Stock Price Data Set Research Articles

High-dimensional macroeconomic forecasting and variable selection via penalized regression

SummaryThis study examines high-dimensional forecasting and variable selection via folded-concave penalized regressions. The penalized regression approach leads to sparse estimates of the regression coefficients and allows the dimensionality of the model to be much larger than the sample size. First, we discuss the theoretical aspects of a penalized regression in a time series setting. Specifically, we show the oracle inequality with ultra-high-dimensional time-dependent regressors. Then we show the validity of the penalized regression using two empirical applications. First, we forecast quarterly US gross domestic product data using a high-dimensional monthly data set and the mixed data sampling (MIDAS) framework with penalization. Second, we examine how well the penalized regression screens a hidden portfolio based on a large New York Stock Exchange stock price data set. Both applications show that a penalized regression provides remarkable results in terms of forecasting performance and variable selection.

A Neural Stochastic Volatility Model

In this paper, we show that the recent integration of statistical models with deep recurrent neural networks provides a new way of formulating volatility (the degree of variation of time series) models that have been widely used in time series analysis and prediction in finance. The model comprises a pair of complementary stochastic recurrent neural networks: the generative network models the joint distribution of the stochastic volatility process; the inference network approximates the conditional distribution of the latent variables given the observables. Our focus here is on the formulation of temporal dynamics of volatility over time under a stochastic recurrent neural network framework. Experiments on real-world stock price datasets demonstrate that the proposed model generates a better volatility estimation and prediction that outperforms mainstream methods, e.g., deterministic models such as GARCH and its variants, and stochastic models namely the MCMC-based stochvol as well as the Gaussian-process-based, on average negative log-likelihood.

Time-consistency of risk measures with GARCH volatilities and their estimation

Abstract In this paper we study time-consistent risk measures for returns that are given by a GARCH(1,1) model. We present a construction of risk measures based on their static counterparts that overcomes the lack of time-consistency. We then study in detail our construction for the risk measures Value-at-Risk (VaR) and Average Value-at-Risk (AVaR). While in the VaR case we can derive an analytical formula for its time-consistent counterpart, in the AVaR case we derive lower and upper bounds to its time-consistent version. Furthermore, we incorporate techniques from extreme value theory (EVT) to allow for a more tail-geared statistical analysis of the corresponding risk measures. We conclude with an application of our results to a data set of stock prices.

A hybrid fuzzy time series model based on granular computing for stock price forecasting

Given the high potential benefits and impacts of accurate stock market predictions, considerable research attention has been devoted to time series forecasting for stock markets. Over long periods, the accuracy of fuzzy time series model forecasting is invariably affected by interval length, and formulating effective interval partitioning methods can be very difficult. Previous studies largely relied on distance partitioning, but this approach neglects the distribution of datasets and can only handle scalar forecasting. But the magnitude of stock price movements is often severe and difficult to predict. Thus, the distribution of stock price datasets is always skewed and the straightforward partitioning method is not well suited to these types of time series datasets. In this research, a novel fuzzy time series model is used to forecast stock market prices. The proposed model is based on the granular computing approach with binning-based partition and entropy-based discretization methods. The proposed model is verified using experimental datasets from the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), Dow-Jones Industrial Average (DJIA), S&P 500 and IBOVESPA stock indexes, and results are compared against existing fuzzy time series models, three different SVM models, and three modern economic models - GARCH, GJR-GARCH, and Fuzzy GARCH. Compared to other current forecasting methods, the proposed models provide improved prediction accuracy and the results are verified by paired two-tailed t-tests. The experimental results clearly provide improvements for obtaining optimized linguistic intervals and ensuring the accuracy of the proposed model.

Hybrid Network of Neuro-Fuzzy based Decision Tool for Stock Market Analysis

Prediction of stock market return is an important issue in finance. Fuzzy and Artificial neural networks have been used in stock market prediction during the last decade. Studies were performed for the forecast of stock index values as well as daily direction of change in the index. This work compares fuzzy and arrangement of ANN model and makes these models to train with the past 5 years stock price datasets of various companies like (TCS, HCL) and the prediction of future stock price of company has been found. Membership functions (LOW, MEDIUM, HIGH) based fuzzy model will give recommendation for investor which says the current situation of the stock market. The Root Mean Square error (RMSE), Mean Absolute Performance (MAPE) metrics calculates the error rate value of each model. The proposed Hybrid Network model has expecting to given high performance.

The Finite and Moving Order Multinomial Universal Portfolio

An upper bound for the ratio of wealths of the best constant -rebalanced portfolio to that of the multinomial universal portfolio is derived. The finite- order multinomial universal portfolios can reduce the implementation time and computer-memory requirements for computation. The improved performance of the finite-order portfolios on some selected local stock-price data sets is observed.

An Ensemble Model of Multiple Classifiers for Time Series Prediction

Time series forecasting is a challenging task in many fields. Due to the complex non-linear relationship between the multidimensional features of the time series data, improved time series forecasting requires a forecasting model that combines multiple prediction models. Ensemble learning performs better than single learning model and discovers regularities in dynamic and non-stationary data. In literature, single level neural network ensembles are used for the prediction problems (1- 5). This paper introduces a novel two level ensemble learning approach based on Radial Basis Function networks (RBF), K - Nearest Neighbor (KNN) and Self Organizing Map (SOM) for time series prediction with the aim of increasing the prediction accuracy. The evaluation of the proposed Pattern Prediction Ensemble Model (PAPEM) using three input datasets such as, Mackey dataset, Sunspots dataset and Stock Price dataset shows that the proposed PAPEM model performs better than the individual classifiers.

A prediction algorithm for time series based on adaptive model selection

HMM (Hidden Markov model) has been used successfully to analyze various types of time series. To fit time series with HMM, the number of hidden states should be determined before learning other parameters, since it has great impact on the complexity and precision of the fitting HMM. However this becomes too difficult when there is not enough prior knowledge about the observed series, which will lead to the increasing mean error in prediction process. To overcome this shortcoming, a prediction algorithm PAAMS for time series based on adaptive model selection is proposed. In PAAMS, the model can be dynamically updated when the prediction mean error increases. During the update process, an automatic model selection method AMSA is applied to get the best hidden state number and other model parameters. The proposed method AMSA is based on clustering, in which the number of hidden states is considered as the number of clusters. The feasibility and effectiveness of proposed prediction algorithm are explained. Experiments on American stock price data set are done and the results show that the PAAMS algorithm can achieve higher precision than that of previous study on the same data sets based on fixed model techniques.

Highly Interconnected Subsystems of the Stock Market

The stock market is a complex system that affects economic and financial activities around the world. Analysis of stock price data can improve our understanding of the past price movements of stocks. In this work, we develop a method to determine the highly interconnected subsystems of the stock market. Our method relies on a k-core decomposition scheme to analyze large networks. Our approach illustrates that the stock market is a nearly decomposable system which comprises hierarchic subsystems. This work also presents results from the analysis of a network derived from a large data set of stock prices. This network analysis technique is a new promising approach to analyze and classify stocks based on price interactions and to decompose the complex system embodied in the stock market.

A hyperbolic diffusion model for stock prices

In the present paper we consider a model for stock prices which is a generalization of the model behind the Black–Scholes formula for pricing European call options. We model the log-price as a deterministic linear trend plus a diffusion process with drift zero and with a diffusion coefficient (volatility) which depends in a particular way on the instantaneous stock price. It is shown that the model possesses a number of properties encountered in empirical studies of stock prices. In particular the distribution of the adjusted log-price is hyperbolic rather than normal. The model is rather successfully fitted to two different stock price data sets. Finally, the question of option pricing based on our model is discussed and comparison to the Black–Scholes formula is made. The paper also introduces a simple general way of constructing a zero-drift diffusion with a given marginal distribution, by which other models that are potentially useful in mathematical finance can be developed.

UGC: An algorithm for two-stage unconstrained guillotine cutting

The main perfect of this composition is to discover the stylish version to prognosticate the cost of the inventory request. During the procedure of analyzing the colorful ways and variables to remember, we plant that approaches similar as Random woodland, machine help Vector were not absolutely exploited. On this composition, we will introduce and assessment a in addition practicable gadget to prognosticate the motion of shares with lesser delicacy. The first issue we looked at turned into the previous time's stock price dataset. The dataset has been preprocessed and refined for actual analysis. For this reason, our composition can even focus on preprocessing the raw data of the dataset. 2nd, after preprocessing the facts, we are able to observe the use of the arbitrary wood, we can aid the vector machine on the dataset and the results it generates. Similarly, the proposed composition examines the use of the soothsaying device in actual surrounds and the problems related to the delicacy of the overall values handed. The composition additionally provides a system literacy version for prognosticating the lifestyles of shares in a aggressive request. Predicting the success of shares might be a main asset for stock request institutions and could give actual effects to the troubles facing equity investors. By Using Stock Prediction algorithm overall accuracy is 80.3%.