نشریه تحقیقات مالی
پیاپی 54 (تابستان 1398)

This research examines the overconfidence, representativeness and disposition effect biases among individual investors of the Tehran Stock Exchange. The purpose of this research is to investigate the extent to which these biases are prevalent among investors and their relation with investors' performance. 

For this purpose, we examined the portfolio statements of individual investors in the stock market during a five-year period, from 2012 to 2016. We have used multiple indicators to measure the behavioral bias and to examine the relationship between behavioral bias and investors' performance by the portfolio study method. 

The results show that overconfidence, representativeness and disposition effect are relatively prevalent among investors. There is also a significant relationship between the overconfidence and representativeness biases and the performance of investors, although this relationship is not significant for the disposition effect. Specifically, investors with a higher portfolio turnover, as well as a more concentrated portfolio, have earned higher returns. Also, investors who bought past winning stocks have had higher average returns.

Behavioral bias is relatively common among investors, and these biases can affect the performance of the investors.

The goal of this research is to calculate the amount which must be paid for a fair premium based on the principle of equity and the financial solvency ratio of an insurance company based on the principle of equivalence, via potential deviation ratio method as a new method.

The aggregate loss variable has been derived from Severity and Frequency of the losses. In this method, first the actual statistical distributions of these variables are estimated and then the fair premium is calculated based on the principle of equity. Next, the amount of potential deviation ratio, which is required to increase the financial solvency margin of the insurance company, is calculated. The model for calculating the premium, as well as the more precise concept of financial solvency of the insurance company that is provided in this research, is based on scientific foundations and can be used as a new method for all insurance fields.

The results of this research show that the calculated amount of premiums and potential deviation ratio that is required to increase the financial solvency ratio of the insurance company, by estimating the actual distribution of frequency and severity of the claims compared with when these variables are assumed to be normal distributed, are different. The difference is especially important in the higher levels of confidence.

It can be concluded that in the case of calculating premiums and potential deviations based on assuming the normal distribution for the data, the real financial solvency ratio would be different from the apparent calculated financial solvency ratio of the insurance company. Furthermore, lacking the ability to precisely price the premiums may cause the insurance company to quickly fall down to bankruptcy.

In the present era, businesses have developed to a large extent which has, in turn, forced them to manage their resources and expenditures wisely for the sake of competition. This is mainly because the competitive market has severely reduced the flexibility of companies, which means that their ability respond to different economic situations has reduced and this puts most firms at the constant risk of bankruptcy and contraction. Therefore, in this study, we have tried to predict the bankruptcy of manufacturing companies through preventing the occurrence of such risks. 

In this study, the "Kernel Extreme Learning Machine" has been used as one of the artificial intelligence models for predicting bankruptcy. Given that machine learning methods require an optimization algorithm we have used one of the most up-to-date, "Gray Wolf Algorithm" which has been introduced in 2014.

The above model has been implemented on the 136 samples that were collected from the Tehran Stock Exchange between 2015 and 2018. All of the performance evaluation criteria including the classification, accuracy, type error, second-order error and area under the ROC curve showed better performance than the genetic algorithm which was presented and its significance was confirmed by t-test.

Considering the gray wolf algorithm’s high accuracy and its performance compared to the genetic algorithm, it is necessary to use the gray wolf algorithm to predict the bankruptcy of Iranian manufacturing companies either for investment purposes and for validation purposes, or for using internal management of the company

This study is aimed at examining the relative performance of trading halts and price limits in the Tehran Stock Exchange. In this research, we determined if the mechanisms involved in trading halts and price limits in the Tehran Stock Exchange affect the market variables or not. This was done through studying the trading activity, liquidity, volatility and abnormal returns. We have also examined the efficiency of the mechanisms involved in trading halts with respect to the price limits.

In this study, the pre and post events period’s average changes were compared to test the relative performance of the trading halts and price limits. We examined 400 trading suspensions and 893 price limits related to 80 companies from 1392 to 1396, and the data was selected by the omissive sampling method. The hypothesis test has been performed using the SPSS software, the mean comparison method and Wilcoxon test.

Through testing the hypotheses it was concluded that the trading activity reduces after trading halts but increases after price limits. On the other hand, liquidity increases after the trading halt, but reduces after prices limits. The results also show that volatility reduces after trading halts although there is no significant difference after price limits. Meanwhile the result also show that abnormal returns increase after trading halts but decreases after price limits.

According to the volatility and liquidity criteria, trading halts are more efficient, but based on the trading activity and abnormal returns criteria, price limits seem to be more efficient. Therefore, in order to compare the relative performance of the trading halts and price limit in the Tehran Stock Exchange, we cannot express a definitive opinion.

Efficient investment is one of the company's most important responsibilities, consequently making investment decisions is an incentive for future cash flows and the ultimate evaluation of the company. Thus, if a firm’s investment is inefficient, it will result in the lack of optimal allocation of the company’s resources and will have adverse effects on the shareholders' interests. Therefore, this research aims at studying the various aspects of the economic consequences of inefficient investment decisions and provides a model for measuring the investment efficiency in Iranian firms.

In this research, the factors affecting investment are extracted based on previous theories and researches. Each of these factors, which are closely related to investment, is investigated to measure investment efficiency. The output of the primary models is firstly tested in terms of accurate detection of under and over-investment in firms. Secondly, the model is validated through the sources and compared to the consequences of inefficiency and investment efficiency based on previous theories and research. This process continues until an optimal model for investment efficiency is acquired .  

The results showed that among the 18 variables studied, only sales growth, growth opportunities, annual returns, dividends, and investments in the past year were able to express the current year's investment changes and were used for measuring investment efficiency. The native model of investment efficiency has a power of 88.14% and 92.89%, respectively, for an accurate detection of prone companies of over and underinvestment. Also, based on the findings, agency costs lead to an increase in over-investment and on the other hand financial constraints lead to increase under-investment, as well as the investment efficiency which has a positive impact on economic value-added and firm value . 

According to the results, investments in Iran, in addition to common factors with foreign economic environments, are influenced by multiple factors that can be useful in explaining investment efficiency.

The main objective of this study is to compare the Harvey Logistic Growth Models, Harvey, and the Nonlinear Autoregressive Neural Network, and to design and find a model with better predictive accuracy for the Tehran Stock Exchange data. This model is nevertheless highly dependent on past values, has high fluctuations, and shows nonlinear motion patterns which have been repeatedly neglected.

In this study, the "Harvey Logistic" Growth Models, Harvey and the addition of nonlinear components based on the Taylor series expansion for trigonometric functions were studied to compare the accuracy and prediction of these models based on prediction criteria and its results with the nonlinear autoregressive neuronal network. Daily data of fluctuations from 1393 until 1395 of the total stock index, which was divided into two categories, were used as the sample pool in this study.

The results of the unit root tests such as Dickey-Fuller and BDS test show that the data is stationary and has a nonlinear property. In the estimation stage, the goodness of fit for the Logistic and Harvey models show that both models have a high root mean square error and low coefficient of determination for the four data types. By adding the nonlinear parts to the Harvey model, a good fit was obtained for the stock index with a coefficient of determination of about 99.8 percent and minimum root mean square error, even when compared with the nonlinear autoregressive neural network.

The results of the research show that combining the Harvey model with the nonlinear component could be considered as one of the models which predict the Tehran Stock Exchange index better than the other models.

Liquidity plays a major role in financial markets, specifically in easing the burden of sharing the risks and also in improvement of efficiency of transactions. Liquidity risk can be defined as the risk of being unable to buy or sell an asset in a specific time frame without it losing value. The main goal of the present study is to to study the effects of liquidity-adjusted Capital Asset Pricing Model (LCAPM) with Bullish and Bearish of Tehran Security Exchange Market to find the better model to explain expected return of stocks. 

One of the new areas in capital asset pricing models are the "liquidity-adjusted capital asset pricing models" which help us in this regard using the factor-based modeling tools. In this study, Amihud illiquidity measure is used as the indicator of liquidity of transactions of shares and time structure breakdown has been used to determine the type of the market.

The results of the study shows that firstly, Amihud measure is in comparison one of best measures for liquidity risk estimation. Also, cumulative liquidity measure has a reverse relationship with stock returns. Meanwhile, it has been shown, for both bear and bull markets separately, that liquidity has a positive effect on stock returns.

In contrast to heterogeneous effects of liquidity risk of capital market on expected rate of return, by separating the market into bear and bull markets, the positive effects of liquidity risk on expected rate of returns can be observed. Therefore, one can conclude that effect of this type of risk on expected return is positive in the bear markets and is higher than bull markets.