stock index

The purpose of this study is to investigate and identify the periods of formation and bursting of speculative bubbles in Iran's capital market by creating a state space model and two-mode switching regime (mode 1 is bubble growth and burst stage and mode 2 is the time of bubble loss) during the period from April 2011 to March 2018. The Oxmetrics 7 software is used to investigate the existence of multiple bubbles and research objective. The results of the study of the state space switches confirm the bubble of the capital market in Iran during four periods in the research domain. The life span of the first speculative bubble is 2 months from October to November 2011, the second is 8 months from March to October 2013, the third is 3 months from December 2015 to February 2016, and the fourth period of bubble is 5 months from August to December 2017. Therefore, the result of the research stipulates that the stock index of the Iranian capital market in the realm of research time period has had 18 months of bubbles and has spent 66 months in balance.

This research sought to investigate the assumption that commodities operate as hedging and safe-haven for stocks -during various periods of stock market volatility. In this regard, market test regression models and daily data from 21/03/2009 to 19/03/2020 were used. The researcher was able to test both hypotheses of commodities as hedging and safe-haven simultaneously using these three models. According to the market test model results, "periods of relatively high and low volatility," gold coin futures contracts are viewed as a strong safe-haven for Changes in Tehran stock exchange returns, yet they lack the property of hedging. According to the results of the market test model “Low return periods," gold commodities and other petroleum products serve as safe-haven. Furthermore, “during times of crisis," commodities such as polymer, copper, and gold (cash and futures) had a consistent relationship with the stock market returns. They can be regarded as a strong safe haven for Changes in stock returns. Gold, in general, provided a safe-haven property for the stock index returns in all market test models, and it can serve as a stabilizing force for financial systems by reducing the casualties caused by extreme negative market shocks. The findings indicated that commodities can be used as risk management tools during economic and financial crises. Regarding hedging, the commodity market performed poorly compared to the stock market. Hedging does not always represent a safe haven for the stock market return, and vice versa.

Recent studies on financial markets have demonstrated that technical analysis can help us effectively predict the stock market index trend. Business systems are widely used for stock market analysis. This paper uses a genetic algorithm (GA) to develop a stock market trading optimization system. Our proposed system can generate a decision-making strategy for buying, holding, and selling stocks for each day and generate high returns for each stock. The system consists of two stages: removing restricted stocks and producing a stock trading strategy. Accordingly, evolutionary computation, like GA, is highly promising because of its intelligence, flexibility, and search strength (fast and efficient). The multiple-objective nature of the utilized algorithm can be regarded as the center of gravity of the research question. The proper functioning or malfunctioning of the resulting portfolio management can be employed as a benchmark for selecting or discarding the algorithm. On the other hand, the research question is focused on the application of technical analysis indicators. Therefore, both aspects of the research question, namely the multiple-objective nature of the algorithm in terms of the analysis method and technical indicators in terms of features selected for analysis, must be taken into account.

This study seeks to rigorously assess the relationship between Iran’s stock market index and monetary policy within the framework of the Markov Switching Vector Autoregressive (MS-VAR) model. The MS-VAR methodology is particularly well-suited for capturing regime-dependent dynamics and structural shifts in macroeconomic and financial time series. For this purpose, quarterly data spanning from Spring 2009 to Fall 2023 have been employed. All estimations were conducted using EViews 12 and OX Metrics 7 software. As a preliminary step, the Hodrick-Prescott filter was applied to differentiate between two distinct market regimes. Combined with a univariate Markov Switching model, this approach enabled the identification of cyclical fluctuations in the stock market, distinguishing bull from bear market phases. The results indicate that Regime 1 (bear market) demonstrates greater persistence and stability relative to Regime 2 (bull market), suggesting asymmetric market dynamics. Subsequently, the study investigates the effects of monetary policy—proxied by the interbank market rate and liquidity growth—on the growth of the stock market index within the MS-VAR framework. The findings suggest that monetary policy has different effects during bull and bear market phases. The stock index exhibits a prompt and asymmetric response to changes in both the interbank market rate and liquidity growth. Specifically, in both bull and bear market regimes, an increase in the interbank interest rate exerts a contractionary effect on stock index growth, with a more pronounced negative impact observed during periods of market recession. Moreover, liquidity growth consistently contributes positively to stock index growth across both regimes, with a more pronounced effect under bull market conditions. Variance decomposition analysis further reveals that, in both regimes, shocks to the stock index itself account for the largest proportion of its fluctuations. Nonetheless, the relative importance of monetary policy instruments varies by regime: In both expansionary and recessionary phases of the stock market, shocks stemming from the interbank market rate play a more prominent role in explaining stock index volatility compared to liquidity shocks. Finally, the presence of nonlinear interactions among the variables is statistically validated based on the Likelihood Ratio (LR) test.