Survivorship Bias in Crypto Trading

Survivorship Bias in Crypto Trading

Definition:

Imagine you're trying to learn how to build a successful business. You read biographies of incredibly successful entrepreneurs, like Elon Musk or Jeff Bezos. You learn about their strategies, their successes, and the amazing companies they've built. But what about all the businesses that failed? What about the countless entrepreneurs who tried and didn't make it? If you only focus on the successes, you're missing a huge part of the picture. This is, in essence, survivorship bias. It’s the tendency to focus on the entities that have “survived” a process, while overlooking those that didn’t, leading to a skewed understanding of the underlying circumstances.

Key Takeaway: Survivorship bias leads to inaccurate analysis by focusing solely on successful outcomes and ignoring the failures, resulting in flawed trading strategies and unrealistic expectations.

Mechanics

Survivorship bias operates by selectively presenting data. In the context of crypto trading, this means focusing on the performance of existing cryptocurrencies (e.g., Bitcoin, Ethereum, Solana) while ignoring the numerous altcoins that have failed, been delisted from exchanges, or simply faded into obscurity. These failures are crucial for a complete understanding of the market, as they reveal the risks, the pitfalls, and the factors that contribute to both success and failure.

Here’s a breakdown of how it works:

1. Data Selection: The trader or analyst only considers data from cryptocurrencies that are still active and trading. This could be historical price data, trading volume, market capitalization, or any other relevant metric.
2. Omission of Failures: Data from failed projects, scams, and delisted coins is excluded. This creates a biased dataset, as it doesn't represent the full spectrum of market outcomes.
3. Inflated Performance: Because the failures are omitted, the remaining data often portrays a more favorable picture of the market. Overall returns appear higher, volatility might seem lower, and the perceived success rate of various trading strategies is inflated.
4. Misleading Conclusions: Based on this biased data, traders draw conclusions about market trends, the effectiveness of trading strategies, and the overall risk-reward profile of crypto investing. These conclusions are often inaccurate and can lead to poor investment decisions.

Definition: Survivorship bias is the logical error of concentrating on the people or things that made it past some selection process and overlooking those that did not, typically because of their lack of visibility.

Trading Relevance

Survivorship bias significantly impacts trading decisions in multiple ways:

• Overestimation of Returns: When backtesting a trading strategy, using only the data from surviving cryptocurrencies can lead to an overestimation of potential returns. The strategy might look highly profitable in the backtest, but in reality, it could have performed poorly if it had been applied to a broader range of assets, including those that failed.
• Underestimation of Risk: The absence of failed projects in the analysis can lead to an underestimation of the risks associated with crypto investing. The volatility of the market and the potential for significant losses are often masked by the focus on successful projects.
• Flawed Strategy Development: Traders might develop strategies based on the performance of a select group of successful cryptocurrencies, assuming that these strategies will work consistently across the entire market. However, the strategies might be specific to the characteristics of the surviving assets and not applicable to the broader crypto landscape.
• Misleading Performance Metrics: Performance metrics, such as Sharpe ratio or Sortino ratio, can be inflated by survivorship bias. These metrics are used to evaluate the risk-adjusted returns of an investment, but they provide a distorted view when based on biased data.

Risks

Failing to account for survivorship bias in crypto trading exposes traders to several significant risks:

• Unrealistic Expectations: Believing that the market is consistently profitable and that successful strategies are easily replicable.
• Poor Asset Allocation: Investing heavily in assets based on misleading performance data, leading to a poorly diversified portfolio.
• Increased Losses: Implementing trading strategies that are ineffective in the broader market, leading to significant financial losses.
• Overconfidence: Developing an overconfident attitude about trading abilities based on the perceived success of strategies, leading to reckless decision-making.
• Missed Opportunities: Ignoring potentially valuable insights from the failures of other projects, such as early warning signs of scams or unsustainable business models.

History/Examples

Survivorship bias is a pervasive issue in financial markets and has numerous historical examples, including:

• Early ICOs: In the initial coin offering (ICO) boom of 2017, many projects raised substantial funds but ultimately failed. If an investor only analyzed the performance of successful ICOs, they would have a very skewed view of the overall market. The failures, which often involved scams, mismanagement, or lack of adoption, were crucial to understanding the risks involved.
• Backtesting Trading Strategies: Imagine backtesting a strategy using only the price data of Bitcoin since its inception. While the strategy might appear highly profitable, it wouldn't account for the numerous altcoins that have failed during the same period. This would lead to an unrealistic assessment of the strategy's true potential and risk.
• The Dot-com Bubble: The dot-com bubble of the late 1990s and early 2000s provides a classic example of survivorship bias. Many internet companies went public and experienced rapid growth, but a vast majority ultimately failed. If you only looked at the successful companies like Amazon or Google, you would miss the crucial lessons learned from the failures, such as unsustainable business models and excessive valuations.
• Fund Performance Analysis: When evaluating the performance of crypto funds, it's essential to consider the funds that have closed or merged. If you only analyze the surviving funds, you might overestimate the performance of the entire industry.
• Coin Delistings: When coins are delisted from major exchanges, this can be a sign of underlying problems. Ignoring these events and only focusing on the coins that remain listed can lead to a distorted view of the market's health and the risks associated with certain projects.

Addressing survivorship bias requires a conscious effort to seek out and analyze data from both successful and unsuccessful projects. This includes researching failed ICOs, delisted coins, and projects that have been abandoned. By incorporating this comprehensive data into your analysis, you can develop a more realistic understanding of the crypto market, mitigate risks, and make more informed trading decisions.