مجله راهبرد مدیریت مالی
سال دوازدهم شماره 1 (پیاپی 44، بهار 1403)

According to the research literature of initial public offerings (IPOs), despite the high Performance of them in the short-term, their long-term performance will probably be lower than the average performance of the relevant market {or industry} and this issue, if confirmed, requires the special attention of capital market activists. By using factor models, the current research has focused on the long-term performance of IPOs and, accordingly, the phenomenon of their underperformance in the long-term. In particular, in order to create a broad and reliable insight into the phenomenon " long-term underperformance of IPOs”, analyzes have been carried out at various levels, including the market and industry levels.

The research was conducted on 235 initial public offerings (IPOs) between February 2015 and December 2020 (57 months in total). The analyzes have been done with the approach of factor models and the use of monthly time series of portfolios consisting of initial public offerings (IPOs). To investigate the phenomenon of low or high Performance of IPOs, the intercept level of the models has been used. Due to the lack of agreement among researchers regarding the length of the long-term period of IPOs, this research has used three periods (12-24 and 36 months).Also, according to the existence of the different models explaining performance of the IPOs and the possible effects of the model on the results, three different and conventional models have been used, including Fama and French (2003 and 2015) and Hou et al.'s model (2015).The stock and over-the-counter markets have been investigated separately, and since the research data only made possible the portfolio of 4 chemical, pharmaceutical, agricultural, and basic metal industries, the analysis of industries was carried out only at the level of these 4 industries.

Validation test of the models supported their validity and the results of fitting them showed that all the investigated models have adequate explanatory power and the minimum adjusted explanatory power (adj R2) is 69% and the maximum is 85%. As the definition of the long-term period becomes longer, their explanatory power decreases. IPOs move almost in line with the market, and the most important factor in predicting their return is market risk and profitability, and other factors have little effect on explaining the long-term return of IPOs. Regarding the investigation of the under-performance phenomenon, it should be said that sufficient evidence was not obtained to support the phenomenon of low or high performance of initial public offerings. And the intercept of the models in all periods and for all models lacked statistical significance. Breaking the data into the market and industries level did not create a special difference in the results. The analysis of the 4 mentioned industries showed the phenomenon is not statestically significant in any of the industries, although in the case of the chemical industry, a borderline significance level with a positive coefficient was obtained in some periods. Whether the IPOs are listed in stock market or over-the-counter (OTC), does not make a difference in their long-term performance.

The evidence of the current research does not support the existence of the phenomenon of “IPOs long-term underperformance” its reverse form (over- performance) in any of the tested cases, and the results for periods of more than one year (12-24 and 36 months) and for both stock and over-the-counter (OTC) markets and for all four industries (, chemical, pharmaceutical, agriculture and basic metals), are consistent and the model used, also has no effect on it so it must be said that the existence of under or over-performance of IPOs in Iran's financial markets is not confirmed. In more technical terms, in periods of more than one year, the market does not recognize the difference between the IPOS and the other companies due to factors other than known fundamental factors. This issue challenges the need to adjust individual decision-making models in the face of initial public offerings, and on the other hand, provides guidelines for use by legislators when legislating processes related to the capital market and especially initial offerings

Despite the revision of Iran's Accounting Standard No.15 and the requirement of Standard No.42, fair value measurement, since the beginning of 1400, marketable investments in financial statements are generally identified at the lower of cost and net sales value.According to the disclosure requirements of the Securities and Exchange Organization, investment companies are required to disclose the current price of investments on the CODAL site at monthly intervals.The purpose of this study is to investigate the appropriate criteria for this important item of assets in investment companies.that's mean, it compares the current practice of fair value disclosure in the notes to the financial statements and CODAL against its recognition.This study is based on the relative and incremental value relevance of competing measures (cost and market value) from the point of view of measurement using linear regression of accounting variables on the stock price.Robust regression method was also used to test the robustness of the results.The time period is monthly from 2018/03/21 to 2022/04/30 and the statistical population is all investment companies that were accepted to the Tehran Stock Exchange market before 2018.Finally, according to the available information of 47 companies, the population has been limited to 2350 Company - month.The evidence of this study showed that the market value of the marketable investments portfolio has both a relative and incremental value relevance compared to its cost.The findings of the additional tests also showed that the investors had an appropriate reaction to the crisis by reducing the value relevance of the numbers related to the market value of the marketable securities portfolio.that's mean, at the time of crisis when the value of stocks in the investment portfolio of companies falls sharply, this decrease in the value of assets is reflected with a gentle slope in the stock price of the investing company.

Wealth and its place in countries has always been considered because it is produced with the support of countries's wealth and overshadows other macroeconomic variables. One of the components of wealth is financial wealth, which can affect the pattern of changes in economic variables and affect the consumption, savings, investment and production of countries, and also affect future incomes. Therefore, the purpose of this study is to investigate the interaction of innovation, globalization and the quality of governance institution on financial wealth in the top 48 science-producing countries in the world during the period 2011-2020. For this purpose, the research model has been estimated using integrated data and generalized torque method. The results show that the effect of all indicators of innovation (institutions, human capital and research, infrastructure, market complexity and business complexity) on financial wealth is positive and significant. Also, the interaction of all indicators of innovation, globalization and governance on financial wealth is positive and significant. In addition, the interaction of globalization and governance with the components of innovation on financial wealth is positive and significant. Therefore, it is recommended that the governments of the studied countries with low financial wealth help to increase financial wealth by improving the quality of governance and taking advantage of globalization opportunities and paving the way for improving the components of innovation.

The interplay between various industrial groups plays a crucial role in determining the optimal investment portfolio for investors. Identifying which group carries or accepts risk within a specific time period and performance range allows for necessary adjustments in the investor's portfolio to achieve maximum returns. In this regard, the present study examines the impact of banking, automotive, cement, basic metals, and petroleum products groups on a symmetric, positive, and negative performance basis from January 5, 2015, to February 17, 2023. The results of the study indicate that in recent years, the overall index of these mentioned groups has shown more negative performance than positive performance. Moreover, banks and basic metals have acted as guiding and risk-transferring entities to other groups. On the other hand, the automotive and petroleum products groups have been risk-accepting, and their performance can be explained by the two groups of banks and basic metals.

Market sentiment or investor sentiment is investors' general perception towards a particular security or the entire financial market. Investors' emotions are considered one of the critical factors affecting the stock price, but the behavioral tendencies of humans have made the measurement of emotions face challenges; Therefore, the main problem of this research is to extract a quantitative index to measure sentiment tendency in Tehran Stock Exchange. This research is considered to be descriptive-correlational from the point of view of practical purpose and from the point of view of data collection. In this research, using the principal component analysis (PCA) method, an index was extracted to measure the emotional tendency of Tehran Stock Exchange investors based on monthly data during the period of 2019-2018. The emotional variables used in the final index are the number of stock transactions, the net cash flow of mutual funds, the ratio of small investors' transactions, the discount of investment companies' shares, the price-to-earnings ratio, the psychological line index, the relative strength index, and the volume of stock transactions.

The Tehran Stock Exchange and its industries have contributed to the risk of tail events occurring in recent years, such as the U.S. and European sanctions against Iran and the COVID-19 pandemic. Industries affected by these crises have become more connected. Crisis episodes have raised risk spread within the financial system. Whenever an impulse hits an industry, it is quickly transmitted to other industries through a contagion mechanism, which ultimately causes fluctuations in the whole system. Stress situations suggest that promoting financial stability should be a priority for regulators and academic researchers. For this purpose, analyzing the systemic risk contagion and tail risk interconnectedness among industries is important to enhance the stock market’s safety and design the related macroprudential policies. The stock market plays a vital role in the financial system by allocating resources and facilitating price discovery. However, Tehran Stock Exchange is a completely immature market, which greatly increases the possibility of risk contagion to the entire market. For example, the collapses of stock price bubbles in the Tehran Stock Exchange over the past two decades resulted in huge losses in the net worth of investors and was followed by a recession. The financial turmoil highlights the importance of measuring systemic risk in the stock market.Interconnections among industries in a network imply that a shock in one industry may lead to risk transmission between related industries, causing risk contagion to the entire stock market. Therefore, investigating the risk interrelationships between industries and identifying the systematically linked industries is essential to effectively predict the systemic risks and control risk spread in the stock market.This study analyzes the risk interdependence among the main industries listed on the Tehran Stock Exchange. In particular, the risk contagion effects among industries are analyzed under extreme market conditions.

The incentives of fund managers and their superior capabilities in order to invest and manage them are two explanations for the risk changes of joint venture funds in different market conditions. To this end, in this research, through the analysis of 21 mutual funds in stock for the period of 79 months from August 2011 to March 2018, using the least squares method, we examine the incentives for risk managers of mutual funds in the stock and its effect on return on investment. The results of the research show that in the bull markets, there is a motivation to compensate for losses, as managers of these funds increase the level of risk in the future, Causing increase the returns of the funds Future period. Against the managers of winning funds, using the exhibit superior investment ability, reduce the risk of fund and increase the returns of the Future period.According to the results, In bearish markets, managers will reduce their future returns by increasing the risk of the fund, which reflects the organizational weakness or weakness of the managers' skills in investing. In contrast managers who reduce the risk level of the fund seek to increase the return on the fund's future period, which is motivated by the managers' career concerns.

The purpose of this research is to use the ensemble learning model to combine the predictions of random forest models, short-term long memory and recurrent neural network to provide an algorithmic trading system based on its. In this research, a prediction model based on ensemble machine learning model is presented and its performance is compared with each of the sub-algorithms and real data. In this research, in the first stage, using three machine learning models, the price top and bottom of Bitcoin have been predicted. In the second stage, the outputs of the models are presented as feature variables to the XGboost and LightGBM models to predict the roof and floors. Then, in the third stage, the outputs of the second stage are combined with the collective voting classification pattern to predict the next ceiling and floor. Bitcoin price top and bottom data in the 1-hour time frame from 1/1/2018 to the end of 6/30/2022 have been used as target variables and 31 technical analysis indicators as feature variables for three models in the first stage. Finally, forecast values and algorithmic trading systems were evaluated and compared with real data for 3 models and the introduced ensemble learning model. The obtained results show the improvement of the precision and accuracy of the proposed collective learning model in predicting the top and bottom of Bitcoin, as well as its better performance than the sub-algorithms.

The main purpose of this study is to investigate the impact of economic uncertainty and government policies on the price volatility of the banking sector in the Tehran Stock Exchange, considering the structural instability in the model parameters. In this regard, in order to estimate the uncertainty of the variables, a variety of symmetric, asymmetric and non-linear GARCH models were estimated, and finally, based on the information criteria and the significance of the asymmetry coefficients, the EGARCH model was selected as the optimal model. In the following, the effect of uncertainty Indicators on the instability of the banking sector index was investigated by the time-varying parameter autoregressive (TVP-VAR). In this research, monthly data were used in the period of 2010:7-2020:9. The results of the final estimation of the research model indicate that the impact coefficients of uncertainty indicators on the instability of the banking sector are variable. In such a way that the effect of the uncertainty of government policies (uncertainty of tax revenues) on the instability of the banking sector estimated negative at the beginning and positive at the end of the period. Based on the impulse response function (IRF), the effect of inflation uncertainty as the indicator of economic uncertainty on the instability of the banking sector is positive. Also, the impact of government policy uncertainty through the exchange rate uncertainty channel has had a positive impact on the volatility of the banking sector in the stock exchange. However, the uncertainty of government policies based on the uncertainty index of tax revenues has had a negative impact on the price volatility of the banking sector, and this impact has gradually decreased.

Non-linearity feature and high fluctuations in financial time series have made the forecasting of stock prices and financial indicators face many challenges. However, recent developments in deep learning (DL) models with structures such as long-short-term memory (LSTM) and convolutional neural network (CNN) have made improvements in the analysis of this type of data. Another approach that can be effective in the analysis of financial time series is the decomposition of capital market signals through algorithms such as complete integrated empirical mode decomposition (CEEMD). Considering the importance of forecasting in the financial markets, in this research, by combining deep learning models and complete integrated empirical mode decomposition (CEEMD), The hybrid CEEMD-DL(LSTM) model has been used to forecast the Tehran Stock Exchange index. In this regard, the daily data of the total index of the Tehran Stock Exchange in the period of 2012/12/01 – 2022/02/20 be used and the results were compared with the results of competing models based on efficiency measurement criteria. Based on the obtained results, the introduced model (CEEMD-DL(LSTM)) has higher efficiency and accuracy in stock exchange index forecasting. Accordingly, the use of this model in financial forecasts is suggested.