Indian Financial Market Research Articles

Stock Market Index Prediction Using Artificial Neural Network

Often, nonlinearity exists in the financial markets while Artificial Neural Network (ANN) could be used to expect equity market returns for the next years. ANN has been improved its ability to forecast the daily stock exchange rate and to investigate several feeds using the back propagation algorithm. The proposed research utilized five neural network models, Elman network, Multilayer Perceptron (MLP) network, Elman network with Self-Optimizing Map (SOM), MLP with SOM filter and simple linear regression, for estimating new values. Results were examined to investigate the predicting ability and to provide an effective feeds for future values. The result of the proposed simulation showed that SOM could greatly improve the convergence of the neuron networks; whereas Elman network did a better performance to capture the temporal pattern of the symbolic streams generated by SOM.A benchmark of linear regression model was also employed to show the ability of neural network models to generate higher accuracy in forecasting financial market index.

Financial Constraints and Investment Financing Decisions among Indian Firms in Post Sub Prime Crisis Era

The study investigated the sources of investment financing by Indian non-financial companies and the extent of dependency on internal funds to finance their investment projects since the U.S. Sub-prime mortgage crisis. A balanced panel of 359 firms for the period of 2006-2020 was estimated by GMM, Fixed Effect, Random Effect and OLS Pooled technique for three groups of firms. The result indicated that internal funds were highly important for all categories of firms. Further, higher investment cash flow sensitivity for the smaller firms demonstrated that these firms did not have easy access to capital markets compared to larger firms despite significant reform measures in the Indian financial market since 1991.

The Volatility Transmission Between Cryptocurrency And Global Stock Market Indices: Case Of Covid-19 Period

The uncertainty originated by the COVID-19 pandemic and the unpredictability of both real and financial market indicators have increased the volatility of global financial markets. As a result of globalization, the determination of risk and information transfer between financial markets has gained importance during the pandemic process. In this context, the spread of volatility between the cryptocurrency market and the global stock markets was analyzed by considering the pandemic process. Bitcoin, which represents 42% of the total market cap, was used to represent the cryptocurrency market in the analysis. S&P500, FTSE100, SSEC and NIKKEI indices, which are among the world's leading indices in terms of market cap, were used to represent the global stock market. Constant Conditional Correlation Multivariate GARCH model was used for the analysis of volatility transmission. Daily closing prices covering the date range from 1st December 2019 to 1st July 202 were used for the analyses. The model results were positive and significant for all predicted conditional correlation parameters. In this context, there is volatility transmission and information transfer between BTC and stock returns. The model findings are expected to be a supporting element for financial market participants to make the right decision in the optimal portfolio allocation process.

Predicting power of ticker search volume in Indian stock market

This study examines the ability of online ticker searches to serve as a valid proxy for investor sentiment and forecast stock returns and trading volumes in the Indian financial market. In contrast to the findings in prior research, we observe that ticker search volumes do not exhibit any predictive value for future excess stock returns. However, we find a weak but significant positive effect of ticker search volumes on trading volume with a two-week lag. A battery of robustness checks supports our findings. Our work warns the investors from possible misleading insights arising from search volume and stock returns related studies.

Nexus Between Indian Financial Markets and Macro-economic Shocks: A VAR Approach

This paper studies the nexus between asset returns volatility in six major segments of Indian financial markets (viz. money, equity, gsec, forex, equity and banking stocks) and macro-economic shocks (viz. GDP, Inflation, Current Account Deficit, market capitalisation to GDP ratio, US Treasury Yield and Foreign Portfolio Investment). The period of study is from April 2002 to March 2021, a period covering four instances of significant economic and financial market stress. Findings of the study are generally aligned to economic theory, except for the case of gsec market. Besides, macro-variables were found to be exerting greater impact when they are in their weaker/unstable state and the behaviour of US treasury yield and FPI flows were found be more significant factors during stress periods and recovery immediately thereafter. Therefore, there is a need to focus on maintaining macroeconomic stability as a policy to foster financial market stability. Besides, there is a need to monitor a customized and dynamic list of macroeconomic variables in respect of each of the financial market segments to decide on the timing, type and quantum of policy and regulatory responses from time to time. This study contributed towards financial markets public policy, particularly during periods of uncertainties.

Approximate amplitude encoding in shallow parameterized quantum circuits and its application to financial market indicators

Efficient methods for loading given classical data into quantum circuits are essential for various quantum algorithms. In this paper, we propose an algorithm called Approximate Amplitude Encoding that can effectively load all the components of a given real-valued data vector into the amplitude of quantum state, while the previous proposal can load only the absolute values of those components. The key of our algorithm is to variationally train a shallow parameterized quantum circuit, using the results of two types of measurement: the standard computational-basis measurement plus the measurement in the Hadamard-transformed basis, introduced in order to handle the sign of the data components. The variational algorithm changes the circuit parameters so as to minimize the sum of two costs corresponding to those two measurement basis, both of which are given by the efficiently computable maximum mean discrepancy. We also consider the problem of constructing the singular value decomposition entropy via the stock market data set to give a financial market indicator; a quantum algorithm (the variational singular value decomposition algorithm) is known to produce a solution faster than classical, which yet requires the sign-dependent amplitude encoding. We demonstrate, with an in-depth numerical analysis, that our algorithm realizes loading of time series of real stock prices on quantum state with small approximation error, and thereby it enables constructing an indicator of the financial market based on the stock prices.

A convolutional neural network based approach to financial time series prediction.

Financial time series are chaotic that, in turn, leads their predictability to be complex and challenging. This paper presents a novel financial time series prediction hybrid that involves Chaos Theory, Convolutional neural network (CNN), and Polynomial Regression (PR). The financial time series is first checked in this hybrid for the presence of chaos. The chaos in the series of times is later modeled using Chaos Theory. The modeled time series is input to CNN to obtain initial predictions. The error series obtained from CNN predictions is fit by PR to get error predictions. The error predictions and initial predictions from CNN are added to obtain the final predictions of the hybrid model. The effectiveness of the proposed hybrid (Chaos+CNN+PR) is tested by using three types of Foreign exchange rates of financial time series (INR/USD, JPY/USD, SGD/USD), commodity prices (Gold, Crude Oil, Soya beans), and stock market indices (S&P 500, Nifty 50, Shanghai Composite). The proposed hybrid is superior to Auto-regressive integrated moving averages (ARIMA), Prophet, Classification and Regression Tree (CART), Random Forest (RF), CNN, Chaos+CART, Chaos+RF and Chaos+CNN in terms of MSE, MAPE, Dstat, and Theil’s U.

The Effect of Dependence on European Market Risk. A Nonparametric Time Varying Approach

Multivariate dependence measures are crucial for risk management, where variables usually have heavy tails and non-Gaussian distributions. We propose a multivariate time varying Kendall’s tau estimator in a nonparametric context, allowing for local stationary variables. Consistency and asymptotic normality of the estimator are provided. A simulation study is conducted which supports the idea of better performance than other related methods in many complex scenarios. The proposal is used to draw up a daily estimation of the dependence between European financial market indexes. Nonparametric conditional quantiles are estimated to detect any influence of the degree of dependence on the market returns. That dependence emerges as an important factor in the Euro Stoxx distribution. It is noteworthy that the Kendall’s tau only depends on the multivariate copula, so the effect is not due to hidden effects of the marginals. Local Granger causality is tested and evidence is found that the degree of dependence affects the Euro Stoxx returns in the left tail of the distribution. We believe that these results encourage further research into the effect of diversification in quantiles, linked to the factors behind systemic risk. Additionally, there is a noteworthy increase in dependence following the outbreak of COVID-19.

Heterogeneous Effects of Financial Development on Tax Revenues: Accounting for Institutional Quality in Sub-Saharan Africa

Several studies have shown that financial development improves efforts to increase the tax base and tax revenue through different channels. This paper analyzes the effect of financial development on tax revenue conditional on institutional quality for a panel of developing countries over the period 1990-2019. Using a threshold approach, the results indicate that the influence of financial development on non-cash tax receipts is not monotonic and is sensitive to level of institutions. Financial development improves the tax base and tax revenue when the quality of institutions is better. The results point out as well heterogeneousness across countries and over the years counts. Estimates with financial market index confirm the evidence that countries that benefit most from increased tax revenues are those that improve their institutional quality.

Effective Practices of Macroprudential Stress Testing as a Tool of Increasing the Stability of Russian Financial System in the Context of Macroeconomic Shocks

The article examines the evolution of the prudential approach to banking regulation, examines the practical contribution of the Basel Committee on Banking Supervision to the development and implementation of internationally unified practices and procedures for stress testing and supervision. The authors share the point of view that the existing methods and practices of stress testing still need improvements and methodological improvements, since they regularly allow the practical implementation of adverse scenarios leading to financial shocks and global crises. As a significant disadvantage of many actively used stress testing models, it is noted that they are often focused on complex, highly bureaucratic procedures for the preparation and analysis of financial statements, the main purpose of which is to assess the probabilities and sizes of losses and identify scenarios for the development of the situation for each specific bank, and no risks for the financial system as a whole. The authors come to the conclusion that it is advisable to prioritize the use of alternative stress testing models in crisis and post-crisis conditions, the forecasts within which are based on the actual values of financial market indicators, macroeconomic variables, and other open data. Special attention is paid to the stylized CLASS model, based on simple econometric models, as well as stress testing the current market value of V-lab. Based on the results of the study, the authors come to a number of conclusions that the role of the macroeconomic component in the procedures, methods, and algorithms for macroprudential stress testing used in Russia should increase, the degree of involvement and the sphere of responsibility for its results of key institutional units of the public administration system should expand, and macroprudential stress testing itself should not be limited to supervisory stress testing in everyday practice.

Dependence structure between Indian financial market and energy commodities: a cross-quantilogram based evidence

Given the developing nations are moving towards attaining the sustainable energy future, the reliance on renewable energy solutions is rising. Therefore, the dependence on traditional fossil fuel-based solutions is getting reduced, and this might have an impact on the energy market commodities. Analyzing this impact might divulge several insights regarding the portfolio decisions, in presence of the transformations in developmental trajectory. In this study, we analyze the cross-quantile dependence of the returns on the energy market commodities and the market returns for Indian financial market over July 31, 2008 to March 31, 2020. For this purpose, we adopt a novel three-stage methodology comprising Dynamic Conditional Correlation GARCH, Cross-quantilogram, and Wavelet Coherence-based models. We find that the market returns have negative effect on returns on the energy market commodities. This impact has been found to be asymmetric in nature. Moreover, the moderating impact of policy uncertainty has been analyzed has been analyzed through partial cross-quantilogram approach, and the outcome shows that the impact remains same under extreme market conditions. The findings have significant portfolio decisions in an energy transition context.

The impact of financial development on CO2 emissions: new evidence from developed and emerging countries.

Taking into account the complicated and multidimensional nature of financial development, this study aims to investigate the impact of overall financial market development, institution development, and their sub-indices on CO2 emissions. To advance knowledge about the nexus between financial development and CO2 emissions, four financial market indices (overall financial market development, FM-access, FM-depth, and FM-efficiency) and four financial institution indices (overall financial institution development, FI-access, FI-depth, and FI-efficiency) are used. The study used two-stage system GMM and panel data of developed and emerging countries over the period 2000-2018. The empirical results reveal that the overall financial market development and its sub-indices (FM-access, FM-depth, and FM-efficiency) reduce CO2 emissions in developed and emerging countries. The results further show that the overall financial institution development and its sub-indices such as FI-access, FI-depth, and FI-efficiency foster the environment quality in developed economies, while these indices impede the environmental quality in emerging economies. The usage of renewable energy is found to be a viable solution to mitigate the CO2 emissions in both groups of countries. Additionally, policies related to sustainable development are also discussed in the paper.

Sectoral herding behaviour in the Indian financial market

Sectoral herding behaviour in the Indian financial market

Consumer Grievance Redress in Indian Financial Markets

Consumer Grievance Redress in Indian Financial Markets

An Analysis on Stock Market Volatility in an Emerging and Developing Economy During Global Pandemic

Unavoidably, COVID-19's global epidemic brought about a significant drop in every sphere of the economy. This study examines the impact of COVID-19 on Indian stock market performance by using a moving average and historical values of the Nifty 50 and S&P BSE SENSEX. The investigation's goal is to pinpoint the Indian Stock Exchange's BSE and NSE weaknesses and the volatility during that time span. The results demonstrate that the pandemic had a major influence on the industries included in this research. But the rise in COVID-19 cases starting in January 2020 and the deaths that followed in 2021 caused the biggest stock market decline ever seen. Despite that, the market saw a record-breaking high on both the NSE and the BSE by the end of 2021. Following the Covid's impact, the Indian financial market also experienced a sharp downturn. During this time, the Nifty dropped 7500 points and the BSE dropped about 920 points. This research focuses on the volatility of the stock market across this time frame. By the afternoon of April 5th, 2021, investors had lost more than Rs. 4.54 lakh crore. The investors lost a total of 7.36 lakh crore in just 7 months.

Option pricing using Data-Driven Machine Learning Approaches: Empirical Evidence from Indian Financial Market.

Option pricing using Data-Driven Machine Learning Approaches: Empirical Evidence from Indian Financial Market.

Mutual funds: Performance evaluation of select debt schemes

Mutual funds are one of the important financial instruments for the financial sector. Mutual funds provide an opportunity for investors to invest in a variety of diversified portfolio investments and also help to achieve their financial objectives. The mutual fund industry has been responding veritably fast in the Indian financial market in the last ten years as it provides further promising results to investors. The Indian mutual fund industry has over the last year seen a dramatic improvement in terms of volume as well as the quality of the product and service offerings in recent times. The study measures the performance of Mutual Fund (MF) schemes in India with special reference to public and private debt mutual fund schemes. A total of 16 schemes were selected from the four asset management companies. The evaluation was attained by applying various financial tools like Rate of Return, Standard Deviation, Beta, Sharpe Ratio, Jenson Ratio, and Treynor Ratio. The data contained comes from the various authorized websites and factsheets of funds. When public and private mutual funds were analogized, the private mutual fund companies were commencing much better than public mutual fund companies for the investors to invest in.

Sectoral herding behavior in Indian financial market

Sectoral herding behavior in Indian financial market

Online Scams in Financial Market-A study with reference to Indian Financial Market

Online Scams in Financial Market-A study with reference to Indian Financial Market

Measuring the impact of exchange rate volatility on the depth and efficiency of the financial sector: evidence from Nigeria

This study examines the effect of exchange rate volatility on the depth and efficiency of Nigeria's financial sector over the period of 1986-2018 via the autoregressive distributed lag cointegration technique. The study employs four models that consist of financial institution and financial market indicators of depth and efficiency. The results from the study show that exchange rate volatility exerts a negative effect on financial institutions' depth and efficiency in both the short run and long run. The findings also show that exchange rate volatility wields a negative effect on financial market depth in the short run, albeit with no long run effect, whereas exchange rate volatility has an adverse effect on financial market efficiency in both the short run and long run. The paper recommends that the Central Bank of Nigeria should put in place adequate measures to keep the exchange rate stable in order to broaden the depth and efficiency of the financial sector.