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 favorable risk-return trade-off (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 which the investor gets for bearing this uncertainty. Securities or stocks having low risk tend to yield fewer returns, like government securities or bonds. However, long-term high-risk stocks tend to provide more returns (Gupta, 2012).

Risk-return relationship
Figure 1: Risk-return relationship (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 behavior 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 out the coefficients used for measuring risk i.e.

Risk coefficientsDescriptionFormula
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 deviationShows 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 CoefficientExplains 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
Table 1: Risk Coefficients (Gorbunova, 2016)

Trend based analysis of stocks

Gautami & Kalyan, (2018) compared the risk and return of selected stocks of 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.

Trend based analysis of stocks

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

Momentum based analysis of stocks

This comparison enables assessing the performance and volatility of stocks. Larsen, (2010) further emphasised on 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 behavior 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 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

CAPM based analysis of stocks

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 it into under-priced, over-priced, and neutral category. With the opportunity of 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 period. 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 but mostly the stocks for shorter period having high risk tended to yield higher return.

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

An optimal decision of investment is dependent on the analysis of the 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
Riya Jain
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