McKinney Chapter 8 - Practice for Section 05

FINA 6333 for Spring 2024

Author

Richard Herron

1 Announcements

  1. Joining Data with pandas, our fourth and final DataCamp course, is due by Friday, 2/16, at 11:59 PM
  2. We will use class next week, from 2/19 through 2/23, for group work on Project 1, so there is no lecture video or pre-class quiz
  3. Project 1 is due by Tuesday, 2/27, at 8:25 AM
  4. Please complete the anonymous ungraded survey on Canvas to help me help you learn better:

2 10-Minute Recap

Chapter 8 of McKinney covers 3 topics.

  1. Hierarchical Indexing: Hierarchical indexing helps us organize data at multiple levels, rather than just a flat, two-dimensional structure. It helps is work with high-dimensional data in a low-dimensional form. For example, we can index rows by multiple levels like “ticker” and “date”, or columns by “variable” and “ticker”.
  2. Combining Data: We can combine datasets on one or more keys.
    1. We will use the pd.merge() function for database-style joins, which can be inner, outer, left, or right joins.
    2. We will use the .join() method to combine data frames with similar indexes.
    3. We will use the pd.concat() to combine similarly-shaped series and data frames.
  3. Reshaping Data: We can reshape data to change its structure, such as pivoting from wide to long format or vice versa. We will most often use the .stack() and .unstack() methods, which pivot columns to rows and rows to columns, respectively. Laster in the course we will learn about the .pivot() method for aggregating data and the .melt() method for more advanced reshaping.

3 Practice

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas_datareader as pdr
import yfinance as yf
%precision 4
pd.options.display.float_format = '{:.4f}'.format
%config InlineBackend.figure_format = 'retina'

3.1 Download data from Yahoo! Finance for BAC, C, GS, JPM, MS, and PNC and assign to data frame stocks.

Use .rename_axis(columns=stocks.columns.names = ['Variable', 'Ticker']) to assign the names Variable and Ticker to the column multi index. We could instead use stocks.columns.names = ['Variable', 'Ticker'], but we can chain the .rename_axis() method.

stocks = (
    yf.download(tickers='BAC C GS JPM MS PNC')
    .rename_axis(columns=['Variable', 'Ticker'])
)
[*********************100%%**********************]  6 of 6 completed
stocks.tail()
Variable Adj Close Close ... Open Volume
Ticker BAC C GS JPM MS PNC BAC C GS JPM ... GS JPM MS PNC BAC C GS JPM MS PNC
Date
2024-02-12 33.6200 53.9200 392.6400 175.7900 86.8700 149.1400 33.6200 53.9200 392.6400 175.7900 ... 385.0000 174.7800 85.8600 147.7700 34160400 17162300.0000 2797200.0000 8539300.0000 7888000.0000 1612700.0000
2024-02-13 32.7500 52.7600 378.7500 174.2600 83.9700 145.2600 32.7500 52.7600 378.7500 174.2600 ... 387.5900 175.3200 85.8600 146.7800 43801500 17672100.0000 3030800.0000 8397600.0000 11339400.0000 2121400.0000
2024-02-14 33.1300 53.9800 378.0400 176.0300 84.0000 147.8700 33.1300 53.9800 378.0400 176.0300 ... 380.8800 175.0700 84.5400 146.6300 27833900 14891900.0000 2041300.0000 7056700.0000 5973700.0000 1265300.0000
2024-02-15 34.0700 55.2100 385.4200 179.8700 85.6700 149.6300 34.0700 55.2100 385.4200 179.8700 ... 379.4200 176.1500 84.4500 148.7500 41683100 16865000.0000 2218900.0000 8723400.0000 7993500.0000 1991800.0000
2024-02-16 34.0900 54.8500 384.4400 179.0300 86.5000 148.8500 34.0900 54.8500 384.4400 179.0300 ... 383.2400 179.6100 85.5200 148.4200 33146357 11419637.0000 2302099.0000 7927785.0000 9628557.0000 1362728.0000

5 rows × 36 columns

3.2 Reshape stocks from wide to long with dates and tickers as row indexes and assign to data frame stocks_long.

By default, .stack() stacks the inner column index to the inner row index.

stocks_long = stocks.stack()
stocks_long.tail()
Variable Adj Close Close High Low Open Volume
Date Ticker
2024-02-16 C 54.8500 54.8500 55.1950 54.5500 54.9600 11419637.0000
GS 384.4400 384.4400 387.5800 380.9450 383.2400 2302099.0000
JPM 179.0300 179.0300 179.9800 178.1600 179.6100 7927785.0000
MS 86.5000 86.5000 86.7900 85.0700 85.5200 9628557.0000
PNC 148.8500 148.8500 149.9100 147.6900 148.4200 1362728.0000

3.3 Add daily returns for each stock to data frames stocks and stocks_long.

Name the returns variable Returns, and maintain all multi indexes. Hint: Use pd.MultiIndex() to create a multi index for the the wide data frame stocks.

stocks['Adj Close'].columns
Index(['BAC', 'C', 'GS', 'JPM', 'MS', 'PNC'], dtype='object', name='Ticker')
_ = pd.MultiIndex.from_product([['Returns'], stocks['Adj Close'].columns])
stocks[_] = stocks['Adj Close'].iloc[:-1].pct_change()
stocks.tail()
Variable Adj Close Close ... Volume Returns
Ticker BAC C GS JPM MS PNC BAC C GS JPM ... GS JPM MS PNC BAC C GS JPM MS PNC
Date
2024-02-12 33.6200 53.9200 392.6400 175.7900 86.8700 149.1400 33.6200 53.9200 392.6400 175.7900 ... 2797200.0000 8539300.0000 7888000.0000 1612700.0000 0.0166 -0.0013 0.0218 0.0045 0.0114 0.0093
2024-02-13 32.7500 52.7600 378.7500 174.2600 83.9700 145.2600 32.7500 52.7600 378.7500 174.2600 ... 3030800.0000 8397600.0000 11339400.0000 2121400.0000 -0.0259 -0.0215 -0.0354 -0.0087 -0.0334 -0.0260
2024-02-14 33.1300 53.9800 378.0400 176.0300 84.0000 147.8700 33.1300 53.9800 378.0400 176.0300 ... 2041300.0000 7056700.0000 5973700.0000 1265300.0000 0.0116 0.0231 -0.0019 0.0102 0.0004 0.0180
2024-02-15 34.0700 55.2100 385.4200 179.8700 85.6700 149.6300 34.0700 55.2100 385.4200 179.8700 ... 2218900.0000 8723400.0000 7993500.0000 1991800.0000 0.0284 0.0228 0.0195 0.0218 0.0199 0.0119
2024-02-16 34.0900 54.8500 384.4400 179.0300 86.5000 148.8500 34.0900 54.8500 384.4400 179.0300 ... 2302099.0000 7927785.0000 9628557.0000 1362728.0000 NaN NaN NaN NaN NaN NaN

5 rows × 42 columns

The easiest way to add returns to long data frame stocks_long is to .stack() the wide data frame stocks! We could sort stocks_long by ticker and date (to sort chronologically within each ticker), then use .pct_change(). However, this approach miscalculates the first return for every ticker except for the first ticker. The easiest and safest solution is to .stack() the wide data frame stocks!

stocks_long = stocks.stack()
stocks_long.tail()
Variable Adj Close Close High Low Open Volume Returns
Date Ticker
2024-02-16 C 54.8500 54.8500 55.1950 54.5500 54.9600 11419637.0000 NaN
GS 384.4400 384.4400 387.5800 380.9450 383.2400 2302099.0000 NaN
JPM 179.0300 179.0300 179.9800 178.1600 179.6100 7927785.0000 NaN
MS 86.5000 86.5000 86.7900 85.0700 85.5200 9628557.0000 NaN
PNC 148.8500 148.8500 149.9100 147.6900 148.4200 1362728.0000 NaN

3.4 Download the daily benchmark return factors from Ken French’s data library.

pdr.famafrench.get_available_datasets()[:5]
['F-F_Research_Data_Factors',
 'F-F_Research_Data_Factors_weekly',
 'F-F_Research_Data_Factors_daily',
 'F-F_Research_Data_5_Factors_2x3',
 'F-F_Research_Data_5_Factors_2x3_daily']
ff = (
    pdr.DataReader(
        name='F-F_Research_Data_Factors_daily',
        data_source='famafrench',
        start='1900'
    )
    [0]
    .div(100)
)
C:\Users\r.herron\AppData\Local\Temp\ipykernel_28096\2049483829.py:2: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.
  pdr.DataReader(
ff.tail()
Mkt-RF SMB HML RF
Date
2023-12-22 0.0021 0.0064 0.0009 0.0002
2023-12-26 0.0048 0.0069 0.0046 0.0002
2023-12-27 0.0016 0.0014 0.0012 0.0002
2023-12-28 -0.0001 -0.0036 0.0003 0.0002
2023-12-29 -0.0043 -0.0112 -0.0037 0.0002

3.5 Add the daily benchmark return factors to stocks and stocks_long.

For the wide data frame stocks, use the outer index name Factors.

# stocks.join(ff)
# MergeError: Not allowed to merge between different levels. (2 levels on the left, 1 on the right)
_ = pd.MultiIndex.from_product([['Factors'], ff.columns])
stocks[_] = ff.iloc[:-1].pct_change()
stocks.tail()
Variable Adj Close Close ... Returns Factors
Ticker BAC C GS JPM MS PNC BAC C GS JPM ... BAC C GS JPM MS PNC Mkt-RF SMB HML RF
Date
2024-02-12 33.6200 53.9200 392.6400 175.7900 86.8700 149.1400 33.6200 53.9200 392.6400 175.7900 ... 0.0166 -0.0013 0.0218 0.0045 0.0114 0.0093 NaN NaN NaN NaN
2024-02-13 32.7500 52.7600 378.7500 174.2600 83.9700 145.2600 32.7500 52.7600 378.7500 174.2600 ... -0.0259 -0.0215 -0.0354 -0.0087 -0.0334 -0.0260 NaN NaN NaN NaN
2024-02-14 33.1300 53.9800 378.0400 176.0300 84.0000 147.8700 33.1300 53.9800 378.0400 176.0300 ... 0.0116 0.0231 -0.0019 0.0102 0.0004 0.0180 NaN NaN NaN NaN
2024-02-15 34.0700 55.2100 385.4200 179.8700 85.6700 149.6300 34.0700 55.2100 385.4200 179.8700 ... 0.0284 0.0228 0.0195 0.0218 0.0199 0.0119 NaN NaN NaN NaN
2024-02-16 34.0900 54.8500 384.4400 179.0300 86.5000 148.8500 34.0900 54.8500 384.4400 179.0300 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

5 rows × 46 columns

stocks_long = stocks_long.join(ff)
stocks_long.loc['2023-12'].tail(12)
Adj Close Close High Low Open Volume Returns Mkt-RF SMB HML RF
Date Ticker
2023-12-28 BAC 33.8800 33.8800 33.9700 33.7700 33.8200 21799600.0000 0.0012 -0.0001 -0.0036 0.0003 0.0002
C 51.0329 51.5200 51.8000 51.4000 51.4000 10218500.0000 0.0012 -0.0001 -0.0036 0.0003 0.0002
GS 386.4100 386.4100 387.7600 383.6300 384.5200 1024700.0000 0.0050 -0.0001 -0.0036 0.0003 0.0002
JPM 169.2563 170.3000 170.6600 169.0000 169.3500 6320100.0000 0.0053 -0.0001 -0.0036 0.0003 0.0002
MS 92.7316 93.6400 93.9500 93.2400 93.3100 4089500.0000 -0.0002 -0.0001 -0.0036 0.0003 0.0002
PNC 154.0486 155.6300 156.2100 154.9500 155.4400 1153300.0000 0.0041 -0.0001 -0.0036 0.0003 0.0002
2023-12-29 BAC 33.6700 33.6700 33.9900 33.5500 33.9400 28037800.0000 -0.0062 -0.0043 -0.0112 -0.0037 0.0002
C 50.9537 51.4400 51.6100 51.2200 51.5600 13147900.0000 -0.0016 -0.0043 -0.0112 -0.0037 0.0002
GS 385.7700 385.7700 386.6400 383.5700 385.5700 881300.0000 -0.0017 -0.0043 -0.0112 -0.0037 0.0002
JPM 169.0575 170.1000 170.6900 169.6300 170.0000 6431800.0000 -0.0012 -0.0043 -0.0112 -0.0037 0.0002
MS 92.3454 93.2500 93.7700 93.0600 93.4900 4772100.0000 -0.0042 -0.0043 -0.0112 -0.0037 0.0002
PNC 153.2765 154.8500 156.1100 154.5200 155.2700 1572600.0000 -0.0050 -0.0043 -0.0112 -0.0037 0.0002

3.6 Write a function download() that accepts tickers and returns a wide data frame of returns with the daily benchmark return factors.

We can even add a shape argument to return a wide or long data frame!

def download(tickers, shape='wide'):
    if shape.lower() not in ['wide', 'long']:
        raise ValueError('Invalid shape: must be "wide" or "long".')

    stocks = yf.download(tickers)

    factors_all = (
        pdr.DataReader(
            name='F-F_Research_Data_Factors_daily',
            data_source='famafrench',
            start='1900'
        )
    )
    
    factors = factors_all[0].div(100)
    
    # if stocks has a multi index on the columns because we asked for more than one ticker
    # then we have more work to do
    if type(stocks.columns) is pd.MultiIndex:
        
        # whether we want a wide or long data frame, it is easier to add returns to a wide data frame
        _ = pd.MultiIndex.from_product([['Returns'], stocks['Adj Close'].columns])
        stocks[_] = stocks['Adj Close'].pct_change()

        # if we want a wide data frame, then we need a factors multi index
        if shape.lower() == 'wide':
            _ = pd.MultiIndex.from_product([['Factors'], factors.columns])
            stocks[_] = factors
            return stocks.rename_axis(columns=['Variable', 'Ticker'])

        # if we want a long data frame, then we need to stack stocks and join factors
        # because we tested for wide and long above, we know that here shape must be long
        else:
            return stocks.stack().join(factors).rename_axis(columns=['Variable'], index=['Date', 'Ticker'])
            
    # if stocks does not have a multi index on the columns
    # then we only have join factors
    else:
        return stocks.join(ff).rename_axis(columns=['Variable'])
download(tickers=['AAPL', 'TSLA'], shape='long')
[*********************100%%**********************]  2 of 2 completed
C:\Users\r.herron\AppData\Local\Temp\ipykernel_28096\3667605341.py:8: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.
  pdr.DataReader(
Variable Adj Close Close High Low Open Volume Returns Mkt-RF SMB HML RF
Date Ticker
1980-12-12 AAPL 0.0992 0.1283 0.1289 0.1283 0.1283 469033600.0000 NaN 0.0138 -0.0001 -0.0105 0.0006
1980-12-15 AAPL 0.0940 0.1217 0.1222 0.1217 0.1222 175884800.0000 -0.0522 0.0011 0.0025 -0.0046 0.0006
1980-12-16 AAPL 0.0871 0.1127 0.1133 0.1127 0.1133 105728000.0000 -0.0734 0.0071 -0.0075 -0.0047 0.0006
1980-12-17 AAPL 0.0893 0.1155 0.1161 0.1155 0.1155 86441600.0000 0.0248 0.0152 -0.0086 -0.0034 0.0006
1980-12-18 AAPL 0.0919 0.1189 0.1194 0.1189 0.1189 73449600.0000 0.0290 0.0041 0.0022 0.0126 0.0006
... ... ... ... ... ... ... ... ... ... ... ... ...
2024-02-14 TSLA 188.7100 188.7100 188.8900 183.3500 185.3000 81203000.0000 0.0255 NaN NaN NaN NaN
2024-02-15 AAPL 183.8600 183.8600 184.4900 181.3500 183.5500 65434500.0000 -0.0016 NaN NaN NaN NaN
TSLA 200.4500 200.4500 200.8800 188.8600 189.1600 120831800.0000 0.0622 NaN NaN NaN NaN
2024-02-16 AAPL 182.3100 182.3100 184.8500 181.6650 183.4200 48627624.0000 -0.0084 NaN NaN NaN NaN
TSLA 199.9500 199.9500 203.1700 197.4000 202.0600 110528448.0000 -0.0025 NaN NaN NaN NaN

14319 rows × 11 columns

3.7 Download earnings per share for the stocks in stocks and combine to a long data frame earnings.

Use the .earnings_dates method described here. Use pd.concat() to combine the result of each the .earnings_date data frames and assign them to a new data frame earnings. Name the row indexes Ticker and Date and swap to match the order of the row index in stocks_long.

tickers = yf.Tickers(tickers='BAC C GS JPM MS PNC')
earnings  = (
    pd.concat(
        objs=[tickers.tickers[t].earnings_dates for t in tickers.tickers],
        keys=tickers.tickers,
        names=['Ticker', 'Date']
    )
    .reset_index()
)
earnings.tail()
Ticker Date EPS Estimate Reported EPS Surprise(%)
67 PNC 2024-04-16 08:00:00-04:00 3.0800 NaN NaN
68 PNC 2024-01-16 06:00:00-05:00 2.9400 3.1600 0.0737
69 PNC 2023-10-13 06:00:00-04:00 3.1100 3.6000 0.1586
70 PNC 2023-07-18 07:00:00-04:00 3.2800 3.3600 0.0230
71 PNC 2023-04-14 06:00:00-04:00 3.6700 3.9800 0.0847

3.8 Combine earnings with the returns from stocks_long.

Use the tz_localize('America/New_York') method add time zone information back to returns.index and use pd.to_timedelta(16, unit='h') to set time to the market close in New York City. Use pd.merge_asof() to match earnings announcement dates and times to appropriate return periods. For example, if a firm announces earnings after the close at 5 PM on February 7, we want to match the return period from 4 PM on February 7 to 4 PM on February 8.

returns = (
    stocks_long
    .reset_index()
    [['Date', 'Ticker', 'Returns']]
    .assign(
        Date=lambda x: x['Date'].dt.tz_localize('America/New_York') + pd.to_timedelta(16, unit='H')
    )
)
returns.tail()
Date Ticker Returns
62021 2024-02-16 16:00:00-05:00 C NaN
62022 2024-02-16 16:00:00-05:00 GS NaN
62023 2024-02-16 16:00:00-05:00 JPM NaN
62024 2024-02-16 16:00:00-05:00 MS NaN
62025 2024-02-16 16:00:00-05:00 PNC NaN 
surprises = pd.merge_asof(
    left=earnings.sort_values(['Date', 'Ticker']),
    right=returns.sort_values(['Date', 'Ticker']),
    on='Date',
    by='Ticker',
    direction='forward'
)
surprises.tail()
Ticker Date EPS Estimate Reported EPS Surprise(%) Returns
67 JPM 2025-01-15 08:00:00-05:00 NaN NaN NaN NaN
68 JPM 2025-01-15 08:00:00-05:00 NaN NaN NaN NaN
69 C 2025-01-15 11:00:00-05:00 NaN NaN NaN NaN
70 PNC 2025-01-16 08:00:00-05:00 NaN NaN NaN NaN
71 PNC 2025-01-16 10:00:00-05:00 NaN NaN NaN NaN

3.9 Plot the relation between daily returns and earnings surprises

Three options in increasing difficulty:

  1. Scatter plot
  2. Scatter plot with a best-fit line using regplot() from the seaborn package
  3. Bar plot using barplot() from the seaborn package after using pd.qcut() to form five groups on earnings surprises
(
    surprises
    [['Surprise(%)', 'Returns']]
    .mul(100)
    .plot(kind='scatter', x='Surprise(%)', y='Returns')
)

plt.xlabel('Earnings Surprise (%)')
plt.ylabel('One-Day Return (%)')
plt.title(f'Earnings Surprise Analysis\n {', '.join(tickers.tickers)}')
plt.show()

import seaborn as sns
sns.regplot(
    data=surprises[['Surprise(%)', 'Returns']].mul(100),
    x='Surprise(%)',
    y='Returns'
)

plt.xlabel('Earnings Surprise (%)')
plt.ylabel('One-Day Return (%)')
plt.title(f'Earnings Surprise Analysis\n {', '.join(tickers.tickers)}')
plt.show()

sns.catplot(
    data=(
        surprises[['Surprise(%)', 'Returns']]
        .dropna()
        .mul(100)
        .assign(
            surprise_bin=lambda x: pd.qcut(x['Surprise(%)'], q=5, labels=False)+1
        )
    ),
    x='surprise_bin',
    y='Returns',
    kind='bar'
)

plt.xlabel('Earnings Surprise Bin (1=Lowest, 5=Highest)')
plt.ylabel('One-Day Return (%)')
plt.title(f'Earnings Surprise Analysis\n {', '.join(tickers.tickers)}')
plt.show()

3.10 Repeat the earnings exercise with the S&P 100 stocks

With more data, we can more clearly see the positive relation between earnings surprises and returns!

_ = pd.read_html('https://en.wikipedia.org/wiki/S%26P_100')[2]['Symbol'].to_list()
tickers_2 = yf.Tickers(tickers=[t.replace('.', '-') for t in _]) # Yahoo! Finance uses "-" to indicate share classes instead of "."
earnings_2  = (
    pd.concat(
        objs=[tickers_2.tickers[t].earnings_dates for t in tickers_2.tickers],
        keys=tickers_2.tickers,
        names=['Ticker', 'Date']
    )
    .reset_index()
)
earnings_2.tail()
Ticker Date EPS Estimate Reported EPS Surprise(%)
1207 XOM 2023-04-28 06:00:00-04:00 2.5900 2.8300 0.0943
1208 XOM 2023-01-31 06:00:00-05:00 3.2900 3.4000 0.0319
1209 XOM 2022-10-28 06:00:00-04:00 3.7900 4.4500 0.1733
1210 XOM 2022-07-29 06:00:00-04:00 3.7400 4.1400 0.1077
1211 XOM 2022-04-29 06:00:00-04:00 2.1200 2.0700 -0.0247 
returns_2 = (
    yf.download(tickers=[t for t in tickers_2.tickers])
    .rename_axis(columns=['Variable', 'Ticker'])
    ['Adj Close']
    .pct_change()
    .stack()
    .to_frame('Returns')
    .reset_index()
    [['Date', 'Ticker', 'Returns']]
    .assign(
        Date=lambda x: x['Date'].dt.tz_localize('America/New_York') + pd.to_timedelta(16, unit='H')
    )
)
[*********************100%%**********************]  101 of 101 completed
returns_2.tail()
Date Ticker Returns
1018569 2024-02-16 16:00:00-05:00 V -0.0086
1018570 2024-02-16 16:00:00-05:00 VZ -0.0025
1018571 2024-02-16 16:00:00-05:00 WFC -0.0025
1018572 2024-02-16 16:00:00-05:00 WMT 0.0063
1018573 2024-02-16 16:00:00-05:00 XOM 0.0000 
surprises_2 = pd.merge_asof(
    left=earnings_2.sort_values(['Date', 'Ticker']),
    right=returns_2.sort_values(['Date', 'Ticker']),
    on='Date',
    by='Ticker',
    direction='forward'
)
surprises_2.tail()
Ticker Date EPS Estimate Reported EPS Surprise(%) Returns
1207 USB 2025-10-16 09:00:00-04:00 NaN NaN NaN NaN
1208 USB 2025-10-16 09:00:00-04:00 NaN NaN NaN NaN
1209 RTX 2025-10-20 06:00:00-04:00 NaN NaN NaN NaN
1210 USB 2026-01-20 09:00:00-05:00 NaN NaN NaN NaN
1211 USB 2026-01-20 09:00:00-05:00 NaN NaN NaN NaN 
(
    surprises_2
    [['Surprise(%)', 'Returns']]
    .mul(100)
    .plot(kind='scatter', x='Surprise(%)', y='Returns')
)

plt.xlabel('Earnings Surprise (%)')
plt.ylabel('One-Day Return (%)')
plt.title('Earnings Surprise Analysis\n S&P 100 Stocks')
plt.show()

There are a few large negative and positive earnings surprises that make our plot difficult to interpret! We have these huge outliers because surprises are defined as the percent change relative to the expected. If expected is a very small number, then the surprise can be a very large number! We would use different surprise definitions for a more rigorous analysis. Here we can drop observations less than the 0.01 quantile and greater than the 0.99 quantile.

low_2 = surprises_2['Surprise(%)'].quantile(0.01)
high_2 = surprises_2['Surprise(%)'].quantile(0.91)

cond_2 = surprises_2['Surprise(%)'].between(low_2, high_2)
(
    surprises_2
    .loc[cond_2, ['Surprise(%)', 'Returns']]
    .mul(100)
    .plot(kind='scatter', x='Surprise(%)', y='Returns', alpha=0.5)
)

plt.xlabel('Earnings Surprise (%)')
plt.ylabel('One-Day Return (%)')
plt.title('Earnings Surprise Analysis\n S&P 100 Stocks')
plt.show()

sns.regplot(
    data=surprises_2.loc[cond_2, ['Surprise(%)', 'Returns']].mul(100),
    x='Surprise(%)',
    y='Returns'
)

plt.xlabel('Earnings Surprise (%)')
plt.ylabel('One-Day Return (%)')
plt.title('Earnings Surprise Analysis\n S&P 100 Stocks')
plt.show()

sns.catplot(
    data=(
        surprises_2
        .loc[cond_2, ['Surprise(%)', 'Returns']]
        .dropna()
        .mul(100)
        .assign(surprise_bin=lambda x: pd.qcut(x['Surprise(%)'], q=5, labels=False) + 1)
    ),
    x='surprise_bin',
    y='Returns',
    kind='bar'
)

plt.xlabel('Earnings Surprise Bin (1=Lowest, 5=Highest)')
plt.ylabel('One-Day Return (%)')
plt.title('Earnings Surprise Analysis\n S&P 100 Stocks')
plt.show()