Download or read book Forecasting India s NIFTY IT Index written by Rajveer Rawlin and published by . This book was released on 2021-05-28 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master's Thesis from the year 2021 in the subject Business economics - Banking, Stock Exchanges, Insurance, Accounting, grade: 1, language: English, abstract: The purpose of this research is to forecast the following day's closing price for a specific share of a company in the stock market using the "Hidden Markov Model". In this paper, the "Hidden Markov Model" is used to predict some of the stocks of interconnected airline markets. The researchers have developed the "Hidden Markov Model" for forecasting time series. As a result of its ability to model dynamic systems, this model is widely used for the recognition of model and problem classifications. In this article, the researchers examined trends in the historical data set. They inserted the appropriate neighboring prices to the datasets and predicted the next day's exchange. Data collection was secondary. The secondary market was collected from Southwest Airlines for 1.5 years (approximately) from September 17, 2002, to December 16, 2004. The observations of the input data are continuous rather than discrete. The sample size is 4 airline firms (British Airlines, Delta Airlines, Southwest Airlines, and Ryanair Holdings Ltd.) The NIFTY IT index captures the performance of the Indian Information Technology (IT) companies. The NIFTY IT index consists of 10 companies listed on the National Stock Exchange (NSE). The IT sector in India has been recording tremendous growth over the years, where it accounts for a growth rate of 7.5 percent per annum. Time series analysis is a statistical tool that can be used in forecasting the prices of financial assets. In the current study, the NIFTY IT index was forecasted from past data collected over a 10 year period spanning from 2011 to 2020. An ARIMA model is fit and used to forecast the NIFTY IT index. Forecasted values were different from actual prices, suggesting that more influencing independent variables must be include, to improve the model accuracy.

Download or read book Forecasting Indian Stock Market Index Using Singular Spectrum Analysis written by Suwarna Shukla and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we focus on analyzing the predictive accuracy of three different types of forecasting techniques, Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), and Singular Spectral Analysis (SSA), used for predicting chaotic time series data. These techniques have different origins. ARIMA, ANN and SSA roots to Statistical Time Series Analysis, Computational Biology and Signal Processing respectively. The objectives of the paper can be explained in two parts: (1) To present the use of Singular Spectral Analysis (SSA) as a forecasting tool for predicting the index value of Indian Stock Market. (2) To compare the forecasting results from SSA in comparison to a parametric model, say Autoregressive Integrated Moving Average (ARIMA) and a non-parametric model, say Artificial Neural Network (ANN). In order to understand the processes of these techniques, we start with an example where, the SSA, ARIMA and ANN are provided with NSE Nifty 50 daily closing index data for 14 years from 1st January 1998 to 30th June 2014 that consists of 4123 data points. The Data is truncated into 4000 data points as input for above mentioned models and 123 data points as a scale for comparing the forecasting results from the above models. Later on we run Simulation to measure the Consistency and Accuracy of Performance of SSA, ARIMA and ANN. The accuracy and performances are validated by running the technique on 100 randomly generated time series with 2500 data points each. For each time series, the technique is compared on the basis of Root Mean Squared Error (RMSE). We find Predictability accuracy and performance of ANN better than SSA and ARIMA, and SSA better than ARIMA.

Download or read book Price Forecasting Models for IShares S P India Nifty 50 Index Fund INDY Stock written by Ton Viet Ta and published by . This book was released on 2020-08-23 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt: Do you want to earn up to a 325% annual return on your money by two trades per day on iShares S&P India Nifty 50 Index Fund INDY Stock? Reading this book is the only way to have a specific strategy. This book offers you a chance to trade INDY Stock at predicted prices. Eight methods for buying and selling INDY Stock at predicted low/high prices are introduced. These prices are very close to the lowest and highest prices of the stock in a day. All methods are explained in a very easy-to-understand way by using many examples, formulas, figures, and tables. The BIG DATA of the 2706 consecutive trading days (from November 20, 2009 to August 21, 2020) are utilized. The methods do not require any background on mathematics from readers. Furthermore, they are easy to use. Each takes you no more than 30 seconds for calculation to obtain a specific predicted price. The methods are not transient. They cannot be beaten by Mr. Market in several years, even until the stock doubles its current age. They are traits of Mr. Market. The reason is that the author uses the law of large numbers in the probability theory to construct them. In other words, you can use the methods in a long time without worrying about their change. The efficiency of the methods can be checked easily. Just compare the predicted prices with the actual price of the stock while referring to the probabilities of success which are shown clearly in the book (click the LOOK INSIDE button to read more information before buying this book). Depending on the number of investors who are interested in this book, the performance of the methods from the publication date will be added to the book after one year, and will be stated here in the description of the book too. You will then see that the methods in this book are outstanding or not. The book is very useful for Investors who have decided to buy the stock and keep it for a long time (as the strategy of Warren Buffett), or to sell the stock and pay attention to other stocks. The methods will help them to maximize profits for their decision. Day traders who buy and sell the stock many times in a day. Although each method is valid one time per day, the information from the methods will help the traders buy/sell the stock in the second time, third time or more in a day. Beginners to INDY Stock. The book gives an insight about the behavior of the stock. They will surely gain their knowledge of INDY Stock after reading the book. Everyone who wants to know about the U.S. stock market.

Download or read book Volatility Modeling and Forecasting for NIFTY Stock Returns written by Gurmeet Singh and published by . This book was released on 2016 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, an attempt has been made to model the volatility of NIFTY index of National Stock Exchange (NSE) and forecast the NIFTY stock returns for short term by using daily data ranging from January, 2000, to December, 2014, which comprises 3736 data points for the analysis by using Box-Jenkins or ARIMA model. The volatility in the Indian stock market exhibits characteristics similar to those found earlier in many of the major developed and emerging stock markets. It is shown that ARCH family models outperform the conventional OLS models. ADF test and unit root testing is done to know the stationarity of the series, later the AR(p) and MA(q) orders are identified with the help of minimum information criterion as suggested by Hannan-Rissanen. As per the analysis, ARIMA (1,0,1) model was found to be the best fit to forecast the volatility of NIFTY stock returns. The model can be used by the investors to forecast the short run NIFTY stock returns and for making more profitable and less risky investments decision.

Download or read book Modeling and Forecasting of Time Varying Conditional Volatility of the Indian Stock Market written by Srinivasan Palamalai and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Volatility forecasting is an important area of research in financial markets and immense effort has been expended in improving volatility models since better forecasts translate themselves into better pricing of options and better risk management. In this direction, the present paper attempts to model and forecast the volatility (conditional variance) of the S&P CNX Nifty index returns of Indian stock market, using daily data for the period from January 1, 1996 to January 29, 2010. The forecasting models that are considered in this study range from the simple GARCH(1, 1) model to relatively complex GARCH models, including the Exponential GARCH(1, 1) and Threshold GARCH(1, 1) models. Based on out-of-sample forecasts and a majority of evaluation measures, the results show that the asymmetric GARCH models do perform better in forecasting conditional variance of the Nifty returns rather than the symmetric GARCH model, confirming the presence of leverage effect. The findings are consistent with those of Banerjee and Sarkar (2006) that relatively asymmetric GARCH models are superior in forecasting the conditional variance of Indian stock market returns rather than the parsimonious symmetric GARCH models.

Download or read book Forecasting the Stock Index Movements of India written by Marxia Oli Sigo and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Prediction of financial markets, especially prediction of highly volatile stochastic stock market indices, plays a crucial role in identifying profitable investment avenues by the financial investors at large. The investing community encompasses retail investors, financial institutions, investment banks and Foreign Institutional Investors who look for the creation of wealth in the form of capital appreciation and earning the title of ownership of business enterprises by investing in the securities market, through buying and selling of shares of stock exchange listed corporate entities. The forecasting of dynamic financial market movements is one of the scientific endeavours which demands a great deal of market intelligence, financial acumen and domain knowledge of the characteristics of behavioural finance in a wider spectrum. This paper aims to discuss the non-linear movement pattern/trend of the most active two stock indices of India, namely, the Sensex and Nifty, during the study period from 2009-2015 by applying the traditional logistic regression method and one of the neural network tools, namely, k-nearest neighbourhood algorithm. This study would help the investors to streamline their investment patterns and strategies in order to take well informed investment decisions and optimize their stock returns by using the relevant market information.

Download or read book Forecasting Stock Index Movement written by Manish Kumar and published by . This book was released on 2006 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: There exists vast research articles which predict the stock market as well pricing of stock index financial instruments but most of the proposed models focus on the accurate forecasting of the levels (i.e. value) of the underlying stock index. There is a lack of studies examining the predictability of the direction/sign of stock index movement. Given the notion that a prediction with little forecast error does not necessarily translate into capital gain, this study is an attempt to predict the direction of Samp;P CNX NIFTY Market Index of the National Stock Exchange, one of the fastest growing financial exchanges in developing Asian countries. Random forest and Support Vector Machines (SVM) are very specific type of machine learning method, and are promising tools for the prediction of financial time series. The tested classification models, which predict direction, include linear discriminant analysis, logit, artificial neural network, random forest and SVM. Empirical experimentation suggests that the SVM outperforms the other classification methods in terms of predicting the direction of the stock market movement and random forest method outperforms neural network, discriminant analysis and logit model used in this study.

Download or read book Returns Volatility of Sectoral Indices of Nifty written by Dr. T. Peddanna and published by Readworthy. This book was released on with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Generally, the fund managers prefer to include Nifty-listed securities in their portfolio, because they are the leading stocks of the nation, using these companies constructed 11 sectors of stock indices. On the whole, the analysis of 12-year data starting from April 2002 to March 2014 established two phases of sectoral indices of Nifty; they are pre and post-recession periods in the light of sub-prime financial crisis that cropped up across the globe during 2008-09. As this study revealed sector-wise return exposure under different economic conditions, it helps investors to diversify their funds to various sectors which give average return to their portfolios and at lower risk element. However, this study is helped in understanding the risk-return relationship between different sectors of Nifty, as well as ARCH and GARCH models to estimate the volatility in the near future in great detail. The direction of the Nifty index is mainly determined by a few sectors in the long run like Bank, Pharma and Capital Goods indices. Finally, this study is enabled the investors to understand the risk and returns of sectoral indices of Nifty to make effective portfolio decisions under different economic conditions to sustain the portfolio with the same objectives till its tenure. This book is useful for Portfolio Managers, Fund Managers, Investment Managers and Policy makers, Academicians, Research scholars; Post graduate students and other commerce and Management students those working on Returns and volatility of stock market indices and securities."

Download or read book Forecasting of Stock Market Indices Using Artificial Neural Network written by Dr.Jay Desai and published by . This book was released on 2013 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper presents a computational approach for predicting the S&P CNX Nifty 50 Index. A neural network based model has been used in predicting the direction of the movement of the closing value for the next day of trading. The model presented in the paper also confirms that it can be used to predict price trend of the stock market. After studying the various features of the network model, a suitable model for stocks forecast is proposed. The model has used the preprocessed data set of closing value of S&P CNX Nifty 50 Index. The training data set encompasses the trading days from 1st January, 2010 to 30th November, 2011. The test data set encompasses the trading days from 1st January, 2011 to 31st December, 2011. Accuracy of the performance of the neural network is compared with buy and hold return of the index. The model generated returns of 59.84% against buy and hold return of -26.08%. The average accuracy of target forecasting is found to be 82%.

Download or read book Forecasting Financial Markets in India written by Rudra Prakash Pradhan and published by Allied Publishers. This book was released on 2009 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: Papers presented at the Forecasting Financial Markets in India, held at Kharagpur during 29-31 December 2008.

Download or read book Support Vector Machines Approach to Predict the S P CNX NIFTY Index Returns written by Manish Kumar and published by . This book was released on 2007 with total page 19 pages. Available in PDF, EPUB and Kindle. Book excerpt: The present study, investigates the predictability of Samp;P CNX NIFTY Index returns using Support vector machines (SVM). The performance of the SVM model in forecasting Nifty index returns is rigorously evaluated in terms of widely used statistical metrics like mean absolute error, root mean square error, normalized mean square error, correctness of sign and direction change (Pesaran and Timmermann (1992, DA test), and equal forecast accuracy using Diebold and Mariano (1995, DM test) by comparing its performance with those of neural network, random forest regression and a linear ARIMA model. The four competiting models are also examined in terms of various trading performance and economic criteria like annualized return, Sharpe ratio, maximum drawdown, annualized volatility, average gain/loss ratio etc via a trading experiment. The findings of the study reveal that SVM model achieves greater forecasting accuracy and improves prediction quality compared to other models experimented in the study. The SVM model can be used as an alternative forecasting tool for Nifty Index returns and it will lead to better returns based on the traditional forecasting accuracy measures, such as root mean squared errors, and financial criteria, such as risk-adjusted measures of return.

Download or read book Prediction of Stock Market Index Movements with Machine Learning written by Nazif AYYILDIZ and published by Özgür Publications. This book was released on 2023-12-16 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book titled "Prediction of Stock Market Index Movements with Machine Learning" focuses on the performance of machine learning methods in forecasting the future movements of stock market indexes and identifying the most advantageous methods that can be used across different stock exchanges. In this context, applications have been conducted on both developed and emerging market stock exchanges. The stock market indexes of developed countries such as NYSE 100, NIKKEI 225, FTSE 100, CAC 40, DAX 30, FTSE MIB, TSX; and the stock market indexes of emerging countries such as SSE, BOVESPA, RTS, NIFTY 50, IDX, IPC, and BIST 100 were selected. The movement directions of these stock market indexes were predicted using decision trees, random forests, k-nearest neighbors, naive Bayes, logistic regression, support vector machines, and artificial neural networks methods. Daily dataset from 01.01.2012 to 31.12.2021, along with technical indicators, were used as input data for analysis. According to the results obtained, it was determined that artificial neural networks were the most effective method during the examined period. Alongside artificial neural networks, logistic regression and support vector machines methods were found to predict the movement direction of all indexes with an accuracy of over 70%. Additionally, it was noted that while artificial neural networks were identified as the best method, they did not necessarily achieve the highest accuracy for all indexes. In this context, it was established that the performance of the examined methods varied among countries and indexes but did not differ based on the development levels of the countries. As a conclusion, artificial neural networks, logistic regression, and support vector machines methods are recommended as the most advantageous approaches for predicting stock market index movements.

Download or read book Building a Sustainable Future Roadmap for India s Progress Prosperity written by Dr.Shalini Choithrani and published by SHREE VINAYAK PUBLICATION. This book was released on 2024-03-14 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Artificial Intelligence in Forecasting written by Sachi Mohanty and published by CRC Press. This book was released on 2024-07-19 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting deals with the uncertainty of the future. To be effective, forecasting models should be timely available, accurate, reliable, and compatible with existing database. Accurate projection of the future is of vital importance in supply chain management, inventory control, economic condition, technology, growth trend, social change, political change, business, weather forecasting, stock price prediction, earthquake prediction, etc. AI powered tools and techniques of forecasting play a major role in improving the projection accuracy. The software running AI forecasting models use machine learning to improve accuracy. The software can analyse the past data and can make better prediction about the future trends with higher accuracy and confidence that favours for making proper future planning and decision. In other words, accurate forecasting requires more than just the matching of models to historical data. The book covers the latest techniques used by managers in business today, discover the importance of forecasting and learn how it's accomplished. Readers will also be familiarised with the necessary skills to meet the increased demand for thoughtful and realistic forecasts.

Download or read book Machine Learning and Metaheuristics Algorithms and Applications written by Sabu M. Thampi and published by Springer Nature. This book was released on 2021-02-05 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Second Symposium on Machine Learning and Metaheuristics Algorithms, and Applications, SoMMA 2020, held in Chennai, India, in October 2020. Due to the COVID-19 pandemic the conference was held online. The 12 full papers and 7 short papers presented in this volume were thoroughly reviewed and selected from 40 qualified submissions. The papers cover such topics as machine learning, artificial intelligence, Internet of Things, modeling and simulation, disctibuted computing methodologies, computer graphics, etc.

Download or read book Machine Learning Approaches in Financial Analytics written by Leandros A. Maglaras and published by Springer Nature. This book was released on with total page 485 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Cybernetics Cognition and Machine Learning Applications written by Vinit Kumar Gunjan and published by Springer Nature. This book was released on 2021-03-30 with total page 439 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes the original, peer reviewed research articles from the 2nd International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA 2020), held in August, 2020 at Goa, India. It covers the latest research trends or developments in areas of data science, artificial intelligence, neural networks, cognitive science and machine learning applications, cyber physical systems and cybernetics.