International Journal of Finance and Managerial Accounting
Volume:3 Issue: 12, Winter 2019

Improving out-of-sample forecasting is one of the main issues in financial research. Previous studies have achieved this objective by increasing the number of input variables or changing the kind of input variables. Changing the forecasting model is another possible approach to improve out-of-sample forecasting. Most researches have focused on linear models, while few have studied nonlinear models. In the present study, we have reduced the number of variables and at the same time applied a nonlinear forecasting model. Oil prices have been used as predictors to predict return by application of a new artificial neural network nonlinear model named Deep Learning and its comparison with OLS and ANN methods. Results indicate that the applied non-linear model has higher accuracy compared to historical average model, OLS and ANN. It also indicates that out-of-sample prediction improvement does not always depend on high input variables numbers. On the other hand when using a smaller number of input variables, it is possible to improve this forecasting capability by changing the model and applying nonlinear models.

In Recent Decades, The Financial Sphere has entered a New Era of Contempt for Some of the Assumptions of Modern Economics and Finance. One of These Assumptions is the Rationale of the Investors, which has been Seriously Challenged and Is Now Being Strengthened by the Fact That Prices are Determined more by Attitudes and Psychological Factors than for Fundamental Variables and Therefore the Study of Market Psychology has Become More Important. The Purpose of This Research is to assess the Reaction of Investors to Price Changes by Measuring the Speed of Price Adjustment Compared to the General Information in Tehran Stock Exchange and a Model for Yielding Additional Returns in Futures. The five-year Research Period is from 2011 to 2016. The Statistical Population of the Study is the Companies listed in Tehran Stock Exchange and the Results Show that the Rate of Adjustment of Prices is Different from Each other Based on Specific Company Variables, and the Difference in the Rate of Price Adjustment can Cause Make Extra Returns.

Supply chain companies are one of the most important elements of the economy of each country. These companies play an important role in the expansion and activities of other companies through the provision of capital, customers, credit and even raw materials and technology. Therefore, the main goal of this research was to examine the impact of contagion of return and volatility in the return of the automobile companies supply chain listed in Tehran Stock Exchange. For doing so, Iran Khodro and SAIPA automobile supply chain companies were investigated separately. In addition to the main companies (Iran Khodro and SAIPA), three other supply chain companies were selected for research. The results of the multivariate GARCH model applied for daily data in time interval of 2013/3/21 to 2017/3/21 showed that both the return and the volatility of stock returns of SAIPA and Iran Khodro supply chain companies affected the return and volatility of these two companies stock return. This finding confirms the research hypothesis providing that the return and volatility of Iran Khodro and SAIPA companies are affected by these companies supply chain. In this research the risk contagion resulting from fluctuations in return has also been examined. It can be interpreted that the risk is contagious as the same as the different shares return.

The aim of this study is to provide a new two-stage DEA model with fuzzy multi-objective programming approach for evaluating the performance of companies listed in the Tehran Stock Exchange. In this study, a two-stage DEA model, different from the traditional model, we introduce for performance analysis. In this regard, the stable operation of companies, into two sub-process, have divided, which includes the profitability (first phase) and the value creativity (the second phase), which can be used to identify the status of the company's operations and potential for future growth. Therefore, the profitability, including two entrances (the ratio of total debt, the ratio of total equity) and two outputs (ROA, ROE) and the value creativity (the second stage) includes two outputs (the ratio of book value to market value of B / M, the cost income ratio E / P) consider, that is, the outputs of the first stage are inputs for the second stage. The decision matrix proposed in this study, can clearly define the benchmark that can be emulated by inefficient companies and help managers to develop appropriate strategies needed to enhance their overall efficiency. The results show that due to general inefficiency, ineffectiveness was in one of the two sub-processes. The results show that the multi-phase two-stage DEA model is able to identify the causes of inefficiencies and provides a scale to compare performance.

For many cases, grading and prioritizing the projects are so important in project-based organizations. In fact, it means prioritizing some projects and allocating organizational resource only to those projects to reach the organization profit up to maximum level through such allocation and decision. There are many different factors contribute in choosing the best project combination for organization too. Considering the fact that the value of criteria in projects are usually unclear and vague, and through observing, studying, interview and top managers’ brain storm meetings, the present paper tries to identify the factors affecting manufacturing investment projects, and then to prioritize projects of MAPNA Locomotive Engineering and Manufacturing Company using TOPSIS method. In present study, Shnnon’s weight entropy method is used for determination of weight of parameters. The results of this research show that identifying four main economic, technical, manufacturing and marketing criteria are among the factors influence on choosing a project, and of 19 manufacturing projects of MAPNA Locomotive Engineering and Manufacturing Company P19, P1, P2, P12, and P11 are prioritized from 1st to 5th grade, respectively.

In this research, we proposed a new metaheuristic technique for stock portfolio multi-objective optimization employing the combination of Strength Pareto Evolutionary Algorithm (SPEA), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Arbitrage Pricing Theory (APT). To generate the more precise model, ANFIS has implemented to envisage long-term movement values of the Tehran Stock Exchange (TSE) indices including TSE TEPIX and TSE TEDIX making use of technical indicators. The SPEA is exerted to choose several characteristics of technical indicators that these types of chosen essential characteristics strengthen the overall performance of the forecasting model. This research applied the suggested model in Tehran Stock Exchange. The research sample contains panel data for top 50 Companies of Tehran Stock Exchange over a ten-year interval from 2007 to 2017. The efficient procedures on actual market information are examined and explain the performance of the offered model under true limitations from the experiential assessments; we clearly discover that SPEA-ANFIS-APT forecasting technique considerably performs better than the other portfolio optimization models. The suggested hybrid optimization approach provides considerable enhancements and also innovation in the portfolio management and investment strategies under unpredictable and uncertain stock exchange without human interference, with a diversification procedure, thereby supplying satisfactory and ideal returns with minimum risk. Furthermore, the planned portfolio model SPEA-ANFIS-APT attains appropriate and acceptable functionality among diverse portfolio models despite oscillations in a stock exchange conditions. In comparison with the outcomes of various other approaches, the supremacy of the offered model is approved.

The purpose of this study is designing a model based on Tobit regression, DEA, Artificial Neural Network, Genetic Algorithm and Particle Swarm Optimization to evaluate the efficiency and also benchmarking the efficient and inefficient units. This model has three stages, and it uses the data envelopment analysis combined model with neural network, optimized by genetic algorithm, to evaluate the relative efficiency of 16 regional electric companies of Tavanir. A two-staged approach of data envelopment analysis and Tobit regression has been used to measure the effects of environmental variables on the mean efficiency of companies. Finally we use a hybrid model of particle swarm algorithm and genetic algorithm to benchmark the efficient and inefficient units. The mean efficiency of regional electric companies have increased from 0.8934 to 0.9147, during 2012 to 2017, and regional electric companies of Azarbayjan, Isfahan, Tehran, Khorasan, Semnan, Kerman, Gilan and Yazd, had the highest mean efficiency of 1, and west regional electric companies and Fars had the lowest efficiency of 0.7047 and 0.6025, respectively.

Development of downstream operations in the Iran's petroleum industry has always been considered as a necessity in to create more value-added. One of ways to accomplish the misson, especially in the current situation, is exploiting the capacity of petroleum startups. Considering that these companies need to be valued for financing, and since the traditional valuation methods do not provide efficiency, identification of valuation drivers for these startups as the main objective of research is an important step towards creating common literature between investors and venture capital company in order to use qualitative methods of valuation and facilitate financing process. The present study seeks to examine the influential factors affecting the valuation of petroleum startups in Iran. To this end, after reviewing theoretical foundations and interviewing with some experts and venture capitalists, environmental (contingent) effective factors were identified. Then a questionnaire was developed and distributed over statistical sample. The empirical findings revealed that the business team, size of the opportunity, marketing, sales & partner's channels, competitive environment, product power and the intellectual properties, time for idea implementation, investment rounds, as well as laws and regulations, have the most explanatory power in the valuation of Iranian petroleum startups, respectively. We provided some suggestions and policy implications in this regard.