Reliance Industries (NSE: RIL) has been in limelight for quite some time following a streak of investments in its Jio and Retail arms. In spite of there being more than 5,000 listed stocks on Indian bourses, RIL itself accounts for almost 7% of the total Assets Under Management (AUM) of all Equity Mutual Funds (MFs). Have you ever thought why investors are so overweight on RIL? Let’s find out.
This entire analysis is a part of my initial R projects. All views expressed are my own and are not to be used as formal investment advice. Personal research is advised before investing.
Volatility & Track Record
RIL is a highly liquid scrip allowing the market to discover the ‘fair price’ on its own once any material information reaches the exchanges.
RIL has shown good volatility, ranging between 5-10% MoM for the last 10 years, and has been a favorite for both short and long term investors. It exerts a cyclical trend, creating opportunities for investors to exploit a complete trough – crest phase before moving to the next cycle.
“Past performers tend to perform better in the future as well” – Investors who believe in trends often look for high growth multi-baggers for the long term. RIL has shown a consistent track record of growth in Revenues and Bottom line (except some minor disruptions in some quarters). Its diversification from an Oil & Gas company to include new business lines like Telecom, Retail, Broadband and Apparel has been appreciated by investors who have lapped up RIL shares in spite of its rising prices.
To understand whether RIL plays a significant role in maximizing our desired returns, we perform a Portfolio Optimization exercise using 5 large-cap stocks from different GICS sectors:
Reliance Industries (RIL)
HDFC Bank (HDFCBK)
Tata Consultancy Services (TCS)
Cipla Pharmaceuticals (CIPLA)
Hindustan Unilever (HUL)
Our aim is to maximize risk-adjusted-returns for the above portfolio, thus obtaining weights for each of the 5 stocks to maximize Sharpe Ratio (excess return over the risk free rate per additional unit of risk). We’ll also look at a defensive outlook (Minimum variance portfolio) and interpret how RIL plays a role in both of the portfolio expectations.
Before creating a portfolio, we look at the correlation of the above 5 stocks with Nifty 50 to track their respective relative movements w.r.t. the index.
As expected, RIL and HDFC Bank, being the two largest Nifty 50 constituents, exhibit a strong correlation with the index. However, CIPLA and HUL show defensive behavior by exhibiting low correlation, which indicates that these 2 stocks can be used to create a defensive hedge against unexpected downside of the first 2 stocks.
Optimal Portfolio Allocation
We use an iterative process to allot weights to each of the stocks and obtain portfolio returns & risk for that iteration. Finally, the program calculates the Sharpe Ratio and assigns it to the respective vector.
Sharpe Ratio = (Portfolio returns – Risk free return)/Portfolio risk
Here, we have taken 10 year G-sec yield of 5.9% p.a. as the Risk free rate of return. The assignment of Sharpe ratio values to the vector nodes continues till the entire port is filled and then we perform a maxima search (for Tangency portfolio) or risk minima search (for Minimum variance portfolio). Finally, the optimum weights are tabulated and the Tangency portfolio weights are plotted.
# Confine our observations to begin at the base year and end at the last available trading day horizon <- paste0(as.character(base_year), "/", as.character(Sys.Date())) stock <- stock[horizon]
# Calculate monthly arithmetic returns data <- periodReturn(stock, period = "monthly", type = "arithmetic")
# Assign to the global environment to be accessible assign(ticker, data, envir = .GlobalEnv) } # Call our function for each stock a <- monthly_returns("RELIANCE.NS", 2017) b <- monthly_returns("HDFCBANK.NS", 2017) c <- monthly_returns("TCS.NS", 2017) d <- monthly_returns("CIPLA.NS", 2017) e <- monthly_returns("HINDUNILVR.NS", 2017)
# Get Nifty 50 Data f <- monthly_returns("^NSEI", 2017)
# Merge all the data and create Corrplot returns <- merge.xts(a,b,c,d,e,f) colnames(returns) <- c("RIL", "HDFCBK", "TCS", "CIPLA", "HUL", "NIFTY50") corrplot::corrplot(cor(returns))
#Optimal allocation plots (Max SR) com <- c(RIL,HDFCBank,TCS,Cipla,HUL) opwt <- c(max_sr$RIL,max_sr$HDFCBK,max_sr$TCS,max_sr$CIPLA,max_sr$HUL) dtf <- data.frame(com,opwt) print(dtf) ggplot() + geom_point(data=dtf, aes(x=dtf$com,y=dtf$opwt),size=5,color="green")+labs(title = "Dynamic Portfolio Allocation", x = "Stock", y = "Weight")
Interpretation of Results
Table: Final portfolio weights for Optimum (max Sharpe Ratio) and Minimum variance portfolios
a) Optimum (Maximum Sharpe ratio) portfolio:
A maximum Sharpe ratio of 2.16 is obtained with maximum portfolio allocation (54.5%) to RIL. This result supports the investment approach followed by most Fund managers as well, i.e. RIL is one of the prime cuts if you’re looking to beat the market consistently. The high weightage to RIL is partially balanced by the allocation to HUL, which is a defensive FMCG stock and brings down the overall risk of the portfolio.
b) Minimum variance portfolio:
By drawing down on the portfolio allocation to RIL, we obtain a significantly lower (25% reduction) in overall risk. The entire allocation in Tangency portfolio now gets distributed between HDFC Bank, CIPLA and HUL. As observed from the earlier Correlation plot, CIPLA & HUL exhibit slightly negative correlation, thus partially negating each other’s downside risk and bringing down portfolio risk by 25%.
Portfolio managers looking to maximize risk-adjusted-returns (Aggressive Equity) are thus likely to favor RIL as a key driver of the portfolio returns. Looking at the historical performance and key prospects (like 5G onboarding, JioMart, OTT tie-ups, increasing ARPU), this over-weightage on RIL is likely to continue in the coming days as well. However, relatively conservative managers like Pension Funds may opt for an allocation in between the 2 extreme results presented in this analysis. Transferring weight from RIL to CIPLA or HUL is likely to bring down overall portfolio risk and generate a moderate return, keeping undue risk in check.