How did Markets and Public Sentiment React During Demonetization? Study of a Significant Event in the Indian Economy

The present study aims to determine the impact of shock of demonetization which happened in November 2016 in India. It has been observed in literature that while the market moves due to unforeseen events, market movements are largely affected by news reports on such events. Considering these two threads and the association between them, the study follows mixed method research methodology and assesses the impact of demonetization on stock market movement through time series analysis and text analytics of news items generated during the period. This study examines, through time series analysis, the impact of demonetization as an unexpected event on stock market movement. Time series analysis evaluates the impact on overall stock market movements and on sectoral indices, liquidity shocks in the emerging Indian economy due to demonetization. This study integrates time series analysis with robustness tests and follows text analytics, news analytics and sentiment analytics to gauge public sentiment (influenced by media coverage) during the event. These evaluations validate negative movements in the market and most of the sectors due to the negative sentiment of people about demonetization

2016 Rupee Demonetization (Dn): It’s a Success!

The November 2016 Dn of the rupee is a phenomenal event in India’s monetary history with spillovers into every nook and corner of Indian economy, and yet it has hardly received the attention it warrants from economists and other professionals. To better understand the socioeconomic consequences this paper compiles and evaluates the positives and negatives of Dn in spite of challenges in quantifying them. There was uproar at the outset because of the market turmoil notebandi caused, but some twenty months later Indian economy is doing well on the Dn stress test. This is evident in a) the broadening of the tax base, b) the relatively higher degree of compliance with filing income tax returns and reduction in black money, c) the new business regulations related to Goods and Services Tax (GST) leaving little scope for corrupt ways, d) increasingly higher rates of growth in GDP and notably, e) the swift transformational change in economic behavior triggering better tax compliance as well as the exodus to digital payment modalities, and the concurrent reduction in habitual need for cash. The five structural changes above normally have a long gestation. However, the successful ongoing makeover in India in so short a time by itself calls for an intensive study tempting one to suspect if nation-state pride is one of the drivers of reform

Data Analytics in advancing Accounting Profession and Business Information for Decision Making

The nature of the risks and opportunities facing business has changed over time. Much of the global value today is more of technology service and knowledge based than it was 40 years ago. The study examines data analytics in advancing accounting profession and business information for decision making. Two specific objectives guided the study, the study used a survey research design approach, and the population consists of 300 respondents made up of 50 each from academics, financial analysts, accountants, business owners, investors and big data analysts. Descriptive statistics was used to analyses the data while Z test was used to test the hypotheses. The findings from the study shows that the two hypotheses tested has a high acceptance degrees level of an average percentage of (92.4%) and (86.98%) respectively, this goes to shows that the issue of big data analytics in advancing accounting profession and business information for decision making is very much germen. This also was observed in the results of Z-Test of the Standard Deviation of (0.412) and (0.303) respectively, which leads to the acceptance of the two alternatives hypotheses and rejecting of the null hypotheses. The study concluded that big data analytics improves and help business organizations take informed decisions to enhance their operational efficiency, also, that the world accepted the slogan that data is the new oil. Those who are able to gain out of that will remain in the business ie, survival of the fittest. This is the implication to the new millennium environment where the professional accountant finds itself. Therefore, should be able to deal with the complex procedures, so that the accountant will be a big data analytics professional too. The study recommended among others that all the stakeholders (academics, financial analysts, professional association bodies, accountants, business owners, investors and government) should be involves in the necessity of teaching big data and business analyses in management sciences in our higher institutions to promote students' knowledge, the continues enlightenment, holding workshops, training and retraining  courses for researchers and academics of the importance of analyzing big data and how to process, store, manage and use the analyzed data in the financial and accounting field, since using big data can lead to better disclosure which in turn  enhance investor trust

Effective and ethical use of strategic data analysis for the purposes of election engineering in India.

There has been a rise in the use of data analytics in the election campaigning process, the potential of which can be seen with the 2012 US presidential election. This dissertation aims to do an examination into the effective and ethical use of data analytical models for the purpose of election engineering in an Indian context. It appraises the current analytical models used in India through the collection of primary data such as semi-structured interviews which were conducted with people working with the different political organizations in India. In total, 6 semi-structured interviews were carried out among representatives from 3 political parties, at both the state level and the national level. The study revealed the usage of data analysis by the national level parties with different strategic approaches towards it, whereas, the state party focused more on traditional strategic methods of data analytics. The recommendation for improving the effectiveness of current data analytical models in India, is through providing a structured method to be followed which could be tailored for specific political party requirements. The ethical recommendation involves monitoring and controlling data analytics from breaching the data privacy of individuals through the use of governmental institutions and regulatory bodies.Houve um aumento no uso de análise de dados no processo de campanha eleitoral, cujo potencial pode ser visto nas eleições presidenciais de 2012 nos EUA. Esta dissertação tem como objetivo fazer um exame sobre o uso efetivo e ético de modelos analíticos de dados para fins de engenharia eleitoral em um contexto indiano. Ele avalia os modelos analíticos atuais usados na Índia através da coleta de dados primários, como entrevistas semi-estruturadas que foram realizadas com pessoas que trabalham com as diferentes organizações políticas na Índia. No total, foram realizadas 6 entrevistas semi-estruturadas entre representantes de 3 partidos políticos, em nível estadual e nacional. O estudo revelou o uso da análise de dados pelas partes de nível nacional com diferentes abordagens estratégicas, enquanto o partido do estado se concentrou mais em métodos estratégicos tradicionais de análise de dados. A recomendação para melhorar a eficácia dos atuais modelos analíticos de dados na Índia é através do fornecimento de um método estruturado a ser seguido, que pode ser adaptado para requisitos específicos de partidos políticos. A recomendação ética envolve monitorar e controlar a análise de dados por violar a privacidade de dados de indivíduos através do uso de instituições governamentais e órgãos reguladores

A Netnography of the Social Media Presence of Brand Netflix, India

Social media marketing is growing at a rapid pace. Marketers need to keep up with technology-savvy consumers and help the brands in reaching greater heights with the help of social media practices. This manuscript presents how an Over-the-Top platform like Netflix India uses social media as a digital tool for enhancing consumer engagement and building strong consumerbrand relationships using netnography as a tool. This manuscript further attempts to classify the content generated by Netflix India on its social media platforms- Instagram, Facebook, Twitter into diverse content typologies, identify the most significant content typology, and understand how the number of likes, comments, shares, retweets (in case of Twitter) relating to most significant content typology impacts the enhancement of consumer engagement amongst Netflix audience. Thereafter, the research study classifies the content generated by Netflix into three categories, namely- informational content, promotional content, and relational content. The study is further able to empirically illustrate how a greater number of likes, shares, comments, and retweets on relational content has a significant impact on consumer engagement of the brand Netflix

Intelligent Monitoring and Controlling of Public Policies Using Social Media and Cloud Computing

Part 3: Government and InfrastructureInternational audienceLack of public participation in various policy making decision has always been a major cause of concern for government all around the world while formulating as well as evaluating such policies. With availability of latest IT infrastructure and the migration of government think-tank towards realizing more efficient cloud based e-government, this problem has been partially answered, but this predicament still persists. However, the exponential rise in usage of social media platforms by general public has given the government a wider insight to overcome this long pending dilemma. This paper presents a pragmatic approach that combines the capabilities of cloud computing and social media analytics towards efficient monitoring and controlling of public policies. The proposed arrangement has provided us some encouraging results, when tested for the policy of the century i.e. GST implementation by Indian government and established that proposed system can be successfully implemented for efficient policy making and implementation

Rushing to Regulate: Rethinking the RBI\u27s Directives on Peer-to-Peer Regulations in India

Almost half of India still does not have a bank account, leaving millions of Indians unable to access traditional sources of credit. For these unbanked Indians, peer-to-peer (P2P) lending platforms have become an important alternative credit source. A recent boom in P2P platforms caused the Reserve Bank of India (RBI) to create a regulatory framework for the P2P sector. This Comment seeks to address some of the issues concerning regulating an unconventional industry that provides a crucial service. First, it is argued that the RBI fundamentally mischaracterizes both the services P2P\u27s provide, and how P2P\u27s provide these services. The Comment then discusses challenges P2P regulation poses for the RBI, arguing that the RBI\u27s framework both over- and underregulates P2P platforms. Finally, this Comment recommends India adopt U.S. P2P regulations, allowing for an exemption-based approach to lending. Given that alternative credit is much needed in India, this comment hopes to better tailor current regulations, in order to avoid a total regulatory overhaul

Mobile Commerce: Secure Multi-party Computation & Financial Cryptography

Abstract: The basic objective of this work is to construct an efficient and secure mechanism for mobile commerce applying the concept of financial cryptography and secure multi-party computation. The mechanism (MCM) is defined by various types of elements: a group of agents or players, actions, a finite set of inputs of each agent, a finite set of outcomes as defined by output function, a set of objective functions and constraints, payment function, a strategy profile, dominant strategy and revelation principle. The mechanism adopts a set of intelligent moves as dominant strategies: (a) flexible use of hybrid payment system which supports cash, e-payment and m-payment, (b) secure multi-party computation to ensure information security and privacy and (c) call intelligent analytics to assess and mitigate possible threats on m-commerce service. The mechanism supports three different types of transaction processing protocols (P1, P2 and P3) and calls a cryptographic protocol (Pc). The cryptographic protocol performs a set of functions sequentially such as authentication, authorization, correct identification, privacy verification and audit of correctness, fairness, rationality, accountability and transparency of secure multi-party computation on each m-transaction. The basic building blocks of the cryptographic protocol are signcryption, proofs of knowledge, commitments and secret sharing. This work also presents the complexity analysis of the mechanism in terms of computational cost, communication cost, security and business intelligence. Keywords: Secure multi-party computation, Financial cryptography, Mobile commerce mechanism, Threat analytics, Digital econom

Combination of Facebook Prophet and Attention-Based LSTM with Multi- Source data for Indian Stock Market Prediction

The stock market prediction has been the subject of interest to various researchers and analysts due to its highly unpredictable nature and serves as a perfect example for time series forecasting. Over the years deep learning models such as Long-Term Short-Term Memory and statistical models such as Autoregressive Integrated Moving Average have shown promising results in predicting future stock prices. But the results from these models cannot be generalized as they fail to incorporate the dynamics of the market and influence of several external factors such as political, social, investor\u27s emotion, etc on stock markets. Recently Facebook’s creation of the Prophet model solely for time series forecasting has been successful in fitting the trends and seasonality of the data accurately compared to vanilla models. This research proposes a unique combination of the newly developed Facebook Prophet model and Attention-Based Long-Term Short-Term Memory model to predict the adjacent closing price of NIFTY 50 stocks to fit both the seasonality and non-linearity component of stock price data. Further to encompass both market and investor sentiments influencing stock prediction, data from five sources are collected from 01/01/2015 to 31/12/2019 namely historic stock price, technical indicators, news articles scraped from multiple news sources, and tweets collected from a verified Twitter account. To extract sentiments from unlabelled news and tweet data this research takes upon an unsupervised approach by implementing a pre-trained Bidirectional Encoder Representations from Transformers base uncased model. The proposed model is trained and validated on eight combinations of the dataset created by merging data from multiple sources and compared with the performance of the baseline Facebook Prophet model trained and tested with data from a single source i.e., historic stock prices. The proposed model resulted in the least Mean Absolute Percentage Error ranging from 3.3 to 7.7 for all the combinations of the data in comparison to the baseline model which achieved the highest Mean Absolute Percentage Error of 11.67