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The stock market crash is a sudden dramatic decline of stock price due to uncertainty in the stock market. The stock prices are the influence of many factors, such as global trends, local trends, and economic conditions. The identification stock crisis is a challenging task for stock traders and investors. The goal of this paper is to forecast stock crisis events. The experiment is carried out in two steps. First is the least square (LS) method, and the least absolute deviation (LAD) is considered to identify a correlation between mean and median. Based on the correlation between mean and median, the GARCH (General autoregression conditional heteroskedasticity) model proposed to calculate the error distribution in stock returns. To identify the appropriate error distribution, we have varied the degree of t distribution parameters. In the second step, the volatility of stock prices is given as input to the GARCH model to forecast the future crisis events. To carry out the proposed experiment, we have considered Infosys and sbi stock. Experiment results reduce the error in predicting stock crises events.

The stock market prices are volatile due to influence by many factors such as global trends, local trends, and economic conditions. Identification of Generalized autoregressive conditional heteroscedasticity(GARCH) order for stock data is a challenging task due to more fluctuation in stock prices and high variance in data. GARCH is considered to model the conditional volatility of a stock time series. Stock markets data often exhibit volatility clustering. Though many models which belong to autoregressive conditional heteroscedasticity (ARCH) family has proposed, but all the previous studies gave their affirmative consent on the performance of GARCH (1,1), which is considered the standard model, maybe because of the belief held by many researchers that the first lag of conditional variance can capture all the volatility clustering. This can be highly misguiding, especially when the stock market data has high order variance. The focus of this work is to make use of existing, well-known Information Criteria (IC) to identify the stock indices data-generating-process whenever the GARCH effect is present. Akaike Informations Criteria (AIC), Bayesian Information Criteria(BIC), and Hannan-Quinn information(HQ) criteria have used for this experiment. We studied different models with different parameter values and observed the abilities of information criterion in choosing the correct model from a given pool of models. For higher-order GARCH models and high sample sizes, AIC was able to correctly predict the model with high probability, while BIC and HQ performed well for smaller order models.

Predicting stock price movements is difficult due to the speculative nature of the stock market. Accurate predictions of stock prices allow traders to increase their profits. Stock prices react when receiving new information. During the trading day, it is difficult to understand the up and down movements signaled by stock prices. This paper addresses the problem of fluctuations in stock prices. We proposed the method to identify stock movement trend in data, and this method considered the combination of candlestick data and technical indicator values. The outcome of this method is given as inputs to a deep neural network (DNN) to classify a stock price’s up and down movements. National Stock Exchange, India, datasets are considered for an experiment from the years 2008 to 2018. The work is carried out using H2O deep learning on an RStudio platform. Experimental results are compared with a three-layer artificial neural network (ANN) model. The proposed five-layer DNN model outperforms state-of-the-art methods by 8–11% in predicting up and down movements of a given stock.

The failure of safety critical systems like Nuclear Power Plant can lead to disastrous effects which may lead to death of people and harm to the environment. Thus it becomes very important to model these systems to meet the reliability, safety requirements without compromising with the high performance. Petri Nets(PNs) is a type of modelling languages which is widely used for validation of real time systems. Timed Petri Nets(TPNs) are popularly use to model such critical systems primarily for non-functional analysis. In this paper have used Timed petri net and Markov chains methodology for reliability and performance analysis for Nuclear Power Plant Injection Systems. This paper will be finding the time spent by the tokens in the Petri Net System as an evaluation metric.

Optimizing the Time complexity of any program is still the most researched and sought area for researchers. At the industry level, the Software execution timing is the dominant criteria for Workload selection. One prominent method for reducing the Time complexity of a program is by using program slicing configuration, without affecting the program flow. Program slicing is the process of slicing a program in such a way that it reduces the time of debugging. This paper presents a timed-based analysis of a program with and without slicing with the help of different verification tools, namely KLEE, DIVINE, and Frama-C. This paper aims to compare these tools based on the timing of debugging and validity of a program before and after slicing.

Volatility is the degree of variation in the stock price over time. The stock price is volatile due to many factors, such as demand, supply, economic policy, and company earnings. Investing in a volatile market is riskier for stock traders. Most of the existing work considered Generalized Auto-regressive Conditional Heteroskedasticity (GARCH) models to capture volatility, but this model fails to capture when the volatility is very high. This paper aims to estimate the stock price volatility using the Markov regime-switching GARCH (MSGARCH) and SETAR model. The model selection was carried out using the Akaike-Informations-Criteria (AIC) and Bayesian-Information Criteria (BIC) metric. The performance of the model is evaluated using the Root mean square error (RMSE) and mean absolute percentage error (MAPE) metric. We have found that volatility estimation using the MSGARCH model performed better than the SETAR model. The experiments considered the Indian stock market data.

In this paper we are going to discuss the prediction of the financial time series using the Markov chain changing transition matrix model using genetic algorithm. During initial phase of the algorithm we will create the window of fix size with fixed number of state. The basic aim of this paper is to reduce the time taken to find the best window size and best number of states in the window by using the genetic algorithm. This paper produce the approach so that investor can save their time to predict the series without manual activity. To demonstrate the genetic algorithm optimisation we used the historical index data: national stock exchange(NSE50). The Nifty data contained 1239 candles starting from January 1,2015 and ending December 31, 2019. Data was downloaded from [https://www1.nseindia.com/]. In this case we observed the better investment strategy using the first order Markov chain model and reducing the execution time by using the genetic algorithm.

With the increase in the number of open-source and commercial code in the market, copyright and license infringement cases are on the rise. The lack of availability of source code makes identification a difficult task as existing techniques heavily rely on the source code. We propose two similarity measurement methods using the instruction log and the call-trace of each executable using Intel Pin tool. A Software Plagiarism Detector(SPD) was developed using the Intel Pin instrumentation tool and we have tested this approach on different small executable single-threaded and multi-threaded files. The results portray the validity of this method. We also talk about the possibility to expand this method for bigger software.

Accurate stock price predictions can help investors take correct decisions about the selling/purchase of stocks. With improvements in data analysis and deep learning algorithms, a variety of approaches has been tried for predicting stock prices. In this paper, we deal with the prediction of stock prices for automobile companies using a novel TFD—Time Series, Financial Ratios, and Deep Learning approach. We then study the results over multiple activation functions for multiple companies and reinforce the viability of the proposed algorithm.

Stock price prediction is one of the challenging tasks for researchers and academics due to frequent changes in stock prices. The stock prices are speculation, and it purely depends on the demand and supply of the market during the trading session. Most of the existing work approach is foresting stock prices using machine learning methods. There has been a limited number of studies on stock crisis identification. Log periodic power law (LPPL) is one of the approaches to identify bubbles in the stock market before crises happened. By looking at existing work, we found that LPPL has not applied in the Indian stock market. In this paper, we have considered LPPL to identify a bubble in the Indian stock market. Due to fluctuation in the market, stock price follows the nonlinearity behavior, hence LPPL is considered to fit the equations. The experiment is carried out R Studio platform.

Stock price prediction has been a major area of research for many years. Accurate predictions can help investors take correct decisions about the selling/purchase of stocks. This paper aims to predict and gauge stock costs and patterns, utilizing the power of machine learning, content examination and fundamental analysis, to give traders a hands-on tool for keen speculations particularly for the volatile Indian Stock Market. We propose a technique to analyze and predict the stock price with the help of sentiment analysis and decomposable time series model along with multivariate-linear regression.

The recent success of the application of Artificial Intelligence in the financial sector has resulted in more firms relying on stochastic models for predicting the behaviour of the market. Everyday, quantitative analysts strive to attain better accuracies from their machine learning models for forecasting returns from stocks. Support Vector Machine (SVM) and Random Forest based regression models are known for their effectiveness in accurately predicting closing prices. In this work, we propose a technique for analyzing and predicting stock prices of companies using the aforementioned algorithms as an ensemble. Datasets from India's National Stock Exchange (NSE) containing basic market price information are preprocessed to include well known leading technical indicators as features. Feature selection, which ranks features based on their degree of influence on the final closing price has been incorporated to reduce the size of the training dataset. Additionally, we evaluate the effectiveness of considering the public opinion of a company by employing sentiment analysis. Using a trained Word2Vec model, company specific hash-tagged posts from Twitter are classified as positive or negative. Our proposed ensemble model is then trained on a new dataset which combines the technical indicator data along with the aggregated number of positive/negative tweets of a company over time. Our experiments indicate that in some scenarios, the ensemble model performs better than the constituent models and is highly dependent of the nature and size of the training data. However, combining technical indicator data with aggregated positive/negative tweet counts has a negligible effect on the performance of the ensemble model.

Information sharing and review platforms has generated large volumes of opinionated data which is usually in unstructured form. With the help of Sentiment Analysis, this data can be transformed into structured data which can be useful for commercial applications such as product reviews and feedback, marketing analysis, etc. The purpose of this work is to analyzes the performance of three classifiers(SVM, Naive Bayes, and Logistic Regression) with respect to providing positive or negative sentiment for three different scenarios(Movie Reviews, Election Opinions, and Food Reviews). The three classifiers are compared using fixed set of preprocessing steps and four different weighting schemes(Term frequency inverse document frequency (TFIDF), Term frequency inverse class frequency (TFICF), Mutual Information (MI), and X2 statistic (CHI)). The controlled experimental results showed that Logistic Regression classifier performs better in terms of overall accuracy when MI is used as weighting scheme.

Efficient performance of a disk I/O operation involves a multitude of factors such as the type of the disk, I/O scheduling, and the type of the file system used. Due to the various types of file systems available, with each having different structure and logic, properties of speed, flexibility, security, size and more, it becomes imperative to have an objective overview of the merits and demerits of each file system according to the needs of the users. In this work, we present a thorough performance evaluation of ext4, NTFS and Btrfs filesystems along with CFQ, NOOP and Deadline I/O schedulers tested on regular hard disk drives and SSDs.