Iranian Journal of Finance

Abstract Many researchers have attempted to explain the phenomenon of medium-term return continuation using modern financial theories. The excess return gained in the momentum investment strategy, in fact, compensates for unknown risks that current theories are unable to explain. Research indicates that various strategies can be beneficial at different maintenance periods. Various strategies generally involve a simple method in which they are formed based on the criterion of return over a certain period in the past and are maintained for a corresponding period in the future. Each investment strategy tends to generate excess returns based on the predictability of short-term price movements, as indicated by past performance. The purpose of this study is primarily to investigate the usefulness of residual momentum and conventional momentum strategies in the short-term and long-term. The time period of this study is from 2009 to 2018, and the general approach for calculations is based on the method described by Jegadeesh and Titman (1993), Blitz et al. (2011), and Blitz et al. (2020). The results of this study show no significant difference between residual and conventional momentum strategies in both short-term and long-term periods, indicating that both approaches exhibit similar risk-adjusted performance and forecasting capabilities.

Abstract Herding behavior is typically described as the inclination of investors to follow the actions of others in their investment decisions. Herding represents a behavioral tendency in which investors rely on collective rather than private information. Herd literature shows that return jumps can serve as a representation of information arrival, leading to significant price changes. This proposition is introduced due to its potential impact on investor sentiment, assuming greater awareness among other investors as a factor related to the occurrence of herding. Furthermore, it is believed that, in conditions of negative market returns, market participants are more inclined to mimic the behavior of others due to the stress induced by the risk incurred. In the background of previous research, evidence indicates the occurrence of herd behavior on days with return jumps and negative returns. In this study, we investigated herding behavior and its asymmetry through the utilization of return jumps, employing the CSAD method. Under circumstances in which there were no occurrences of return jumps and without taking into account negative market returns, our research was unable to verify the existence of herding at the market level. Nevertheless, when return jumps and negative market returns were present, the occurrence of herd behavior was proven, and the asymmetry of herd behavior was also verified.

Abstract Examining the importance and influence of financial market companies is one of the main issues in the field of financial management because sometimes the collapse of a stock exchange company can affect an entire financial market. One systematic way to analyze the significance and impacts of companies is to use complex networks based on Interaction Graphs (IGs). There are different methods for quantifying the edge weight in an IG. In this method, the graph vertices represent the stock exchange companies that are connected by weighted edges (corresponding to the extent to which they relate to each other). In this paper, using the GARCH model (1,1) and the Clayton copula, we obtained the lower tail dependence interaction network of the first 52 companies of the Tehran Stock Exchange in terms of average market value, between June 2017 and October 2020. Then, based on the minimum spanning tree of the interaction network, we divided the companies into different communities. Using this classification, it was observed that the companies of the first group (Food Industry) and the second group (Oil Refinery) have the greatest impact on other companies. We also calculated the central indexes of the minimum spanning tree for each company. According to the results, the companies of the third group (Steel) have the highest average in the central indicators.