نشریه تحقیقات مالی
پیاپی 67 (پاییز 1401)

While inefficiencies in the financial markets are the leading cause of capital misallocation, options market efficiency is a major area of interest within this field of study. Since arbitrage efforts to take advantage of arbitrage profit opportunities in the options market lead the market to efficiency, the current study seeks to explore arbitrage strategies that can be used in this regard.

In the literature, there are two strategies to do the options market efficiency tests. The first strategy is to compare the market price with the implied price derived from theoretical models such as the Black-Scholes model. The second strategy is no-arbitrage options pricing relations violation test based on the hypothesis that there is no arbitrage opportunity in the market (i.e., the selected approach in this research). The advantage of examining no-arbitrage options pricing relations is that they can provide a simple way of testing the efficiency of the options market without imposing restrictions on the preferences of investors or distributional assumptions on the return of the underlying asset. The description of variables measured in empirical studies changes over time and between the different countries and research conditions. The meta-analysis approach (based on studies published from 1978 to 2019) was used to identify arbitrage strategies and the amount of profit from each strategy. In addition, the analysis of variance and a one-sample t-test was used to rank and compare the strategies. In total, 54 empirical studies with the support of more than 3.7 million options transactions and 1,315 effect sizes (research sample) were meta-analyzed.

The results showed that in the whole sample, the options market is always efficient. This means that the cumulative effect size for the whole sample (empirical tests performed in different countries and different time periods) is significantly different from zero. In addition, in combined strategies (which include both buy and sell options), there is more arbitrage profit opportunity than in simple strategies. The reliability of the tests was evaluated by applying 26 experimental conditions.

In all cases, the superiority of combined strategies was confirmed. On the other hand, the reasons for the divergence of efficiency in the options market were discovered. First, combined strategies in which options are traded simultaneously have made the options market inefficient. Accordingly, it is argued that market efficiency is underestimated in cases where no-arbitrage relations are based on trading strategies including simultaneous trading of put and call. On the other hand, in such strategies, investors earn more arbitrage profit. It was pointed out that the most likely reason for this result is the complexity of these strategies, which requires the simultaneous efficiency of the call market, the put market, and the spot market. For example, in different countries, assuming transaction costs in the presence of dividends and etc., the research results remain robust.

 In recent years, in particular after the 2008 financial crisis, factor investing received widespread attention from asset managers around the world. Due to the lack of enough research in this area in the Iranian capital market, the purpose of this study is to evaluate the performance of factor investing in the Tehran stock exchange (TSE).

In this research, the performance of five single-factor portfolios and three multi-factor portfolios were evaluated on Tehran Stock Exchange from 2014 to 2021. First, the five single-factor portfolios were made based on momentum, value, size, quality, and low volatility. Then, using the results of single-factor portfolios, three multi-factor portfolios were made based on equal weight (1/N), inverse volatility, and equal risk contribution (ERC). The results of single-factor and multi-factor portfolios were compared to overall and equal-weight indices.

The results achieved from all single-factor portfolios, except those from momentum, and all three multifactor portfolios were better than the overall index with a 95% confidence level. However, compared to the equal-weight index, the results were mixed and only the size factor and equal-weight multi-factor portfolios had better performance within a 95% confidence interval.

The results of this study indicate that, during the time frame of this study, factor investing showed better performance than the overall index. Therefore, this type of investing can be considered a practical type of investment by investors and asset managers on the Tehran Stock Exchange.

 In asset pricing models, it was traditionally assumed that there is a linear relationship between return and explanatory variables. Therefore, estimating the coefficient in a nonlinear setting would be inconsistent and bias-oriented. In this study, the predictive power of the nonlinear and linear Fama-French Five Factor Model was estimated in the period from March 2010 to March 2020.

The semi-parametric method was used to estimate the nonlinear expected return in FF Five Factor Model. The expected return was also calculated based on the linear FF Five Factor Model.

The estimated return was compared with the realized returns. Then the mean absolute percentage error was used to measure the predictive power of research models. The results show that mean of the mean absolute percentage error in the semi-parametric model is lower than the linear model.

Despite the lower error of the semi-parametric FF Five-Factor model compared to the linear model, no significant difference was observed between the predictive power of these two models. Therefore, the estimating methods will not have a significant impact on the predictive power of the Five Factor Model.

The purpose of this paper is to determine the effective factors influencing the value of FinTech start-ups from the perspective of venture capitalists in the field of financial technology in the early stage of their life cycle.

This research is qualitative that uses the Grounded theory method. In this study, by interviewing 19 venture capitalists active in the field of financial technology, 46 final codes were extracted via open coding. In the next step, through axial coding, a paradigm model was presented for the main value of the start-up FinTech company.

This study showed that the value of FinTech start-ups is majorly influenced by the intervening factors related to their main buyers, i.e. banks and financial institutions because the benefits of buying fintech companies are strategic for banks and financial institutions and can create a competitive advantage for them compared to other banks and financial institutions. In addition, it was found that underlying conditions such as investor exit routes, the need for reinvestment, and the consequences of their merger and acquisition also affect the value of FinTech start-ups.

The value of FinTech start-ups is more affected by the intervention of their buyers than other technology start-ups, because the benefits of buying FinTech companies are strategic for banks and financial institutions, and it can give them a competitive advantage over other banks.

One of the most important issues for all investors, including individual and institutional investors in the stock market, is finding the optimal portfolio. Identifying the optimal portfolio in the stock market can be considered a two-objective optimization problem. This problem maximizes and minimizes the return and risk of the portfolio, respectively. Like other multi-objective optimization problems, the portfolio optimization problem can be solved by multi-objective evolutionary algorithms (MOEAs). Accordingly, the Non-dominated Sorting Genetic Algorithm ( ) and Strength Pareto Evolutionary Algorithm ( ), as the two advanced algorithms of multi-objective evolution algorithms, can be used to solve this problem. These algorithms identify the optimal solution by ranking and archiving solutions located on the Pareto frontier. The purpose of this research is to compare the performance of  and  in mean-variance and mean-semi-variance approaches to identify the optimal stock portfolio.

This research investigated 241 stocks enlisted on the Tehran Stock Exchange (TSE). It was conducted within 174 months from September 2006 to March 2019. The researchers first identified the optimal portfolio using NSGAII and SPEA2 algorithms through two approaches including mean-variance and mean-semi-variance. Then, by conducting a statistical hypothesis test on the average Sharp ratio of extracted portfolios, the performance of NSGAII and SPEA2 algorithms were compared. To confirm the research findings, a robustness test was done by comparing the performance of the SPEA2 algorithm with the traditional Markowitz model. Also, to ensure the stability of research findings, the performance of two algorithms in the mean-variance and mean-semi variance approaches were compared with quarterly data ending March 2022.

According to the obtained results, the SPEA2 algorithm has better performance than the NSGAII algorithm in both approaches. Backtesting the real data for the quarter ending inMarch 2022 confirmed the findings of the present study. Also by doing robustness tests, the researchers found the SPEA2 algorithm as the superior algorithm in this research with better performance than Markowitz's basic model.

The results indicated that the 2 algorithm has better performance in selecting the optimal portfolio than the  algorithm in both the mean-variance and mean-semi variance approaches. Regardless of how the stock returns are distributed, this study recommends that individual and institutional investors use the SPEA2 algorithm to determine the optimal portfolio arrangement.

One of the important factors that investors are faced with in a stock exchange is the complexity of companies.  Complexity can disrupt the process of extracting and using the required financial information. Increasing market uncertainty influences stock pricing. The main purpose of this research is to investigate the effect of informational, operational, and corporate governance complexity components in firms on stock pricing.

The statistical sample of this research consists of 180 companies enlisted in Tehran Stock Exchange (TSE) from 2009 to 2019.

The achieved results showed that the components of company informational complexity (i.e. divergence, negative operating cash flow, accruals, consolidated financial statements, the standard deviation of operating cash flow, and standard deviation of net changes in operating assets) have a positive and significant effect on the incorrect stock pricing. Also, among the components of operational complexity, three indices (i.e. cycle length, export sale ratio, and product variety of the company) have a positive and significant relationship with incorrect stock pricing. However, the present study could identify no significant relationship between production technology level and incorrect stock pricing in Iran. Finally, the components of leadership complexity (i.e. political communication, CEO tenure, and ownership concentration) were found to have a positive and significant relationship with incorrect stock pricing.

The results showed that the complexity of companies affects the mispricing of stocks, which increases the risk of making wrong stock choices for investors.

Financing is one of the factors of future success for small and medium-sized technology businesses. Because of the immaturity of these businesses, the majority of their assets are intangible, rely on technical knowledge, and lack significant financial and credit records. To control the probable risks, financing systems traditionally rely on patterns based on maximum authentication of physical assets, which do not fulfill the needs of these firms and must be altered. The fundamental purpose of this research is to create a model for assessing and determining the collaterals of technical projects and knowledge-based firms so that finances could be more easily obtained and the risk of non-refunding could be managed. In this research, in the first step, the criteria for evaluating and assessing the intended project and company were defined. By using a fuzzy expert system, the appropriate method of dealing with companies was determined. In the second step, in order to create a learning system, the results of the first step were used as input data in the form of three neural network algorithms implemented in MATLAB software and ANFIS algorithm, with 93% accuracy compared to the input data.

The final neuro-fuzzy model was tested according to the repayment data available in Iran National Innovation Fund. The statistical population included knowledge-based firms receiving facilities. By using The Cochran formula, 103 companies were selected as a sample. The results obtained by testing and evaluating the model, in 85% of cases, could correctly identify the companies' performance in repaying resources and suggest appropriate collateral (Real estate collateral or Bank Guarantees).

The model presented in this study can be used to evaluate, validate, and determine the collaterals of technological projects and knowledge-based firms. It can facilitate their access to financial resources and also help them with managing the attendant risks. It also employs new optimization methods and has the ability to learn.