Strategies for Creating Value: Portfolio Optimization in the Indian Automotive Sector

Abstract

This study investigates strategies for creating value through portfolio optimization in the dynamic Indian automotive industry. This research focuses on the creation of portfolios that effectively balance risk and return in an industry subject to constant change and global economic factors. Using modern portfolio theory, the study collect and analyze financial data from a subset of automotive manufacturers, focusing on past stock performance, volatility, and correlations[1]. The study construct efficient portfolios suited to various risk-return preferences, ranging from conservative to aggressive, through rigorous analysis. This research reveals the complex differences of the automobile industry, where company performance is influenced by macroeconomic variables and market trends[2]. The constructed portfolios illustrate the benefits of diversification and the returns-and-risk balancing act. It discuss the implications for investors, with an emphasis on the importance of strategic asset allocation for managing sector-specific risks. This research equips investors and financial experts with crucial tools and insights for optimizing their portfolios in the automotive industry. It contributes to a broader comprehension of portfolio management in sector-specific contexts and provides practical advice for navigating an industry that is constantly evolving.

Introduction

I. INTRODUCTION

Risk is defined as the level of uncertainty or potential loss that is inherent in making an investment choice[1], [3]. Typically, when the level of investment risks increases, investors tend to pursue elevated returns in order to adequately offset the inherent risks associated with their investments. Each savings and investment option carries varying levels of risk and potential returns. Distinguishing factors encompass the ease with which investors can access their funds, the rate at which their investments will appreciate, and the level of security associated with their investments[4], [5]. Nevertheless, the process of diversifying a portfolio, specifically within the realm of the stock market, presents a viable strategy for mitigating risk. Prior to engaging in the stock market, it is imperative for investors to familiarize themselves with fundamental knowledge pertaining to risk, the diverse array of dangers they may encounter, and other relevant particulars surrounding their prospective investments[6]. Within this particular setting, a multitude of investment ideas have been presented to investors, among which the Sharpe ratio and Treynor ratio stand out as notable examples of efficient theories. The Sharpe ratio is a quantitative metric utilized to evaluate the performance of an investment instrument or portfolio relative to a risk-free asset, subsequent to the incorporation of risk adjustments[7]–[10]. The Sharpe ratio is a financial measure that is formulated as: Sharpe Ratio Formula= (R(p)-R(f))/SD.

The variable R(p) represents the historical return of the fund for which the Sharpe Ratio is being computed. Returns can be observed across various time periods; nevertheless, it is often more advantageous to consider long-term periods[11]. The variable R(f) represents the rate of return on an investment that is considered to be risk-free. The Treynor ratio examines the potential excess return (return) relative to the anticipated future uncertainty (risk)[12]. Hence, comprehending the beta coefficient of a stock holds significant importance for investors, as it offers vital insights into the stock's risk profile and its prospective performance relative to the overall market. Through the process of calculating beta, investors are able to make educated decisions regarding the construction of a well-diversified portfolio that is in line with their individual risk tolerance and investment objectives[1]. 

Numerous investors endeavor to maximize their profits within the prevailing financial climate by the pursuit of more speculative investments, driven by the commonly embraced belief that there exists a positive correlation between risk and return[13], [14]. In order to achieve successful investments, it is imperative for investors to possess not only the financial resources, but also a comprehensive understanding of stock market analysis and various theoretical frameworks pertaining to the risks and rewards associated with equities. To address this demand, a comprehensive study was undertaken to provide a comprehensive understanding of estimating (beta) values for a wide range of equities across several industries[15].

The primary objective of this study is to assist inexperienced investors with a high-risk profile in constructing well-diversified portfolios and optimizing their investment returns, while considering the presence of uncontrollable risk factors inherent in the market[16]. The study aims to identify and analyze the objectives that guide the research to assess risk and return by estimating the beta coefficient of different stocks. In order to select stocks from the automobile sector and conduct an analysis of their β values, one must undertake a systematic approach[17]. The objective is to construct a well-diversified investment portfolio by selecting stocks with lower beta values.

II. LITERATURE REVIEW

The objective of this work is to create portfolios by employing multiple risk models, evaluate the performance of these portfolios, and draw conclusions regarding the suitability of these models in various economic scenarios. To fulfill this requirement, the study has conducted a detailed reviews covering the following inputs to come for a valuable conclusion. The risk models employed in constructing the portfolios encompassed Value at Risk (VaR), Conditional Value at Risk (CVaR), and Expected Shortfall (ES)[18]. Subsequently, the portfolios were assessed in terms of their performance. The findings of the study demonstrated that the CVaR model exhibited superior performance compared to other models in generating risk-adjusted returns[1]. The researchers employed a composite model, integrating the autoregressive integrated moving average (ARIMA) model and the support vector regression (SVR) model, to generate precise forecasts of future returns[3]. The proposed model outperformed the baseline model in terms of both return predictions and transaction costs.

The research employed a Monte Carlo simulation to assess the risk level associated with a portfolio of equities selected from the ISE-100 index of the Istanbul Stock Exchange. The study employed the use of Value at Risk (VaR) as a measure to assess the level of risk associated with the portfolio[6], [7]. The study proposed the implementation of risk management strategies such as diversification and hedging as a means to mitigate risk, as the results indicated a significant level of risk exposure within the investment portfolio. The utilization of the Conditional Value at Risk (CVaR) metric serves as a risk indicator inside a valuable methodology for optimizing portfolios. The research employed a genetic algorithm to ascertain the optimal portfolio[4], [5]. The findings of the study indicate that, when considering risk-adjusted returns, the proposed model exhibited superior performance compared to other models[19].

The study present an optimal framework for portfolio management that leverages both deep learning and machine learning techniques to predict future returns. The study utilized a genetic algorithm to find the optimal portfolio allocation, while employing a deep neural network (DNN) to predict return rates[11]. The findings of the study indicate that, when considering risk-adjusted returns, the proposed model exhibited superior performance compared to other models. This study aims to ascertain the most effective approach for constructing an investment portfolio in the Indian stock market by employing the Sharpe single index technique[20]. Another study utilized evolutionary algorithms to construct an optimal investment portfolio by leveraging data obtained from stocks traded on the Nigerian exchange market. The strategy included significant elements such as risk, liquidity ratio, returns, diversification, and asset allocation[4].

Several studies have been undertaken with the aim of constructing an optimal investment portfolio of Indian stock companies that exhibit correlation with several economic sectors. One study in the field of manufacturing was conducted[2]. The present study effectively employs the Mean-Variance, Downside-Variance, and Semi-Variance Portfolio Optimization techniques to analyze data obtained from the Turkish Day-Ahead Electricity Market over a span of two consecutive years[6], [11]. The utilization of diverse optimization strategies, commonly found in financial literature but comparatively restricted in the context of power market optimization, serves as a strategic approach for risk management through diversification. It is feasible to construct efficient frontiers, utility functions, and corresponding optimal portfolio solutions for each optimization technique[4].

Investment involves the allocation of current financial resources with the expectation of generating future financial returns. Investors engage in the purchase of stocks with the expectation of obtaining future profits, which may arise through the appreciation of stock prices or the receipt of dividend payments[1]. These individuals are commonly identified as investors due to their involvement in various investment activities. Realized returns, which are obtained from the returns on equities, involve the allocation of current financial resources in the pursuit of future financial gains[7]. Investors engage in the purchase of stocks with the expectation of obtaining future profits through the appreciation of stock prices or the receipt of dividend payments. These individuals are commonly identified as investors due to their involvement in investment activities[3]. Realized returns, derived from historical data and employed as performance metrics for enterprises, pertain to the returns generated by stocks. The accurate returns are crucial in predicting expected earnings for potential investors[11]. It is imperative to acknowledge that every investment entails a certain degree of risk or uncertainty.

When making investments, there is a possibility that the actual returns may not align with the initial expectations[21]. Hence, the current research is developed for selecting the best performing shares in automotive sector and related Companies in order to have better returns with the lowest betas. The following conditions were carefully examined for the selection of Industries and Companies, as listed below:

• Sector specific fundamental analysis for the selection of stocks.
• Historical returns, Standard Deviation, Beta of the concerned companies (Automobile) ten companies taken from sector
• There are 9 years companies return and Index return (SENSEX)

Further to the above, the following are the methodology have been implemented in the study.

III. INDUSTRY AND COMPANY ANALYSIS

This study presents a comprehensive risk-return analysis of automotive sector in the market, exploring the relationship between risk and return within each sector. By examining automotive sector. The study aim provide insights into the potential benefits and challenges associated with diversifying automotive industries. The study delves into historical data, statistical metrics, and portfolio simulations to help investors make informed decisions for optimizing risk-adjusted returns in their investment strategies[22]. This research investigates the most favorable equity in the Indian capital markets as of December 2022.

It considers various objectives and constraints while analyzing the return and risk data from the past Ten years. The study aims to validate the risk-return correlation for these optimal stocks and sheds light on the nature of this relationship, providing reasoned justifications for its findings. In our pursuit to construct a good stock returns, the study meticulously selected 10 stocks Automobile, This portfolio aimed to strike an equilibrium between risk and return, catering to investors who sought both stability and growth.

Table 1: Performance of the category 1 stocks

Source: Calculated

Table 2: Performance of the category 2 stocks

Source: Calculated

The following comprehensive insights emerged from this study. The research highlighted the compelling interplay between risk and return. Stocks with higher betas exhibited the expected trend of offering higher returns, underscoring the principle of risk-reward trade-off[3], [23]. Notably, Eichers Motors Ltd and Mahindra and Mahindra ltd with betas of 1.98 and 1.83 respectively, showcased substantial returns that far exceeded the risk-free rate. This phenomenon underscores the potential for investors to achieve healthy returns even within relatively low-beta stocks.

The inclusion of Force Motors and Ashok Leyland in this portfolio provided an intriguing insight. With a negative beta of -2.63 and -75.51, the stock's performance appeared to deviate from the general market trend. This could signify that Force Motors and Ashok Leyland performance was inversely correlated to the broader market, suggesting it might act as a hedge during market downturns.

The analysis unveiled the strategic choices made by certain companies, such as Force Motors and TVS Motors. Displaying a beta of -2.63 and remarkable returns of 18% and 4.16 and 26%. These highlights how companies willing to undertake higher risk might have the potential to reward investors with substantial returns. Aggressive Growth Portfolio was curated with a focus on achieving exceptional returns by embracing higher-risk stocks with elevated betas. This approach catered to investors who were comfortable with volatility in pursuit of potential high rewards.

IV. SUMMARY AND FINDINGS

In summary, this research study has conducted an extensive examination of portfolio optimization in the dynamic Indian automobile industry. The research utilized modern portfolio theory to examine financial data from a specific group of car manufacturers, with a particular emphasis on historical stock performance, volatility, and correlations[24]. The study has provided insights into the intricacies of the automotive sector, wherein the financial performance of companies is impacted by macroeconomic factors and market dynamics.

The primary outcomes of this research underscore the significance of portfolio diversification and the intricate equilibrium between risk and return within the automobile industry[3], [7], [25]. The study conducted a comprehensive examination of 10 automobile businesses, wherein stocks were classified according to their beta values. This classification served to emphasize the diverse levels of risk and return associated with each group[1], [3], [25].

The research findings indicate that stocks with greater beta values exhibit a positive correlation with higher returns, which aligns with the risk-reward trade-off principle[7]. Eicher Motors and Mahindra & Mahindra, for example, demonstrated significant gains that surpassed the risk-free rate, highlighting the potential for favorable returns even within equities with relatively low beta. Moreover, the incorporation of equities such as Force Motors and Ashok Leyland, which exhibit negative betas, indicates their potential as hedging instruments during periods of market decline.

The investigation unveiled the strategic decisions undertaken by specific corporations, like Force Motors and TVS Motors, who opted for higher-risk equities and achieved significant returns. This discovery underscores the tendency of investors who are tolerant of volatility to pursue greater returns by investing in companies that are ready to assume higher levels of risk.

This research provides investors and financial specialists with essential tools and insights to enhance the optimization of their portfolios within the dynamic Indian automobile industry[2], [3]. This statement highlights the significance of employing strategic asset allocation and diversification as effective approaches for controlling risks associated with specific sectors[2], [19], [26]. Investors can make well-informed decisions on the construction of their portfolios by taking into account several elements such as beta values, historical performance, and the trade-offs between risk and return[2], [5], [6], [11], [19], [26]. This enables them to align their investment objectives and risk tolerance with the composition of their portfolios[3], [7]. In its entirety, this study enhances the comprehension of portfolio management within sector-specific settings and provides practical recommendations for effectively addressing the complexities and prospects inherent in this dynamic business.

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Copyright

Copyright © 2023 Manjunath M, Riya Kyal, Dr. Gopalakrishna Chinnaswamy , Dr. S. Vinoth. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.