FINANCIAL RESEARCH
Year:2022 | Volume:23 | Issue:4|Papers Count:7

Objective: Enhanced index Tracker portfolio, as one of the investment management strategies, is trying to combine the benefits of both active and passive approaches. This research is going to provide a two-stage model that can first reproduce the index performance with a smaller number of index-forming shares and, secondly, calculate the Enhanced index tracker portfolio weights. Methods: In the first step, using a binary mathematical programming model to create clustering of time series, an index tracker portfolio was created. Coppola-based correlation coefficients and mutual information were used as time series similarity measures at this stage. In the second stage, the weight of investment in the selected shares was determined in a way that the return on the portfolio surplus was maximized relative to the index created in the first stage. The uncertainty resulting from the estimation of the excess stock returns in the second phase was considered by using a robust optimization approach. Results: The results obtained by applying the out of sample test on the 50 more active companies in the Tehran Stock Exchange from the spring of the Iranian calendar year of 1394 to spring of 139, using the tracking error and market ratio, indicate that in addition to the success of the similarity criteria in time series clustering and index tracking, at a confidence level of 99%; Enhanced index Tracker portfolios based on normal, T and Clayton Copula correlation coefficients have a positive significant difference with the index. Conclusion: According to this study, to develop an enhanced index tracker portfolio, it is practical to apply copula-based correlation coefficients and try a robust optimization approach to take into account the uncertainty of the parameters.

Objective: Nowadays, the measurement of the risk of the marketplace has a significant effect on investments; however, the inadequate evaluation of this risk will cause a financial crisis and possible bankruptcy. One of the typical approaches to measure this risk is the probability-based risk measurement method, known as Valueat-Risk (VaR), for estimating and backtesting of which there are various methods. The purpose of this paper is to put forward a comprehensive test for backtesting and analyzing the sensitivity of VaR based on the number of samples (n) and confidence levels (N). Methods: First, the VaR of Tehran Stock Exchange data was estimated by applying GARCH-Copula, DCC, and EVT. Next, by using the multinomial backtesting in two steps the accuracy of VaR estimation and ranked the models were tested. Thereafter, considering the number of samples (n) and the confidence levels (N), the sensitive analysis of the backtesting result demonstrated the accuracy of the estimated VaR by selecting the most appropriate parameters. Results: Sensitive analysis findings indicated that in all three models, increasing the parameter "N" will result in an increase in the error rate. On the other hand, sensitive analysis of parameter "n" proved that its value depends on the technique used to estimate VaR, but generally, any increase in it leads to validation of VaR estimation models. The results also showed that according to the EVT method, at least 29% of the data is required to be used as a test sample in VaR estimation; however, the amount is equal to 22% in the DCC and GARCH-Copula methods. Conclusion: The result of the sensitivity analysis indicated that the reliability of different estimating VaR techniques relies on "n" and "N" parameters and different amounts of these two parameters can generate inaccurate and uncertain outcomes for each model. In addition, ranking these methods by using the loss function, GARCHCopula, EVT and DCC methods ranked first to third, respectively.

Objective: Passive management is an investing strategy that tracks a market value-weighted index or portfolio. It seeks to minimize the cost of investment fees and to avoid undesirable repercussions of the unpredictability of future trends. Active portfolio management tries to beat the market while passive portfolio management pursues a similar risk-return pattern to that of the market index. Index tracking is a passive investment strategy in the stock market that aims to make a portfolio using constituents of an index. It seeks to mimic its behavior without purchasing all of its constituents. This study aimed to track Tehran Exchange Dividend & Price Index (TEDPIX). Methods: In this study, portfolios were tracked and their performances were examined by applying a two-tail mixed conditional value-at-risk model (main model). Optimizing TMCVaR is a linear program that minimizes the upper deviation and the downside deviation from the benchmark index. The investigated sample included the weekly data gathered from 2011/3/21 to 2018/20/3. The data was divided into 26-time frames including 52 in-sample data and 12 out-ofsample data. Results: Statistical tests confirmed the portfolios resulting from the main model were successful in tracking the index. As a result, the investigated model was recognized as capable of tracking the index. However, due to the tracking error and information ratio, the two models were not statistically different. In the present study, the two models showed the same performance in tracking the index. Conclusion: In this study, a linear mathematical programming model was proposed to form index tracking portfolios. The results showed that although the main model was successful in index-tracking it did not outperform the mean absolute deviation model in terms of reduction in tracking error and increasing information ratio.

Objective: This study seeks to optimize stock portfolios at the industry level for an intended investment company by considering some limitations (the amount of liquidity of each industry in a month, transaction costs, portfolio turnover, and tracking error) in practice. Methods: The research hypothesis was initially tested. In the first stage, the optimization was implemented without considering the restrictions. Then, the optimization was implemented by imposing all the constraints except the tracking error. In the third stage, the optimization was implemented by placing all the constraints. Results: The obtained results proved portfolio optimization statistically significant and indicated that it had a higher Sharpe ratio than the construction of a random portfolio. The first step of this study showed that the intended company was far from the efficient frontier. Also, to maximize returns, minimize risks, and maximize the Sharpe ratio, the weights of the industries were needed to be changed (the weight of the sugar and pharmaceutical industries are recommended to be increased). The second phase approved that the company was still far from the efficient frontier and the efficient frontier had become smaller and moved downwards and to the right (the weight of the sugar and pharmaceutical industry are recommended to be increased more than the weight of others). The third step showed that the company was still far from the efficient frontier and the efficient frontier had become smaller and moved downwards and to the right (the weight of the metal and chemical industry are required to be higher than others).

Objective: Identifying the correct asset pricing model has long been an important topic in the thematic literature of financial economics. Such a model not only explains stock returns but also increases the ability to predict abnormal returns. The first models for estimating returns date back to the 1960s, when Markowitz (1952)'s new theory of securities attracted the attention of researchers. The first model for estimating returns was The capital asset pricing model (CAPM) which was presented by William Sharp (1964). In his research, William Sharp showed that return on asset was a function of line of market risk premium. But from 1975 to 1990, deviations and anomalies related to the CAPM model gradually became apparent. Following the recognition of these anomalies in accounting, in this study, based on the research of Penman and Zhou (2018), a expected return factor based on accounting characteristics is introduced. The purpose of this research is to evaluate the possibility of improving the performance of q-factor and adjusted q-factor with the expected investment growth factor’ s models for explaining the difference between stock returns by adding the expected return factor based on accounting characteristics. Methods: This is an applied research in terms of purpose and an inferential and descriptive research, in terms of method. To achieve the research objective, data of 345 companies enlisted in the Tehran Stock Exchange (TSE) and Iran Farabourse market, from 2006 to 2020, were gathered. Then based on consumption theory and accounting principles and assumptions, accounting characteristics affecting earnings growth and expected return were identified. After, the data was experimentally tested. In the following, the relevant characteristics in a factor are summarized as the expected return factor based on accounting characteristics to be used in the development of multi-factor pricing models. Finally, to evaluate the performance of multi-factor asset pricing models, test assets were classified into two categories (once considering the expected return, and once without considering the company’ s expected return factor). Results: Numerous cases from both groups of test assets showed that the expansion of qfactor and adjusted q-factor with the expected investment growth factor’ s models with the expected return factor based on accounting characteristics increases the probability value of GRS statistic, decreases GRS statistic, and increases their adjusted coefficient of determination. This indicated an improvement in performance and a considerable increase in the explanatory power of models containing the expected return factor based on accounting characteristics compared to their respective models. Conclusion: The results of this research showed that the added expected return factor based on accounting characteristics to q-factor and adjusted q-factor with the expected investment growth factor’ s models improves their performance in explaining the stock returns. Also, the test assets that considered the company’ s expected return characteristic performed better compared to those that did not.

Objective: The human decision-making process is complex and influenced by various factors. Investors' emotional decisions cause stock prices to deviate from real ones, leading to incorrect stock pricing and consequently inefficient investment. In response to criticisms of traditional financial theories, behavioral finance seeks to explain the anomalies in financial markets by considering the fact that investors are not entirely rational and their behavioral biases influence their decisions. Due to the necessity of the subject and the lack of a comprehensive model that shows the factors influencing the decision-making of investors in the Iranian capital market, the present study developed this model. Methods: The multi-grounded theory method was used in the current study. In-depth semi-structured interviews with professional experts were arranged to collect data. The statistical population of the present study included individual investors active in the Iranian capital market. The process of conducting interviews, analyzing them, and presenting the model started in March 2019 and continued by the end of 2021. Finally, 67 main categories and 218 concepts were extracted from conducted interviews that formed the overall model of the research. Results: Causal factors which influence investors’ decision making include: regret aversion, greed, fear, cognitive dissonance, reputation, anchoring, self-attribution biases, loss aversion, gambling pleasure, investment thinking, herding behavior, endowment bias, representativeness biases, false excitement, overconfidence biases, hindsight bias, regency bias, mental accounting, illusion of control bias, trading asymmetry, similarity error, magnet effect, gambler's fallacy, motivation, time horizon, first profit effect, experience, age, gender, are analyzed. Conclusion: Based on the research results, individual investors can improve the quality of their decision-making and make more effective investment decisions by implementing the presented strategies, and also by identifying and reducing their mental biases. These strategies include self-analysis, note-taking, capital market knowledge, concentration, holistic, patience, acceptance, mistakes review, self-reliance, consulting, openness to criticism, flexibility, study, diversification, and distract.

Objective: Efficient investment in human resources and the factors affecting it are among the topics receiving less attention in the academic literature. This study seeks to investigate the impact of institutional investors and ownership concentration; as the two regulatory elements of corporate governance, on labor investment efficiency. Methods: To test the research hypotheses, a panel data model was used, and to measure the labor investment inefficiency, Jung Lee and Weber (2014) model was applied. The research sample, after imposing the intended restrictions, included 179 companies enlisted on the Tehran Stock Exchange, in the period from 2010 to 2019. Results: The results indicated that institutional ownership has no impact on the labor investment efficiency, while ownership concentration reduces it. This effect existed in the case of overinvestment but it was not observed in the case of underinvestment. Conclusion: Conflict of interest and information asymmetry between managers and owners increase the risk of inefficient decisions, especially in the context of investing in human resources and the risk is likely to be reduced by strengthening regulatory mechanisms. Corporate governance mechanisms, such as ownership concentration, lead to more careful observation on management and reduce inefficiency in the made decisions.