For report in pdf pdf_link

For notebook notebook

The aim of this project is to perform an in-depth analysis assosiated with the assumptions of Modern Portfolio Theory and how financial assets interact within the portfolio using sttistical methods

Harry Markowitz introduced the mean-varinace portfolio selection theory in 1952.Since then the Modern Porfolio theory is cornerstone in the field of modern finance

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import requests
sns.reset_defaults()

Twelve stock quoted on NYSE are used for the purpose of analysis. The data is between the period of 1st January 2008 to 31st December 2017. The data is downloaded using Twelvedata API in the json format. The stocks are divident adjusted

# dowloading data using twelve data api
tickers = ["AAPL", "AXP", "BA", "GE","JNJ", "JPM", "KO", "MCD", "MSFT", "VZ",  "WMT", "XOM"]
api_key = "*************************************"
interval = "1day"
start = "2008-01-01"
end = "2018-01-01"
stocks = []
def stock_data(tick):
    url = f'https://api.twelvedata.com/time_series?symbol={tick}&start_date={start}&end_date={end}&interval={interval}&order=ASC&apikey={api_key}'
    data = requests.get(url).json()
    return data["values"]

df = pd.DataFrame()
df["datetime"] = pd.DataFrame(stock_data("AAPL"))["datetime"]
for tick in tickers[:6]:
    df[tick] = pd.DataFrame(stock_data(tick))["close"]

for tick in tickers[6:]:
    df[tick] = pd.DataFrame(stock_data(tick))["close"]

# datetime as index
df["datetime"] = pd.to_datetime(df["datetime"])
df.index = df["datetime"]
df.drop(["datetime"], axis=1, inplace=True)

df.head()

# any na values
df.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 2518 entries, 2008-01-02 to 2017-12-29
Data columns (total 12 columns):
 #   Column  Non-Null Count  Dtype 
---  ------  --------------  ----- 
 0   AAPL    2518 non-null   object
 1   AXP     2518 non-null   object
 2   BA      2518 non-null   object
 3   GE      2518 non-null   object
 4   JNJ     2518 non-null   object
 5   JPM     2518 non-null   object
 6   KO      2518 non-null   object
 7   MCD     2518 non-null   object
 8   MSFT    2518 non-null   object
 9   VZ      2518 non-null   object
 10  WMT     2518 non-null   object
 11  XOM     2518 non-null   object
dtypes: object(12)
memory usage: 255.7+ KB

df.isna().sum()

AAPL    0
AXP     0
BA      0
GE      0
JNJ     0
JPM     0
KO      0
MCD     0
MSFT    0
VZ      0
WMT     0
XOM     0
dtype: int64

df = df.astype("float")

We calculate the daily change in prices of stock using the given formulae

$$r_{asset} = \frac{P_t - P_{t-1}}{P_{t-1}}\times100$$

where

$P_t$ is present value of adjusted close price of individual stock

$P_{t-1}$ is previous day adjusted close price of individual stock

$r_{asset}$ daily return on assest

df = df.pct_change().dropna()
df = df*100

According to Random Walk Theory stock price change are independent of past movement trends and therefore next day price cannot be predicted from previous price changes

We plot a simple scatterplot for our twelve assets where we compare the previous day return against the present value and it shows absolutely no correlation for any of the assets which proves the assmption to be true

fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(8,10))
tickers = df.columns
x = 0
for i in range(4):
    for j in range(3):
        ax = sns.scatterplot(x=df[tickers[x]], y=df[tickers[x]].shift(1),color="deepskyblue",alpha=0.5, ax=axes[i,j])
        ax.set_title(tickers[x])
        ax.set_xlabel("t")
        ax.set_ylabel("t+1")
        x = x + 1
plt.tight_layout()

The daily return on assets are collected and represented in the form of histogram given in the figure below, from initial analysis it seems that most of the stocks closely resemble normal distribution. Most of these returns are concentrated towards the mean value but a small portion of these return are scattered at the end of the distribution showing few days where there is large swing in price

fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(8,8))
tickers = df.columns
x = 0
for i in range(4):
    for j in range(3):
        ax = sns.histplot(df[tickers[x]], bins=25, color="navy", alpha=0.7, ax=axes[i,j])
        ax.set_title(tickers[x])
        x = x + 1
plt.tight_layout()

from scipy import stats
tickers = df.columns
xnor = np.arange(-10,10, 0.1)
ynor = stats.norm.pdf(xnor)
x = 0
fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(10,8))
for i in range(4):
    for j in range(3):
        ax = sns.kdeplot(df[tickers[x]],color="violet", fill=True, ax=axes[i,j])
        sns.lineplot(x=xnor, y=ynor, lw=1.5, color="navy", ax=axes[i,j], label="Normal")
        ax.set_xlim(-4,4)
        ax.set_title(tickers[x])
        ax.set_xlabel("")
        x = x + 1
plt.tight_layout()

pd.DataFrame(df.mean(), columns=["MeanReturn"]).T

plt.figure(figsize=(7,3))
ax1 = sns.barplot(x=df.columns, y=df.mean(), color="violet")
ax1.set_title("Mean Returns b/w 2008-17");

In the context of finance the risk can be defined as the uncertainty in the market. Mathematically it can defined as the deviation away from the expected historical returns during a particular time period. A more preferred definition in our context of our analysis is the degree of making loss on a potential investment.There are several statistical methods use to measure financial risk

Standard Deviation is the most commonly employed method for calculating financial risk. It can be expressed as.

$$sd_{asset} = \sqrt{\frac{\sum_(x_i - \mu)^2}{N}}$$

$N$ is the total number of daily returns observed,

$x_i$ is daily observed return,

$\mu$ is mean return observed during N days.

The greater the Standard Deviation of the security the more risky the asset is (in relative terms). The Standard Deviation calculates the historical volatility of the security, greater the dispersion from the mean value indicates more volatile asset. From the table we can observe that JNJ is least risky asset with standard deviation of 1.0321% while JPM is the most risky asset with standard deviation of 2.733%.

pd.DataFrame(df.std(), columns=["SD"]).T

plt.figure(figsize=(7,3))
ax1 = sns.barplot(x=df.columns, y=df.std(), color="navy", alpha=0.7)
ax1.set_title("Mean Returns b/w 2008-17");

A more preferable method is using the daily volatility clustering technique which helps in visualizing the volatility at specific periods in time.From Figure we can observe that the period between the year 2008 to 2010 was extremely volatile period due to rapid change in the stock prices from their mean value, the contributing factor for this rapid movement was in response to the subprime mortgage crises in US due to which their was a large scale financial panic in the market. After the US government intervened in the financial markets to calm down the rapid volatility in the market, the price movement started to become less intense.

fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(10,8))
tickers = df.columns
x = 0
for i in range(4):
    for j in range(3):
        ax = sns.lineplot(x=df.index, y=df[tickers[x]],color="royalblue",alpha=1, ax=axes[i,j])
        ax.tick_params(labelrotation=45)
        ax.set_title(tickers[x])
        ax.set_xlabel("")
        ax.set_ylabel("SD")
        x = x + 1
plt.tight_layout()

Markowitz (1952) argued that the individual risk pertaining to an asset is not as much important as the contribution of risk from each asset to the aggregate portfolio.In the financial markets only those risk are compensated that cannot be avoided.

In the financial market no two financial assets are either completely correlated or completely independent of each other, to some degree each asset is interrelated to the movement of the prices of another asset , some assets have very high degree of co-movement while some have less exposure to each other. The Modern Portfolio Theory attempts to analyze the interrelationship between different investments. It utilizes statistical measures such as correlation to quantify the diversification effect on portfolio

The extent to which the risk of the portfolio can be reduced largely depends on the covariance between different assets. Portfolio assets which have low correlation coefficients are considered to be less risky than pairs with high coefficients

Within the portfolio ofN assets there are N variances and N(N-1) covariance. As the number of assets in the portfolio increases the number of covariance increases rapidly. This large number of covariance are very importanttowards determining the risk of the portfolio.

In the Figure we can observe that at the start of the year 2008 the correlation among the assets were lowas the crises took of , the correlation start to become stronger between the various pairs of asset. In the year between 2009 and 2013 we can see a high correlation among the assets which can be attributed to the increase in systematic risk due to panic in the US financial industry. After 2013 the correlation among the pairs of asset start to decline due to decrease in systematic risk. During the time of financial stress the correlation among the pairs of the portfolio becomes very high due to intense volatility in the market.

fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(8,8))
periods =["2008-12-31", "2009-12-31","2010-12-31","2011-12-31","2012-12-31","2013-12-31","2014-12-31","2015-12-31","2016-12-31"]
x = 0
for i in range(3):
    for j in range(3):
        ax = sns.heatmap((df[:periods[x]].corr()), lw=0.5, cmap="YlGnBu", cbar=False, ax=axes[i,j])
        ax.set_title(periods[x][:4])
        x = x + 1
plt.tight_layout()

Sharpe ratio give insight to the investor what he is getting as a return on a security for the unit amount of risk he is taking. Sharpe ratio is calculated using

$${Sharpe Ratio} = \frac{R_{asset}-R_f}{\sigma_{assest}}$$

$R_{asset}$ return on asset

$R_f$ risk free rate

$\sigma_{asset}$ SD of asset

Modern Portfolio theory sttes that adding assets to a portfolio that have low correlation can decrease portfolio risk without sacrificing the return. The larger the sharpe ratio the better it is for portfolio

pd.DataFrame(df.mean()/df.std(), columns=["Sharpe Ratio"]).T

plt.figure(figsize=(7,3))
ax1 = sns.barplot(x=df.columns, y=df.mean()/df.std(), color="violet", alpha=0.7)
ax1.set_title("Mean Returns b/w 2008-17");

The effect of diversification is of central importance to Modern Portfolio Theory. The process of diversification suggests the investor to invest in multiple assets across different asset classes and industries. As the number of securities in the portfolio increases the portfolio become less risk inherent.

But the decreases is non linear in nature. As in the Figure we can observe that as the number of assets increase (equal weighted portfolio) the standard deviation decreases at a diminishing rate, after adding a certain number of assets if we add more assets to the portfolio it does not have much impact in decreasing the portfolio risk because some risk will always remain in the form of systematic risk which cannot be reduced.

dec_sd = df.std().sort_values(ascending=False).index
wport_sd =[]
for i in range(1,13):
    equal_weight = np.repeat(1/i, i).reshape(-1,1)
    covar = df.loc[:,dec_sd[:i]].cov().to_numpy()
    sd = np.sqrt(np.dot(equal_weight.T,np.dot(covar, equal_weight)))
    wport_sd.append(sd)
wport_sd = np.array(wport_sd).reshape(12,)

plt.figure(figsize=(8,4))
sns.scatterplot(x=range(1,13), y=wport_sd, color="red", s=50, legend=False)
sns.lineplot(x=range(1,13), y=wport_sd, color="black", legend=False);

From Volatility clustering we can conclude that portfolio returns are much less volatile than any single stock return

dates = df.index
freq_assets = df.shape[1]
daily_returns = []
weight = np.repeat(1/freq_assets, freq_assets).reshape(-1,1)
for date in dates:
    asset_return = df.loc[date].to_numpy()
    port_return = np.dot(weight.T, asset_return)
    daily_returns.append(port_return)

portfolio_return = np.array(daily_returns).reshape(df.shape[0],)

fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(10, 10),dpi=120)
tickers = df.columns
x = 0
for i in range(4):
    for j in range(3):
        ax = sns.lineplot(x=df.index, y=df[tickers[x]],color="deepskyblue",alpha=1, ax=axes[i,j])
        sns.lineplot(x=df.index, y=portfolio_return, color="black",linewidth=0.5, alpha=0.8, ax=axes[i,j], label="Portfolio")
        ax.tick_params(labelrotation=45)
        ax.set_title(tickers[x])
        ax.set_xlabel("")
        ax.set_ylabel("SD")
        x = x + 1
plt.tight_layout()

Markowitz concluded that out of infinite portfolio that can be constructed by assigning different weights to the portfolio (of the 12 stocks) there will be one efficient portfolio which gives an investor maximum return for a given amount of risk. The investor would always prefer the portfolio over the other inefficient portfolios because it maximizes his utility

Using computer simulation we have generated more than 50,000 randomly weighted portfolios out of the 12 stocks and plotted their expected return and standard deviation.

From the Figure we can observe that these random portfolios constitutes a parabolic curve. At the edge of these curve lie the efficient portfolios which investor prefer over other portfolios based on their utility.

row = 50000
col = 12
random = np.random.normal(size=row*col).reshape(row,col)
sum_ = random.sum(axis=1)
weights = []
for idx in range(row):
    weight = random[idx]/sum_[idx]
    weights.append(weight)
weights = np.array(weights)

mean = []
sd = []
covar = np.array(df.cov())
daily_mean = np.array(df.mean())
for idx in range(weights.shape[0]):
    w = weights[idx].reshape(-1,1)
    port_mean = np.dot(w.T,daily_mean)
    port_sd = np.sqrt(np.dot(w.T, np.dot(covar,w)))
    mean.append(port_mean)
    sd.append(port_sd)
mean = np.array(mean).reshape(row,)
sd = np.array(sd).reshape(row,)
sharpe = mean/sd

plt.figure(figsize=(5,7),dpi=120)
ax = sns.scatterplot(x=sd, y=mean, color="red", size= 2,alpha=0.5, hue=sharpe, palette="PuRd", legend=False)
ax.set_xlim(0.85,3)
ax.set_ylim(-0.1,0.16);