Indian Market Research Articles

Market resilience in turbulent times: a proactive approach to predicting stock market responses during geopolitical tensions

PurposeThe Indian stock market can be tricky when there's trouble in the world, like wars or big conflicts. It's like trying to read a secret message. We want to figure out what makes investors nervous or happy, because their feelings often affect how they buy and sell stocks. We're building a tool to make prediction that uses both numbers and people's opinions.Design/methodology/approachHybrid approach leverages Twitter sentiment, market data, volatility index (VIX) and momentum indicators like moving average convergence divergence (MACD) and relative strength index (RSI) to deliver accurate market insights for informed investment decisions during uncertainty.FindingsOur study reveals that geopolitical tensions' impact on stock markets is fleeting and confined to the short term. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.47% accuracy in forecasting stock market values during such events.Originality/valueTo the best of the authors' knowledge, this model's originality lies in its focus on short-term impact, novel data fusion and high accuracy. Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of geopolitical tensions on market behavior, a previously under-researched area. Novel data fusion: Combining sentiment analysis with established market indicators like VIX and momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods. Advanced predictive accuracy: Achieving the prediction accuracy (98.47%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.

Urban air pollution and systemic risk of the real estate market in China

Air pollution is a major environmental risk to the economy nowadays. Given that urban air pollution is one of the determinants of urban housing price, and that both urban air pollution and housing price have significant spatial spillover effects, this paper explores the impact of inter-city air pollution spillover on systemic risk of the real estate market, using housing price data of 79 cities in 8 major urban agglomerations in China. This paper innovatively introduces the principal component analysis into the VAR model and appropriately conducts historical decomposition to construct a new systemic risk measurement index of the real estate market, which can identify both direction and magnitude of the impact. The results show that, on the time dimension, air pollution is found to contribute most time when systemic risk of the real estate market reaches a high level. On the spatial dimension, this paper identifies the urban agglomerations and node cities whose systemic risks of the corresponding real estate markets are more susceptible to air pollution spillover. On the spillover channels, this paper finds that the past cultivation of environmental awareness and the implementation of environmental protection policies in China have alleviated the negative impact of air pollution spillover on the systemic risk of the real estate market.

Harnessing AI and Predictive Analytics: Transforming the Electric Vehicle Market in India

The electric vehicle (EV) market in India is undergoing a transformative phase, driven by growing environmental concerns, government incentives, and the rapid integration of advanced technologies such as Artificial Intelligence (AI) and Predictive Analytics. These technologies are proving essential in optimizing various aspects of EV development, including battery management, predictive maintenance, and customer experience personalization. This research article investigates the influence of AI and Predictive Analytics on the Indian EV market, highlighting their roles in improving operational efficiency, reducing costs, and enhancing safety and user satisfaction. Statistical data and case studies of leading Indian EV companies, such as Tata Motors, Ola Electric, and Ather Energy, illustrate the impact of these technologies on market growth and performance. Despite facing challenges like high initial costs and regulatory barriers, AI and Predictive Analytics are poised to play a pivotal role in the future expansion of the EV market in India. The findings suggest that with increased adoption and ongoing technological advancements, AI and Predictive Analytics will significantly contribute to the growth and sustainability of the EV sector in India. Keywords: Electric Vehicles (EVs), Artificial Intelligence (AI), Predictive Analytics, Battery Management Systems (BMS), Predictive Maintenance

TECHNOLOGICAL ADVANCEMENTS AND THEIR INFLUENCE ON CONSUMER DECISION-MAKING IN HEARING AID ACQUISITION

This paper reviews the impact of technological advancements on consumer decision-making regarding hearing aid acquisition, focusing specifically on the Indian context. While innovations like smartphone connectivity, AI-driven features, and rechargeable batteries increase purchase intention (Manchaiah et al., 2021), a complex interplay of cultural, socioeconomic, and accessibility factors influences adoption rates. The paper examines how the urban-rural divide (Kumar et al., 2020), affordability challenges (Reddy & Nair, 2019), and cultural nuances (Sharma & Patel, 2022) shape technology acceptance in India. It also explores the potential of Tele-audiology (Chopra & Sharma, 2021) and government initiatives (Kumar et al., 2021) in promoting wider access to advanced hearing aids. The review highlights the need for manufacturers to adopt segmented approaches, healthcare providers to expand telecom-services, and policymakers to implement inclusive policies to address the unique needs of the Indian market.

Effect of Behavioural Biases on Investment Decisions of Individual Investors in India

The current study "effect of behavioural biases on investment decisions of individual investors in India" aims to uncover and quantify the influence of behavioural biases on individual investors' investment decisions. The study's goal is to examine the influence of behavioural biases on individual investors' investing decisions. It is discovered that five components are formed from the thirteen items as a consequence of heuristics factor analysis, namely Availability, Representativeness, Overconfidence, Anchoring, and Gambler's Fallacy. The prospective factor analysis also reveals that three components are retrieved and loaded from the eleven items, namely Loss Aversion, Regret Aversion, and Mental Accounting. According to the findings of this study, behavioural biases affect individual investors while making investment decisions in the Indian securities market. Investor behaviour in making investment decisions is influenced to a larger extent by availability, representativeness, overconfidence, anchoring, and loss aversion. To be successful, an investor must first understand his or her own investment behaviour, which begins with recognising and avoiding behavioural biases from their own experiences, followed by setting realistic and achievable goals through a diversifiable portfolio, and taking into account all financial market mechanisms.

The Future of Digital Currency: Implications for Global Financial Systems and Trade

This article explores the evolving landscape of digital currencies, including Central Bank Digital Currencies (CBDCs) and cryptocurrencies, and their potential impacts on global financial systems and international trade. As digital currencies become increasingly significant, their influence on monetary policy, financial stability, and transaction efficiency warrants a thorough examination. The article provides a comprehensive review of existing literature, identifies gaps, and outlines the potential implications of digital currencies, particularly focusing on the Indian market. The discussionincludes theoretical frameworks, practical implications, and strategic recommendations for policymakers and businesses. This conceptual analysis aims to offer insights into how digital currencies could reshape financial systems and trade dynamics in both developed and emerging markets.

Starbucks Market Entry Plan to India

This report examines Starbucks' strategy for expanding into the Indian market. Starbucks Corporation, headquartered in Seattle, Washington, is a globally recognized American coffeehouse and roastery chain, and it holds the title of the largest coffeehouse chain worldwide. The report begins by analyzing the rationale behind choosing India as the host country, with a focus on the cultural and economic factors that were considered in the environmental scanning process. It then outlines a market entry strategy, proposing a potential joint venture between Starbucks and the prominent Indian company, TATA Global Beverages Limited. The discussion extends to a comprehensive marketing strategy, utilizing the 4Ps framework to strategically guide Starbucks' entry into the Indian market. Lastly, the report addresses potential challenges and presents a contingency plan to mitigate them.

Why Tata Nano Failed? From Ascent to Ashes: Unraveling the Tata Nano Saga – A Chronicle of Triumph, Tragedy, and Missteps

The Tata Nano launched in 2008 was initially marketed as the world's cheapest car, designed to provide an affordable transportation solution for the masses in India. It was a revolutionary concept that aimed to bring mobility to a wider segment of the population. Ratan Tata, the former Chairman of Tata Sons, envisioned the Nano as a symbol of innovation and social impact. Under his leadership, Tata Motors endeavored to create a compact, economical car that could transform the way people in India commuted. The global ambition of the Nano was to showcase India's engineering capabilities and demonstrate how a low-cost, frugal design approach could result in a groundbreaking product. Ratan Tata's vision extended beyond the domestic market, aiming to position the Nano as a viable option for consumers worldwide. However, despite the initial hype and anticipation surrounding the Nano, various factors contributed to its failure in the Indian market. Issues such as quality concerns, safety perceptions, marketing challenges, and changing consumer preferences played a significant role in the Nano's inability to meet sales expectations and establish a sustainable presence in the automotive industry. Ratan Tata made a roapmap to sell Minimum of 3 lacs of Tata Nano car in the first Years, but the reality is Tata Nano couldn’t cross the selling of 3 lacs units even after 8 years, before its get retired. Keywords: Anticipation, Envisioned, Groundbreaking, Innovation, Revolutionary

Whether ARIMA-based Portfolio Can Beat the Market Index

Portfolio optimization is a long-term topic in the financial field, which can maximize returns while minimizing risks. It is widely used in production and daily life, such as stock investment, production optimization, and engineering models. This article selects data from 10 stocks, namely KO, PG, PEP, CL, MDLZ, STZ, PM, KMB, GIS, and K, from January 24, 2024, to June 14, 2024. Firstly, this article uses the ARIMA model to learn the first 70% of the data and predict the last 30% of the data, and then uses portfolio strategy to evaluate the results. This article obtained the maximum Sharpe ratio model on the Monte Carlo effective boundary. By combining the maximum Sharpe ratio model with the SP 500 index within the same time frame, this article concludes that the market performs better. This article provides different portfolio allocation strategies for risk averse investors seeking stable positive returns in turbulent markets.

Pure contagion vs. financial interconnection in the subprime crisis context: Short- and long-term dynamics

This paper examines the difference between pure contagion and financial interconnection by studying the U.S. and some American and Asian markets in the subprime crisis context. These markets are affected by the mortgage crisis, with data available from January 1, 2003 to December 30, 2011. The paper first identifies the turmoil period via the wavelet technique and adopts cointegration and Granger causality approaches by estimating vector autoregressive (VAR) and vector error correction models (VECM) models. Based on daily returns from stock market indices in five American countries (Mexico, Brazil, Canada, Argentina, and the U.S.) and eight Asian ones (Hong Kong, Japan, India, Indonesia, Malaysia, Singapore, Korea, and China), the results show eight cases of pure contagion and 10 cases of financial interconnection. In addition, there were high co-movements in the short term and low co-movements in the long term for financial interconnection cases. These findings have several implications for investors looking to diversify their portfolios internationally and for portfolio managers to expect and limit market risk. The results provide additional guidance to regulators and policymakers.

Analyzing the Selective Stock Price Index Using Fractionally Integrated and Heteroskedastic Models

Stock market indices are important tools to measure and compare stock market performance. The Selective Stock Price (SSP) index reflects fluctuations in a set value of financial instruments of Santiago de Chile’s stock exchange. Stock indices also reflect volatility linked to high uncertainty or potential investment risk. However, economic shocks are altering volatility. Evidence of long memory in SSP time series also exists, which implies long-term persistence. In this paper, we studied the volatility of SSP time series from January 2010 to September 2023 using fractionally heteroskedastic models. We considered the Autoregressive Fractionally Integrated Moving Average (ARFIMA) process with Generalized Autoregressive Conditional Heteroskedasticity (GARCH) innovations—the ARFIMA-GARCH model—for SSP log returns, and the fractionally integrated GARCH, or FIGARCH model, was compared with a classical GARCH one. The results show that the ARFIMA-GARCH model performs best in terms of volatility fit and predictive quality. This model allows us to obtain a better understanding of the observed volatility and its behavior, which contributes to more effective investment risk management in the stock market. Moreover, the proposed model detects the influence volatility increments of the SSP index linked to external factors that impact the economic outlook, such as China’s economic slowdown in 2012 and the subprime crisis in 2008.

The Relationship between the Return of Energy Companies Listed on the Kazakhstan Stock Exchange and the Exchange Rate, KASE Index, and Gold Return: ARDL Bounds Value Approach

In a free market economy, stock market indices are influenced not only by national economic developments, but also by economic indicators such as gold, exchange rates, and oil. It is important to consider these indicators when analyzing the returns of companies traded on the stock exchange. Internal factors that impact stock market returns include the company's estimated earnings and changes in the company's financial structure. External factors include macroeconomic variables such as exchange rates, interest rates, gold prices, and Gross Domestic Product (GDP). A study analyzed the relationship between the returns of energy companies traded on KASE and the KASE index, exchange rate, and gold return during the period of 01.01.2023-01.04.2024 (328 trading days) using the ARDL Bounds Value Approach. The research findings indicate that the stock market composite index, foreign exchange, and gold returns have a long-term effect on the returns of energy companies. Particularly, the long-term effect of the stock market composite index return is found to be significant and positive for all three assets. Additionally, the effect of companies' past values has been observed as negative. According to the error correction model analysis, a key finding is that shocks to company returns will reach equilibrium in a short time, approximately one trading day. These results can provide decision support, especially for investors, when investing in energy companies.

Green cryptocurrencies versus sustainable investments dynamics: Exploration of multifractal multiscale analysis, multifractal detrended cross-correlations and nonlinear Granger causality

Within this study, we analyse green cryptocurrencies versus sustainable investments dynamics by calculating a multifractal multiscale analysis (MMA) with Hurst surfaces paired with powerlaw distributional coherence tests for each series. Next, we determine multifractal cross-correlations by applying a maximal overlap discrete wavelet transform (MODWT) based trend-filtered variation of a multifractal detrended cross-correlation analysis (MF-DCCA). Finally, to determine the strength and directionality of potential causations, we determine the results of a nonlinear Granger causality test. The results for the MMA state q-dependent unstable multifractality for each series. The coherence tests indicate that the series follow powerlaws or exponentially nested powerlaws and yield fat-tails in some cases. Moreover, we find strong scaling and multifractal cross-correlations between the cryptocurrencies and the sustainability series. Finally, the nonlinear Granger causality tests across four lags indicate a complex interplay between some of the selected cryptocurrencies and various indices. These results suggest the existence of potential predictive powers of these cryptocurrencies on the market indices.

THE EFFECT OF FINANCIAL STRESS ON STOCK MARKETS: AN EXAMPLE OF MINT ECONOMIES

Increasing uncertainties due to developments in financial markets lead to uncontrollable financial behaviors. Financial stress indices are created by taking into account many different financial indicators directly related to the financial system. So, it is important to examine the impact of financial stress indices on stock markets, which have an important share in financial markets. The paper aims to investigate the short and long-term effects of emerging markets' financial stress index (EFSI) and global financial stress indices (GFSI) on the stock markets of MINT (Mexico, Indonesia, Nigeria, and Turkey) economies. For this purpose, analyses were made using the ARDL (Autoregressive Distributed Lag) Bounds Test method using weekly data for the period 10/01/2014-26/04/2024. It has been determined that EFSI and GFSI negatively affected the benchmark stock market indices of all MINT economies in the short term and that the negative effect continued in the long term but it has been significant for only EFSI in all MINT economies, as important results of the analysis. It has been determined that following financial stress indices can be a leading indicator for stock market investors. It is hoped that the results of the paper may be useful for financial market actors who are considering investing in these markets.

All are interesting to invest, I fear of missing out (FOMO): a comparative study among self-employed and salaried investors

Purpose This paper aims to examine how fear of missing out (FOMO) and investment intention mediate the relationship between behavioral biases and investment decisions of retail investors in the Indian stock market. Design/methodology/approach The present research comprises two cross-sectional quantitative studies, where Study A involves data from 405 self-employed and Study B involves 393 salaried investors. Data was attained through questionnaires – the partial least squares structural equation modeling was used for data analysis. Findings The outcomes show that herding, overconfidence and loss aversion bias significantly impact investment intention and FOMO on both studies. Furthermore, the outcomes also indicate that herding and loss aversion bias significantly influence investment decisions in studies (A and B); however, overconfidence bias insignificantly affects the investment decisions in Study A. Besides, the results also reveal a substantial relationship between FOMO, investment intention and investment decision. Practical implications The findings of this paper assist practitioners (financial analysts and retail investors) in considering the various ways of analyzing investment decision outcomes by considering the joint effect of several biases. Originality/value This paper is an initial attempt to propose a new theoretical framework and empirically examine the impact of behavioral biases on investment decisions by considering the FOMO and investment intention among self-employed and salaried investors. This study also contributes to the behavioral finance literature; other researchers may find it valuable to attain their goals.

Accurate and efficient stock market index prediction: an integrated approach based on VMD-SNNs

The stock market index typically mirrors the financial market's performance. Hence, accurate prediction of stock market index trends is essential for investors aiming to mitigate financial risk and enhance future investment returns. Traditional statistical approaches often struggle with the non-linear nature of stock market index data, leading to potential inaccuracies in long-term predictions. To address this issue, we introduce the TCN-LSTM-SNN (TLSNN) model, a hybrid framework that integrates Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) for robust feature extraction, within a highly efficient Spiking Neural Network (SNN) architecture. Additionally, we employ the Subtraction-Average-Based Optimizer (SABO) to refine the Variational Mode Decomposition (VMD) technique, thereby separating the periodic and trend components of stock indices, reducing noise interference, and establishing a decomposition ensemble framework to bolster the model's resilience. The experimental results show that the VMD-TLSNN hybrid model suggested in this study surpasses other individual benchmark models and their hybrid models in prediction accuracy. Additionally, it demonstrates notably lower energy consumption compared to other hybrid models.

Are Indian markets insulated from the impact of cryptocurrencies? Unveiling the volatility linkages through multi‐index dynamic multivariate GARCH analysis

AbstractThis paper investigates the dynamic relationships between the volatility of Bitcoin and major Indian stock market indices. Employing a dynamic conditional correlation–generalized autoregressive conditional heteroskedasticity (DCC‐GARCH) model, we explore how volatility shocks and information flow influence the correlations between these asset classes. Our findings reveal a key characteristic: volatility spillovers tend to be short‐lived, indicated by a relatively low DCC‐GARCH parameter (dcca1). This suggests that while a surge in volatility in one market might lead to a temporary increase in correlation with the other, this heightened correlation is unlikely to persist for extended periods. However, the model also highlights a high DCC‐GARCH parameter (dccb1), signifying that the correlations themselves are responsive to new information. This implies that volatility linkages can adjust rapidly in response to market events or economic data releases. To enhance accessibility for a broad audience, we translate these findings into economic intuitions. We illustrate how the model can be interpreted through real‐world examples, such as the impact of sudden policy changes in India or global market flash crashes. By understanding the short‐lived nature of volatility spillovers and the responsiveness of correlations, investors in the Indian markets can make more informed decisions when considering the potential influence of Bitcoin's volatility while contributing to a deeper understanding of the dynamic interactions between cryptocurrency and traditional financial markets in the Indian context.

Does speculation impair informational efficiency? New evidence from the Indian agricultural commodity market

Purpose This study explores the association between market efficiency and speculation. The government of India temporarily banned the futures trading of various commodities several times citing the presence of speculation. Many controversies exist about this topic; thus, this study clarifies the association between market efficiency and speculation and investigates whether market reforms altered this association.Design/methodology/approach The data for nine commodities is collected from the National Commodity and Derivative Exchange (NCDEX) for 2005–2022. Regression analysis and Automatic Variance Ratio (AVR) were adopted to inspect the informational efficiency and influence of speculation in the commodity market. Furthermore, this study uses different sub-samples to understand the changes in the market microstructure and its effects on market quality.Findings The results confirm an inverse and significant relationship between information efficiency and speculation and a deviation from the random walk process observed. Therefore, return predictability exists in the market. This study confirms that market reforms do not reduce the influence of speculation on market efficiency. The study concludes that the market is not weak-form efficient.Research limitations/implications This study has certain limitations, since this study is empirical in nature, it may possess the limitations of empirical research.Originality/value This paper has dual novelty. First, this study investigates the effects of market reforms. Second, this study captures the influence of speculation in the Indian agricultural commodity market by considering the market microstructure aspects.

Businiss Analytic Techniques of Apparels Industries and its impact on Global Business

The present study deals with “Business analytic techniques and its impact of global business in apparels industries.” The sale products and financial performance of Raymond industries application of tools and techniques of tools and cost management differ on 2021-22 and 2022-23. Results was that Raymond apparels industries application of tools and technique of cost management 2021-22 and 2022-23 net profit 77.76%, 7.76% on Indian market and 16.09% on Global market. The annual reports Raymond apparels industries in multiple products is very high as compared to single product. Application of tools and techniques of cost management 43.15 on 2022-23 financial years.

Patterns in the Chaos: The Moving Hurst Indicator and Its Role in Indian Market Volatility

Estimating the impact of volatility in financial markets is challenging due to complex dynamics, including random fluctuations involving white noise and trend components involving brown noise. In this study, we explore the potential of leveraging the chaotic properties of time series data for improved accuracy. Specifically, we introduce a novel trading strategy based on a technical indicator, Moving Hurst (MH). MH utilizes the Hurst exponent which characterizes the chaotic properties of time series. We hypothesize and then prove empirically that MH outperforms traditional indicators like Moving Averages (MA) in analyzing Indian equity indices and capturing profitable trading opportunities while mitigating the impact of volatility.