نشریه تحقیقات مالی
پیاپی 56 (زمستان 1398)

Increasing the competitiveness of countries in the world can be reached only through innovation and the financial aspect is the most important pillar of a national innovation system. Hence, the role of venture capital in developing knowledge-based institutions is vital. However, startup portfolio selection and venture capital firms’ syndication have always been critical challenges in VC industry. Hence, the need for integrated methods, based on sophisticated quantitative techniques, is always being felt. In this research, the simulation of startup portfolio optimization is much more similar to real world and the preferences of startups as decision-makers and the interaction between investees and investors are considered. The results could shed light on which investors regarding their attributes and the startup's attributes should syndicate together and how much is their shares.

Considering the complexity of the problem, the best-known model to simulate the problem is an agent-based modeling. By applying two different computational engines based on ANFIS and ANFIS tuned by PSO and also through the utilization of modified HS, the optimization procedure is preceded.

The proposed solution method is applied to about four various samples and has been executed five times independently. Regarding analysis, the computational engine based on ANFIS tuned by PSO is more efficient and the optimum portfolio is achieved based on it.

Regarding the assumptions of the problem and the agent’s attributes in venture capital, the investors’ portfolios and their syndication has been optimized in order to lessen risk and increase return on investment.

While recent studies (Petkova and Zhang (2005) and Choi (2013)) provide evidence using U.S. data for the differential behavior of the value and growth firms, they do not explain the sources and causes underlying such differences. Further, it is unclear whether such differences still persist in other countries’ capital markets. In this paper, we address both of these questions by examining the dynamic behavior of value and growth firms’ conditional asset and levered betas over multiple periods of stable and adverse market conditions for the period 2000 -2017 in the Tehran Stock Exchange (TSE).

Panel regressions and parametric tests are conducted for sub samples of strong value and growth firms to examine the influence of key financial and market variables such as leverage, return on assets, sales growth, market to book, and market sentiments.

During adverse market conditions, value firms’ asset betas are negatively affected by financial leverage and positively by operating leverage measures. On the other hand, growth firms’ asset betas are only positively affected by their profitability. These results support the notion that high financial and operating leverage plays a key role in elevating value firm’s riskiness during adverse economic conditions.

We provide evidence that value and growth firms’ levered and unlevered risks are differentially effected by market and financial factors. Specifically, we find that operating and financial leverage significantly constraint value firms’ ability to respond effectively during adverse economic conditions.

This research is aimed at introducing firms' life cycles as a new and effective factor on stock return and comparing the performance of the new multifactor asset pricing models (augmented by firm's life cycle factor) with corresponding conventional multifactor asset pricing models in explaining stock returns.

To this end, Dickinson's cash flows pattern has been used to measure the firm's life cycle. A Firm's life cycle factors are constructed based on the difference in average returns of firms in maturity stage and firms in other firm's life cycle stages. Then, this factor waS combined with the conventional multi-factor pricing model, namely the Fama and French three-factor model, Carhart four-factor model, Fama and French five-factor model and Ho, Xue, and Zhang four-factor model. In the following, using time series regression approach, the performance of augmented multifactor asset pricing models and corresponding conventional ones are compared. For this purpose, the accounting and market data from companies listed in Tehran stock exchange and Iran Fara Bourse between the years 2004 and 2018 and the variety of test assets based on different firm's characteristics were used

The results show that augmented multifactor pricing models have a better performance compared to corresponding multifactor pricing models in explaining stock returns and outperformance is more evident when test assets are formed using firm's life cycle compared to test assets formed without the firm's life cycle.

The addition of a firm's life cycle factor improves the performance of conventional multifactor pricing models in explaining stock returns.

Analyzing the behavior of Tehran Stock Market, based on the daily asset return for the duration between 1387 and 1397 has been the main aim of this research.

Tehran Stock Market daily asset return can be considered as a time-series and therefore all existing models can be applied to it. Considering the distributional and temporal properties of such series, it can be shown that the series is stationary. Hence, Hidden Semi-Markov Model, which is widely used for analyzing time series, could be employed to analyze this series.

Based on Kolmogorov-Smirnov test and Akaike and Bayesian indices, the best density function for the series is a three parameter Gussian Mixture. Moreover, employing three-state Hidden Semi-Markov Model would be the suitable method for modeling. In addition, it was found that Tehran Stock Market followed three states namely bull, bear, and sidewalk and the definitions for such states have been given, while the probability of being in each state has also been provided.

Tehran Stock Market was in sidewalk state around half of the analyzed duration and the luckiest state after both bear and bull states was sidewalk. The market almost never came to bull state after the bear state. Moreover, the chance of getting into bear state from sidewalk was three times more than the chance of getting into the bull market.

Left-tailed risk illustrates the probability of unfavorable events that could occur in a range wider than three variances of the distribution function. Although such events have a very low occurrence probability, they would cause significant losses in case of occurrence. This research aims at examining the cross-sectional effects of left-tailed risk on expected excess returns. The present research also examines the probability of the persistence of left-tiled risk in the future.

In this research two proxies of value at risk and expected shortfall are used to measure left-tailed risk. For this purpose, a sample of 120 companies listed in the Tehran stock market in the period of the years 2010-2017 have been selected. Research hypotheses were examined with the use of Fama and Macbeth regression. Transition matrix was used to determine the probability of left-tailed risk persistence in the future.

According to the findings of the research, left-tailed risk has a significant and negative effect on the expected excess returns. The findings also suggested that the negative returns of the left tail will have a persistence probability of over 50% in the future.

The findings of the present research illustrate a new anomaly in the financial area, which is the negative effect of left-tail risk on the expected excess returns, and persists in the future.

The purpose of this study is to determine the effect of financial inflexibility on value anomaly. According to the research literature, three related sources of financial inflexibility have been identified, and we have created a composite inflexibility index, based on the variables of Investment irreversibility, total leverage and financial constraint.

In order to achieve the research goals, the monthly data of a 450 year - firm has been used during the period from 2008 to 2017. To test the research hypotheses, Fama and French (1993) three-factor Asset Pricing model was used, and by following the Poulsen, Faff and Gray (2013) studies financial inflexibility, the fourth factor has been added to it. In order to investigate the role of financial inflexibility on value anomaly, the above models were used once by using combined data; and once again, it is fitted with a time-series method.

Financial inflexibility has a significant impact on Stock and portfolio risk premium.

The results of the research show that the financial inflexibility leads to a positive risk premium in the stock level and investment portfolios and value firms gain higher future returns than growth firms due to the compensation for the risk of financial inflexibility, and Finally, the positive relationship of financial inflexibility factor with inflexible portfolios and negative relationship with flexible portfolios indicates that financial inflexibility independently subjects firms to common shocks.