Stock market indices are considered to be a powerful economic indicator. These can classified based on the methodology of weight allocation for each stock and rules governing entry, retention exit criteria various stocks in index. This paper presents descriptive an exploratory analysis carried out daily returns data NASDAQ 100 (^NDX) index shortlist 20 Random sampling was conducted at sector le...

The article considers an autoregressive process AR(1) with finance application. solution of many control problems comes down to process. We study the first-order application S&P500 and NASDAQ indices for 01/01/2017-01/08/2021. main purpose is investigate effects Covid-19 pandemic on stock indices. For statistical computing visualization, R language environment are used.

Multifractal analysis and extensive statistical tests are performed upon intraday minutely data within individual trading days for four stock market indexes (including HSI, SZSC, S&P500, and NASDAQ) to check whether the indexes (instead of the returns) possess multifractality. We find that the mass exponent τ(q) is linear and the singularity α(q) is close to 1 for all trading days and all index...

This paper proposes a new approach for estimating and forecasting the moments and probability density function of daily financial returns from intraday data. This is achieved through a new application of the distributional scaling laws for the class of multifractal processes. Density forecasts from the new multifractal approach are typically found to provide substantial improvements in predicti...

This paper proposes a fuzzy logic based DSS for stock market. This system will help investors of the stock market to take the correct buy/sell/hold decisions. The results obtained from the proposed fuzzy logic model were satisfactory. A fuzzy tuning methodology was introduced to enhance the accuracy of the decisions. The tuning methodology which uses genetic algorithms is presented also in this...

Training neural networks to capture an intrinsic property of a large volume of high dimensional data is a difficult task, as the training process is computationally expensive. Input attributes should be carefully selected to keep the dimensionality of input vectors relatively small. Technical indexes commonly used for stock market prediction using neural networks are investigated to determine i...