1 Introduction¶
1.1 Case Study¶
On May 13, 2022, Elon Musk, the CEO of Tesla and a prominent figure in the cryptocurrency community, made a significant announcement on Twitter. In his tweet, Musk stated that Tesla would no longer accept Bitcoin as a form of payment. This unexpected decision sent shockwaves through the Bitcoin community and had a noticeable impact on the price of Bitcoin.
To analyze the causal relationship between Musk's tweet and Bitcoin's price and what would be the alternative sceinario of the Bitcoin prices if Elon Musk did not make the tweet, we will employ the "Google Causal Impact" using Python.
1.2 Google Causal Impact¶
The "Google Causal Impact" is a statistical approach used to estimate the causal impact of a particular event on a target variable, while accounting for various other factors that may influence the outcome. It is based on Bayesian structural time series models and is particularly effective in evaluating the impact of stock/crypto price change, marketing campaigns, policy changes, or other interventions. Google Causal Impact package was initially developed for "R" by Google team, later the packege was imported to "python"
By using this technique, we can isolate the effect of Elon Musk's tweet on Bitcoin's price, controlling for other factors such as market trends, trading volume, and external events. The analysis will provide insights into the direct influence of the tweet on Bitcoin's price movement, allowing us to understand the magnitude and significance of the impact. And help to visualize the what-if scenario for the Bitcoin price.
2 Model Implementation¶
2.1 Libraries, Dates, Data¶
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import yfinance as yf
from causalimpact import CausalImpact
training_start = "2021-04-12"
training_end = "2021-05-12"
treatment_start = "2021-05-13"
treatment_end = "2021-05-20"
end_stock = "2021-05-21"
y_stock = ["BTC-USD"]
y = yf.download(y_stock,
training_start,
end_stock,
interval="1d")
y.head()
y = y["Adj Close"].rename("BTC")
y.head(2)
X_stocks = ["MSFT", "NVDA", "AI", "EA", "SQ", "CRSP", "GOOG", "WMT"]
X = yf.download(X_stocks,
training_start,
end_stock,
interval="1d")
X.head()
💡 Here in confounders (X) stocks we are excluding stoks related to crypto currency & Elon Musk for remove selection biases
X = X["Adj Close"]
X.head(2)
X.index = X.index.tz_localize(None)
df = pd.concat([y,X],axis=1).dropna()
df.head()
2.2 Analysis¶
training_df = df[df.index <= training_end]
training_df.head()
differencing = training_df.pct_change().dropna()
differencing.head()
💡 In 90% of the cases time series datas are non stationary. So we are assuming this dataframe containing non stationary data
sns.heatmap(data=differencing.corr(),
annot=True,
fmt=".1g",
cmap="Blues")
plt.show()
final_df = df.drop(columns=["EA", "GOOG", "WMT"])
final_df.head(2)
pre_period = [training_start,training_end]
post_period = [treatment_start, treatment_end]
impact = CausalImpact(data = final_df,
pre_period = pre_period,
post_period = post_period)
sns.set_theme()
impact.plot(figsize=(23,14))
plt.show()
💡 Since we are working with stock datas, we will not consider the "Cumelitive Effect" and only conside the "Relative Effect"
print(impact.summary())
2.4 Interpretation¶
The average value of the response variable during the post-intervention period was around 43971.47. If the intervention had not taken place, we would have expected an average response of 49543.17.The 95% interval for this expected value ranges from 47823.92 to 51362.75. That means if Elon Musk did not make that tweet the Bitcoin avarage price would be 49543.17 around that period, but because of the tweet the actual average Bitcoin price fall to 43971.47
By subtracting this expected value from the observed response, we estimate the causal effect of the intervention on the response variable to be -5571.7, with a 95% interval of -7391.28 to -3852.45. That indicates there is an negative effect on the Bitcoin price wich is around -5571.7
In relative terms, the response variable showed a decrease of approximately 11.25%. The 95% interval for this percentage change is from -14.92% to -7.78%. This indicates that the observed negative effect during the intervention period is statistically significant.
The probability of obtaining this effect by chance alone, as indicated by the Bayesian one-sided tail-area probability (p-value), is very small at 0.0. Therefore, the causal effect can be considered statistically significant.
3 Conclusion¶
By applying the Google Causal Impact we are able to prove there is a negative effect between the Elon Musk's Bitcoin tweet and the Bitcoin price fall. Understanding and analyzing causal relationships in cryptocurrency markets are crucial for investors, traders, policymakers, and researchers alike. By leveraging techniques like Google Causal Impact, we can gain a deeper understanding of the dynamics within these markets and make more informed decisions. This analysis serves as a reminder of the interconnectedness of social media, public figures, and the cryptocurrency landscape, and the potential implications of their interactions on market behavior.
Overall, the case study showcases the power of data analysis techniques like Google Causal Impact in studying and quantifying the impact of specific events or interventions on cryptocurrency prices. This knowledge can provide valuable insights for market participants and contribute to a more comprehensive understanding of the complex and dynamic nature of the cryptocurrency market.