A Comparative Analysis of Market Depth for Futures & Spot on Top Centralised Exchanges By Simplicity Group

‍

Executive Summary

‍

This research explores the impact of centralized exchange (CEX) listings on token prices by analyzing the post-listing performance of 34 tokens across Binance, ByBit, OKX, KuCoin, Coinbase, Gate.io, and MEXC. The study specifically focuses on the price behavior during the first week after listing, drawing on quantitative data to identify trends, volatility patterns, and potential correlations between token price performance. The purpose of the study was to identify whether there is a causal relationship between an exchange’s brand and initial token price performance.

We analyzed the performance of all tokens on the exchanges individually, and then ran comparative analysis between the exchanges, before finishing with analysis of two tokens which were listed on all 7 exchanges on the same day.

Our analysis reveals several key findings. First, the average token experienced a significant price increase within the first 24 hours of listing, and for most exchanges, it proceeded to retrace immediately after on the second day. However, some exchanges that experienced smaller listing pumps had more stability at higher price levels, unlike those which had much higher pumps followed by severe corrections.

Second, some exchanges experienced eerily similar price movements for their tokens, whilst others had much more variability. We’re not entirely sure what this is indicative of.

Third, exchange stipulations are crucial to price performance. In particular, listing a token hours or days after other exchanges meant that price performance on that late exchange would be worse.

Last, some exchanges have far more outliers than others, indicating that an exchange’s internal due diligence process is vital for the overall performances of its listings.

Our main recommendation based on this research for token listings is to not tie the reputation of an exchange to potential price performance.

In conclusion, all exchanges had different average price performances for the same tokens due to a plethora of factors, but by running statistical analysis, we gain valuable insights for projects considering CEX listings as part of their market entry strategy, as well as for investors looking to understand the opportunities and risks associated.

‍

1.1 Introduction

‍

The cryptocurrency industry is characterized by its dynamic nature, boasting a vast number of ecosystems, apps, and communities, with thousands of cryptocurrencies and tokens. For Web3 projects, tokens are more than just a vessel for fundraising. They're crucial parts of most ecosystems, encapsulating different types of value: monetary, political, social, informational, and speculative.

Yet, there is a dominant force that has a significant impact on how retail perceives these values: centralized exchanges. The importance of securing a listing on a "high-tier" exchange cannot be overlooked from a Web3 project's perspective. There is an intense competition between projects trying to secure a spot for listing, as the milestone is often seen as the front door to vast liquidity, credibility, and recognition.

Getting listed on a major exchange is not only an indicator of trustworthiness, it often symbolizes the beginning of true mass adoption, or at least, that's the widely held assumption in the crypto community. However, many confuse this credibility with token price performance, with a key assumption from retail and institutional investors being that bigger exchanges lead to better initial price performance, which means more returns from the TGE unlocks, and better publicity around the launch. Our research is meant to test this conjecture.

In this report, we present a comprehensive and data-driven analysis on the first week of price action of more than 30 tokens after listing on the leading cryptocurrency exchanges: Binance, ByBit, OKX, KuCoin, Coinbase, Gate.io, and MEXC. Our research uncovers key trends and unexpected insights that challenge the conventional perception around token listings held by the crypto community.

We aim to figure out what happens to an average token being listed, as well as comparing the exchanges on a macro level, to provide valuable insights for traders, investors, and Web3 project teams. This will offer a clearer understanding of expected outcomes of exchange listings and how they can leverage these opportunities.

All of this data and analysis is useful to the majority of entities in the industry for a variety of reasons:

1. Projects: The impact that listing on given exchanges has on token performance is important to know in order to build a successful launch strategy and tokenomics that lead to the healthiest token performances.
   ‍
2. VCs: Investors put a lot of weight on the exchanges that projects will be listed on for many reasons, one of which is the amount of capital they can liquidate on launch from their TGE unlock. This research will provide more accurate expectations.
   ‍
3. Market Makers: Understanding typical price action allows for better liquidity management and trading strategies.
   ‍
4. Exchanges: Seeing how one's tokens perform, in particular when compared to the competitors, is useful data when designing internal listing processes and stipulations.
   ‍
5. Traders: Knowing typical token performance after exchange listings is useful for trading recently launched tokens.
   ‍
6. Retail: There is a lot of noise around CEX listings, but this data shines a light on what really happens and helps retail investors who don't know about presales avoid losing their money.

‍

1.2 Methedology
‍

Data Collection Process

For this analysis, data was collected from TradingView, one of the most trustworthy and comprehensive financial market analysis platforms that integrates trading tools, technical analysis, and market data. The focus was on extracting the price behavior of the tokens listed on top crypto exchanges.

The data collection process began by reviewing token listings on Binance. We cross-referenced each of the tokens listed on Binance with those that were also listed on OKX and Coinbase until we reached a sample size of at least 30 tokens. In the end, a total of 34 tokens were included in the analysis.

The initial analysis scope was to evaluate the effects of listings on Binance, Coinbase, and OKX, as these were identified as the top three exchanges for various reasons. However, to expand the analysis, we extended the coverage by including four additional reputable exchanges: ByBit, KuCoin, Gate.io, and MEXC.

It's important to note that in the end, not all exchanges had listed every token in the dataset. While the 34 tokens were listed on Binance, Coinbase, and OKX, there were exceptions. OKX, for example, recently delisted SPELL (Abracadabra) and POLS (Polkastarter), which meant that data was no longer available at the time of writing. When we added ByBit, KuCoin, MEXC, and Gate.io to the analysis, similar data gaps appeared, but we decided to continue the analysis as the overall trends could still be appreciated. MEXC lacked ALCX (Alchemix), whereas ByBit had the most missing tokens, including METIS, RONIN, LQTY, API3, CVS, CLV, ALCX, MLN, ERN, LPT, POLS, FORTH, and BADGER.

Nonetheless, this data collection approach ensured that we had a robust dataset for analyzing the effects of token listings across multiple exchanges.

‍

Data Analysis and Visual Tools

a. Python Libraries Used

Python was chosen as the primary tool for this analysis due to its efficiency in managing a database of over 4,800 data points. Its extensive set of libraries made it possible to handle the dataset, perform statistical analysis, and create comprehensive visualizations.

• Pandas: A library for data manipulation and analysis, providing data structures like DataFrames to handle structured data efficiently.
• NumPy: For numerical computing, it offers fast array-processing and mathematical functions to perform operations on large datasets.
• SciPy: Used for advanced statistical analysis and scientific computing, providing tools for complex data processing and functions like Z-score calculation.
• Matplotlib: A core library for creating visualizations that allowed the generation of plots and charts to represent data.
• Seaborn: Built on top of Matplotlib, Seaborn simplified the creation of more aesthetically pleasing statistical visualizations.

Together, these libraries were integral in processing, analyzing, and visualizing the data to draw the insights and conclusions mentioned in the report.

‍

b. Line Plots

Line plots are used to display data points connected by straight lines, making them ideal for visualizing trends and changes over time. They helped in identifying patterns and fluctuations by providing a clear way to visualize the trends and anomalies in our analysis.

‍

c. Box Plots

Box plots were used to visualize the distribution of a dataset by displaying its median, quartiles, and whiskers. They were effective for comparing the price performance across different timeframes, as they provided a visual summary of central tendencies, distribution, and variability of data.

‍

Statistical Analysis

a. Percentage Increase Calculation

The reference point for calculating the price changes is the listing price of tokens on each exchange. We call this the "initial price." This approach allowed us to assess the token's price behavior in relation to the price it was listed at.

Next, the high from the first daily candle was measured (Day 1 % Change) to visualize the listing pumps. To evaluate whether the listing's impact is maintained or diminished over time, we looked at the open price of the daily candles after 24 hours (Day 2 % Change), 48 hours (Day 3 % Change), and one week after listing (Week % Change).

These considerations led to the formulation of a general equation for calculating the percentage price changes:

It's important to note that when referring to price increases or percentage changes on the different timeframes, each timeframe's performance is always measured relative to the initial listing price, not to the price of the previous day.

For example, if it's said that "On Day 1 the price increase was +250%, and on Day 2 the price was +20%," this means that on Day 1, the price rose to a high of +250% from the listing price, and the next daily candle opened only +20% from the listing price.

‍

b. Identification of Outliers

In this analysis, we employed the Z-score, also known as the standard score, to identify potential outliers in the dataset. A Z-score is a statistical measure that quantifies the number of standard deviations a data point is from the mean of the dataset. This allows for the standardization of data points, facilitating consistent comparisons across the dataset.

In a normal distribution, most data points are expected to concentrate around the mean, with fewer points appearing as they move further away from it. The Z-score helps identify these deviations. The data distribution in a normal curve is typically divided as follows:

• Approximately 68% of the data points will fall within 1 standard deviation away from the mean, leading to Z-scores between -1 and 1.
• Around 95% of the data will fall within 2 standard deviations, with Z-scores between -2 and 2.
• About 99.7% of data will fall within 3 standard deviations, with Z-scores within -3 and 3.

Thus, since a Z-score of 2 or greater places the data point in the outer 5% of the distribution, it makes the observation significantly distant from the mean, suggesting it's a statistical outlier. For this reason, in this analysis, data points with Z-scores that exceeded 2 were considered outliers and were removed from the dataset when conducting the analysis of exchange performance to ensure a more robust and accurate approach. Without this adjustment, extreme data points could have affected the charts and statistics, leading to misleading interpretations of the performance of exchanges that could be attributable only to a select few tokens.

In certain cases, it became necessary to lower the Z-score threshold from 2, or even manually remove certain tokens from analysis, to conduct deeper analysis. While adjusting the threshold isn't statistically accurate, the purpose of this was to be able to see clearly and then analyze the "average" tokens — i.e., tokens that didn't experience extraordinarily bullish price movement.

To ensure we still provide statistically significant analysis overall, we ensured not to adjust the Z-scores when comparing the mean token performances of the exchanges against each other, and when analyzing the two tokens that listed on all exchanges at the same time, to provide the cleanest comparison possible.

‍

Tokens Used in Analysis

Below is a table that shows how many tokens were listed on each exchange, how many fell outside of the Z-score of 2, and how many were used in the individual exchange analysis with box plots. We also calculated the delta between the number of starting tokens and tokens used in the end analysis, and worked it out as a percentage of the starting tokens. This shows us a ratio of extraordinary tokens to average tokens on an exchange, which is important context to keep in mind.

‍

Limitations

1. Token Selection

There are only 34 tokens selected. Whilst we tried to select tokens that were common across the exchanges, there are more tokens that could have been analyzed.

There is also an argument to be made that looking at tokens that launched on only one exchange at a time would’ve given more accurate results, but for two reasons we didn’t do this. Firstly, there is less data, as most projects choose to list on multiple CEXs at the same or similar time. Secondly, we believe that doing so would have skewed the results due to the stipulations of listing on the particular exchanges being related to attributes of tokens that impact price far more than the exchange itself. In other words, certain token attributes, like project quality and budget for listing fees and marketing, impact whether a token is listed on a given exchange; these attributes also impact token price more than a particular exchange listing.

‍

2. Exogenous Factors

There are many factors that are very difficult to control for, from the movement of Bitcoin at the time of some of these launches or trends in narratives, all the way to the state of the global economy. These were not included in the analysis, but we tried to include tokens that were listed across different times, in different narratives, with varying exchange listing combinations, to minimize the impact of these factors on the averages.

‍

3. Listings

The tokens analyzed throughout this report were listed on varying combinations of exchanges either simultaneously or asynchronously. These listing anomalies would have skewed the apparent performances of the tokens individually and the overall performance of the exchanges. This is partially due to the observation that some exchanges generally engage in primary listings whilst others do not, concluding in different performances due to the listing lifecycle. For example, a token may be listed on MEXC at TGE and perform well in its launch week, but by the time it launches on Coinbase, there is much less speculative demand.

However, it is important to note that whilst the above is an important limitation, the analysis is based on real outcomes that occur due to the processes set by each exchange. The exchange’s respective performances remain reflections of their stipulations, as well as brand, and are accurate representations of what we aim to compare: the real token performances immediately after listing across these select exchanges.

‍

4. Market Maker Strategies

Whilst exchanges are key for price performance, the market maker of choice for each project also plays a role. We didn’t have data for the market makers involved with each listing and could not account for differing strategies during the listing process.

‍

5. Python’s Smoothing

We use Python’s smoothing to visualize the charts clearer, but it may be confusing at times as it creates certain peaks and troughs that do not exist.

We use Python’s smoothing to visualize the charts clearer, but it may be confusing at times as it creates certain peaks and troughs that do not exist. The data is reflected in where the price lines cross the x-axis timeframes.

‍

6. Period

The data was gathered for tokens from 2021–2024. Four years ago was a different time for the industry, and the exchanges and their performances may be different if we were to only look at 2024 listings.

‍

1.3 Exchange Introductions

‍

Binance

• Year founded: 2017
• Avg. monthly visitors (as of Sept 4th): 53.9Mn
• Registered users: 200Mn
• Spot 24h volume (as of Sept 4th): $13.8Bn
• Total token pairs: 1,264
• USDT pairs: 393

Founded in July 2017 by Changpeng Zhao and Yi He, both of whom had prior experience at OKCoin, Binance's leadership brought a wealth of industry expertise. Changpeng Zhao, an alumnus of a prestigious Canadian university with a background in software engineering, previously held roles at Blockchain.com and the Tokyo Stock Exchange, which equipped him with a solid foundation for spearheading Binance's rise.

The platform's success was catalyzed by an Initial Coin Offering (ICO) in July 2017, raising $15M through the sale of Binance Coin (BNB) at an initial price of $0.115 per token. Since then, BNB has experienced exponential growth, reaching $224 by October 2023 and currently ranking as the fourth-largest altcoin by market cap.

Within just six months of its inception, Binance became the world's largest cryptocurrency exchange by trading volume; a position it has retained ever since. Over the years, Binance has secured a dominant role within the crypto sector, widely recognized as the most popular and successful platform for digital asset trading.

‍

ByBit

• Year founded: 2018
• Avg. monthly visitors (as of Sept 4th): 27.5Mn
• Registered users: 40Mn
• Spot 24h volume (as of Sept 4th): $5.1Bn
• Total token pairs: 593
• USDT pairs: 491

ByBit is a prominent cryptocurrency and derivatives exchange that was launched in late 2018 and quickly became a go-to platform for trading perpetual contracts with up to 100x leverage. Despite being relatively new, ByBit has rapidly grown in popularity, earning a reputation as one of the most liquid and trusted exchanges globally.

Specializing in derivatives, ByBit offers perpetual contracts for major cryptocurrencies like Bitcoin, Ethereum, EOS, and Ripple. Since its inception in March 2018, the platform's growth has been impressive, drawing traders from around the world with its user-centric approach and innovative trading features. ByBit's journey from a newcomer to a major player in the industry is marked by key milestones and achievements that continue to shape its success.

The exchange was founded by Ben Zhou, who brought with him extensive experience from the Forex industry. Recognizing the need for a more stable and reliable trading platform in the cryptocurrency space, he set out to create a solution that catered to both experienced traders and newcomers alike.

From day one, ByBit focused on enhancing the trading experience. The platform introduced features like advanced risk management tools, lightning-fast order execution, and deep liquidity to ensure smooth and efficient trading. Additionally, ByBit's interface is fully customizable, allowing traders to tailor their experience according to their preferences, which has become one of the exchange's standout qualities.

‍

OKX

• Year founded: 2017
• Avg. monthly visitors (as of Sept 4th): 16.9Mn
• Registered users: 18Mn
• Spot 24h volume (as of Sept 4th): $2.05Bn
• Total token pairs: 529
• USDT pairs: 308

OKX, formerly OKEx (rebranded in 2022), is a major player in the cryptocurrency exchange world, established in 2016. With a recent rebranding and platform upgrade, it’s time for a fresh look at what OKX offers, as they continue rolling out new features.

Initially known for spot trading, OKX now provides access to futures, perpetual swaps, options markets, and more. Beyond trading, users can earn, explore NFTs, take out crypto loans, join Jumpstart launchpads, use trading bots, and participate in DOT slot auctions. These additions have given traders a wide array of tools to engage with the crypto market. However, it’s important to note that regions like the U.S. and other restricted areas don’t have access to OKX’s derivatives.

Founded in Hong Kong, OKX relocated to Malta in 2018, driven by favorable crypto regulations, while maintaining its Seychelles headquarters. Available in over 100 countries, the exchange serves both institutional and retail traders and handles over $2Bn in daily spot volume, making it the fourth exchange in terms of trading volume.

‍

KuCoin

• Year founded: 2017
• Avg. monthly visitors (as of Sept 4th): 5.8Mn
• Registered users: 34Mn
• Spot 24h volume (as of Sept 6th): $540Mn
• Total token pairs: 1,101
• USDT pairs: 784

KuCoin, founded in 2017 by Chun “Michael” Gan and Ke “Eric” Tang, has quickly established itself as a prominent player in the crypto industry. By 2020, it became one of the top exchanges globally, serving over 30M users across the world. KuCoin has facilitated over $1.2Tn in cumulative trading volume since its foundation.

In 2018, KuCoin raised $20M as part of a Series A funding round which helped its rapid growth. In 2022, they secured an additional $150M during their B Series which led to a $10Bn valuation, further positioning KuCoin as a top player in the industry.

However, on March 26th, 2024, the United States Department of Justice (DOJ) filed criminal charges against KuCoin and its founders, Chun Gan and Ke Tang, for violations of the Bank Secrecy Act (BSA) and operating an unlicensed money transmittance business, marking an ongoing legal challenge for the company. We are yet to see what happens next.

‍

Coinbase

• Year founded: 2012
• Avg. monthly visitors (as of Sept 4th): 36.6Mn
• Registered users: 110Mn (in Q4 2022)
• Spot 24h volume (as of Sept 4th): $2.08Bn
• Total token pairs: 401
• USDT pairs: 40

Coinbase, one of the world’s leading cryptocurrency exchanges, has become a key player in legitimizing the blockchain industry. When it went public on Nasdaq in April 2021, it marked a milestone for the crypto world, symbolizing its growing acceptance in traditional finance. The so-called "Coinbase effect," where newly listed cryptocurrencies supposedly see price surges, underscores its influence.

Founded in June 2012 by Brian Armstrong, a former AirBnB engineer, and Fred Ersam, a former Goldman Sachs trader, Coinbase began by offering simple Bitcoin transactions via bank transfers. Over time, it expanded to support many more cryptocurrencies and introduced services like Coinbase Pro for professional traders and Coinbase Prime for institutional clients. The platform’s ease of use and secure features quickly attracted attention, leading to significant investments from major firms like Andreessen Horowitz and Union Square Ventures.

Coinbase’s rapid growth was fueled by the cryptocurrency boom of 2017 and the subsequent surge in trading volume. By 2020, during the pandemic-driven crypto rally, the company saw record profits, leading to a $100Bn valuation. Despite challenges like regulatory scrutiny and market volatility, Coinbase continues to push the boundaries with initiatives like its L2 network, Base. Today, Coinbase serves millions of users worldwide and partners with major companies like Expedia, Dell, and PayPal to facilitate Bitcoin payments.

‍

Gate.io

• Year founded: 2013
• Avg. monthly visitors (as of Sept 4th): 20Mn
• Registered users: 17.4Mn
• Spot 24h volume (as of Sept 4th): $1.64Bn
• Total token pairs: 3,424
• USDT pairs: 2,268

Gate.io is one of the longest-standing cryptocurrency exchanges, originating in 2013 under the name Bter in China. It rebranded as Gate.io in 2017, adapting to the shifting regulatory landscape. Today, it ranks among the top ten exchanges globally, holding the fifth spot in trading volume.

In its early days, Gate.io was one of the fastest-growing crypto platforms, pioneering beyond simple trading. After regulatory pressures forced it out of China, the exchange temporarily moved operations to Virginia in the U.S. However, tightening regulations prompted another relocation, with the company now headquartered in the Cayman Islands.

In 2019, Gate.io raised $64M through an ICO for its exchange token, allowing it to significantly expand its services and become a strong competitor against platforms like Binance and KuCoin.

‍

MEXC

• Year founded: 2018
• Avg. monthly visitors (as of Sept 4th): 6.4Mn
• Registered users: 10Mn
• Spot 24h volume (as of Sept 4th): $989Mn
• Total token pairs: 2,526
• USDT pairs: 2,423

MEXC, a cryptocurrency exchange launched in 2018, quickly gained popularity for its high-speed transaction processing, serving over 10M users across the planet. With a powerful trading engine developed by experts from the banking sector, it processes up to 1.4M transactions per second. The platform is user-friendly, supports multiple languages, and is known for its extensive range of cryptocurrencies and trading pairs.

MEXC has rapidly grown, capturing a 5% global market share and earning accolades such as the "Best Crypto Exchange Asia" award at the 2021 Crypto Expo Dubai. However, despite its growth and recognition, MEXC has encountered significant regulatory issues. In 2023, it faced warnings from financial authorities in British Columbia, Austria, and Germany.

Moreover, its Estonian license was revoked, and in 2024, Hong Kong regulators flagged the exchange for operating without proper authorization. These regulatory hurdles have raised concerns about its compliance practices, despite its global presence and rapid expansion. We are yet to see what happens next.

‍

2.1 Binance Analysis

‍

All Tokens

The general trend observed in the chart below indicates that the majority of tokens experience a quick price increase on the first day after their listing, followed by a considerable price decline on the second day. This behavior suggests an initial surge in buying activity, likely due to exposure to the exchange's wide trader base, increased liquidity and hype, which slows down as the market begins to correct itself.

These peaks help identify certain tokens, particularly BONK, APE, ICP, JTO, SHIB, SUI, and ACH, that had extreme price movements, significantly differing from the rest. These show abnormal volatility, possibly classifying them as outliers. To ensure a more robust and representative identification of outliers, the Z-scores for each time frame were calculated.

‍

Tokens Within 0.94 Z-Score

The table below shows the specific observations that categorize these tokens as outliers based on the process specified in the methodology section.

However, the process of identifying outliers and filtering them out was slightly different for Binance: while the standard Z-score to be considered an outlier on this analysis was 2, the data for Binance presented some instances in which a token with visually extreme performance had a relatively low Z-score. For this reason, the Z-score threshold was lowered to 0.94; the tokens that fall outside of this threshold are shown below.

As it can be appreciated in the plot lower down, focusing on the remaining tokens enabled a more clear analysis of the general performance for tokens listed on Binance.

Interestingly, the tokens on Binance experienced eerily similar price movements to each other during their first week after listing. We are not sure why that is, especially considering the other exchanges showed greater randomness and variability.

With the chart above, it is possible to visualize the trend in performance that tokens tend to experience. As mentioned previously, the first-day increase in price generally is considerable, with many of the tokens surpassing a +50% price increase, and some even going past the +100% mark.

However, in this chart, the Day 3 percentage changes are much more visible. For many tokens, the price begins to stabilize and come back down to launch, albeit still remaining on the upside. The tokens’ performance between day 2 to the 1-week mark generally experienced downward movement, which points to reduced hype after the initial trading period as the market finds equilibrium price.

Overall performance on tokens listed on Binance can be visualized in the following boxplot as it breaks down the tokens' day-to-day price changes following their listing. Let’s dive into the detailed analysis of how these tokens performed from launch through the end of the week, complemented with descriptive statistics.

‍

‍

Day 1 Percentage Change

The Day One boxplot shows that tokens experience significant change in price. The interquartile range (IQR) shows the price change spanning from approximately +35.5% to +100.0%, indicating that 50% of the tokens experienced these price changes.

The median price change for Day 1 is around +66.2%, showing that most tokens gained significant value immediately after their listing. The upper whisker extends up to +257.1%, highlighting that there were still tokens with unusual increases in their price even after evident outliers were removed.

‍

Day 2 Percentage Change

By Day 2, the upward trends decreased notably, with the IQR decreasing to between +5.8% and +59.3%.

The median price change is around +29.5%; the tokens are coming down to launch price, fast, with the whiskers extending from -15.6% to 214.0% showcasing that some projects already fall below launch. With fewer and less extreme outliers, we can see that tokens start to congregate to similar price changes, experiencing alike performance to each other.

‍

Day 3 Percentage Change

On Day 3, we can see the continuation of what was happening on Day 2. The IQR is even more narrow, ranging from +2.9% to +48.7% while the median price change falls lower to only be up by +18.1% from launch. The whiskers suggest that most tokens’ price changes fall between -27.8% and +164.0%, with very few outliers, underscoring the ongoing consolidation.

‍

1 Week Percentage Change

By the end of the week after listing, the IQR ranged from -4.1% to +29.3% further solidifying the consolidation trend seen in Day 2 and 3, with the median price change now only +15.0% from launch. With the whiskers extending to around -46.3% to +186.0%, minus a couple outliers that went up between Day 3 and 1 Week, the volatility seems to have ceased, and the tokens’ prices have reached a more stable price range, trending negatively.

Overall, Binance has solid price gains at the beginning which taper down to listing price over the week. Interestingly, the 1 Week box plot has the tightest IQR, implying that after the initial volatility of Day 1, the tokens follow similar price movements in relation to their launch price, getting closer and closer together by the end of the week.

‍

3.1 Bybit

‍

All Tokens

ByBit was missing the following tokens: METIS, RONIN, LQTY, API3, CVS, CLV, ALCX, MLN, ERN, LPT, POLS, FORTH, and BADGER. For the tokens listed on ByBit, there are two tokens that have a clear abnormal performance. Based on the chart below, it is clear how JTO and IMX skew the data.

‍

Tokens Within 1 Z-score

In this case, the Z-score threshold was lowered to 1 as a Z-score of 2 only classified JTO as an outlier, but the chart was still highly ineffective to see the performance of all other tokens. With the lower Z-score, JTO and IMX are classified as outliers based on their performance, but so is BICO, which peaked on Day 2—less than JTO and IMX on Day 1, but far above the other tokens on Day 2; hence, it falls within the Z-score of 1.

After removing the outliers, the chart appears to be easier to understand in its majority, albeit some tokens are still skewing the performance of the rest; given they’re not statistically out of bounds by previous metrics, they must remain in the chart.

‍

Below we see the descriptive statistics of the chart above, allowing for easier analysis. We use this to chart a box plot as always, but it does look a bit weird because of the upper bounds of price growth.

‍

Day 1 Percentage Change

The mean is high at +806.3%, and the IQR ranges from +4.6% to +1,685.7%; however, the maximum value observed is +4,900.0% even after removing statistical outliers (e.g., JTO reached around +1,160.0%), which skews the results to the upside. Nonetheless, 25% of tokens seeing price increases higher than +1,685.7% is remarkable.

To get a better understanding of a typical token performance, we can look at the median, which sits at +45.7%—much lower than the mean, indicating that while some tokens have extraordinary returns on Day 1, the average token experiences only modest gains, suggesting a vastly unpredictable Day 1 performance.

‍

Day 2 Percentage Change

The price change decreases significantly on Day 2 compared to Day 1, evidenced by the maximum value of +1,698.0% being a fraction larger than the 3rd quartile the day before. Some tokens continue to experience substantial gains, but this is less common. Moreover, while there’s still high variability, the values are much more compressed with the IQR dropping from +4.6%-+1,685.7% down to +2.49%-+290.0%.

The median value, at +27.4%, is closer to the lower end of the IQR, suggesting that most tokens do not sustain the high Day 1 gains.

‍

Day 3 Percentage Change

By Day 3, the price changes further decrease in both mean and variance. The maximum value is lower at +1,366.0%, indicating that the token price momentum continues to decline. The IQR narrows further, this time close from the bottom end of the tokens increasing the 25th percentile value from +4.6% to +6.1%, reflecting decreasing volatility as time passes by.

‍

1 Week Percentage Change

By the end of the week, the price change data continues to show more stabilization, appreciated by the mean and median being closer in value, and an overall low median of 17.0%.

Based on the percentiles, 25% of tokens experience price losses of at least -2.3% and a median of +16.96%. This indicates that the main trend is more modest a week after listing day.

Overall, the trend for ByBit reveals that tokens experience the highest price changes on Day 1 after being listed, with a notable drop in subsequent days. However, interestingly, the vastly more volatile and bullish tokens don’t experience a convergence to the performance of the average tokens, remaining very bullish, signaling that ByBit seemingly picks more winners and/or impacts token prices with more upward significance.

‍

4.1 OKX Analysis

‍

All Tokens

The line chart for the performance of tokens listed on OKX shows a single token, IMX obscuring the performance of all other tokens, reaching an extremely high price on its listing day of over 100,000%.

‍

Tokens Within 2 Z-score

Similar to ByBit's case, while the standard Z- score to be considered an outlier on this analysis was 2, some of the tokens still present highly abnormal performance.

As it can be seen below, even after removing IMX and BICO, the overall trend for the great majority of tokens is hard to identify due to the high price increases of other tokens, of which one, BLUR, surpasses the 8,000% mark.

Interestingly, BLUR clearly reaches almost the same height as BICO does (albeit on Day 1 instead of Day 2); however, it is within 2 standard deviations (i.e., within the Z-score of 2) because IMX pulled the mean up so high. Whilst BICO was removed due to statistical significance, BLUR managed to stay in.

‍

‍

Remaining Tokens

As we just showcased with BLUR above, the presence of outliers is persistent even after we kept trying to reduce the Z-score to try and do what we did for Binance. For this reason, we decided to remove the outliers manually based on the line chart.

We are fully aware that this change skews the apparent performance results against OKX, but we want to dive deeper into OKX's average performance rather than merely highlight OKX's many big winners. During ByBit’s analysis, we ran into a similar situation; however, the number of tokens we would describe as “average performers” was much lower for ByBit in contrast to OKX, meaning, if we were to remove ByBit’s outliers manually, we’d be left with 12 tokens. In contrast, after manually removing these outliers for OKX, we are left with 24 tokens, a sufficient amount for thorough analysis.

In total, the removed observations in the following chart included IMX, AGLD, APE, BICO, BLUR, SUI, ENS, and SHIB.

‍

Based on the boxplot and descriptive statistics provided below, we can analyze the Day 1, Day 2, Day 3, and the 1-week change for tokens listed on OKX, omitting the effect of the aforementioned outliers.

‍

Day 1 Percentage Change

The first day shows a strong initial performance for tokens listed on OKX, with a median increase of +39.1% and a mean of +59.3%. The manual removal of outliers obviously played a role in the fact that the median and mean are this low, but this was done to see how the less impressive tokens perform, and we can still see solid movement with more than 75% of tokens experiencing +22.3% or more on the first day.

The 25th percentile shows that a quarter of tokens are up only +15% from their initial value, and, according to the 75th percentile, only a quarter of tokens experience increases greater than +40.3%.

Some tokens are now beginning to experience significant negative action, dropping down to as low as -51.3%.

‍

Day 2 Percentage Change

By Day 2 after listing, tokens generally experience much less increases in price relative to the listing price as evidenced by the drop in median and mean to +26.9% and +37.0%, respectively, suggesting that the already-modest gains from Day 1 are not sustained and the post-listing hype and buying pressure ceases.

This pattern is also seen in the removed outliers by looking at the original and the second price charts.

‍

Day 3 Percentage Change

The trend continues into Day 3, with the median and mean remaining similar to Day 2’s at +25.9% and +33.0%, respectively.

‍

1 Week Percentage Change

After a week of listing, the mean remained positive at +32.5%, but the performance of tokens started to vary significantly, with the minimum and maximum prices increasing from the day before in both directions to -63.0% and +188.0%. This shows that after a week, the similarities in price action fade, and in the case of OKX, there is a higher chance of tokens starting to climb in price again after the Day 3 lows.

Overall, the initial listing on OKX generally results in decent gains, averaging +59.3%, with even the lower-performing tokens presenting positive gains on Day 1. A week after listing, a few tokens experienced losses, and while some held up better than others, the average price change was +32.5%. Once again, this is without outliers, some of which comfortably held their price at over 4-figure-percent gains.

‍

5.1 Kucoin Analysis

‍

All Tokens

The line chart for the 1 week performance of tokens listed on KuCoin showcases a single token, MASK, that experienced an astounding price increase. Due to its rare performance, the rest of the tokens are barely noticeable, making it necessary to identify and treat this and other potential outliers.

‍

Tokens Within 2 Z-score

As per a Z-score of 2, only two tokens were classified as statistical outliers: JTO and MASK.

‍

‍

Remaining Tokens

Despite iterating through lower Z-score thresholds, there were still tokens that made it hard to see the performance of an average token. For this reason, we proceeded to manually remove the outliers that we could identify visually on the line chart apart from the already-identified outliers: BLUR, SUI, and ARB.

The chart on the next page showcases token performance on KuCoin after manually removing outliers, making it much easier to interpret all of the average tokens' performances. Additionally, there are visual trends that help understand the overall performance for tokens listed on KuCoin. The main trend is that tokens experience a price increase in the first 24 hours after listing and seem to hold the new floor price well.

‍

‍

‍

Day 1 Percentage Change

The first day of being listed shows a mean of +100.3%, a commendable price increase. The 25th and 75th percentiles are +40.0% and +136.0%, showing strong bullish price action.

The price increases on this day were between +0% and +425.0%, showing that the tokens’ performance can vary widely, either experiencing no gains or significant ones.

‍

Day 2 Percentage Change

By Day 2, the 25th percentile remains almost unchanged, but the 75th percentile converges significantly, implying a decrease in buying pressure as it sits at +60.3%—a considerable decrease from Day 1’s 75th percentile.

On the flip side, the whiskers actually diverge, ranging between -2.5% and +435.1%, slightly wider compared to Day 1. This shows that whilst the average token behaves similar to the pack on Day 2, the edge cases remain bold and far away from the average.

‍

Day 3 Percentage Change

Day 3’s IQR width remains almost the same but at a slightly lower price range, between +25.9% to +53.2%. The mean dropped down to +67.6%, and the median is close behind at +40.7%. The edge cases are also starting to converge as indicated by the closing whiskers.

1 Week Percentage Change

One week after listing, the mean remains steady at +62.0% with a higher median, at +48.5%. Evidently, the tokens hold a comfortable increase after listing. On the other hand, the IQR actually widens, and as we can see by the line chart, that is due to the tight trend of token performance beginning to fray. Whilst the edge cases come back down from their highs over the week, further indicated by a lower mean but higher median from Day 3, the average tokens start to pick up momentum either to the upside or the downside.

Overall, the average token listed on KuCoin experiences modest price growth, but growth that is sustained over the week after listing, as indicated by the means and medians that remain relatively constant.

‍

6.1 Coinbase Analysis

‍

All Tokens

Most tokens exhibit a notable initial price surge, particularly within the first day after listing. reflecting the market enthusiasm and increased demand that accompanies being listed on an exchange like Coinbase; this is more evident in tokens like SHIB and CLV, among others, which have a huge peak.

‍

Tokens Within 2 Z-score

As per the Z-score of 2, we find three outliers.

After removing the outliers, the chart shows in more detail that tokens experience a rapid correction after their initial surge. By Day 2, a significant number of tokens either stabilize or decline sharply to their initial listing price.

It is important to note that Coinbase seems to delay their listings. For example, APE listed at $1.00 on all exchanges except Coinbase that listed the token 4 hours later at a price point of $7.85. This means that APE’s line chart is vastly different from its performance on other exchanges, and this is partially why Coinbase seems to have such subpar performance—not only is the initial price higher, but the arbitrage opportunities mean that from Coinbase’s perspective, tokens are experiencing negative price action, whereas from the perspective of other exchanges, those tokens are still comfortably positive.

The aforementioned delayed listing is likely a conscious choice made by Coinbase to minimize volatility; as Coinbase is a publicly traded company, there are likely regulations to which they are beholden.

By the end of the week, the performance of most tokens moves toward a less volatile state, with percentage changes becoming more uniform.

‍

‍

Overall performance of tokens listed on Coinbase can be visualized in the following boxplot as it breaks down the tokens’ day-to-day price changes following their listing.

‍

‍

Day 1 Percentage Change

The first box plot shows the widest range of price changes, with several tokens extending far above the main body of the data, implying that a minority of tokens peak much higher than the rest.

The mean on Day 1 is +24.4%, far lower than the previous exchanges, and the 75th percentile shows that 25% of the tokens saw a price change greater than a mere +16.7%, which is very low, and means the majority of tokens don’t experience great increases on Day 1 like with the previous exchanges. As mentioned above, the reason seems to be due to Coinbase’s delayed listings and the methodology used for this report, but if you are a trader that wants to trade a token launch, your best bet is to go on other exchanges.

The minimum price change is 0% further indicating that at least one observation didn’t see any increase at all.

‍

Day 2 Percentage Change

On the second day, the average and median price changes remained positive, with the mean being slightly higher than on Day 1, at +26.2%.

The lower quartile shows that a quarter of the tokens had negative price action, as the 25th percentile is slightly below zero at -0.5%. On the other hand, the 75th percentile indicates a price change greater than +18.0%, higher than the day before, surprisingly, but still quite low.

‍

Day 3 Percentage Change

By Day 3, the mean decreased to almost half of the price change seen in Day 1 to +10.8%, and the volatility is further reduced based on the minimum and maximum observations. However, the data seems to indicate that lower or even negative price performance is most common by this timeframe, with 50% of the observations seeing a negative performance of below -0.2%.

‍

1 Week Percentage Change

On the weekly mark, overall performance turns downwards with both the mean, -1.9%, and median, -4.4%, in the negative. The interquartile range shows a similar picture, ranging between -23.4% to +6.4%. The minimum and maximum prices have also converged closer, indicating a similar trend for all tokens to lose volatility after a week as they chart downwards.

For Coinbase, price performance isn’t the best across all timeframes after a token gets listed. On most timeframes, the 25th and 50th percentiles are rather low or even negative in some cases meaning that 50% of tokens are below listing price. As we mentioned above, it’s mostly due to Coinbase listing later than other exchanges, for a reason we were unable to find out for certain.

‍

7.1 Gate Analysis

‍

All Tokens

The line chart for tokens listed on Gate.io (Gate from here on out) initially shows optimistic performance, although, as the case with the others, this is skewed by a few very bullish tokens. However, with Gate, it seems like 10 out of 34 tokens have very strong performance, which is almost a third of our sample size.

‍

Tokens Within 2 Z-score

Just like other exchanges, a few tokens listed on Gate still show atypical performance.

As can be seen below, the performance of the average token is not appreciated correctly due to some extreme price changes that remain even after the initial identification and treatment of outliers.

‍

Tokens Within 0.63 Z-score

We proceeded to lower the Z-score further to 0.63, which is low but allows us to see what happens to the average token easier. The tokens that fall below this Z-score threshold were APE, BONK, CLV, ENS, ERN, ICP, JTO, LDO, RPL, and SPELL. As we showed in the introduction of this report, Gate had the highest number of tokens removed from analysis below, with a ratio of 29.4% of tokens being classified as outliers due to their very bullish price action.

This is crucial to keep in mind because after removing so many tokens it’s questionable whether what we’re looking at below can be considered the “average token” after all.

For a pure statistical analysis without manual outliers, look in the next section that compares the exchanges with no manual manipulation.

‍

‍

Day 1 Percentage Change

The first day after listing on Gate shows a strong initial performance, with a median increase of +39.5% and a mean of +82.9%, indicating that tokens tend to see a strong price surge on Day 1. The IQR ranges between +14.2% to +87.1% whilst the maximum observed data point goes as high as +538.9%, indicating that the tokens perform very differently to one another.

The bullish edge cases are now also starting to cease as the tokens converge in performance, with the upper whisker dropping from +440.0% on Day 2 down to +170.0%.

‍

Day 2 Percentage Change

By Day 2, the median price change drops to +10.4%, and the mean decreases to +33.2%, showcasing a fast convergence down to near listing price. Day 2 also sees the start of the downward trend, with the lower quartile at -9.0%, indicating that at least a quarter of all tokens are below listing, and the minimum going as far down as -67.0%.

‍

Day 3 Percentage Change

The Day 3 median and mean values dropped even lower to +9.5% and +17.4%, respectively; while some tokens are still above listing, the overall trend is clearly downward. Furthermore, the 25th percentile fell lower to -19.1%, indicating that the bottom quarter of tokens are on a steady journey down by Day 3.

‍

1 Week Percentage Change

By the end of the week, the median is now below listing at -6.4%, with the 25th percentile showing a pronounced decline of -32.0%. The mean further decreases to a mere -0.5%, kept afloat by a couple bullish edge cases and some tokens that turned their downward momentum around, but the overall downward trajectory to below listing is evident.

Overall, Gate has some great token performances, holding a strong 3 or 4-figure gain floor price, which is an impressive feat. Only by not including these tokens, a reduction from the sample of 34 tokens down to 24, we see the mediocre results of your average tokens. Generally these tokens see a strong initial increase in price on listing day, followed fast by either a significant reduction in said gains or even losses on Days 2 After this, the tokens converge with volatility stabilizing, and continue to trend downwards below listing price.

‍

8.1 MEXC Analysis

‍

All Tokens

The line chart for MEXC showcases that a lot of tokens experience a significant increase in price after listing, indicating strong initial buying pressure and interest from the market. Despite this general observation, the chart shows very diverse performance of the top movers.

‍

Tokens Within 2 Z-score

After removing the outliers from the database, we see the vastly varied price performance compared to other exchanges. There is still a general pattern of significant increase after listing, followed by a steady decline, but there is much more consolidation and varied outcomes, with some tokens going negative almost immediately, others growing throughout the week, and others holding their peaks for longer than the initial day.

Below are the descriptive statistics for each timeframe in the analysis alongside the box plot.

‍

Day 1 Percentage Change

Day 1 has a mean of +405.1% with a median of +77.9%, indicating that tokens listed on MEXC experience a strong price increase on the first day of being listed. Regarding percentiles, the 25th percentile is at +153.3%, meaning that 75% of tokens saw price increases above this value, which is incredibly bullish.

Price changes on listing day on MEXC vary widely from -64.0% (only ACH) to +1,680.0%, highlighting the potential for either sharp declines or massive spikes immediately post-listing. But overall as seen with the percentiles, the majority of tokens experience great performance.

‍

Day 2 Percentage Change

During Day 2, the mean is slightly lower than Day 1, at +390.49%, but still exceptionally high. The price changes on the second day still show high volatility, ranging from -90.0% to +1,610.8%. By analyzing the percentiles, the 25th percentile is very similar to Day 1’s only slightly lower at +152.3%.

‍

Day 3 Percentage Change

The mean is +377.0%, continuing the slight downward trend and suggesting that the average token continues to stabilize and experience pullbacks. However, it still reflects strong performance for many tokens. The minimum does further decline to -94.0%, this time with 2 more tokens turning negative, but the maximum slightly increases to +1,617.6%.

‍

1 Week Percentage Change

The mean values decrease from +405.1% on Day 1 to +330.8% after one week of being listed, implying considerable strength. The IQR grows, ranging between +121.3% - +397.2%, an increase in both directions from +143.4% - +362.2% on Day 3. This means that although the mean does trend downwards, it is not without continued volatility.

Overall, the performance in price for tokens after listing on MEXC shows that tokens experience solid gains that are largely sustained over the first week after listing. Nevertheless, performance seems to be volatile and varies widely as it can be seen in the minimum and maximum values observed.

‍

9.1 Exchange Comparision

‍

All Tokens

To compare the exchanges' overall performances against each other, we were more systematic; for this analysis, the outliers for each exchange were identified only as anything outside of 2 standard deviations. Therefore, the performance observed in the exchange comparison may vary from the individual exchange analysis which was more focused on observing the tokens that did not face extraordinary price performance.

But, firstly, for perusal, the chart below shows the mean performance for exchanges with their respective outliers.

‍

Tokens Within 2 Z-score

After addressing the statistical outlier tokens on each exchange outside of a Z-score of 2, the main trends in the chart persist, although KuCoin's and OKX's performance appear less drastic than initially observed, whilst ByBit remains incredibly bullish.

Let's dive into the total mean percentage change across different time frames to analyse what's really happening.

‍

The Biggest Price Movement

ByBit shows the most significant average price changes for tokens across all timeframes, especially with a dramatic price increase on Day 1 at +2,739.9% (even after removing statistical outliers) and remaining high through the first week at +539.6% from the initial date. This suggests that whilst tokens listing on ByBit experience extreme volatility, they still maintain a high performance over the week.

‍

Second and Third Biggest Price Movement

If we’re looking at Day 1, OKX and KuCoin take the lead, respectively.

• OKX follows ByBit with the second highest average percentage change. It has a strong Day 1 change at +630.6%, which decreases to a comfortable +174.8% after a week. This indicates that tokens listed on OKX also experience significant bullish pressure, although not as extreme as ByBit.
• KuCoin showed very similar performance to OKX, making it the exchange with the third highest price increase on Day 1 at 603.1%. By Day 2, the prices come down heavily as they did for ByBit and OKX—to about a third from Day 1, and a week after the tokens saw on average an increase of +152.3% compared to the listing price, a solid performance.

However, looking at the end of the week, MEXC comfortably takes second place. MEXC surpasses OKX and KuCoin combined (+327.1%) at +330.8% after a week, indicating that the tokens are much less volatile and are able to maintain their highs much better.

‍

Moderate Movement

Both Gate and MEXC show moderate performance on Day 1, with Gate having a better performance initially reaching a peak of +414.9% but gradually decreasing to +86.8% by the end of the week. MEXC, as discussed above, has one of the most consistent performances throughout the week, surpassing almost every exchange by the end.

‍

Lowest Movement

Binance and Coinbase have the lowest average percentage changes. Binance shows some fluctuation, starting at +111.2% on Day 1 but dropping significantly to +17.6% by the end of the week. Coinbase displays the least impact, with a very low initial change at +24.4% and even turning negative by the end of the week at -1.9%.

This can be attributed primarily to the fact that these exchanges rarely do primary listings, and as we discussed in the introduction, this plays a significant role in the performance that we analyse in this report.

‍

10.1 Individual Token Listing Dates

‍

Looking into the individual tokens is another valuable analysis to conduct when assessing the performance of each exchange. Below is a big index of the individual token charts, but first, we need to explain the unique listing dates of the tokens.

The term “Unique Listing Date” refers to how many different days a token was listed on the exchanges. This means:

• “7” refers to 7 unique listing dates, meaning the token was listed on 7 different days and there is no overlap for an exact day.
• “6” indicates that one of the exchanges’ listing dates coincided with another exchange’s date. For these cases, the token was listed on 2 exchanges on the same day, but all other exchanges listed it on different days.
• “5” could mean several possible combinations:
  • 3 exchanges’ listing dates coincided, with all other exchanges having different dates.
  • 2 pairs of exchanges’ listing dates coincided.
• “4” is similar:
  • 4 exchanges’ listing dates coincided, with the others all being different.
  • 3 pairs of exchanges’ listing dates coincided.

We can see that 25 tokens have 4 or more unique listing dates.

Identifying these overlaps is important as it could mean maximized visibility, liquidity, and therefore, a bigger price impact for tokens on listing, but it can also make future exchanges struggle to see the same buy pressure as the earlier exchanges did.

For the sake of this analysis, the tokens mentioned previously can provide unbiased data for the analysis of exchanges’ impact on price.

However, there were other cases in which 3 or more exchanges’ listing dates coincided, introducing uncertainty for the analysis as the impact will be from several exchanges.

• “3” unique listing dates could have the following scenarios:
  • 3 exchanges listing the token on the same day, 2 exchanges listing it on a second day, and other 2 exchanges list it on a third day.
  • 4 exchanges list the token on the same day, 2 exchanges on another second date, and a single exchange listing by itself on another day.
• “2” unique listing dates:
  • 4 exchanges list on one day, while the other 3 exchanges list it on a second day.
  • 5 exchanges’ listing dates coincide, and the other 2 coincide.
• “1” unique listing date means that all exchanges listed the token on the same day.

‍

‍

‍

10.2 Token Analysis

‍

It is clear that there is a simple way to analyse the exchanges without any exogenous factors (market, listing cycle, hype cycle, etc.) playing a role, and that is to look at APE and ARB token charts. These tokens launched on all 7 exchanges on the same day.

First, it is important to state again how this analysis was done. We look at the initial listing price, Day 1 high, Day 2 open, Day 3 open, and Day 7 open. We compare all of these to the initial price to arrive at the percent price changes.

Second, it is crucial to remember that if a token has a lower listing price on a certain exchange, then it will perform better on our chart.

Third, a lack of sufficient arbitrage means that the differences in liquidity on an exchange impact token prices significantly, leading to vastly different Day 1 highs. These highs are achieved within the first hour of listing, quickly mitigated by arbitrage.

Last, it is clear that the slight differences in token prices on Day 2 onwards can be attributed by a lack of sufficient arbitrage, which means the prices across exchanges don’t match all the time. Hence, there are slight differences in price performance on the chart, even for tokens that were listed at the same initial price.

By comparing these tokens, in particular the Day 1 highs, we are getting direct insight into how the exchanges’ liquidity combined with the buy pressure they bring impact token prices on listing. There are other factors that still play a role, such as market maker strategies, but we do not have access to enough information to make any final statements.

Thus, this analysis is more niche compared to the rest of the report, which looks at a macro view of mean token performance.

We can see below the two tables that showcase the dollar prices of the tokens APE and ARB respectively. We have rounded the prices to the nearest dollar value for APE, and nearest 1 d.p. for ARB, but only in the tables below, so it’s clearer for the reader to analyse; the price charts and percentage tables are not rounded.

‍

APE Analysis

Out of the exchanges that APE launched at $1, the highest Day 1 price increase was reached by both ByBit and MEXC, with the price going up by +4,900.0%. Although ByBit seems to not appear in the chart, the line representing the exchange is not visible due to it having almost identical performance with MEXC and its corresponding line is obscured by MEXC’s.

Gate follows this performance, with APE rising by +4,146.6%. This is interesting because APE listed at $5 on Gate, but at $1 on MEXC and ByBit, implying that Gate’s liquidity in relation to buy pressure must have been low, further evidenced even by Python’s smoothing making the line negative.

Despite listing at $1, APE showed an increase of +2,700.0% on both Binance and OKX, likely due to having deeper liquidity in relation to buy pressure than ByBit and MEXC.

Other exchanges saw much lower gains, with KuCoin at +425.0% and Coinbase lagging far behind with a +15.2% increase, both due to higher listing prices but with sufficient liquidity, unlike Gate.

While Binance’s and OKX’s Day 1 price increase were not as significant as ByBit’s and MEXC’s, they had much less volatility; all of them concluded on the same +1,346.0%.

Gate showed the most volatile pattern. After the significant +4,146.6% jump on Day 1, the price dropped sharply to +254.1% by Day 2, eventually showing a price change of +185.8% by the 7-day mark. It experienced the percentage highs of the four exchanges above, and then the percentage lows of KuCoin and Coinbase.

‍

‍

ARB Analysis

On its listing day, ARB only listed at the same price on Binance and OKX, with the other prices varying greatly.

ARB saw the most significant price increase on KuCoin, skyrocketing by +10,472.7%, due to being listed at a much lower price of $0.1 relative to other exchanges. ByBit came second at +1,775%, after being listed at $0.4.

Despite being listed at $0.5 on Binance and OKX, the highs reached on Day 1 were different, with OKX experiencing +292.0%, whilst Binance at +220.0% was closer to MEXC’s 226.8%, despite MEXC listing at $3.1, over six times higher.

MEXC reached $10.0, almost as high as KuCoin’s $11.6, indicating low liquidity for the amount of buy pressure.

Gate and Coinbase experienced much lower gains, with ARB rising +107.3% on Gate and a minimal +5.4% on Coinbase. Despite listing at relatively similar prices, $1.9 and $1.5, they reached the highs of $4.0 and $1.6, respectively, indicating Coinbase’s vastly superior liquidity provision.

By Day 2, prices began to correct for KuCoin, OKX, ByBit, and Binance, but have dropped below listing for MEXC at -48.7%, and for Gate to -18.7%. On the flip side, Coinbase remained perfectly still at 5.4%.

On Day 3, the trends remained consistent. MEXC’s performance continued its decline, dropping further to -57.2%, with Gate at -32.1%. However, now even Coinbase dipped into negative territory at -12.1%.

After a week, KuCoin led the exchanges with a +1,200.0% increase from the listing price, while ByBit and OKX remained positive at +257.5% and +186.0%, respectively. MEXC, Gate, and Coinbase did not recover and ended the week with negative performance, with -53.3%, -25.9%, and -4.03%, respectively.

For ARB, throughout the whole week, Binance and Coinbase were the least volatile exchanges, implying they had the highest liquidity.

‍

‍

11.1 Closing Remarks

‍

Looking at 34 tokens across 7 different exchanges, we analysed how each exchange performed, how the average tokens on the exchanges performed, how the exchanges compare against each other, and then a couple of individual tokens.

Whilst it is without doubt that there are limitations to the research, in particular the intersectionality of listings and exogenous factors that impact results, we can nonetheless deduce some interesting takeaways:

1. The results of the research challenge commonly-held assumptions about the performance of tokens after listing on different exchanges. While some tokens perform better on what are normally deemed as top exchanges, others experience stronger price increases on a percentage basis. For example, ByBit recorded the overall best increase in price on the first day after listing, whilst exchanges like Binance and Coinbase, despite having the largest user bases, do not typically have the highest percentage increases or rises in price.
2. On the other hand, MEXC, an exchange which ranks lower in terms of active users and volume, shows more modest initial gains but maintains the most stable, and second highest performance overall. These observations were further confirmed by the analysis for APE, which experienced the highest price change on MEXC and ByBit, and ARB, where MEXC had higher percentage growth than Binance and Coinbase, despite being listed at a higher price.
3. KuCoin’s performance was the best in the exchange comparison.
4. However, after removing outliers, ByBit was superior; partially because several of the analysed tokens are not listed on the exchange, sitting at 20 out of 34, whereas KuCoin was on 31 out of 34.
5. This could suggest that ByBit is more selective in its vetting process for listing tokens, or that it chooses focus liquidity on fewer projects to ensure better performance.
6. Tokens classified as outliers were often either listed across all exchanges on a single date or had high overlaps. This raises the possibility that listings across multiple exchanges on a single day may have influenced the observed price performance. Future research could explore this further by exclusively analysing tokens with identical listing dates to better understand how simultaneous listings impact token performance. This approach could reveal unique market dynamics that occur when tokens are introduced to multiple platforms at the same time.
7. Liquidity is an important factor for the volatility on the day of listing, although it becomes almost irrelevant by the second day.

We aim to keep producing reports to uncover the ultimate truths about this clouded industry.

Yours truly,
Simplicity Group

Here’s the extracted text formatted for your blog:

‍

Endnotes

Sources

• www.coingecko.com/en/exchanges
• www.tradingview.com
• www.machinelearningplus.com/machine-learning/how-to-detect-outliers-with-z-score/
• www.geeksforgeeks.org/z-score-for-outlier-detection-python/
• https://pandas.pydata.org/docs/index.html
• https://numpy.org/doc/
• https://docs.scipy.org/doc/scipy/
• https://matplotlib.org/stable/index.html
• https://seaborn.pydata.org/

‍

Disclaimer

This document, along with the accompanying materials, has been produced by Simplicity Group—exclusively for informational purposes. It does not constitute financial advice, nor should it be construed as such.

The content herein is intended solely to present observations from individuals who are not certified financial advisors. It is an effort to share perspectives and should not be perceived as professional financial guidance.

Furthermore, this document should not be considered a comprehensive resource for making investment decisions. It is not exhaustive and does not cover all potential risks or facets of investment opportunities.

Investors are advised to conduct their own thorough research and assessment of all associated risks before making any investment decisions. This document is not intended to serve as the sole or primary basis for any investment strategy or decision-making process.

‍

Additional Disclosures

1. Regulatory Information: This material has not been reviewed by any regulatory authority. The provision of this document does not imply that it is compliant with all applicable laws and regulations, or that it has been tailored to adhere to the investment needs and objectives of any particular investor.
   ‍
2. Changes and Updates: The information provided herein is based on the conditions and market scenarios at the time of this document’s creation. Future changes in market conditions or new information could render this material outdated or inaccurate.
   ‍
3. Warranties: The information is provided “as is” without any guarantees, warranties, representations, expressed or implied, of any kind. Simplicity Group and its affiliates are not responsible for any direct or consequential loss arising from the use of this document or its content.
   ‍
4. Statement of Intent: None of the statements made within this document are intended to defame, misrepresent, misquote, or systematically devalue any individual, organizations, or technologies mentioned or discussed. The information is presented to inform and educate the intended reader based on available data and research.
   ‍

Simplicity Group has worked in good faith, with a steadfast commitment to unbiased observations and rigorous research of the emerging technology discussed herein. It is the goal of this report to broaden knowledge and understanding while fostering transparency and allowing readers to understand how research methodologies impact results.

‍

‍

‍