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 Although previous research has examined the application of artificial intelligence (AI) across various areas of finance, there remains limited empirical evidence regarding its impact on corporate financial asset allocation. This gap is particularly evident when considering the organisational capabilities that enable firms to utilise AI technologies effectively. In the rapidly evolving technological landscape, artificial intelligence (AI) has emerged as a pivotal force driving innovation and transformation within corporate financial management. By embedding AI into organisational processes, companies have fundamentally reshaped their financial decision-making frameworks. However, the exact mechanisms through which AI adoption shapes the allocation of financial assets are still not fully understood. This study examines how artificial intelligence (AI) technologies influence the allocation of financial assets within corporations, with a specific focus on the moderating influence of three dynamic organisational capabilities: absorptive capacity, innovation capability, and adaptability. Based on panel data collected from companies listed on the Tehran Stock Exchange between 2020 and 2024, AI adoption is measured through textual analysis of management commentary reports obtained from the Codal system. The dependent variable comprises a set of financial ratios, including the proportion of financial assets relative to a firm’s total assets. The analysis employs multiple regression models with interaction terms to test the proposed hypotheses. Findings indicate that the adoption of AI substantially enhances the effectiveness of distributing financial assets. Moreover, absorptive capacity and innovation capability strengthen the association between AI adoption and the allocation of financial assets within firms' performance, while adaptability shows no statistically significant moderating effect. These results highlight the importance of both technological infrastructure and internal capabilities for leveraging advanced technologies to their fullest potential. This research not only enriches the academic discourse with fresh empirical insights but also offers valuable implications for financial managers, capital market regulators, and policymakers engaged in organisational digital transformation strategies.

Abstract This study explores the considerable significance of candlestick chart patterns as a foundational asset within the realm of stock market analysis and prediction. As a graphical representation of historical price movements and patterns, Candlestick charts offer a distinct and valuable perspective for understanding how the financial market operates. This perspective assists us in accurately pinpointing the most advantageous times for making decisions to buy or sell financial securities, such as stocks or bonds. These charts provide insights into market trends and potential trading opportunities. We adopt an innovative approach by harnessing image processing techniques to extract and analyze patterns from Candlestick charts systematically. Our findings underscore the pivotal role of visual data in financial analysis, particularly in times of market volatility and uncertainty. Investors often resort to technical analysis strategies when confronted with erratic market trends, often relying on insights derived from chart-based analysis to guide their decision-making processes. By meticulously extracting essential insights from candlestick charts, our study aims to provide investors with more efficient and less error-prone tools. Ultimately, this endeavor contributes to the enhancement of decision-making precision and the mitigation of risks inherent in participating in the dynamic stock market landscape.

Abstract This work aims to analyze the relationship between stocks in the financial market of the Tehran Stock Exchange embedded in their transfer entropy. In this regard, the behavior of the transfer entropy between indices of 180 corporations of the Tehran Stock Exchange has been studied. Then the footprint of crises of the market has been searched in the trends of the transfer entropy. The result has been compared with the result of the analysis imposed on the stocks included in the Dow Jones industrial index in the stock exchanges of the United States. In order to investigate the financial crisis of the Tehran Stock Exchange, the stock price data of 180 companies in this market that were active in the period from 2008 to 2018 are analyzed. It is observed that the average pairwise transfer entropy of indices in the Dow Jones group declines over the financial crises in the United States. In Iran, despite the United States, the financial crises have not left a footprint in the pairwise transfer entropy over the studied period. Such an observation suggests future studies on the pairwise and possibly collective behaviors of indices in Iran and the United States.

Abstract The stock market plays an important role in the economic development of countries. Network analysis is one of the latest methods in analyzing the stock market. It is a new concept for a macro view of the whole market in quantitative science literature. Therefore, this research analyzes the available Shareholder network in the Tehran Stock Exchange from 2013 to 2017. This research is based on a type of data collected and analyzed is quantitative research. And, its’ type is network analysis. The research results indicate that many of shareholders are connected to each other, although a class structure governs their relations. Some of the shareholders, in comparison with others, have a better position. Having a better position caused them to encounter fewer mediators in gaining access to other shareholders, and also easier access to available resources. The shareholders’ ability in gaining access to information through the cluster of network members enhances too. Therefore, it is claimed that these shareholders can play the role of key actors in the governing structure. Also, the results of the Pareto distribution indicate that the distribution of power among the Shareholders is approximately 25/75, that is, 75 per cent of the strength in the hands of 25 per cent of the Shareholders.

Abstract In recent financial scandals, related parties transactions (RPTs) have been as one of the major concerns, so that the targeted use of these transactions and lack of their disclosure or insufficient disclosure are some of the factors in the failure of the corporates. In RPTs, there is a risk that the related party may be favoured with terms that could harm the interests of the company’s shareholders. The purpose of this study was to investigate the effects of different types of related parties transactions on the firm value with the moderating role of the audit committee incorporates listed in Tehran Stock Exchange. The research statistical sample consists of 100 listed firms in the Tehran Stock Exchange in 6 years of 2013-2018. This research, based on the nature and content, is a descriptive/ correlational research. Using Panel data and multiple regression, the results of the research show that there is a negative relationship between RPTs and the firm value. The findings also show that there is a positive relationship between the audit committee and the firm value. Also, the findings show that different types of RPTs have a different effect on the firm value. The results also show that the audit committee does not affect the relationship between RPTs and the firm value. 

Abstract In order to survive in the modern world, organizations must be equipped with the mechanisms that not only maintain their competitive advantage, but also result in their progress and improvement. Prediction of banks’ performances is an important issue, and a poor performance in banks may primarily lead to their bankruptcy, thereby affecting national economics.
The bank performance prediction model uses scientific and systematic approaches to diagnose the financial operations of institutes. According to a precise and strict evaluation, the model can detect the weakness of institutions in advance and provide early warning signals to related financial governments. In the present study, we have used three data mining models to predict the future performance of the banks accepted in Tehran Stock Exchange (TSE) and Iran Fara Bourse. Initially, 53 financial ratios were selected and, consequently, reduced to 28 using the fuzzy Delphi technique. The statistical population included 18 banks listed on TSE and Iran Fara Bourse, which   provided their financial statements during the period of 2011 to 2017. Data were collected from the Codal site based on 28 financial ratios using C4.5 decision tree, AdaBoost, and Naïve Bayes algorithm. According to the findings, the Naïve Bayes algorithm was the optimal predictive model with the accuracy of 88.89%.

Abstract This study aims to present a model for predicting corporate default among Tehran Stock Exchange’s selected industries. To do this, corporate default drivers were identified and selected by referring to previous research findings and using experts’ opinions. These drivers were divided into five categories: accounting ratios, market variables, macroeconomic indicators, nonfinancial factors, and earnings quality measures. Structural equation modeling (SEM) technique was used to derive the prediction model. In this technique, corporate default drivers were used as latent independent variables, and their constituent factors were considered as observable indicators of the above variables. In addition, corporate default, as the latent dependent variable, was calculated by a measure based on the Black-Scholes-Merton (BSM) option pricing model. After implementing structural equation modeling (SEM) technique by use of Smart PLS software, a prediction model that contains influential drivers of corporate default was derived and presented for each of the selected industries.

Abstract The purpose of this paper is to analyze the relationship between default risk and momentum effect using data from companies listed on Tehran Stock Exchange.To calculate default risk,we used Black-Scholes-Merton (BSM) option pricing model. To describe momentum effect, by determining the formation period to be 6 months, and the holding period to be 3,6, or 12 months, we firstlyexamined the profitability of short term (3/6), midterm (6/6), and long term (12/6) momentum strategies and found that during 2010-2015 time period, only midterm momentum strategy is profitable.Then,we showedthere is no relationship between default risk andmomentum effect.

Abstract Market efficiency paradigm and time patterns concerned, as "calendar anomalies" is a contradictory issue for researches. TSE's market participants have a negative understanding of the 6^th and 12^th month of the fiscal year and this issue is rooted in the obliged credit settlement of the brokerage industry at the year-end. The purpose of this study is to investigate the TSE's total return before and after brokerage firms' year-end. Using GARCH-PQ, and data of market index in periods between 1390 and 1396, we concluded that periods of1^st to 22^ndof 6^thand 12^th months,and 22^nd to the end of 6^th and 12^th months, have respectivelynegative and positive effectson TSE's stock index.