Analysis Of Stock Market Data Research Articles

Optimized Feature Extraction Based Artificial Intelligence Technique for Empirical Analysis of Stock Market Data

Stock market forecasting is considered as a challenging task in financial time series forecasting. The core idea of a successful stock market forecast is to get the best results with the least amount of input data required and the least complex stock market model. So in this paper, to achieve these goals, this paper proposes a new algorithm Cuckoo Neural for Financial Market (CN_FM), which integrates Cuckoo Search (CS) with a Feed forward back propagation Neural Network (FFBPNN) and build a stock price forecasting proficient system. This research follows some basic steps to design a model for sentiment analysis from Stock market dataset and firstly data is pre-processed to remove the unwanted text. In pre-processing steps the techniques such as normalization, punctuation, stop word removal and tokenization are applied. The extracted features are optimized using CS and in turns used to train the FFBPNN. The results obtained from experiments in the stock market show that the proposed method can reduce the dimensions and produce accurate results for emotion-based text categorization with an accuracy of 99.44%.

Detecting the Fluctuations in Large Samples Using Wavelet Transform

structure break is a famous features in stock market data that gain consideration from many kind of researchers. Generally, it occurs because of unexpected variations in the strategy of the government. Recently, wavelet method (WT) is more popular in the stock market data analysis since it has significant benefits than the other traditional methods. In this research paper, the discrete wavelet transform (DWT) based on Daubechies model will be used to capture the structure break in Amman stocks market /Jordan (ASE) using dataset from 2010 until 2018.

Estimation for a Second-Order Jump Diffusion Model from Discrete Observations: Application to Stock Market Returns

This paper proposes a second-order jump diffusion model to study the jump dynamics of stock market returns via adding a jump term to traditional diffusion model. We develop an appropriate maximum likelihood approach to estimate model parameters. A simulation study is conducted to evaluate the performance of the estimation method in finite samples. Furthermore, we consider a likelihood ratio test to identify the statistically significant presence of jump factor. The empirical analysis of stock market data from North America, Asia, and Europe is provided for illustration.

NSE Stock Market Prediction Using Deep-Learning Models

The neural network, one of the intelligent data mining technique that has been used by researchers in various areas for the past 10 years. Prediction and analysis of stock market data have got an important role in today’s economy. The various algorithms used for forecasting can be categorized into linear (AR, MA, ARIMA, ARMA) and non-linear models (ARCH, GARCH, Neural Network). In this paper, we are using four types of deep learning architectures i.e Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) for predicting the stock price of a company based on the historical prices available. Here we are using day-wise closing price of two different stock markets, National Stock Exchange (NSE) of India and New York Stock Exchange (NYSE). The network was trained with the stock price of a single company from NSE and predicted for five different companies from both NSE and NYSE. It has been observed that CNN is outperforming the other models. The network was able to predict for NYSE even though it was trained with NSE data. This was possible because both the stock markets share some common inner dynamics. The results obtained were com- pared with ARIMA model and it has been observed that the neural networks are outperforming the existing linear model (ARIMA).

Daugiamačio α-stabiliojo dėsnio parametrų vertinimas didžiausio tikėtinumo metodu

Skirstinių, susijusių su α-stabiliaisiais, tyrimai šiuo metu yra ypač aktualūs, nes jie dažnai pasitaiko analizuojant verslo ir finansų duomenis ar informacinius srautus kompiuterių tinkluose. Nustatyta, kad finansiniai duomenys dažnai yra leptokurtotiniai ir pasižymi išskirtimis, tad daugelis autorių (Rachev ir Mittnik, 2000; Kabašinskas ir kt., 2012; Sakalauskas ir kt., 2013) parodė, jog dažnai taikomas normalusis skirstinys nėra pats geriausias variantas ekonominių rodiklių analizei atlikti, todėl siūloma jį keisti bendresniais, pavyzdžiui, stabiliaisiais, modeliais. Kadangi Rachevo ir Mittniko (2000), Kabašinsko ir kt. (2012) bei Sakalausko ir kt. (2013) darbuose α-stabiliojo dėsnio parametrų vertinimas yra ištirtas tik vienmačiu atveju, praktikoje iškilo problema, kaip vertinti daugiamačius duomenis. Nors problema tiriama ir nagrinėjama jau kelis dešimtmečius, tačiau iki galo ji dar neišsemta (Press, 1972; Davydov ir Paulauskas, 1999; Nolan, 1998; Kring ir kt., 2009; Ogata, 2013). Šiame darbe sudarytas didžiausio tikėtinumo metodas daugiamačio α-stabiliojo dėsnio parametrų įvertiniams gauti pritaikius EM algoritmą. Integralai, įeinantys į įvertinių išraiškas, apskaičiuojami pasinaudojus Gauso bei Gauso ir Lagero kvadratūrinėmis formulėmis. Sukonstruotas modelis gali būti taikomas akcijų rinkų duomenų analizei atlikti.

Basic Indicators of Systemic Risk in the EU Banking Sector. Implications for Banking Regulation

Abstract The issue of systemic risk regulation and management has gained substantial attention following the latest financial crisis. In the case of the EU it became crucial to deal with the systemic risk problem on a supranational level since the banking sectors of the member countries are highly integrated. While substantial measures have been undertaken to mitigate systemic risk in the EU, the discussion of further reforms continues. This study’s goal is to assess basic indicators of systemic risk in the EU banking sector by using three complementary methods: a forward-looking stock market data analysis, an EU-stress test analysis for systemically important banks, and an empirical investigation of the relation between banking regulation and systemic risk as measured by bank balance sheet indicators. The results lead to a recommendation of further necessary regulatory reforms, which appear in the conclusion.

Detecting Randomness: A Review of Existing Tests with New Comparisons

In this article we review and compare a number of existing tests for detecting randomness in time series data, with emphasis on stock market index data. By comparing variance ratio tests with traditional statistical tests, we have the most extensive simulation comparison of such procedures. The investigated tests are compared over a diverse group of distributions, models, and stock market applications. In our stock market data analysis, the choice of data transformation can have a noticeable effect on test results. This study provides the reader with a guide as to which test and transformation is most appropriate for their use.

Iterative Filtering Decomposition Based on Local Spectral Evolution Kernel

The synthesizing information, achieving understanding, and deriving insight from increasingly massive, time-varying, noisy and possibly conflicting data sets are some of most challenging tasks in the present information age. Traditional technologies, such as Fourier transform and wavelet multi-resolution analysis, are inadequate to handle all of the above-mentioned tasks. The empirical model decomposition (EMD) has emerged as a new powerful tool for resolving many challenging problems in data processing and analysis. Recently, an iterative filtering decomposition (IFD) has been introduced to address the stability and efficiency problems of the EMD. Another data analysis technique is the local spectral evolution kernel (LSEK), which provides a near prefect low pass filter with desirable time-frequency localizations. The present work utilizes the LSEK to further stabilize the IFD, and offers an efficient, flexible and robust scheme for information extraction, complexity reduction, and signal and image understanding. The performance of the present LSEK based IFD is intensively validated over a wide range of data processing tasks, including mode decomposition, analysis of time-varying data, information extraction from nonlinear dynamic systems, etc. The utility, robustness and usefulness of the proposed LESK based IFD are demonstrated via a large number of applications, such as the analysis of stock market data, the decomposition of ocean wave magnitudes, the understanding of physiologic signals and information recovery from noisy images. The performance of the proposed method is compared with that of existing methods in the literature. Our results indicate that the LSEK based IFD improves both the efficiency and the stability of conventional EMD algorithms.

A note on the mixture transition distribution and hidden Markov models

We discuss an interpretation of the mixture transition distribution (MTD) for discrete-valued time series which is based on a sequence of independent latent variables which are occasion-specific. We show that, by assuming that this latent process follows a first order Markov Chain, MTD can be generalized in a sensible way. A class of models results which also includes the hidden Markov model (HMM). For these models we outline an EM algorithm for the maximum likelihood estimation which exploits recursions developed within the HMM literature. As an illustration, we provide an example based on the analysis of stock market data referred to different American countries.

Robust methods for stock market data analysis

We consider the problem of extraction of trend and chaotic components from irregular stock market time series. The proposed methods also permit to extract a part of chaotic component, the so-called anomalous term, caused by the transient short-time surges with high amplitudes. This provides more accurate determination of the trend component. The methods are based on the M-evaluation with decision functions of Huber and Tukey type. The iterative numerical schemes for determination of trend and chaotic components are briefly presented, resulting in an acceptable solution within a finite number of iterations. The optimal level for extraction of the chaotic component is determined by a new numerical scheme based on the fractal dimension of the chaotic component of the analyzed series. Forecasting from the realized part of the analyzed series and a priori expert information is also discussed.

Copula convergence theorems for tail events

Tail dependence is studied from a distributional point of view by means of appropriate copulae. We derive similar results to the famous Pickands–Balkema–de Haan Theorem of Extreme Value Theory. Under regularity conditions, it is shown that the Clayton copula plays among the family of archimedean copulae the role of the generalized Pareto distribution. The practical usefulness of the results is illustrated in the analysis of stock market data.

The Effects of Automobile Strikes on the Stock Value of Steel Suppliers

This analysis of stock market data for the years 1965–90 indicates that upon announcements of automobile strikes, steel suppliers experienced statistically significant negative returns, about equal to the negative returns experienced by the struck automobile producers. Significantly larger negative interindustry effects were associated with large multi-plant strikes than with smaller one-plant strikes, and the negative effects were significantly greater in the later years of the sample period, a time of relatively weak financial condition, than in the earlier years, a time of stronger financial condition. The author concludes that researchers and policy-makers evaluating the private and social costs of strikes should consider adverse effects on the profitability not only of the struck industries, but also of linked industries, and should take into account the important roles of strike size and industry financial health.

The fast Fourier transform and its applications

The Fast Fourier Transform (FFT) is a mathematical method widely used in signal processing. This book focuses on the application of the FFT in a variety of areas: Biomedical engineering, mechanical analysis, analysis of stock market data, geophysical analysis, and the conventional radar communications field.