Risk return analysis of income, value and growth stocks

For a stakeholder, risk and return are essential considerations in the investment decision-making process. Investors try to minimize systematic (market) and unsystematic risks and optimally choose stocks yielding favourable risk-return trade-offs (Aliu, Pavelkova, & Dehning, 2017). Risk in this context is defined as the variability that could occur in the cash flow of the investment. While the return is the reward that the investor gets for bearing this uncertainty. Securities or stocks having low risk tend to yield fewer returns, than government securities or bonds. However, long-term high-risk stocks tend to provide more returns (Gupta, 2012).

The above figure shows the existence of a direct relationship between risk and return. Based on the attitude of the investor towards risk, the stock would be selected for investment.

Methods to examine the rate of risk-return for a stock

Krishnsprabha & Vijayakumar, (2015) based on their analysis of the Bombay Stock Exchange that included stocks of:

• banking,
• IT,
• automobile,
• consumer goods and,
• pharmaceutical sectors;

stated that the decision-making process of an investor is dependent on risk and return. Thus, in order to understand the behaviour of an investor, it is essential to conduct a risk-return analysis. Krishnsprabha & Vijayakumar stated that return for a period can be computed using the average of return at each sub-period. However, in order to measure risk, either the value of beta (β) can be computed or variance or standard deviation of return can be used. Gorbunova, (2016) supported the method stated by Krishnsprabha & Vijayakumar and listed the coefficients used for measuring risk i.e.

Risk coefficients	Description	Formula
Beta (β)	Depict the change in return from certain stock with respect to change in market price.	Wherein, n: number of observations; X: profitability index; and Y: return on stocks
Alpha (α)	Represents the average change in the prices of a company’s shares.	Wherein, β: beta coefficient; n: number of observations; X: profitability index; and Y: return on stocks
Standard deviation	Shows the magnitude of market risk	Wherein, α: alpha coefficient; β: beta coefficient; n: number of observations; X: profitability index; and Y: return on stocks
Correlation Coefficient	Explains the relationship between the market index return and return from a certain share	Wherein, n: number of observations; X: profitability index; and Y: return on stocks

Trend-based analysis of stocks

Gautami & Kalyan, (2018) compared the risk and return of selected stocks on the Indian stock exchange. Their analysis stated that in order to compare the performance of stocks it is essential to observe the movement in the prices of stocks. For this, trend-based analysis needs to be done for the movement in the prices of the shares. The formula used for tracking this movement is as follows.

This method of movement analysis was also supported by Gupta, (2012) who stated that the trend-based analysis of these movements in stock price helps in keeping a record of the earnings of an investor considering the risk born by them. Shobha & Navaneeth, (2017) further stated that the trend analysis of the returns would help in determining the rate of the volatility of the returns.

Aliu et al., (2017) supported this trend-based risk-return trade-off analysis. They stated that by comparing graphically the weighted return and standard deviation of the returns, the stock’s risk-return trade-off could be compared. The presence of a weighted return graph above the risk (standard deviation) shows that stock provides compensation for the risk borne by the investors.

Momentum-based analysis of stocks

Each investor focuses on first analysing the movement of the stocks in order to determine the possibility of profitability. An investor focuses on minimising risk and for having information about the movement of stock prices in past, it is essential to track its movements (Agrawal, Jindal, & Pilla, 2010). The study of Agarwal, Jindal and Pilla examined the movement of stocks with 15 days and 50 days moving averages using the below-shown formula

This comparison enables assessing the performance and volatility of stocks. Larsen, (2010) further emphasised the relevance of momentum analysis. He stated that by using the moving average at different time periods, one can predict the future movement of stocks.

Vaidya, (2018) worked on the stochastic oscillation method for identifying the nature of stocks. Performing a momentum analysis with a 3-month average, Vaidya stated that with the formation period of 3 months, the short-term comparison between the performance of the stock could be done. Hence, momentum-based analysis of stocks tends to provide information about the historical position of the stocks and predict the future movement of stock prices.

CAPM-based analysis of stocks

Parmar, (2016) focused on the Capital Asset Pricing Model (CAPM) based method of examining the variability of the returns. Studying the behaviour of stocks, Parmar compared the market return with the risk-free return. This comparison of the risk-return helps in determining whether the investment in a given stock is optimal or not. Rui, Rasiah, Yen, Ramasamy, & Pillay, (2018) further tested the link between the risk and expected return using CAPM. Analysis of the risk and return relationship using CAPM tests examines the stated direct linkage between risk and return. The expected return could be computed using the below formula

Furthermore, by comparing the actual return of the stocks with the expected return, the CAPM analysis helps in predicting the nature of stocks by classifying them into the under-priced, over-priced, and neutral categories. With the opportunity for growth in the under-priced stocks, investors prefer to invest in these stocks (Sukono et al., 2018). Kumar, (2020) supported this by comparing the return at different holding periods. CAPM analysis of stocks herein states that as the holding period of the stocks increases, the risk associated with it also decreases. Though the return rate varies with the change in holding period mostly the stocks for a shorter period having high risk tended to yield a higher return.

A combination of trend, momentum and CAPM-based analysis is the optimal method of examining the risk-return trade-off

An optimal decision of investment is dependent on the analysis of past performance, current movements in prices, and the future prediction of the stock position. Together trend, momentum, and CAPM analysis help study the behaviour of stocks along with predicting the movement of stocks in the future.

References

• Agrawal, S., Jindal, M., & Pilla, G. N. (2010). Momentum analysis based stock market prediction using Adaptive Neuro-Fuzzy Inference System (ANFIS). Proceedings of the International MultiConference of Engineers and Computer Scientists 2010, IMECS 2010, (March 2010), 526–531.
• Aliu, F., Pavelkova, D., & Dehning, B. (2017). Portfolio risk-return analysis: The case of the automotive industry in the Czech Republic. Journal of International Studies, 10(4), 72–83. https://doi.org/10.14254/2071-8330.2017/10-4/5
• Gautami, S., & Kalyan, N. B. (2018). A Comparative Study on Risk & Return Analysis of Selected Stocks in India. International Journal Of Management And Economics Invention, 04(05). https://doi.org/10.31142/ijmei/v4i5.03
• Gorbunova, N. A. (2016). Methods of analysis of equity securities risk and return: Issues and prospects. European Research Studies Journal, 19(3), 228–249. https://doi.org/10.35808/ersj/558
• Gupta, N. (2012). A study on the risk-return analysis of HDFC and ICIC securities.
• Krishnsprabha, S., & Vijayakumar, M. (2015). A Study on Risk and Return Analysis of Selected Stocks in India. Iternational Journal of Scientific Research and Management, 3(4), 2550–2554.
• Kumar, A. (2020). Analysis of Risk Return Relationship in Indian Stock Market. 1–60.
• Larsen, J. I. (2010). Predicting Stock Prices Using Technical Analysis and Machine Learning. https://doi.org/10.1080/00420986820080431
• Parmar, C. T. (2016). A Comparative Analysis of Selective Companies : Using Capital Asset Pricing.
• Rui, K. X., Rasiah, D., Yen, Y. Y., Ramasamy, S., & Pillay, S. D. (2018). An analysis of the relationship between risk and expected return in Malaysia stock market: Test of the CAPM. International Journal of Engineering and Technology(UAE), 7(3), 161–170. https://doi.org/10.14419/ijet.v7i3.21.17154
• Shobha, C. V., & Navaneeth, K. (2017). Risk and return analysis of selected banking stocks in India. International Journal of Current Advanced Research, 6(12), 8629–8632. https://doi.org/10.24327/ijcar.2017.8632.1396
• Sukono, Susanti, D., Najmia, M., Lesmana, E., Napitupulu, H., Supian, S., & Putra, A. S. (2018). Analysis of stock investment selection based on CAPM using covariance and genetic algorithm approach. IOP Conference Series: Materials Science and Engineering, 332(1). https://doi.org/10.1088/1757-899X/332/1/012046
• Vaidya, R. (2018). Stochastic and Momentum Analysis of Nepalese Stock Market. Journal of Nepalese Business Studies, 11(1), 14–22. https://doi.org/10.3126/jnbs.v11i1.24196