Liquidity is the ease of converting an asset (physical/digital) into cash or another asset without loss and is shown by the relationship between the time scale and the price scale of an investment. This article examines the illiquidity of Bitcoin (BTC). Bitcoin hash rate information was collected at three different time intervals; parallel to these data, textual information related to these intervals was collected from Twitter for each day. Due to the regression nature of illiquidity prediction, approaches based on recurrent networks were suggested. Seven approaches: ANN, SVM, SANN, LSTM, Simple RNN, GRU, and IndRNN, were tested on these data. To evaluate these approaches, three evaluation methods were used: random split (paper), random split (run) and linear split (run). The research results indicate that the IndRNN approach provided better results.

Type of service Customer service in the field of after-sales service is important for every component Many companies can help with this to improve customer satisfaction; most companies are aware of this and that providing high-quality and balanced after-sales services is effective in customer loyalty and repeat purchases. This research aims to design a credit risk management model for Saipa Yadak Company and its agency network. It uses the component Financial after-sales service and algorithm He has paid innovative ideas and has been a Sample Case Review in this representative research of Saipa company. The research results showed that the component Financials include, service cost, performance, satisfaction account, the amount of the deposit and the amount of buying the agency presentations After-sales service provider on management Credit risk has an effect; And also the night worm algorithm Swing and pre-capable honey bee colony algorithm The vision of credit risk management using the component have financial In this way, the night worm algorithm Tab and the algorithm of the honey bee colony have a high ability (more than 85 %) in the forward direction Optimum management of credit risk using components they have financial resources. The Nightworm Algorithm Swing with 88.01% accuracy and the bee colony algorithm with 87.78% accuracy succeeded and credit risk management using the component advance finances to see.

Stock market forecasting has been a challenging part for many analysts and researchers. Trend analysis, statistical techniques, and movement indicators have traditionally been used to predict stock price movements, but text extraction has emerged as a promising method in recent years. The use of neural networks, especially recurrent neural networks, is abundant in the literature. In most studies, the impact of different users was considered equal or ignored, whereas users can have other effects. In the current study, we will introduce TM-vector and then use this vector to train an IndRNN and ultimately model the market users' behaviour. In the proposed model, TM-vector is simultaneously trained with both the extracted Twitter features and market information. Various factors have been used for the effectiveness of the proposed forecasting approach, including the characteristics of each individual user, their impact on each other, and their impact on the market, to predict market direction more accurately. Dow Jones 30 index has been used in current work. The accuracy obtained for predicting daily stock changes of Apple is based on various models, closed to over 95\% and for the other stocks is significant. Our results indicate the effectiveness of TM-vector in predicting stock market direction.

Business research is facing the challenge of scattered knowledge, particularly in the realm of brand loyalty (BL). Although literature reviews on BL exist, they predominantly concentrate on the pre-sent state, neglecting future trends. Therefore, a comprehensive review is imperative to ascertain emerging trends in BL This study employs a bibliometric approach, analyzing 1,468 papers from the Scopus database. Various tools including R software, VOS viewer software, and Publish or Perish are utilized. The aim is to portray the knowledge map, explore the publication years, identify the top authors and their co-occurrence, reliable documents, institutions, subjects, research hotspots, and pioneering countries and universities in the study of BL. The qualitative section of this research identifies gaps and emerging trends in BL through Word Cloud charts, word growth analysis, and a review of highly cited articles from the past four years. Results showed that highly cited articles mention topics such as brand love, consumer-brand identification, and social networks and the U.S. had the most productions in this field. Besides, most citations were related to Keller with 1,173 citations. Furthermore, in the qualitative section, social networks and brand experiences were found to be of interest to researchers in the field. Finally, by introducing the antecedents and consequences of BL, the gaps and emerging trends in BL were identified, so as to present the di-rection of future research in this area.