VIX and Stock Investment: Data Analysis with Python

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VIX refers to the Volatility Index (VIX Index) for options trading on the S&P 500, which is calculated and published by the Chicago Board Options Exchange (CBOE). It is commonly referred to as a “investor fear gauge” because it reflects investor sentiment, such as a sense of caution about the future of the market. This article explores how VIX can be utilized for stock investment using Python.

We are going to answer the following questions through this article.

• How many days overall does the VIX spike occur?

• What happens to stock prices when the VIX rises?

• How good is the performance of an investment based on the VIX?

1. Environment Setup

First install yfinance.

pip install yfinance

import yfinance as yf
import pandas as pd
import datetime
import matplotlib.pyplot as plt
%matplotlib inline

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2. Download Dataset

Here we download historical data of S&P500 and VIX using yfinance. We use all S&P 500 and VIX data since 1990 to today, when the VIX is available.

# duration
start = datetime.date(1990,1,1)
end = datetime.date.today()

# ticker
codelist=["^GSPC","^VIX"]
# display name
displist=["S&P500","VIX"]

# get data
data_stock = yf.download(codelist, start=start, end=end)["Adj Close"]

# data_stock.to_csv("VIX.csv")
# data_stock = pd.read_csv("VIX.csv", index_col=["Date"], parse_dates=True)

3. VIX index and market psychology: the emotion behind volatility

VIX generally rises when markets are in turmoil or when the economy is in a downturn. On the other hand, when stock prices are rising and no major fluctuations are expected, the VIX index tends to decline or remain stable at low levels. In other words, the VIX index is negatively correlated with stock price movements.

For example, in March 2020, when market sentiment deteriorated following the spread of the new coronavirus, the VIX index rose to a record high of 82.69.

The value of the VIX can be used to infer investor sentiment.

• 0–15
  Generally, the market is optimistic

• 15–20
  Generally indicates market conditions are normal

• 20–25
  Generally indicates increasing market concern

• 25–30
  Generally indicates market turmoil

• 30+
  Generally indicates that the market is very turbulent

Now we check the proportion of each VIX value range.

# probability of each VIX value range
data_stock[codelist[1]].value_counts(bins=[0, 15, 20, 25, 30, 100], normalize=True)

The probability of the VIX being above 30 is 8.2%, which means that on average the market is in turmoil two days a month.

4. Plot Relationship between S&P500 and VIX

Draw a time-series plot of the VIX and the S&P 500 simultaneously to see how stock prices move when the VIX soars.

vix_num=30
fig, ax1 = plt.subplots(figsize=(20, 6),facecolor="white")


ax2 = ax1.twinx()

ax1.plot(data_stock[codelist[0]].dropna(),
color="red", label=displist[0],linewidth=2,alpha=0.5)

ax2.plot(data_stock[codelist[1]].dropna(),
color="blue", label=displist[1],linewidth=2,alpha=0.5)

handler1, label1 = ax1.get_legend_handles_labels()
handler2, label2 = ax2.get_legend_handles_labels()

ax1.legend(handler1 + handler2, label1 + label2, loc=2, borderaxespad=0.5)

ax1.tick_params(axis='x', labelrotation=45)
ax1.set_yscale("log") 
ax1.set_ylim(data_stock[codelist[0]].min()*0.5,data_stock[codelist[0]].max()*1.1)
ax2.set_ylim(data_stock[codelist[1]].min()*0.9,data_stock[codelist[1]].max()*3.0)

ax2.axhline(y=vix_num, color="gray")

df=data_stock[data_stock[codelist[1]] >= vix_num]
for item in df.index:
  ax1.axvline(item,color='limegreen',linewidth=1 ,alpha=0.1)

plt.show()

It can be seen that the VIX soars when there is a sharp drop in stock prices. Also, at a rough glance, the timing of the VIX spike looks pretty good.

6. Performance of buying S&P 500 when the VIX soared

If you had bought the S&P 500 on August 5, when the VIX was most recently above 30, you would have received a 12.8% return.

data_stock.tail(1)['^GSPC'].values / df.tail(1)['^GSPC'].values 
# output: array([1.12819665])

So do we get the same good returns on other VIX soaring days?

We will examine the performance of the S&P 500 in a similar manner for the 722 days that the VIX has been above 30 so far. In the following, we calculate the performance of S&P500 one year after the day when VIX was over 30.

# 1 years later of date when VIX > 30
one_year_later = df.index + datetime.timedelta(days=365)
i = 0
one_year_later_adjusted = []
def check_date(date, trial=0):
  if not date in data_stock.index:
    if trial <= 4:
      return check_date(date + datetime.timedelta(days=1), trial + 1)
  else:
    return date
for d in one_year_later:
  d = check_date(d)
  if d:
    one_year_later_adjusted.append(d)

performance_one_year_later = data_stock.loc[one_year_later_adjusted]['^GSPC'].values / df.head(len(one_year_later_adjusted))['^GSPC'].values


pd.DataFrame(pd.Series(performance_one_year_later.ravel()).describe()).transpose()

plt.hist(performance_one_year_later, bins=10)
plt.ylabel("Frequency")
plt.xlabel("Performance (1 years)")
plt.show()

Surprisingly, an average return of 22.1% can be expected. The maximum return is 74. 7%, and the maximum loss is -41.5%.

Over a five-year period, the average return is 70.3%. VIX-aware investing is one of wise strategies.