There have been several efforts in the literature to extract as much information as possible from the financial networks. Most of the research has been concerned about the hierarchical structures, clustering, topology and also the behavior of the MARKET network; but not a notable work on the network filtration exists. This paper proposes a STOCK MARKET FILTERING model using the correlation - based financial networks in which network nodes represent the potential STOCKs and network edges indicate the correlation coefficients of corresponding STOCK pairs. The model is capable of reducing the basic MARKET size while keeping the diversification and risk - return expectations fairly constant. The novelty of this research is to develop a new MARKET network FILTERING method which exploits Minimum Spanning Tree (MST) to reduce the number of network nodes (graph order) rather than the links (graph size). The proposed method chooses the nodes (STOCKs) based on dangling ends of the constructed MST. In order to verify our proposed model, we applied the model on data of three STOCK MARKETs: New York STOCK Exchange (NYSE), Germany STOCK Exchange (DAX) and Toronto STOCK Exchange (TSE). In conclusion, the numerical results showed that our proposed model can make a subset of the STOCK MARKET in which its performance can imitate the whole MARKET with a rather considerable reduction in size; as a result, we can have a diversified subset of the MARKET compatible with that of the whole MARKET. The performance of the model is confirmed by comparing the portfolio of the filtered MARKET network with the whole MARKET portfolio using the complement of Herfindahl Index as a measure of diversification.

One of the most important problems in modern finance is finding efficient ways to summarize and visualize STOCK MARKET data. Modeling the FILTERING of complex networks in the STOCK MARKET allows this to be achieved by reducing the MARKET size, obtaining reliable information with less disturbance. Since STOCK price changes are not independent of each other, the study of the correlation between STOCK price changes provides a better understanding of MARKET performance for investors. STOCK MARKET analysis based on complex networks allows studying the correlation of STOCK prices. In this paper, using the STOCK MARKET data in the Tehran STOCK Exchange, the Iranian STOCK MARKET network is created by the threshold method, and then the network FILTERING is based on MST. The results show that the filtration modeling of Iran's STOCK MARKET network based on the MST can form a subset of the STOCK MARKET that follows the performance of the entire MARKET with a significant reduction in size and has a similar degree of diversification with the entire MARKET. These analyzes provide a more in-depth insight into the structure of the STOCK MARKET while reducing the size.

Public confidence in the fairness of STOCK MARKETs increases their efficiency and liquidity. Manipulating STOCK MARKET destroys its coherence and decreases public confidence about the authenticity of the MARKET. Manipulation of MARKET leads to false and nominal prices and creates misleading appearance of MARKET activity trend.To support the capital MARKET investors, legal systems principally banned manipulation of STOCK MARKET and have set penalty.Elements of MARKET manipulation crime and prescribed penalties are different in different legal systems with the approval of STOCK MARKET law in 2005 and according to Article 46 of this law, manipulation of STOCK MARKET was considered a crime and its punishment was determined.

This study aimed to evaluate the significance and severity of the relationship between MARKET sentiment and the volatility of the Tehran STOCK Exchange Price Index (TEPIX). We drew on the principal component analysis (PCA) to provide a composite sentiment index using a set of proxies. In addition, ARIMA-E-GARCH hybrid models were applied to model the volatility of the TEPIX and other control variables. Subsequently, GLS regression was used to measure the impact of MARKET sentiment and the control variables variation on the volatility of the TEPIX. The findings showed that the influences of optimistic and pessimistic sentiment on the volatility of TEPIX were both statistically significant and respectively, negative and positive. However, the severity of these negative and positive effects was slight. Furthermore, we found that the STOCK exchange volatility was highly affected by the volatility of the inflation and the liquidity much more than the other variables such as optimistic and pessimistic sentiment.

Objective With the increasing volume of information and the complexity of financial MARKETs, investors are increasingly seeking innovative financial tools to make more informed decisions. These tools should help investors choose the right STOCKs and achieve better returns. In this regard, STOCK recommendation systems are becoming increasingly important. STOCK recommendation systems can assist investors in achieving superior returns by selecting the right STOCKs. However, traditional STOCK recommendation systems often lack the necessary accuracy and efficiency. This research aims to develop a novel approach called STOCK-based Collaborative FILTERING to design a STOCK recommendation system for the Tehran STOCK Exchange. This method is founded on two key assumptions: first, MARKET inefficiency, meaning the STOCK MARKET does not completely and accurately reflect all available information; and second, the presence of hidden information in STOCK movements, indicating that these movements contain valuable insights that can influence the prices of other STOCKs in the MARKET. In this research, assuming the existence of the transmission effect on the Tehran STOCK Exchange, we used the collaborative FILTERING technique, a common algorithm in recommender systems, to design a STOCK recommender system. The purpose is to help investors select the best-performing STOCKs to outperform the MARKET.     Methods This study uses historical STOCK price data of 145 firms listed on the Tehran STOCK Exchange from 2012 to 2021. The collaborative FILTERING algorithm was implemented in two stages: training and testing. In the training stage, the algorithm was trained using data from 2012 to 2016, and in the testing stage, its performance was evaluated on data from 2016 to 2021. Following, buy and sell signals were generated using the STOCK-based collaborative FILTERING algorithm during the same period. Finally, the strategy was evaluated.   Results The algorithm was tested as an investment strategy in both in-sample and out-of-sample periods. The results obtained from the algorithm for the out-of-sample periods show that this strategy can achieve a 25-fold return. The overall index returned 16 times during this period, indicating the excellent performance of the strategy over time. Additionally, the value at risk (VaR) for the selected method during the study period stood at -12.8%, indicating the lower risk of this method.   Conclusion STOCK-based collaborative FILTERING is an active investment strategy. This intelligent algorithm aims to identify undervalued STOCKs and achieve higher returns than the MARKET. This algorithm can serve as a valuable tool for active investors seeking to identify valuable STOCKs and achieve higher returns than the MARKET. Therefore, further research is necessary to examine the performance of this algorithm in different MARKETs and diverse economic conditions. Also, it is recommended to implement risk control strategies and optimize the system's efficacy further.

According to predictions in the literature, sock price of firms with greater product MARKET power is less sensitive to order flows which results in greater STOCK liquidity. This prediction was examined in a sample of 75 listed companies in Tehran STOCK Exchange during 1388 to 1391 using three illiquidity measures (Amihud Ratio, Quoted Spread, Effective Spread) and two proxies for MARKET power. The results of panel regressions showed that STOCK liquidity increases with MARKET share (the first proxy of MARKET power). Findings also showed the relationship between operational income ratio (second proxy of MARKET power) and STOCK liquidity is not meaningful and consistent with theory. It seems that the reputation of firms in product MARKET (respect to MARKET share) results in higher STOCK MARKET liquidity than their profitability. Other results confirmed that advertising in product MARKET doesn’t influence STOCK MARKET liquidity, directly.

while the relationship between STOCK MARKET return and oil price is of great interest to researchers, previous studies do not investigate STOCK MARKET return with petrochemical products MARKET. In this paper, we analyzed the relationship between prices of main petrochemical products and STOCK returns of petrochemical companies in Tehran STOCK exchange. Using a panel data model and GLS estimation method, we investigated the effect of methanol, propane, and urea prices along with financial variables on STOCK returns of six big petrochemical companies during 2001 to 2013. Results show that although changes in prices of petrochemical products have direct effect on STOCK returns of all petrochemical companies, this effect is much higher for smaller companies.