IT Index Research Articles

Unveiling Outperformance: A Portfolio Analysis of Top AI-Related Stocks against IT Indices and Robotics ETFs

In this study, we delve into the financial market to compare the performance of prominent AI and robotics-related stocks against traditional IT indices, such as the Nasdaq, and specialized AI and robotics ETFs. We evaluate the role of these stocks in diversifying portfolios, analyzing their return potential and risk profiles. Our analysis includes various investment scenarios, focusing on common AI-related stocks in the United States. We explore the influence of risk management strategies, ranging from “buy and hold” to daily rebalancing, on AI stock portfolios. This involves investigating long-term strategies like buy and hold, as well as short-term approaches, such as daily rebalancing. Our findings, covering the period from 30 April 2021, to 15 September 2023, show that AI-related stocks have not only outperformed in recent years but also highlight the growing “AI bubble” and the increasing significance of AI in investment decisions. The study reveals that these stocks have delivered superior performance, as indicated by metrics like Sharpe and Treynor ratios, providing insights into market trends and financial returns in the technology and robotics sectors. The results are particularly relevant for investors and traders in the AI sector, offering a balanced view of potential returns against the risks in this rapidly evolving market. This paper adds to the financial market literature by demonstrating that investing in emerging trends, such as AI, can be more advantageous in the short term compared to traditional markets like the Nasdaq.

Dividend Announcement and Stock Prices: An Analysis of Selected IT Companies in India

The stock price movement attracts all stakeholders of the company. There are many factors as well as events that affect stock prices of the company and dividend announcement is one of them. So, the present study focuses on the impact of dividend announcements on share prices of selected IT companies listed in NSE in India. A dividend announcement gives a signal to investors about the company's strength and urges them to invest in the company's stock. The event study methodology is used in this study. Abnormal Return, Cumulative Return, Average Abnormal Return, Cumulative Average Abnormal Return, and t-statics of AR, CAR and CAAR are calculated. A paired t-test is calculated to measure the impact of a divided announcement on the share price before and after the announcement period. For that 21-day event window, 120 days of estimation window and 9 observations from the years 2016-17 to 2018-19 of selected.3 IT companies that are included in nifty50 as well as in nifty IT index and who are paying dividend have been selected. The paired t-test indicates a significant difference between pre and post-announcement periods in the year 2017-18 and shows insignificant differences in the years 2016-17 and 2018-19.

Impact of COVID-19 on the Efficiency of Indian IT Companies

The purpose of this study is to investigate the impact of the COVID-19 epidemic on Indian information technology enterprises using data from the 10 largest IT firms listed on the Nifty IT index. Data were sourced from the PROWESS IQ database for 2017–2022. Input-oriented data envelopment analysis (DEA) using CCR and BCC models was applied by taking gross revenue as an output and labour, costs of fuel, materials, power, administrative and general costs as input variables. The study finds a positive although not statistically significant difference in the efficiency of Nifty IT companies during COVID-19 compared to pre-pandemic levels. Additionally, firms experienced higher scale efficiency compared to production efficiency growth throughout 2020–2022. Around 70% of Nifty IT firms faced a decreased efficiency in input utilization during the COVID-19 outbreak. Further, 50% of Nifty IT firms operated under increasing returns to scale and required expansion of business in 2021–2022. The dimensions of returns to scale exhibited stability in the long run. Companies with increasing returns to scale had a 7% lower average technical efficiency than firms with decreasing returns to scale. Large firms demonstrated better scale efficiency. The results of the study can be used by managers to increase the efficiency of firms.

Inflation and Stock Market Returns: An Empirical Study of Sectoral Indices with Special Reference to India

In this study an attempt has been undertaken so as to establish the relationship between sectoral indices returns and inflation numbers. In order to achieve the objective of the study, all the sectoral indices have been taken in the study except two (02) indices because they were introduced in the National Stock Exchange in the recent past and, therefore, their data is not available for the whole reference period of the study. The indices that are in the study are, (CNX Auto Index, CNX Bank Index, CNX Energy Index, CNX Finance Index, CNX FMCG Index, CNX IT Index, CNX Metal Index, CNX MNC Index, CNX Pharma Index, CNX PSU Bank Index). The study took eight years of reference period throughout which all the indices that are included in the sample were available. The researchers have employed Pearson correlation method so as to establish the relationship between inflation and sectoral indices returns. The findings of the study put forth that most of the indices do possess statistically significant relationship with inflation numbers.

Volatility implications on IT index during pre and post COVID-19 outbreak: Evidence from India

Volatility implications on IT index during pre and post COVID-19 outbreak: Evidence from India

Volatility implications on IT index during pre and post COVID-19 outbreak: evidence from India

Volatility implications on IT index during pre and post COVID-19 outbreak: evidence from India

DEEP LEARNING AND MACHINE LEARNING MODELS TO FORECAST BSE AND NIFTY SENSEX IT INDEX

The primary objective of this work is to build an appropriate mathematical model that helps predict the direction of stock market indices. The stock market is volatile and dynamic, and prediction of its movement will help investors make more optimal strategies and boost their profit. In this context, the data was collected from two major IT indices of India, the BSE IT Index and the NIFTY IT Index. In modeling the time series, autoregressive integrated moving average (ARIMA) was used initially, followed by various machine learning models, like artificial neural network (ANN), recurrent neural network (RNN), and convolutional neural network (CNN). The data analysis exhibited superior performance of ANN models compared to other models for performance criteria such as root mean squared error (RMSE) and mean absolute percentage error (MAPE). This exploratory analysis concluded that ANN models outperform other predicting models to greater accuracy, augmenting previous literature on stock market analysis. This machine learning approach would help investors design optimal strategies and boost their profits in the world of stock market.

Dynamic relationship between equity, bond, commodity, forex and foreign institutional investments: Evidence from India

The interrelationship between equity, bond, commodity and forex movements can provide investors with abundant trading opportunities regardless of whether one market is trending upward or downward. Hence, to understand the interlinkage between markets, this study examines the long-run and causal linkage between forex, G-sec bonds, oil prices, gold rates, foreign institutional investment (FII) flows, and equity market and sectoral index returns. Daily time-series data from August 2012 to August 2021 were considered for empirical analysis. Johansen’s cointegration test revealed that foreign exchanges like USD, Euro, GBP and Yen, oil and gold rates, G-bond returns and FII flows were significantly cointegrated with the stock market and sectoral indices in the long run. Further, Granger causality found a uni-directional relationship between forex rates (i.e., USD, Euro, Yen) and the market, as well as sectoral indices, except Nifty 50 and Nifty IT indices. Oil price movements were found to effectively predict future price changes of Nifty consumer durables, auto, IT indices. Gold prices are useful to predict Nifty-Auto, Bank, Financial Services, Oil & Gas and PSU. The study also found a bi-directional relationship from FII inflows to the stock market and sectoral indices. The findings suggest that forex rates, oil prices and FII flows significantly affect India’s stock market and sectoral performance. The study contributes to the existing literature by comprehensively examining the interlinkage between commodities such as oil and gold, foreign exchanges like USD, Euro, GBP and Yen, G-bond, FII flows and the stock market, and fourteen sectoral indices in the Indian context.

Testing of Semi–Strong Form of Efficiency: an Empirical Study on Stock Market Reaction Around Dividend Announcement

Purpose: The purpose of this study is to examine the efficiency of the Indian stock market of the Nifty IT index over the dividend announcement for five years from 2016 to 2020. Theoretical framework: A reward procured by the shareholders on their equities is, of course, the dividend. A leading area of concern is the dividend announcement. According to the theory of efficient markets, stock prices accurately reflect all available information. This demonstrates that the prices are correct and fair. The market should therefore respond immediately to an event in this instance the dividend announcement. Therefore, depending on publicly available information will not provide investors with the possibility to consistently generate extraordinary returns. Design/ methodology/ approach: The study attempts to validate the event study approach while investigating the semi-strong form of efficiency. Daily share prices of five companies out of ten of the Nifty IT index were observed to test the Efficient Market Hypothesis. 31 days event window has been employed to calculate the abnormal returns of the selected sample around dividend issue announcements also t-test was applied to assess the level of significance. Findings: The study found that the stock market was efficient in its semi strong form and the investors could not make excess returns over the dividend announcement of the Nifty IT index. Research, Practical & social implications: This study eliminates the possibility for investors to beat the average market returns. It is significant since it affects stock market investment choices. Originality/ Values: The majority of studies are only able to analyse the overall average abnormal return and cumulative average abnormal return of chosen companies; it is difficult to locate studies that focus on the abnormal return for each individual company. The t test for each company-wise abnormal returns, overall average abnormal returns, and cumulative average abnormal returns were acquired and tested at the 5% level of significance in order to determine the significance.

A Comparative Evaluation of Performance of Nifty IT Companies in Relation with Nifty IT Index

The Information Technology sector is central to the nation’s security, economy and public health. It is one of the fastest growing sectors in Indian Stock Market. This paper evaluated the performance of companies listed in Nifty IT index with an objective to find out the significance level of each company with Nifty IT index with the help of paired sample T-test. The study covered five years starting from 1s January 2017 to 31 December 2021. Mean returns and standard deviation is calculated to analyze and compare the risk return characteristics of the companies listed in Nifty IT index. Correlation is also calculated to know the relationship of each company with Nifty IT index. The result of the study revealed that L&T Infotech, Mindtree, Mphasis, Coforge and TCS are statistically significant. HCLTech, Infosys, Tech Mahindra, Wipro and L&T Services are not statistically significant. This paper has suggested the investors to invest in those companies which are in consistent with the broader indices. Large cap companies are safe to invest than compared to mid and small cap companies.

Risk Innovation Green Innovation Management of Chinese and Russian Stock Markets Based on Time-Space Kriging Model

In recent years, due to the influence of economic globalization and financial integration, modern financial theory and financial innovation, the Chinese and Russian financial markets have developed rapidly, but at the same time, the financial markets have also shown unprecedented volatility. The Sino-Russian financial markets at the initial development stage will also face even more dramatic fluctuations. In particular, the stock market, which is the largest area for potential financial risk transmission, inevitably becomes the focus of green innovation management for financial risk transmission. This paper uses the modern risk management technology Kriging theory, through the introduction of GARCH Kriging model calculation, analyzes the comprehensive risk transmission of different markets in the Chinese and Russian stock markets, and measures several representative industry risks, indicating that Kriging risk management theory is in China and Russia. The applicability of the stock market and the risk estimation of different industries. At the same time, this paper analyzes the risk composition of the portfolio through Kriging risk analysis of the four heavyweight stocks of Sino-Russian funds, and makes relevant analysis for the selection of the portfolio and the sources of risk transmission and risk avoidance. The experimental results show that for the test sample data T = 510, at a 95% confidence level, the number of Sino-Russian real estate index, manufacturing index, financial index and IT index should not exceed the Kriging critical value nor exceed 36.

Impact of currency fluctuation on the Indian stock market and related investment opportunities

This paper assesses the impact of currency fluctuation on the Indian Stock Market and relative investment opportunities for an investor during the period 2013–2018. For this purpose, a descriptive as well as causal research is conducted where we have tested the hypothesis to come out with the major findings, conclusions, limitations and recommendations of this study. In order to achieve the main objectives of our study that is how currency fluctuation and several other factors impact the Indian Stock Market, how investors design their portfolio by investing in particular sectors to take advantage of currency fluctuation to gain maximum returns andfinally to establish a relationship between exchange rates and Nifty sectors we have used statistical techniques of data analysis like correlation, regression and ANOVA. Data is collected through primary as well as secondary sources. We established that Nifty50 and currencies like USD, Yen, Euro and GBP have a negative correlation and that USD is the most influential currency that affects Nifty50. Also, the auto index, power sector, oil and gas sector show negative correlation and IT index and healthcare show no significant correlation with USD. This paper recommends investors to sell their shares of the power, oil and gas sector when the Indian Rupee depreciates to reduce losses or go on a short position. Similarly, investors can go on a long position if the Indian Rupee appreciates. Also, Investors can either hold their existing positions or exit the market when it comes to IT and pharmaceutical sector as they can move in any direction due to currency fluctuation. All in all, to obtain maximum returns investors can look at inflation or interest rates to predict the currency movements, and thereby design their portfolio in the respective Nifty50 sectors.

A study on Bank and IT nifty influence on Nifty 50

NIFTY 50 is one of the benchmark indices which is been used in India. The index is built upon by the companies which are represented from various sectors. Each sector is given separate weightage depending upon the representations from each sector. This paper focused on to identify Pairwise Granger Causality between NIFTY 50 Index, NIFTY Bank Index and NIFTY IT Index. The researcher also focused on to identify the influence of Bank NIFTY and IT NIFTY and NIFTY 50 Index. The research was carried out with a total of 532 observations (closing value of each index) of spanned across January 2018 – February 2020. The study revealed that Bank NIFTY Granger Cause IT NIFTY and not vice-versa. NIFTY 50 Index is highly influenced by Bank NIFTY IT NIFTY.

Physicochemical Composition and Sensory Quality of Goat Meat Burgers. Effect of Fat Source.

Several strategies for producing healthier meat products have been developed. Reducing fat content, using different fat sources, modifying and improving the fatty acid profile or even replacing saturated fat with oleogels are some of the methods used. Goat meat mainly from animals out of quality brands with low commercial value can be valorized when processed, giving the opportunity to increase its consumption and acceptability. Thus, the aim of this study was to study the effect of the replacement of pork as a source of fat with an olive oleogel in burgers manufactured with goat meat and to compare the goat meat burgers with the most common commercial burgers made with beef. Two replications of the burgers were manufactured at different times, and three samples of each burger type (GOO—goat meat burgers with olive oil; GPF—goat meat burgers with pork fat) were randomly selected from each lot manufactured. Each sample was analyzed in triplicate for each physicochemical analysis. At the time, the manufactured burgers were analyzed simultaneously with the commercial burgers. The burgers with olive oil (GOO) showed higher a* and b* than the burgers with pork fat (GPF) and consequently had lower h° and C*. The ashes, protein and collagen contents of the GOO and GPF burgers were similar to those of the other goat meat products. The effect of the incorporation of oleogel on the physicochemical composition of the burgers in relation to the pork fat was expressed in the fat content, 4 and 2.78% for GOO and GPF, respectively. CH burgers have significantly higher fat content (13.45%) than GOO and GPF burgers. The replacement of pork backfat with a vegetable oleogel modified the fatty acids profile, since the GOO burgers had the highest MUFA and PUFA and the lipidic quality, defined by the IA and IT indices, was 0.38 and 0.99, respectively. Globally, goat burgers were sensorially harder and presented a more difficult chewiness than CH. The replacement of the pork back fat with oleogel significantly decreased hardness and chewiness.

Dynamics of Correlation and Volatility in New Age Technology (Industry 4.0) Sectoral Indices and Traditional Sectoral Indices in US and India

This paper investigates the time-varying correlation and the volatility behaviour of the New Age Technology (Industry 4.0) sectors and, traditional sectors in US (NASDAQ sectoral indices) and India (Nifty sectoral indices) using ADCC/DCC – GARCH models. We also assess the impact of Global Financial Crisis due to sub-prime mortgages and Global Lockdown Crisis due to COVID -19 pandemic on the volatility behaviour and the time – varying correlation of these indices. Risk measures – VaR and Expected Shortfall calculated using Extreme Value Theory are also presented.All the sectoral indices exhibited asymmetric volatility behaviour. Among the indices in the NASDAQ sectoral group, only NASDAQ Computer index did not show a significant increase in volatility during both the crises. Autonomous Vehicles, Clean Technology, Cybersecurity, 3D Printing, Smart Buildings and Virtual Reality indices did not show a significant increase in volatility during Global Lockdown Crisis. While all the traditional sectoral indices except NASDAQ Bank exhibited asymmetric behaviour in correlation, Industry 4.0 indices such as Robotics, 3D printing, Clean Energy and Virtual Reality did not. In India, only Nifty IT and Nifty FMCG indices did not exhibit significant increase in volatility during the Global Lockdown Crisis. Correlation generally increased for all the sectoral indices during the crises.

Did COVID-19 Pandemic Affect the Sentiments expressed in the Financial Statements? - A study of New Age Technology and Nifty IT Firms

This paper investigates the change in the sentiments expressed in the annual reports/10-K filings of the firms in the New Age Technology Sectoral Indices and Nifty IT Index during COVID -19 Pandemic. We use tf.idf Weighted and simple proportion methodologies, and eight word lists from Loughran and McDonald 2020 dictionary – negative, positive, uncertainty, litigious, strong modal, weak modal, constraining and complexity; and negative and positive word lists from General Inquirer (Psychological Harvard IV), Henry 2008 and QDAP dictionaries to measure the change in the sentiments expressed in the annual reports.Friedman and post-hoc Nemenyi multiple comparison tests found significant difference in the FY 20-21 Net Sentiment distributions of Robotics, Cybersecurity, Drones and Space indices. Qualitative analysis indicated that FY 20-21 Net Sentiment scores of Virtual Reality and Nifty IT indices were least affected in FY 20-21.

S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA

This study forecasts the return and volatility dynamics of S&P BSE Sensex and S&P BSE IT indices of the Bombay Stock Exchange. To achieve the objectives, the study uses descriptive statistics; tests including variance ratio, Augmented Dickey-Fuller, Phillips-Perron, and Kwiatkowski Phillips Schmidt and Shin; and Autoregressive Integrated Moving Average (ARIMA). The analysis forecasts daily stock returns for the S&P BSE Sensex and S&P BSE IT time series, using the ARIMA model. The results reveal that the mean returns of both indices are positive but near zero. This is indicative of a regressive tendency in the long-term. The forecasted values of S&P BSE Sensex and S&P BSE IT are almost equal to their actual values, with few deviations. Hence, the ARIMA model is capable of predicting medium- or long-term horizons using historical values of S&P BSE Sensex and S&P BSE IT.

Caracterización de la respuesta a la sequía de líneas segregantes de maíz

El estrés por sequía es una de las principales causas de la reducción del rendimiento del maíz en los trópicos, principalmente en la agricultura que depende de lluvias, por lo que se requiere generar genotipos con tolerancia a sequía. Los objetivos de la investigación fueron cuantificar la respuesta agronómica de un grupo de líneas S2 segregantes de maíz en ambientes de riego y sequía, así como seleccionar líneas con tolerancia al déficit hídrico, con base en dos índices y al promedio de rendimiento de ambos ambientes. Los genotipos de estudio fueron 96 líneas S2 segregantes, dos líneas testigo y dos líneas no seleccionadas para sequía. Las líneas se evaluaron bajo riego y sequía inducida usando un diseño Látice Alfa con cuatro repeticiones, en Ayala, Morelos, México. Se midieron 10 variables morfológicas y se estimaron los índices de susceptibilidad a la sequía (ISS) y el índice de tolerancia (IT). Con base en el rendimiento de grano en riego (3.14 t ha-1), se observó una reducción de 77.7% del rendimiento en sequía (0.7 t ha-1). Los índices ISS e IT identificaron a las líneas LUM69, LUM82 y LUM30 como tolerantes a sequía, solo LUM69 mostró un rendimiento competitivo en ambos ambientes. Los índices de ISS e IT como criterio para identificar genotipos tolerantes, no seleccionaron a las líneas con mejor rendimiento de grano bajo condiciones de riego, por lo que se debe incluir en los criterios de selección al promedio de rendimiento a través de los ambientes de humedad.

An analysis of IT software and service exports from India

PurposeThe aims of the paper are to investigate IT software and service export function for India. First, cointegration tests have been used to investigate the long-run equilibrium relationship of the given variables. Second, long-run coefficients and associated error correction mechanism are estimated.Design/methodology/approachAnnual time series data on IT software and service exports, human capital, exchange rate, investment in IT, external demand and openness index have been used for the present study during the period 1980–2017. The data are collected from the National Association of Software and Service Companies (NASSCOM), Planning Commission of India, University Grants Commission (UGC) of India, real effective exchange rate (REER) database and World Bank development indicators. Auto regressive distributed lag (ARDL) model is used to analyze both short-run and long-run dynamic behaviour of economic variables with appropriate asymptotic inferences.FindingsResults of the analysis show the stable long-run equilibrium relationship among the given variables. It is found that external demand, exchange rate, human capital and openness index have a substantial long-run impact on the IT software and service exports. We also found that the coefficient of error correction term is negative and significant at 1% of the level of significance, which confirms the existence of stable long-run relationship which means adjustment will take place when there is a short-run deviation to its long-run equilibrium after a shock.Research limitations/implicationsThere may be other determinants of software and service exports apart from those considered by the present study. Due to the non-availability of data, the study considers only important determinants that determine the software and service exports in India. The IT exports are an emerging and dynamic field of economic activity and the rate of change is so rapid that the relevance of individual factors may change over time. The study period is also limited to available data.Practical implicationsThe paper has implications for achieving sustainability in IT software and service exports growth. It is recommended that policies directed at improving the performance of IT software and service exports should largely consider the long-run behaviour of these variables.Originality/valueThis paper focuses on originality in the analysis of the relationship among the given variables including IT software and service exports, human capital, exchange rate, investment in IT, external demand and openness index in India. All the work has been done in original by the authors, and the work used has been acknowledged properly.

Risk Return Analysis of Equity Stocks:A Study of Selected Indian IT Companies

The present study is an attempt to diagnose the risk return profile of equity stocks of selected Indian IT companies listed on IT Index of NSE. The risk return profile of selected IT companies has been examined on various parameters including the absolute return, abnormal return, required rate of return as per CAPM model, volatility of return, systematic risk and risk adjusted return. Authors found that Tata Elxsi, Infibeam Avenues and NIIT technologies have offered highest rate of return. The returns of Infibeam avenues, Tata Elxsi and Mindtree are highly volatile. The risk adjusted return is highest in case of Mindtree and Tech Mahindra. Stocks prices of Infosys, HCL technologies and Tech Mahindra are highly sensitive to market movement indicating the highest degree of systematic risk. Except HCL Technologies, Wipro and Oracle Fin Serv, remaining seven IT companies have offered higher rate of return than the minimum required rate of return.