INTRODUCTION:

Portfolio construction is a critical aspect of investment management, aiming to balance risk and return in order to achieve optimal financial outcomes. While diversification across a range of assets is a fundamental principle, sector-specific investments provide unique opportunities and challenges. Different sectors exhibit varying levels of volatility, growth potential, and risk, influenced by both microeconomic factors and broader macroeconomic trends. Understanding how these sectors perform in different market conditions is essential for investors looking to optimize their portfolios.

This research focuses on constructing an optimal portfolio by selecting specific sectors, analyzing their performance, and employing advanced financial models. The study leverages techniques such as mean-variance optimization and the Capital Asset Pricing Model (CAPM) to assess risk-adjusted returns, while also considering the correlations between sectors to improve diversification. Additionally, the research evaluates how factors like interest rates, inflation, and industry-specific developments influence sector performance, offering a comprehensive approach to portfolio optimization.

By focusing on sector-specific strategies, this paper seeks to provide investors with actionable insights into how they can tailor their portfolios to capitalize on growth opportunities while mitigating risk. This approach not only improves the understanding of portfolio dynamics but also enhances decision-making for investors in an increasingly complex and volatile market environment.

This study focuses on building a well-diversified portfolio by selecting stocks from various sectors to optimize returns while managing risk. The process begins with analyzing different sectors based on growth potential and stability. Companies within these sectors are evaluated on their financial health, competitive advantage, and market position. The portfolio aims to balance high-growth companies with stable, dividend-paying stocks, ensuring a mix that reduces risk through diversification. Regular monitoring and rebalancing are essential to adapt to market changes and maintain alignment with the investor's financial goals.

REVIEW OF LITERATURE:

Kumar, R., and Devi, A. (2023) This study used machine learning techniques for sector-based stock selection and portfolio optimization, employing random forests for stock selection and a genetic algorithm for optimizing portfolio weights. Their approach, tested on Indian stock market data, outperformed traditional mean-variance optimization in terms of risk-adjusted returns, and improved diversification by reducing sector-specific risks.

Gupta, S., and Sharma, R. (2023) Gupta and Sharma explored the integration of ESG factors into sector-based portfolio construction in emerging markets, using financial and ESG metrics. Employing a mean-CVaR framework, their analysis of BRICS countries showed that ESG integration enhanced risk-adjusted returns and provided better downside risk protection, especially in environmentally sensitive sectors.

Fernandez, A., and Lopez, C. (2023) The authors investigated time-varying sector correlations and their impact on portfolio diversification using a dynamic conditional correlation model. They demonstrated that adapting portfolio allocations based on changing sector correlations improves risk-adjusted performance, outperforming static allocation strategies in global equity markets.

Moreno, M., and Rodríguez, A. (2023) Moreno and Rodríguez examined the role of sector-specific risk factors in portfolio optimization through a multi-factor model. Using European stock market data, they integrated sector- specific risks into a portfolio framework, finding that this approach enhanced diversification and risk-adjusted returns, particularly during sector-specific market downturns.

Wang, L., and Zhang, H. (2023) Wang and Zhang proposed a deep learning framework for joint sector selection and portfolio optimization. Their end-to-end neural network, tested on the Chinese A-share market, outperformed traditional methods by capturing complex, non-linear relationships between firm-specific and sector-level features, leading to improved risk-adjusted returns.

Singh, A., and Patel, R. (2023) This study applied quantum-inspired evolutionary algorithms (QIEAs) to sector-based portfolio optimization. Singh and Patel's hybrid QIEA model outperformed traditional evolutionary algorithms and mean-variance optimization, particularly in large-scale, multi-sector portfolios, by maximizing returns and minimizing risk in the Indian stock market.

Cohen, L., and Patel, N. (2023) Cohen and Patel used text-based sentiment analysis from financial news and social media to develop a sector-based portfolio optimization model. Tested on the U.S. stock market, their model improved portfolio performance by capturing short-term sector rotations and reducing downside risk during volatile periods.

Lee, J., and Kim, S. (2023) Lee and Kim introduced a machine learning approach to sector-based factor investing and style rotation, using gradient boosting machines. Their dynamic allocation across sectors and factor strategies, tested in the Korean stock market, outperformed traditional sector rotation strategies, with higher information ratios and better downside risk protection.

RESEARCH METHODOLOGY:

This study is both analytical and explorative, relying mainly on data gathered from secondary sources.

OBJECTIVES OF THE STUDY:

The main objectives of the study are to:

· To analyze the effects of market capitalization distribution on portfolio performance and stability.

· To examine the role of technical analysis in identifying promising stocks within each sector.

· To evaluate various stock selection methodologies across different market sectors and their effectiveness in portfolio construction.

SOURCES OF DATA:

This study is pragmatic in nature. The study depends on secondary sources, with data collected from multiple websites, brochures and from the journals like yahoo finance, finology, money control, money today, DALAL street etc. and the tools used are expected returns, coefficient of variation, covariance matrix, variance, standard deviation, sharp ratio etc. for this study.

DATA ANALYSIS – 1.AGROCHEMICAL SECTOR

SECURITY	STOCK RETURN (%)
Dhanuka Agritech Ltd.	46.6
Rallis India Ltd.	25.8
Insecticides India Ltd.	22.8

SECURITY	ANNUAL VARIANCE (%)
Dhanuka Agritech Ltd.	21.1
Rallis India Ltd.	21.8
Insecticides India Ltd.	13.9

SECURITY	ANNUAL STANDARD DEVIATION (%)
Dhanuka Agritech Ltd.	45.9
Rallis India Ltd.	46.7
Insecticides India Ltd.	37.2

Dhanuka Agritech Ltd led with an average return of 46.6%, the lowest risk (COV 0.99), and is allocated the highest weight (50%) in the portfolio. Insecticides India Ltd and Rallis India Ltd contributed to stability and diversification with weights of 29% and 21%, respectively. The portfolio aims for a 36% return, with a standard deviation of 10.6% and a Sharpe Ratio of 2.66, indicating efficient performance.

DATA ANALYSIS – 2. POWER SECTOR

SECURITY	STOCK RETURN (%)
Tata Power Ltd	49.2
REC Ltd	53.9
Borosil Renewables Ltd	102.2

SECURITY	ANNUAL VARIANCE (%)
Tata Power Ltd	16.6
REC Ltd	13.8
Borosil Renewables Ltd	220.3

SECURITY	ANNUAL STANDARD DEVIATION (%)
Tata Power Ltd	43
REC Ltd	48
Borosil Renewables Ltd	49

SECURITY	COEFFICIENT OF ARIATION	RANK
Tata Power Ltd	0.69	1
REC Ltd	0.83	2
Borosil Renewables Ltd	1.45	3

SECURITY	PORTFOLIO WEIGHTAGE (%)
Tata Power Ltd	30.0
REC Ltd	40.0
Borosil Renewables Ltd	30.0

EXPECTD RETURN(RP)	67%
STD DEV(SDP)	17%
SHARP RATIO	3.59

From 2019 to 2024, Borosil Renewables Ltd led with a 102.2% average return but also had the highest volatility. REC Ltd and Tata Power Ltd offered more stable returns with lower risk. The optimal portfolio allocates 40% to REC Ltd, 30% each to Tata Power Ltd and Borosil Renewables Ltd, yielding an expected return of 67% with moderate risk and a high Sharpe Ratio of 3.59, indicating strong risk-adjusted performance.

DATA ANALYSIS–3. AUTO AND ANCILLARY STOCKS

SECURITY	STOCK RETURN (%)
FIEM Industries	40
SANDHAR Technologies	25
LUMAX Auto Technologies Ltd	35

SECURITY	ANNUAL VARIANCE (%)
FIEM Industries	19
SANDHAR Technologies	23
LUMAX Auto Technologies Ltd	24

SECURITY	ANNUAL STANDARD DEVIATION (%)
FIEM Industries	43
SANDHAR Technologies	48
LUMAX Auto Technologies Ltd	49

SECURITY	COEFFICIENT OF VARIATION	RANK
FIEM Industries	0.79	1
SANDHAR Technologies	1.43	2
LUMAX Auto Technologies Ltd	0.92	3

SECURITY	PORTFOLIO WEIGHTAGE (%)
FIEM Industries	40
SANDHAR Technologies	25
LUMAX Auto Technologies Ltd	35

EXPECTD RETURN(RP)	49%
STD DEV(SDP)	11%
SHARP RATIO	3.73

FIEM Industries leads with a 40% average return, followed by LUMAX Auto Technologies Ltd at 35%, both showing strong growth. FIEM Industries has the lowest volatility (43%) and risk (COV 0.79), making it the most stable performer. The optimal portfolio allocates 40% to FIEM Industries, 35% to LUMAX Auto Technologies Ltd, and 25% to SANDHAR Technologies, achieving a high expected return of 49% with a low risk and an impressive Sharpe Ratio of 3.73.

Table showing portfolio construction with selected securities based on risk and return.

STOCKS	AMOUNT	WEIGHTAGE	WEIGHTAGE IN RS	CMP	SHARES
AGROCHEMICAL SECTOR	100000
DHANUKA		50%	50000	1852	27
RALLIS		21%	21000	344.5	61
INSECT		29%	29000	914	32

POWER SECTOR	100000	30%	30000	418.2	72
TATA POWER		40%	40000	587.5	68
REC LTD		30%	30000	514.85	58
BOROSEL

AUTO and ANSCILLERY SECTOR	100000
FIEM		40 %	40000	1302	31
SANDHAR TECH		25 %	25000	656.35	38
LUMAX		35 %	35000	567.3	62

The table shows the construction of a portfolio across three sectors: Agrochemical, Power, and Auto and Ancillary, with each sector allocated ₹100,000. Within each sector, the capital is distributed among selected stocks based on a specified percentage. For example, in the Agrochemical sector, Dhanuka receives 50% of the allocation, resulting in 27 shares purchased at the current market price (CMP) of ₹1,852. The Power sector is allocated 30% of the total, with Tata Power and REC Ltd as key holdings. Similarly, in the Auto and Ancillary sector, the investment is split among Fiem, Sandbar Tech, and Lumax, based on their weightage and CMP.

OPTIMUM PORTFOLIO BY SELECTED HIGH RETURN STOCKS OF EACH SECTOR:

Sector wise Stock	Weightage	Amount
DHANUKA	38.46%	38461.54
REC LTD	30.77%	30769.23
FIEM	30.77%	30769.23
Total	100%	100000.00

RESULT AND DISCUSSION:

The study analyzed the performance of selected stocks from the agrochemical, power, and auto and ancillary sectors over the 2019-2024 period to construct optimal portfolios based on risk-adjusted returns. In the agrochemical sector, Dhanuka Agritech Ltd was the top performer with a 50% portfolio allocation due to its strong returns and low coefficient of variation (COV). In the power sector, REC Ltd and Tata Power Ltd showed efficiency, with REC Ltd receiving the highest allocation (40%), driven by renewable energy trends. The auto and ancillary sector saw FIEM Industries as the most attractive stock with a 40% allocation due to its superior risk-return balance, reflecting the sector’s growth potential in the electric vehicle revolution. Overall, each sector's portfolio achieved high Sharpe ratios, indicating efficient risk management and promising growth prospects.

SUMMARY OF FINDINGS-

· Investor should do the company analysis before investing.

· Investor should clearly see the risk appetite and investment objective before investing.

· Always prefer to invest for the long term to yield a good return.

· Do not invest a whole lot of money in one company or in one sector try to diversify the risk.

· Focus on Stocks with Lower COV: Investors should consider allocating a higher proportion of their portfolio to stocks with lower coefficients of variation, as they provide better risk-adjusted returns. For instance, FIEM Industries and REC Ltd in the auto and ancillary and power sectors, respectively, have demonstrated strong performance relative to their risk.

· Monitoring High-Volatility Stocks: Stocks like Borosil Renewables Ltd, despite their high returns, also present significant risk due to their high volatility. Investors should monitor these stocks closely and consider adjusting their portfolio allocation if volatility increases.

SUGGESTIONS:

· Conduct Company Analysis: Investors should thoroughly analyze a company’s financials and performance before investing.

· Assess Risk Appetite: It's essential to align investments with personal risk tolerance and clear investment objectives.

· Long-Term Investing: Prioritize long-term investments for better returns and to minimize the impact of short-term market fluctuations.

· Diversify the Portfolio: Avoid putting all funds into one company or sector; diversification helps in managing risk.

· Focus on Lower COV Stocks: Allocate more to stocks with lower coefficients of variation, such as FIEM Industries and REC Ltd, for better risk-adjusted returns.

· Monitor High-Volatility Stocks: Keep an eye on stocks with high volatility, like Borosil Renewables Ltd, and adjust portfolio allocations if risks rise

CONCLUSION:

The analysis highlights the importance of balancing return and risk when building a portfolio, focusing on the agrochemical, power, and auto and ancillary sectors. These sectors offer diverse opportunities, with portfolios showing strong performance through high Sharpe ratios, indicating optimal risk-return trade-offs. The study emphasizes the need for diversification and regular portfolio reviews to manage risk and enhance returns. Looking ahead, these sectors are well-positioned for growth, driven by trends in sustainability, technology, and market expansion, with companies like Dhanuka Agritech, REC Ltd, and FIEM Industries likely to lead the way.

REFERENCES:

1. Kumar, R., and Devi, A. Sector-based stock selection and portfolio optimization: A machine learning approach. Journal of Asset Management. 2023; 24(3): 201-218.

2. Cohen, L., and Patel, N. Text-based sector sentiment analysis for portfolio optimization. Journal of Financial Data Science. 2023; 5(2): 78-96.

3. Gupta, S., and Sharma, R. ESG integration in sector-based portfolio construction: Evidence from emerging markets. Sustainable Finance and Investment. 2023; 13(2): 156-179.

4. Moreno, M., and Rodríguez, A. Sector-specific risk factors and their impact on optimal portfolio construction. Journal of Financial Economics. 2023; 148(2): 405-429.

5. Fernandez, A., and Lopez, C. Time-varying sector correlations and their impact on portfolio diversification. International Review of Financial Analysis. 2023; 86: 102412.

6. Singh, A., and Patel, R. Quantum-inspired evolutionary algorithms for sector-based portfolio optimization. Quantitative Finance. 2023; 23(5): 789-806.

7. Lee, J., and Kim, S. Sector-based factor investing: A machine learning approach to style rotation. Journal of Portfolio Management. 2023; 49(5): 135-152.

8. Wang, L., and Zhang, H. Deep learning for joint sector selection and portfolio optimization. IEEE Transactions on Neural Networks and Learning Systems. 2023; 34(6): 2756-2770.

9. Chen, Y., and Liu, W. Entropy-based diversification for sector-allocated portfolios. Journal of Financial Markets. 2023; 59: 100734.

Received on 01.10.2024 Revised on 05.12.2024

Accepted on 16.01.2025 Published on 28.05.2025

Available online from May 31, 2025

Asian Journal of Management. 2025;16(2):101-105.

DOI: 10.52711/2321-5763.2025.00016

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.