Question

Follow a public traded stock and analyze the changes in stock price. Write a presentation and an executive memo of your findings. Choose one of the following companies:

1. Amazon
2. Alphabet
3. Microsoft
4. Facebook
5. Apple

In your presentation you should:

1. Describe graphically the trend of the stock prices during the last 2 months and, in more detail, during the last 3 weeks.
2. Explain the observed trends
3. Compare the trends with relevant indexes
4. Relate these trends to relevant contextual information (example: Covid crisis).
5. Is the development of the prices positive? Do you consider that an investment done two months ago is good? Explain.
6. Regarding this context, briefly forecast the changes in prices for the next month.

Answer 1

Stock selected: Microsoft (MSFT)

Trend analysis using RSI and MACD

Moving Average Convergence Divergence (MACD):
shows relationship between two Moving Averages (MA) for a security's price.

Calculated by subtracting 26-period EMA from 12-period EMA. (EMA - Exponential Moving Average)

Inference- MACD > Signal Line - Buy suggestion; MACD < Signal Line - Sell suggestion

It is advised to use MACD with another indicator such as Relative Strength Index (RSI) to analyse patterns more effectively.

Relative Strength Index (RSI):
measures price change in relation to recent price highs and lows.

Calculated in 2 steps:
RSI(step one) = 100 - [100/(1 + (Avg gain/ Avg loss))]
RSI(step two) = 100 - [100/(1 + (Previous Avg gain * n + current gain/ Previous Avg loss * n + current loss))] n = period - 1 (in above code, period is 14, n will be 13)

Inference- It is a momentum indicator | Overbought > 70% ; Oversold < 30% During downtrend, peak can be considered near 50%.

Limitations of both these indicators is they can give False Positive. One has to be careful and not to be completely reliable on the indicators when making investment decisions.

# Import necessary libraries
import yfinance as yf
import datetime as dt
import matplotlib.pyplot as plt
import numpy as np

# Download historical data for MSFT and S&P500 (benchmark)
ticker1 = "MSFT"  #MICROSOFT
ohlcv1 = yf.download(ticker1,dt.date.today()-dt.timedelta(500),dt.datetime.today())

ticker2 = "^GSPC"  #S&P 500
ohlcv2 = yf.download(ticker2,dt.date.today()-dt.timedelta(500),dt.datetime.today())


#Define Data Points for time period (in days)
t = 44   #for 2 months, means trading days approx 44


#Define function for MACD, a,b,c are parameteres for different periods to calculate EMA
#ewm is used with mean () to calculate exponential weighted moving average for MACD and Signal Line
def MACD(DF,a,b,c):
    """typical values a = 12; b =26, c =9"""
    df = DF.copy()   
    df["MA_Fast"]=df["Adj Close"].ewm(span=a,min_periods=a).mean() #Adj Close column taken from ohlcv data, downloaded from yFinance
    df["MA_Slow"]=df["Adj Close"].ewm(span=b,min_periods=b).mean()
    df["MACD"]=df["MA_Fast"]-df["MA_Slow"]  #formula to calculate MACD
    df["Signal"]=df["MACD"].ewm(span=c,min_periods=c).mean() #Calculation of Signal line
    df.dropna(inplace=True) #removing NaN values
    return df   #return entire dataframe

def RSI(DF,n):
    "function to calculate RSI"
    df = DF.copy()
    df['delta']=df['Adj Close'] - df['Adj Close'].shift(1) #day over day in close price
    df['gain']=np.where(df['delta']>=0,df['delta'],0) #np.where function is similar to IF function in excel
    df['loss']=np.where(df['delta']<0,abs(df['delta']),0)
    avg_gain = []  #creating empty list
    avg_loss = []
    gain = df['gain'].tolist()  #convert data series to list
    loss = df['loss'].tolist()
    for i in range(len(df)): #range is length of Dataframe
        if i < n:
            avg_gain.append(np.NaN)  #for first n = 14, it is definitely NaN value
            avg_loss.append(np.NaN)  #hence used append(np.NaN)
        elif i == n:
            avg_gain.append(df['gain'].rolling(n).mean().tolist()[n])  #calculate average first, then convert into list
            avg_loss.append(df['loss'].rolling(n).mean().tolist()[n])  #append them to empty lists created above
        elif i > n:   #when row is greater than 14
            avg_gain.append(((n-1)*avg_gain[i-1] + gain[i])/n)   #formula
            avg_loss.append(((n-1)*avg_loss[i-1] + loss[i])/n)   #formula
    df['avg_gain']=np.array(avg_gain)   #convert list to array in the dataframe
    df['avg_loss']=np.array(avg_loss)   #convert list to array in the dataframe
    df['RS'] = df['avg_gain']/df['avg_loss']   #Relative Strength Index
    df['RSI'] = 100 - (100/(1+df['RS']))       #Relative Strength Index
    df.dropna(inplace=True)
    return df   #return entire dataframe

# Visualization - plotting MACD/signal along with close price and volume for last 40 data points
df = MACD(ohlcv1, 12, 26, 9)   #calling MACD function for ticker1
df1 = RSI(ohlcv1, 14)   #calling RSI function for ticker1

df2 = MACD(ohlcv2, 12, 26, 9)   #calling MACD function for ticker2
df3 = RSI(ohlcv2, 14)   #calling RSI function for ticker2

fig, axs = plt.subplots(4,2, sharex=True, sharey=False, figsize=(30,15))   #creating subplot figure

#ticker1 Stock graphs
axs[0,0].plot(df.iloc[-t:,4])                               #plotting Adj Close price of ticker1
axs[0,0].set(ylabel='Adj Close')
axs[0,0].set_title(ticker1)
axs[1,0].plot(df.iloc[-t:,-2], label='MACD')                #plotting MACD line for ticker1
axs[1,0].plot(df.iloc[-t:,-1], label='Signal')              #plotting Signal line for ticker1
axs[1,0].legend(loc='lower left')
axs[1,0].set(ylabel='MACD/Signal')
axs[2,0].plot(df1.iloc[-t:,-1])                             #plotting RSI line for ticker1
axs[2,0].axhline(70,color='green',ls='--')                    #plotting overbought indicator line
axs[2,0].axhline(30,color='red',ls='--')                      #plotting oversold indicator line
axs[2,0].set(ylabel='RSI')
axs[3,0].bar(df.iloc[-t:,5].index, df.iloc[-t:,5].values) #plotting Volume chart for ticker1
axs[3,0].set(xlabel='Date',ylabel='Volume')
#ticker2 Stock graphs
axs[0,1].plot(df2.iloc[-t:,4])                              #plotting Adj Close price of ticker2
axs[0,1].set(ylabel='Adj Close')
axs[0,1].set_title(ticker2)
axs[1,1].plot(df2.iloc[-t:,-2], label='MACD')               #plotting MACD line for ticker2
axs[1,1].plot(df2.iloc[-t:,-1], label='Signal')             #plotting Signal line for ticker2
axs[1,1].legend(loc='lower left')
axs[1,1].set(ylabel='MACD/Signal')
axs[2,1].plot(df3.iloc[-t:,-1])                             #plotting RSI line for ticker2
axs[2,1].axhline(70,color='green',ls='--')                    #plotting overbought indicator line
axs[2,1].axhline(30,color='red',ls='--')                      #plotting oversold indicator line
axs[2,1].set(ylabel='RSI')
axs[3,1].bar(df2.iloc[-t:,5].index, df2.iloc[-t:,5].values)#plotting Volume chart for ticker2
axs[3,1].set(xlabel='Date',ylabel='Volume')

Output for the above code:

From the above charts; we can infer that

- MSFT is trading along in the direction of the Benchmark.
- From last 3 weeks, MSFT is coming down from its high, as indicated by chart
- At the same time, RSI was above 70, meaning stock is overbought and hence coming down for correction
- In MACD chart, despite of coming down, signal line is above the MACD, meaning the stock can be bought at this time; this is also supported by RSI level at 40, stock is oversold, and there are chances that it will gain momentum in the near future.

*Disclaimer: ALL VIEWS ARE PERSONAL. NO STOCK RECOMMENDATION IS PROVIDED.