Title = Relationship Between Niffy 50 and Voltas Limited.
Author= Vedika Taware
Introduction=
Voltas Ltd is an Indian multinational company operating under the Tata Group umbrella, specializing in air conditioning and cooling solutions. Established in 1954, it has grown into a prominent player in the industry. Voltas offers a diverse range of products and services including air conditioners, air coolers, commercial refrigeration, water dispensers, and engineering solutions. It has a strong presence both domestically and internationally, exporting its products to over 60 countries. The company prioritizes innovation and sustainability, consistently introducing energy-efficient technologies and environmentally friendly solutions. Voltas is also committed to corporate social responsibility, engaging in various initiatives focused on education, healthcare, and community development. With a track record of strong financial performance and numerous awards and recognitions, Voltas Ltd continues to be a leader in the air conditioning and cooling technology sector.
Objective=
To calculate Beta Voltas Limited and it’s significance.
Literature review=
1) Dahal et al. (2023) presents a comprehensive study aimed at evaluating the impact of incorporating unstructured text data, specifically financial news sentiment, on the accuracy of stock price predictions. The research focuses on comparing the performance of two deep learning architectures, Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), under the same conditions while integrating both structured numerical data and unstructured text data. The study’s key finding is that the performance of stock price prediction models significantly improves when combining fundamental stock market data with financial news sentiment. This conclusion supports the widely held belief that public sentiment and social media can indeed influence stock prices.
2) Meena,et al investigates the impact of risk factors on the financial performance of commercial banks, utilizing key indicators such as CAR, COI, NNPA, ROA, and ROE. Through panel data analysis, it establishes a negative correlation between risk metrics and profitability, emphasizing the significance of oversight and monitoring in mitigating risks. Notably, while capital adequacy ratio (CAR) shows a favorable relationship with profitability, its practical impact is limited. Rising operating expenses, cost-to-income ratios, and non-performing assets (NPAs) pose significant challenges to banks’ financial health, necessitating strategic management to control these factors. The study underscores the importance of policies for loan recovery and credit portfolio monitoring to address NPAs.
Data Collection=
The data for Nifty 50 and Voltas Ltd has been downloaded from the NSE website for the period of 01 February 2023 to 31 January 2024. The data has been manipulated to obtain the Friday closing prices of Nifty 50 and Voltas Ltd. The weekly returns of the NSE index Nifty 50 are represented by X, while the returns of equity for Voltas Ltd are represented by Y. Wherein Y (Equity of Voltas Ltd) is Regressed on X (Nifty 50), and the relationship between the two has been plotted.
Data Analysis =
The equation for the data is-
Y= -0.147365491 + 0.448162894X
N= 48
R Square= 0.103303762
t Stat= 2.35155972
F= 5.529833133
Interpretation=
1)The above equation shows the relationship between Nifty 50 (X) and Voltas Ltd (Y).
2) The positive sign in the equation means that there is a direct relationship between Nifty 50 and Voltas Ltd i.e., if Nifty 50 rises by 1 unit, Voltas Ltd will also rise by 0.4481.
3)Number of observations are 48.
4)R^2 is 0.10330, which meant 10.33% of returns of Voltas Ltd is explained by Nifty 50. Therefore, 89.67% is error due to other variables which are not taken in the model.
5) 5.52983, which is less than table F, which means overall the model is not statistically significant at 5% level.
Conclusion=
As beta =0.44816, is less than 1, therefore we should invest for long term
Reference=
1) Dahal, K. R., Nawa, R. P., Gaire, S., Mahatara, S., Joshi, R. P., Gupta, A., . . . Joshi, J. (2023). A comparative study on effect of news sentiment on stock price prediction with deep learning architecture. PLoS One, 18(4)
2) Meena, V. K., Kumar, S., & Shambharkar, R. T. (2023). Determinants of financial health of commercial banks in india: A study of nifty 50 banks. IUP Journal of Financial Risk Management, 20(2).
