The main purpose of this study is to analyze the strategy of synthesizing the fundamental financial and non-financial variables in order to present a pattern for ranking COMPANIES in Tehran Stock Exchange, and eventually classifying them as winner and LOSER.To confirm the offered ranking and to demonstrate how the COMPANIES were differentiated, the researcher tries to determine whether there are any significant differences in the studies financial and non-financial variables between the winner and LOSER COMPANIES. The hypotheses offered are based on two main axes. First, the variables under study for the winner COMPANIES are much stronger than those of the LOSER COMPANIES. Second, winner COMPANIES enjoy a higher actual return average of 16.3 percent. Besides, their actual return is 17.4 percent higher than that of the market.This research study was conducted in a five-year period, from 1382 through 1386, using the method of average comparison and ranking comparison of a variable in two independent societies (t-Student and U-Mann Whitney tests).The findings of the study revealed that winner COMPANIES possess a more favorable cash status, financial leverage, profitability, and non-financial indexes (indicators). Moreover, it was concluded that activity and market’s value indexes (indicators) were not significantly different between the two groups of COMPANIES.

Objective Nowadays, the volume and speed of transactions in financial markets have grown dramatically and it is hard to track market changes by using traditional methods. Besides the efficiency of traditional methods, the low speed of these approaches is one of the most important deficiencies of them because they cannot adapt to high speed of transactions. To overcome this shortcoming, algorithmic trading techniques have been proposed which online portfolio selection is one of the most important of these techniques. So, the purpose of this research is to propose a new algorithm for online portfolio selection which leads to high risk-adjusted return and speeds up the process of portfolio selection. Methods In this research, two algorithms have been proposed using multi-period mean reversion which is the basis of follow-the-LOSER algorithms. In these algorithms, a set of various experts predict the price relative vector of next period. Then, one of existing algorithms in prediction theory with expert advice is used to assign weights to experts. Then, a learning technique is used for portfolio optimization which leads to portfolio of next period. Results The results show the superiority of the proposed algorithms to other algorithms existing in literature based on return and risk-adjusted return criteria. Conclusion The concept of mean reversion can be better expressed by using multi-period mean reversion. In addition, using different experts’ advices make predictions more accurate and therefore better portfolios are suggested. Also, the use of weighting system indirectly brings robustness in the algorithms because it reduces the weights assigned to experts with poor predictions and transforms it to other experts with proper predictions.

Communicating ideas/news is the primary function of language. However, language does not usually fulfill this as it is expected to. To Dellinger (1995, p. 3) language, “can never appear by itself-it always appears as the representative of a system of linguistic terms, which themselves realize discursive and ideological system.” The present study, analyzing sports articles, aims at investigating the nature and importance of discourse in representing the desired players/ or teams. In other words, it is to examine the ways in which different teams are discursively constructed. More specifically, it shows how ‘our’ team versus ‘other’ (rival) team is shaped discursively. To do this, Hodge and Kress' (1996) model for Critical Discourse Analysis provides the framework with which the following texts have been approached. Four sport extracts, selected from two different issues of two different sport editorials, comprised the corpus of the study. The texts are analyzed with regard to three important properties of texts, i.e., grammar (with regard to two properties: syntagmatic models and transformations), vocabulary (functioning as adjectives, adverbs, and verbs, with their ideological significance), and modality (the degree of authority and certainty of an utterance). The study has revealed how the reporters, while apparently providing the readers with the information about the matches and important events, represent ‘ours’ and ‘others’ in the selected texts  the way they like and, thereby, influence the ideology of the reader.

This research was designed to examine the profitability of the momentum strategy with regard to Industry herding. The existence of momentum, herding and imitation from each other's behavior can intensify the results of momentum strategies. Therefore, using the monthly returns of the COMPANIES listed at TSE during the period from 2008 to 2017, the behavior of winner and LOSER portfolios has been investigated considering the Industry herding. The results showed there is short-term returns of momentum in winner portfolios (in high level of industry herding) higher compared to LOSER portfolios(in low level of industry herding). Although it is significance for periods of one to two months. Also, the comparison of winner COMPANIES with high and low herding shows the short-term momentum. The comparison of the future return of LOSER and winner COMPANIES (with low level of industry herding) also shows that there is a momentum return in some periods, also there is not a obvious pattern. Ultimately, most of the momentum effect relates to the long position of winner (with high level of herding) and the short position of LOSER (with low level of herding).

Momentum strategy, despite its outstanding performance, offers different results at different time intervals. In this study, we aimed to provide an opti-mal model for stock selection based on momentum, reverse and hybrid trad-ing strategies using the data panel model. The present research method was applied on the information of 180 COMPANIES in the period 2011 to 2021 was used to estimate the model. (Eviews12) software has been used to estimate the models. Based on the results of 8 time periods of 3, 6, 9, 12, 24, 36, 48 and 60 months based on different momentum and inverse strategies and a combination of LOSER, winner and LOSER-winner, winner-LOSER were analyzed. According to the data panel method, the studied strategies in small COMPANIES give more additional returns to investors than large COMPANIES. Also, based on the results of hybrid strategies, investors will receive more additional returns in the long run than simple momentum strategies.

This study investigates the long-run reversal effects using the data of 178 listed COMPANIES in Tehran Stock Exchange for a period of 2009-2017. The hypothesis of long-run reversal (contrarian) effect implies that LOSER (winner) portfolios become winner (LOSER) portfolios in the long-run. The result shows the existence of reversal profits in stock returns for a period of 36 months. In addition, this reversal effects are asymmetrical and the result is not true for the winner stocks. In the following, the study examines whether this increase in the future return of LOSER stocks be attributed to their increased risk. In other words, winner stocks will still be LOSERs in the long run even after riskadjustments. For this purpose, the CAPM was used. This model cannot fully explain the long-run reversal profits even by controlling the effects of size, value, and liquidity. In the contrary, multifactor asset pricing models were able to fully explain the long-run reversal profits, in which size has a greater significance effect than other risk factors. These findings suggest that the longrun LOSER portfolio consists of small stocks with a higher risk as compared to the winner portfolio. Therefore, the excess profits from LOSER portfolios are nothing but compensation for the risk held. Accordingly, the contrarian investment strategy cannot be considered as a strategy to earn abnormal returns in Tehran Stock Exchange.