Showing 4 results for rezazade
Dr Alireza Kazerooni, Ali Rezazadeh, Siavash Mohammadpoor,
Volume 2, Issue 5 (10-2011)
Abstract
The main purpose of this article is to investigate the asymmetric effects of the real exchange rate shocks on the non-oil exports of Iran in the period of 1974-2007. For this purpose, using nonlinear Markov-Switching approach, positive and negative shocks of the real exchange rate have been extracted. Based on the results of the Log Likelihood Function and Akaike Information Criterion, the best Markov- Switching model has been specified as MSIH with three regimes for estimating the exchange rate shocks. After extracting the exchange rate shocks, in the next step, the main model of the research has been estimated by using the Johansen-Juselius and DOLS co-integration approaches. Results show that the impact of some explanatory variables (GDP of home country, GDP of Foreign country, Terms of Trade and Openness) on the non-oil exports of Iran has been positive and statistically significant at 95% level of confidence. However, the impact of both positive and negative shocks on the non-oil exports has been negative. Overall, the main hypothesis of symmetrical impacts of the exchange rate shocks has been rejected.
Hossein Asgharpur, Firouz Fallahi, Naser Sanoubar, Ali Rezazadeh,
Volume 5, Issue 17 (10-2014)
Abstract
The main goal of this research is to calculate VaR index with parametric Markov-Switching GARCH approach for accepted companies in Tehran Stock Exchange and also selecting the optimal portfolio of their stocks. To calculate the index, data and information of weekly stock price of 10 representative firms during the period 2008-2014 has been used which account for 332 working weeks.
The results from estimation of VaR and determination of optimal stock portfolio in the non-linear programming framework showed that optimal portfolio of food-industry companies stock, in the context of VaR has higher returns and risk in the first regime (Boom period) compared to the second regime ( recession period). On the other hand, it has had lower weight in both stock portfolios that had lower average returns compared to the rest of the stocks and compared to the stocks which had lower VaR relative to other stocks that has higher weights.
The Kupiec and Lopez back testing using 10 future week data, showed that both of approaches is valid but the parametric approach has better rank. Therefore the optimal portfolios of stocks under parametric VaR will be accepted as final optimal portfolio.
Fatemeh Ansari, Shahab Jahangiri, Ali Rezazade,
Volume 14, Issue 53 (12-2023)
Abstract
Objective: The aim of this research is to provide a practical guide for investing in the Tehran Stock Exchange by combining technical analysis techniques with advanced machine learning methods. Focusing on the analysis of buy and sell signals in selected indices of the Tehran Stock Exchange, the study seeks to evaluate the effectiveness of machine learning models in predicting market trends.
Materials and Methods: In this study, the daily data of six selected indices of the Tehran Stock Exchange, including financial, petroleum products, automotive, pharmaceutical, food, and basic metals indices, were analyzed from 2020 to January 2025. Four machine learning models, including Linear Model, Random Forest, Artificial Neural Network, and Support Vector Regression, were utilized alongside two technical analysis strategies, TEMA and MACD, to generate and evaluate buy and sell signals.
Results: The results indicated that machine learning models, particularly Random Forest and Artificial Neural Network, performed better in identifying buy and sell signals when combined with TEMA and MACD strategies. These models were able to predict market trends with higher accuracy, and the signals they generated were mostly consistent with actual price changes. The food, automotivation and financial and basic metals indices demonstrated greater sensitivity to these analyses.
Conclusion: The combination of machine learning methods with technical analysis strategies can provide investors with a powerful tool for decision-making in the Tehran Stock Exchange. This research demonstrated that using these methods can not only improve the accuracy of buy and sell signals but also reduce investment risk and increase returns. Utilizing these models can be recommended as part of an investment strategy for analysts and investors.
Originality: This research is the first quantitative study that seeks to conceptualize buy and sell signals using the combined method of machine learning and technical analysis as one of the basic tools to guide investors. |
Roghaye Mohsi Nia, Ali Rezazadeh, Yousef Mohammadzadeh, Shahab Jahangiri,
Volume 15, Issue 55 (5-2024)
Abstract
The fundamental aim of this study is to investigate the structural dependence between the cryptocurrency and the stock market index. In this study, the total index of Tehran Stock Exchange has been used as a representative of the developing stock market and the index (S&P500) has been used as a representative of the developed stock market. using daily data during the period from 8 August 2015 to 21 February 2023. The results show that there is no structural dependence between the return Bitcoin and Iran stock market , either in the short term or in the long term. In other words, the changes domain in return of Bitcoin during the low and high ranges on the return of the mentioned index are insignificant. The results indicates that the cryptocurrency market is separated from the main class of financial and economic assets and hence offers various benefits to investors. Also, in the long term, for the return of Bitcoin cryptocurrency and the S&P500 stock index, Clayton's copula function was chosen in the first place as the appropriate model to explain the correlation. There is no correlation between the returns of Bitcoin and the s&p500 stock index in the short term. The findings of this study indicate the important role of cryptocurrencies in investors' portfolios as they act as a diversified option for investors and confirm that cryptocurrencies are a new investment asset class. Furthermore, it analyzes the upside and downside risk spillovers between stock markets and the cryptocurrency market by quantifying market risk measures, namely the conditional VaR (CoVaR) and the delta CoVaR (ΔCoVaR). The results indicate that Bitcoin, Ethereum and Ripple cannot be considered a strong hedge during the time of crisis. The speculative nature of cryptocurrencies and risks embedded in Bitcoin, Ethereum, and Ripple increases the risk flow to stock markets during a crisis, thus rendering the hedging costlier. increases the risk flow to stock markets during a crisis, thus rendering the hedging costlier.
