Purpose Accurate prediction of stock prices is of particular importance due to the wide-ranging effects it has on financial decisions. Design/methodology/approach In this study, long short-term memory (LSTM) neural networks have been used to model and predict future stock prices. These networks are suitable for this application due to their ability to identify complex temporal patterns and retain long-term information. Historical stock price data is used as input to the model for 2023–2024, and a model for price prediction is developed using LSTM neural networks. Findings The results indicate that the LSTM model can predict stock price trends with acceptable accuracy, but still faces challenges such as market fluctuations and sudden changes. Originality/value This research can serve as a supportive tool for financial analysts and investors to improve financial decision-making.

PurposeThe goal of the study is to offer important insights into the dynamics of the cryptocurrency market by analyzing pricing data for Bitcoin. Using quantitative analytic methods, the study makes use of a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and an Autoregressive Integrated Moving Average (ARIMA). The study looks at how predictable Bitcoin price swings and market volatility will be between 2021 and 2023.Design/methodology/approachThe data used in this study are the daily closing prices of Bitcoin from Jan 17th, 2021 to Dec 17th, 2023, which corresponds to a total of 1065 observations. The estimation process is run using 3 years of data (2021–2023), while the remaining (Jan 1st 2024 to Jan 17th 2024) is used for forecasting. The ARIMA-GARCH method is a robust framework for forecasting time series data with non-seasonal components. The model was selected based on the Akaike Information Criteria corrected (AICc) minimum values and maximum log-likelihood. Model adequacy was checked using plots of residuals and the Ljung–Box test.FindingsUsing the Box–Jenkins method, various AR and MA lags were tested to determine the most optimal lags. ARIMA (12,1,12) is the most appropriate model obtained from the various models using AIC. As financial time series, such as Bitcoin returns, can be volatile, an attempt is made to model this volatility using GARCH (1,1).Originality/valueThe study used partially processed secondary data to fit for time series analysis using the ARIMA (12,1,12)-GARCH(1,1) model and hence reliable and conclusive results.

PurposeThe study was done to review the existing literature available on the theme using a popular technique known as a bibliometric review. The purpose was to explore important bibliometric trends such as geographical distribution of research; the most relevant countries and institutions and important collaboration networks, frequently published authors, the most relevant topics/research domains and relationships among these, average citations or per year, the most relevant sources, top authors’ production, authors’ impact by H index and the progression of important keywords over a period of time.Design/methodology/approachThe study analyzed literature published in the English language from 2012 onwards that used the words “cryptocurrency”, “Ethereum” “Bitcoin” along with “investment/s” or “speculation/s” in the Title/ABS/KEY. A specialized approach was followed to retrieve and analyze focused research. The data for analysis was extracted from the Scopus database and was analyzed using Biblioshiny and VOSViewer.FindingsThe study found that the countries such as the UK, Australia, China and the USA have special relevance in terms of the number of citations and collaboration networks. Cryptocurrency/Cryptocurrencies, bitcoin have been the base themes along with other crucial issues such as volatility, hedging, COVID-19 pandemic, Ethereum, blockchain, co-integration, portfolio diversification/optimization, spillover, safe haven, investor attention, gold, etc. There is a lot of interdisciplinary research on the theme.Originality/valueThe current study used a concentrated approach to study the bibliometric literature about the financial implications of cryptocurrency as an asset class and not prominently its technological or legal aspects.

PurposeThe impact of Goods and Service Tax (GST) on the motor and pump exports in the Coimbatore region is measured in this study using various parameters and scales. The data collected from exporters were used to identify the pros and cons of GST, stating their opinions on variables considered by the researchers through extensive literature on GST and exports.Design/methodology/approachThe data were collected from 220 motors and pump exporters through a field survey from the month of January to September 2021. The impacts have been measured using principal component analysis (PCA) and confirmatory factor analysis (CFA). The factor analyses and CFA will derive the positive and negative impact of GST determined through critical empirical evidence in this study. Also, the problems faced by the exporters allied to GST, which the authors could not include in the questionnaire due to certain reasons, are concisely apportioned and discussed.FindingsThe study depicts the major advantages of GST, such as harmonized system, long-run performance, reduction in logistics cost, check-post operation, bonds and ease of doing business. Also, it highlights the disadvantages of GST, such as biases in the indirect tax system, the reimbursement of duty drawback being late or pending and document filing was still a tedious job under the GST regime.Originality/valueThe unavailability of considerable literature on the impact of GST on Indian exports signifies the novelty of this research. So far, this is the first empirical attempt to measure the impact of GST on exports which is a unique and original attempt to highlight the problem that lies under the GST regime and the necessary reforms the tax structure needs in the context of Indian exports.

PurposeWith the developments of the digital era and considerable developments in ICT, advertisers are leveraging data-driven forms of online advertising to target consumers individually. The present study integrates privacy concerns with the constructs from the persuasion knowledge model to investigate consumer avoidance of online behavioural advertising (OBA).Design/methodology/approachThe study employed an online survey method for data collection using a sample size of 345. Reliability and validity of the measurement scales were ensured, and hypotheses developed were tested through PLS-SEM using SMART PLS.FindingsThe results show that persuasion knowledge is a significant predictor of perceived benefits, perceived risks and privacy concerns. Also, privacy concern was found to significantly mediate persuasion knowledge-avoidance behaviour and perceived risk-avoidance behaviour. On the other hand, the perceived benefit was not found significant in influencing privacy concerns for OBA.Practical implicationsThe present study is one of the initial attempts to understand the level of knowledge Indian consumers hold about OBA and how they evaluate and respond to these data-driven forms of advertising. The current study helps advance knowledge of the field and the theories used. Future studies might look at the effect of various demographic and psychographic aspects on consumer avoidance of OBA.Originality/valueAs the country is shifting to digital, it becomes really important to understand the privacy concerns that people perceive in regard with the current advertising practices.