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Author: Rajveer Rawlin
Publisher:
ISBN: 9783346524461
Category :
Languages : en
Pages : 92
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Book Description
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.
Author: Rajveer Rawlin
Publisher:
ISBN: 9783346524461
Category :
Languages : en
Pages : 92
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Book Description
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.
Author: Suwarna Shukla
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
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.
Author: Ton Viet Ta
Publisher:
ISBN:
Category :
Languages : en
Pages : 74
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Book Description
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.
Author: Gurmeet Singh
Publisher:
ISBN:
Category :
Languages : en
Pages : 24
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Book Description
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.
Author: Srinivasan Palamalai
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
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.
Author: Rudra Prakash Pradhan
Publisher: Allied Publishers
ISBN: 9788184244267
Category : Finance, Personal
Languages : en
Pages : 224
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Book Description
Papers presented at the Forecasting Financial Markets in India, held at Kharagpur during 29-31 December 2008.
Author: Manish Kumar
Publisher:
ISBN:
Category :
Languages : en
Pages : 16
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Book Description
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.
Author: Dr. T. Peddanna
Publisher: Readworthy
ISBN: 9388121392
Category : Business & Economics
Languages : en
Pages : 154
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Book Description
"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."
Author: Srikanta Patnaik
Publisher: Springer Nature
ISBN: 9811552436
Category : Technology & Engineering
Languages : en
Pages : 853
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Book Description
This book gathers selected high-quality papers presented at the International Conference on Machine Learning and Computational Intelligence (ICMLCI-2019), jointly organized by Kunming University of Science and Technology and the Interscience Research Network, Bhubaneswar, India, from April 6 to 7, 2019. Addressing virtually all aspects of intelligent systems, soft computing and machine learning, the topics covered include: prediction; data mining; information retrieval; game playing; robotics; learning methods; pattern visualization; automated knowledge acquisition; fuzzy, stochastic and probabilistic computing; neural computing; big data; social networks and applications of soft computing in various areas.
Author: Rajveer Rawlin
Publisher: GRIN Verlag
ISBN: 3656063850
Category : Business & Economics
Languages : en
Pages : 29
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Book Description
Research Paper (postgraduate) from the year 2011 in the subject Business economics - Banking, Stock Exchanges, Insurance, Accounting, grade: 1, language: English, abstract: The Indian stock market S and P CNX Nifty Index (Nifty) is a well diversified index of 50 companies. Foreign Institutional Investors (FII's), wield significant influence over daily trading volumes in both the spot and derivative segments in the Indian markets. This tends to impact market volatility and returns. This study attempted to study the effect of FII transaction amounts, derivative turn over amounts and volatility on the performance of the Nifty index. A strong correlation was observed between derivative turnover and the Nifty but the correlation was relatively weaker between the Nifty and FII transaction amounts and Volatility. FII and F&O activity established important tops ahead of major tops in the Nifty. Volatility remained low during periods of significant upside in the stock market but spiked up during market declines. Linear and Non-linear models using multivariate analysis were fit to estimate the Nifty from the respective independent variables. A non linear model involving all three variables provided the best fit and the least deviation from actual values suggesting that interplay of these and other factors drive the performance of the index. Keywords: Nifty, FII transaction amounts, F&O turnover, Volatility, Nifty forecasting, Linear and Non Linear Models.
