changes within the blockchain operational framework.

This event highlights the subtle relationship between supply (in this case, network fees) and user demand, which is not a simple linear relationship. Demand can shift suddenly, as seen with the BRETT event, or evolve gradually as the ecosystem matures. These changes emphasize the complex interactions between network adjustments and user responses, which are not always predictable and can vary widely due to external shocks or anticipated network upgrades.

Looking ahead, as more exogenous events or known upgrades occur, understanding these fundamental dynamics becomes crucial. Identifying patterns and potential responses of users to changes within the ecosystem can help predict more realistic user dynamics and reactions.