Difference Between Value Stock And Growth Stock: A Comparison Of Risk And Return

Overview of the article

Discuss about the Bayesian estimation of global minimum portfolio.

The article mainly states the difference between value stock and growth stock by stating the return, which is provided by both the stocks. The researcher pointed out the limitations of growth stock and the hindrance it possesses to investors for generate high retune from investment. The comparison between the return and risk of value and growth stock is conducted to determine the actual significance of value stocks. Eugene Fama and Kenneth French proposed that value stock due to the reduces prices and high asset valuation is the best buying option for investors, as share price value of growth stocks is always high due to the demand among investors (Koijen, Lustig and Van 2017).

Fama-French in the article mainly explained the return of stock return, which might allow investors to improve the return from investment. In addition, the Fama-French indicates that there are two factors namely market risk factors and value growth risk factor, which could explain the return, which is provided from investment. This detection of risk factors might help in generating high level of return, which could generate return from investment. Some researchers stated that with the evaluation of risk and return attribute of stock, investors can generate high level of return from investment by controlling risk attributes of their portfolio (Tsuji 2016).

Fama-French focuses on precise risk measure, where they added that market risk factors and value growth risk factor are the major risk, which needs to be evaluated before investing. In addition, the use of market risk factors might help investors in detecting the implications of capital market on return generation capacity of the stocks. This could eventually allow investors to avoid stock with high beta, which could hamper their actual return from investment. The value growth risk factors allow investor to detect stocks with high valuation, which could increase return from investment and raise the investment capital (Sornette 2017).

The researcher in the relevant academic paper indicates the implications of both CAPM model and Fame-French model, which could allow investors to detect risk and return attributes of the stocks. The first implication for the investors regarding CAPM is its simplicity and to view the risk involved in investment. Moreover, it is stated that there are more additional dimensions of risk, which could generate high level of returns from investment. On the other hand, the second implication is that value stock has higher return in comparison with growth stocks, which is detected from evaluating markets around the world. The implication of Fame-French model mainly states that stocks with high value can generate more return in comparisons to stock with growth attributes, as they are undervalued (Shen and Tzeng 2015).

Comparison of value stocks and growth stocks

The evaluation of academic paper “Choosing Factors” by Eugene F. Fama and Kenneth R. French, mainly indicates the issues that has been arising in five factor Fame-French model (Fama and French 2016). The researcher in the academic paper mainly depicts the issues of the investment model, which might hamper risk and return attribute of the investor. The three issues that are identified from the academic paper are depicted as follows.

• Cash profitability (CP) versus operating profitability (OP) as the variable used to construct profitability factors.
• Long – short spread factors versus excess returns on the long or short ends of the spread factors.
• Factors that use the small or big ends of value, profitability, and investment factors versus averages of small and big components.

The overall objective of the academic paper is to detect viability of the identified issues, which might reduce viability of the five-factor Fama and French model. This might hamper financial capability of the investors to generate adequate return from investment. The researcher mainly uses statistical calculations in deriving the viability of factors used in the Fama and French model for detecting stocks with high returns and low risk. The researcher has used max squared Sharpe ratio for model factors for deriving the best possible factor to support the five-factor Fama and French model (Fama and French 2016).

The researcher used Max Squared Sharpe ratio in to marginal contributions for deriving the relevant returns, which could be generated from stocks. In addition, the market used for the evaluation was NYSE, AMEX, and NASDAQ stock. Moreover, the paper explores issues and the choice of profitability factors affects each model max squared Sharpe ratio. Furthermore, analysis of the research indicates that by using max squared Sharpe ratio, the financial viability of the five-factor Fama and French model can be identified. The researcher also indicates that ranking based on max squared Sharpe ratio is accurate, which might improve financial capability of the investor. Therefore, the research indicates that common performance matrix could eventually allow investor to detect viability of the factors used in Fama-French for identifying value stocks with high returns (Fama and French 2016).

The researcher after completing the research indicates that the ultimate winner is the spread factor model of Fama-French, which allows investor to detect accurate stock for investment. In addition, the researcher indicates that operating profitability factor replaces cash profitability, as it might help increase efficiency of the Five factor model of Fama-French. Therefore, from all the three issues mentioned in the academic paper only cash profitability versus operating profitability is detected to be viable, which might be changed in the Five factor model of Fama-French for improving the overall investment model for investors. This detection is only possi9ble with the help of max squared Sharpe ratio used by the researcher in the article. This eventually help in understanding the debt of risk and return attributes of the Five factor model of Fama-French.

Particulars	Exp Return	Stand Dev	Var
Stock Fund	15.000%	32.000%	10.240%
Bond Fund	9.000%	23.000%	5.290%
Correlation	15.000%
Covariance	1.104%

Covariance matrix	Stock Fund	Bond Fund
Stock Fund	5.29%	1.10%
Bond Fund	1.10%	10.24%

Particulars	Value
Weight (Bond)	(10.24% – 1.10%) / ((10.24% + 5.29% – (2 * 1.10%)))
Weight (Bond)	68.58%
Weight (Stock)	1 – 68.58%
Weight (Stock)	31.42%
Standard deviation	SQRT(((31.42%^2) * 15%) + ((68.58%^2) * 9%) + (2 * 31.42% * 68.58% * 1.10%))
Standard deviation	19.94%
Mean	(31.42% * 15%) + (68.58% * 9%)
Mean	10.89%

The above graph mainly helps in detecting the overall minimum variance portfolio graph, which detects the returns and risk involved in investment.

Stock Fund	Bond Fund	Stand Dev	Exp Return	Sharpe ratio
0%	100%	0.2300	0.0900	0.1522
10%	90%	0.2141	0.0960	0.1915
20%	80%	0.2037	0.1020	0.2308
30%	70%	0.1994	0.1080	0.2658
31%	69%	0.1994	0.1089	0.2701
40%	60%	0.2018	0.1140	0.2924
50%	50%	0.2106	0.1200	0.3087
60%	40%	0.2250	0.1260	0.3155
65%	35%	0.2334	0.1288	0.3162
70%	30%	0.2441	0.1320	0.3155
80%	20%	0.2668	0.1380	0.3111
90%	10%	0.2923	0.1440	0.3044
100%	0%	0.3200	0.1500	0.2969

Optimal risky portfolio	Value
Stock -Risk free rate	15% – 5.5%
Stock -Risk free rate	9.500%
Bond -Risk free rate	9% – 5.5%
Bond -Risk free rate	3.500%

Optimal risky portfolio	Value
Weight (Bond)	1- 64.66%
Weight (Bond)	35.34%
Weight (Stock)	((9.5% * 5.29%) – (3.5% * 1.10%)) / ((9.5% * 5.29%) + (3.5% * 10.24%) – ((9.5% + 3.5%) * 1.10%))
Weight (Stock)	64.66%
Standard deviation	SQRT(((64.66%^2) * 15%) + ((35.34%^2) * 9%) + (2 * 64.66% * 35.34% * 1.10%))
Standard deviation	23.34%
Mean	(64.66% * 15%) + (35.34% * 9%)
Mean	12.88%

The graph represents the overall optimal risky portfolio, which could provide high returns with controlled risk.

Particulars	Value
Standard deviation	23.34%
Mean	12.88%
ERc	(12.88% – 5.50%) / 23.34%
ERc	31.62%

Particulars	Value
Target Return	12.00%
ERc	31.62%
T-bill yield	5.50%
Stand-Dev of the portfolio	(12% – 5.5%) / 31.62%
Stand-Dev of the portfolio	20.56%

The detection of standard deviation of the portfolio mainly helps in understanding the risk and return involved in investment (Yang, Couillet and McKay 2015). In addition, the standard deviation of the portfolio is calculated to be at the level of 20.56%.

Particulars	Value
Mean	12.88%
T-bill yield	5.50%
Target Return	12.00%
Proportion with T-bill fund	1- 88.08%
Proportion with T-bill fund	11.92%
Proportion with risky fund	(12% – 5.5%) / (12.88%-5.5%)
Proportion with risky fund	88.08%

The above calculation helps in detecting the overall portfolio of T-bill fund, which is present in the portfolio. In addition, the calculation states that 11.92% of the portfolio comprises of T-bill, while the other 88.08% is fund by risky funds. This might help in detecting the composition of risk-free asset currently present within the portfolio (Bodnar, Mazur and Okhrin 2017).

Reference and Bibliography:

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Björk, T., Murgoci, A. and Zhou, X.Y., 2014. Mean–variance portfolio optimization with state?dependent risk aversion. Mathematical Finance, 24(1), pp.1-24.

Bodnar, T. and Gupta, A.K., 2015. Robustness of the inference procedures for the global minimum variance portfolio weights in a skew-normal model. The European Journal of Finance, 21(13-14), pp.1176-1194.

Bodnar, T., Mazur, S. and Okhrin, Y., 2017. Bayesian estimation of the global minimum variance portfolio. European Journal of Operational Research, 256(1), pp.292-307.

Bodnar, T., Parolya, N. and Schmid, W., 2018. Estimation of the global minimum variance portfolio in high dimensions. European Journal of Operational Research, 266(1), pp.371-390.

Fama, E. F., and French, K. R. 2016. Choosing factors.

Koijen, R.S., Lustig, H. and Van Nieuwerburgh, S., 2017. The cross-section and time series of stock and bond returns. Journal of Monetary Economics, 88, pp.50-69.

Shen, K.Y. and Tzeng, G.H., 2015. Combined soft computing model for value stock selection based on fundamental analysis. Applied Soft Computing, 37, pp.142-155.

Sornette, D., 2017. Why stock markets crash: critical events in complex financial systems. Princeton University Press.

Tsuji, C., 2016. Relations of Japanese Investment Styles and US Investment Styles after the Lehman Bankruptcy: Evidence from Japanese and US Stock Markets. World Journal of Social Science, 3(2), p.42.

Wu, Y., 2016. China’s capital stock series by region and sector. Frontiers of Economics in China, 11(1), p.156.

Yang, L., Couillet, R. and McKay, M.R., 2015. A robust statistics approach to minimum variance portfolio optimization. IEEE Transactions on Signal Processing, 63(24), pp.6684-6697.