diff --git a/data.txt b/data.txt
new file mode 100644
index 00000000000..585322d7de1
--- /dev/null
+++ b/data.txt
@@ -0,0 +1 @@
+{"installed": true}
diff --git a/gamestonk_terminal/stocks/quantitative_analysis/README.md b/gamestonk_terminal/stocks/quantitative_analysis/README.md
index a82c3b02159..4aa3c596c8f 100644
--- a/gamestonk_terminal/stocks/quantitative_analysis/README.md
+++ b/gamestonk_terminal/stocks/quantitative_analysis/README.md
@@ -20,6 +20,9 @@ This menu aims to perform exploratory data analysis on a pre-loaded stock, and t
   * detects abrupt changes using cumulative sum algorithm
 * [acf](#acf)
   * (partial) auto-correlation function differentials
+* [capm](#capm)
+  * capital asset pricing model
+
 
 
 ## summary <a name="summary"></a>
@@ -132,3 +135,15 @@ Auto-Correlation and Partial Auto-Correlation Functions for diff and diff diff s
 * -l : maximum lags to display in plots. Default 40.
 
 ![acf](https://user-images.githubusercontent.com/25267873/112733336-637c5d00-8f37-11eb-9aae-38e75dff9ad2.png)
+
+
+## capm <a name="capm"></a>
+
+```text
+usage: capm
+```
+
+Capital asset pricing model for a given stock.
+
+
+![capm]
diff --git a/gamestonk_terminal/stocks/quantitative_analysis/factors_model.py b/gamestonk_terminal/stocks/quantitative_analysis/factors_model.py
new file mode 100644
index 00000000000..57822c05dfb
--- /dev/null
+++ b/gamestonk_terminal/stocks/quantitative_analysis/factors_model.py
@@ -0,0 +1,78 @@
+"""Factors model"""
+__docformat__ = "numpy"
+
+from urllib.request import urlopen
+from zipfile import ZipFile
+from io import BytesIO
+
+import statsmodels.api as sm
+import yfinance as yf
+import pandas as pd
+import numpy as np
+
+
+def get_fama_raw():
+    """Gets base Fama French data to calculate risk"""
+    with urlopen(
+        "http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_Factors_CSV.zip"
+    ) as url:
+
+        # Download Zipfile and create pandas DataFrame
+        with ZipFile(BytesIO(url.read())) as zipfile:
+            with zipfile.open("F-F_Research_Data_Factors.CSV") as zip_open:
+                df = pd.read_csv(
+                    zip_open,
+                    header=0,
+                    names=["Date", "MKT-RF", "SMB", "HML", "RF"],
+                    skiprows=3,
+                )
+
+    df = df[df["Date"].apply(lambda x: len(str(x).strip()) == 6)]
+    df["Date"] = df["Date"].astype(str) + "01"
+    df["Date"] = pd.to_datetime(df["Date"], format="%Y%m%d")
+    df["MKT-RF"] = pd.to_numeric(df["MKT-RF"], downcast="float")
+    df["SMB"] = pd.to_numeric(df["SMB"], downcast="float")
+    df["HML"] = pd.to_numeric(df["HML"], downcast="float")
+    df["RF"] = pd.to_numeric(df["RF"], downcast="float")
+    df["MKT-RF"] = df["MKT-RF"] / 100
+    df["SMB"] = df["SMB"] / 100
+    df["HML"] = df["HML"] / 100
+    df["RF"] = df["RF"] / 100
+    df = df.set_index("Date")
+    return df
+
+
+def get_historical_5(ticker: str):
+    """Get 5 year monthly historical performance for a ticker with dividends filtered"""
+    tick = yf.Ticker(ticker)
+    df = tick.history(period="5y", interval="1mo")
+    df = df[df.index.to_series().apply(lambda x: x.day == 1)]
+    df = df.drop(["Dividends", "Stock Splits"], axis=1)
+    df = df.dropna()
+    return df
+
+
+def capm_information(ticker):
+    """Provides information that relates to the CAPM model"""
+    df_f = get_fama_raw()
+    df_h = get_historical_5(ticker)
+    df = df_h.join(df_f)
+    df = df.dropna()
+    df["Monthly Return"] = df["Close"].pct_change()
+    df["Excess Monthly Return"] = df["Monthly Return"] - df["RF"]
+    df["Excess MKT-RF"] = df["MKT-RF"] - df["RF"]
+    df = df.dropna()
+    y = df[["Excess Monthly Return"]]
+    x = df["Excess MKT-RF"]
+    x = sm.add_constant(x)
+    model = sm.OLS(y, x).fit()
+    beta = model.params["Excess MKT-RF"]
+    beta2 = beta ** 2
+    m_variance = np.var(df["Excess MKT-RF"])
+    syst = beta2 * m_variance
+    ss = model.ssr
+    deg_free = model.df_resid
+    unsys = ss / deg_free
+    sy = (syst / (syst + unsys)) * 100
+    us = (unsys / (syst + unsys)) * 100
+    return beta, m_variance, sy, us
diff --git a/gamestonk_terminal/stocks/quantitative_analysis/factors_view.py b/gamestonk_terminal/stocks/quantitative_analysis/factors_view.py
new file mode 100644
index 00000000000..99219ee4d1f
--- /dev/null
+++ b/gamestonk_terminal/stocks/quantitative_analysis/factors_view.py
@@ -0,0 +1,15 @@
+"""Factors view"""
+__docformat__ = "numpy"
+
+from gamestonk_terminal.stocks.quantitative_analysis.factors_model import (
+    capm_information,
+)
+
+
+def capm_view(ticker):
+    """A view that displays information for the CAPM model."""
+    beta, m_variance, sy, us = capm_information(ticker)
+    print(f"Beta:\t\t\t{beta:.2f}")
+    print(f"Market Variance:\t{m_variance:.4f}")
+    print(f"Systematic Risk:\t{sy:.2f}%")
+    print(f"Unsystematic Risk:\t{us:.2f}%\n")
diff --git a/gamestonk_terminal/stocks/quantitative_analysis/qa_controller.py b/gamestonk_terminal/stocks/quantitative_analysis/qa_controller.py
index 2a196f69714..202b930d461 100644
--- a/gamestonk_terminal/stocks/quantitative_analysis/qa_controller.py
+++ b/gamestonk_terminal/stocks/quantitative_analysis/qa_controller.py
@@ -24,6 +24,7 @@ from gamestonk_terminal.helper_funcs import (
     parse_known_args_and_warn,
 )
 from gamestonk_terminal.menu import session
+from gamestonk_terminal.stocks.quantitative_analysis.factors_view import capm_view
 
 
 class QaController:
@@ -49,6 +50,7 @@ class QaController:
         "unitroot",
         "goodness",
         "unitroot",
+        "capm",
     ]
 
     CHOICES += CHOICES_COMMANDS
@@ -116,6 +118,7 @@ Rolling Metrics:
 Other:
     decompose   decomposition in cyclic-trend, season, and residuals of prices
     cusum       detects abrupt changes using cumulative sum algorithm of prices
+    capm        capital asset pricing model
         """
         print(help_str)
 
@@ -768,6 +771,26 @@ Other:
         except Exception as e:
             print(e, "\n")
 
+    def call_capm(self, other_args: List[str]):
+        """Process capm command"""
+        parser = argparse.ArgumentParser(
+            add_help=False,
+            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+            prog="capm",
+            description="""
+                Provides detailed information about a stock's risk compared to the market risk.
+            """,
+        )
+
+        try:
+            ns_parser = parse_known_args_and_warn(parser, other_args)
+            if not ns_parser:
+                return
+            capm_view(self.ticker)
+
+        except Exception as e:
+            print(e, "\n")
+
 
 def menu(ticker: str, start: datetime, interval: str, stock: pd.DataFrame):
     """Statistics Menu"""
diff --git a/gamestonk_terminal/stocks/technical_analysis/ta_controller.py b/gamestonk_terminal/stocks/technical_analysis/ta_controller.py
index 0f0954ed1ca..3ca1324e2d4 100644
--- a/gamestonk_terminal/stocks/technical_analysis/ta_controller.py
+++ b/gamestonk_terminal/stocks/technical_analysis/ta_controller.py
@@ -1,6 +1,7 @@
 """Technical Analysis Controller Module"""
 __docformat__ = "numpy"
 # pylint:disable=too-many-lines
+# pylint:disable=R0904
 
 import argparse
 import os
diff --git a/website/content/stocks/quantitative_analysis/capm/_index.md b/website/content/stocks/quantitative_analysis/capm/_index.md
new file mode 100644
index 00000000000..0c63e6f6d75
--- /dev/null
+++ b/website/content/stocks/quantitative_analysis/capm/_index.md
@@ -0,0 +1,12 @@
+```text
+usage: capm [-h]
+```
+
+Capital asset pricing model
+
+```
+optional arguments:
+  -h, --help            show this help message (default: False)
+```
+
+![capm]
diff --git a/website/data/menu/main.yml b/website/data/menu/main.yml
index 051c91d7ccb..e6bf380fe48 100755
--- a/website/data/menu/main.yml
+++ b/website/data/menu/main.yml
@@ -501,6 +501,8 @@ main:
             ref: "/stocks/quantitative_analysis/decompose"
           - name: cusum
             ref: "/stocks/quantitative_analysis/cusum"
+          - name: capm
+            ref: "/stocks/quantitative_analysis/capm"
       - name: prediction techniques
         ref: "/stocks/prediction_techniques"
         sub: