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Open Access Article
1 - The Effectiveness of the Automatic System of Fuzzy Logic-Based Technical Patterns Recognition: Evidence from Tehran Stock Exchange
Abdolmajid Abdolbaghi Ataabadi Sayyed Mohammad Reza Davoodi Mohammad Salimi BaniThe present research proposes an automatic system based on moving average (MA) and fuzzy logic to recognize technical analysis patterns including head and shoulder patterns, triangle patterns and broadening patterns in the Tehran Stock Exchange. The automatic system was MoreThe present research proposes an automatic system based on moving average (MA) and fuzzy logic to recognize technical analysis patterns including head and shoulder patterns, triangle patterns and broadening patterns in the Tehran Stock Exchange. The automatic system was used on 38 indicators of Tehran Stock Exchange within the period 2014-2017 in order to evaluate the effectiveness of technical patterns. Having compared the conditional distribution of daily returns under the condition of the discovered patterns and the unconditional distribution of returns at various levels of confidence driven from fuzzy logic with the mean returns of all normalized market indicators, we observed that in the desired period, after recognizing the pattern, all patterns investigated at the confidence level 0.95 with a fuzzy point 0.5 contained useful information, practically leading to abnormal returns. Manuscript profile -
Open Access Article
2 - Interval Forecasting of Stock Price Changes using the Hybrid of Holt’s Exponential Smoothing and Multi-Output Support Vector Regression
Sayyed Mohammadreza Davoodi Mahdi RabieiGiven the importance of investment in stock markets as a major source of income for many investors, there is a strong demand for models that estimate the future behavior of stock prices. Interval forecasting is the process of predicting an interval characterized by two MoreGiven the importance of investment in stock markets as a major source of income for many investors, there is a strong demand for models that estimate the future behavior of stock prices. Interval forecasting is the process of predicting an interval characterized by two random variables acting as its upper and lower bounds. In this study, a hybrid method consisting of Holt’s exponential smoothing and multi-output least squares support vector regression is used to forecast the interval of the lowest and highest prices in a stock market. First, Holt’s smoothing method is used to smooth the two bounds of the interval and then the residuals of the smoothing process are modeled with multi-output vector support regression. The output of the regression step is the error of the two bounds of the interval. The method is implemented on the weekly data of the overall index of the Tehran Stock Exchange from 1992 to 2016, with the interval defined as the distance between the lowest and highest overall index values. The results demonstrate the high accuracy of the hybrid method in producing in-sample and out-of-sample forecasts for the movement of the two bounds of the interval, that is, the weekly highs and lows of the overall index. Also, the hybrid method has achieved a lower mean squared error than the Holt’s smoothing method, indicating that multi-output vector support regression has improved the performance of the smoothing method Manuscript profile -
Open Access Article
3 - Designing and evaluating the profitability of linear trading system based on the technical analysis and correctional property
CharaghAli Bakhtiyari Asl Sayyed Mohammad Reza Davoodi Abdolmajid Abdolbaghi AtaabadiTraders in the capital market always seek methods to make full use of available information and combine them to find the best buying and selling strategy. The present study uses a linear hybrid system to combine 106 signals from moving averages oscillators and RSI signa MoreTraders in the capital market always seek methods to make full use of available information and combine them to find the best buying and selling strategy. The present study uses a linear hybrid system to combine 106 signals from moving averages oscillators and RSI signals in the technical analysis along with two buy and sell bonds. In addition, the system has correctional property and modifies its parameters over time and according to new information. The result of the research on the Tehran Exchange overall index in the period 1380 to 1397 indicates that the system after the optimal training on training data has an average of daily returns of 0/0025, 0/0048 risk, and a daily Sharp ratio of 0/52, which is better than the individual performance of each signal and market performance in daily average return and sharp ratio criterion. Manuscript profile -
Open Access Article
4 - Evaluation of the Performance of a Dynamic Trading Strategy by Combining the Flag Pattern Detection Technique and an Exponential Moving Average with Cumulative Particle Motion Optimization
sayyed mohammad reza davoodi Sayyede Elnaz Afzaliyan BoroujeniDesigning trading systems with good returns is critical for capital market investors. Trading systems are often based on a combination of several tools to use their combined information. For the first time in Iran, the present study aimed to propose a pattern detection MoreDesigning trading systems with good returns is critical for capital market investors. Trading systems are often based on a combination of several tools to use their combined information. For the first time in Iran, the present study aimed to propose a pattern detection algorithm for a flag pattern based on Japanese candlestick charts and their arrangement. By recognizing the pattern and if the 4- and 10-day moving average is confirmed, a shopping position is developed, and the selling time is determined based on an optimized and dynamic process commensurate with price changes and the data scale. Our objective was to address the question of whether the returns resulting from this strategy have a more significant positive return compared to the purchase and maintenance strategy. The research sample included the daily information of 16 active companies of basic metals in Tehran Stock Exchange during 2007-2019, extracted from the database of Novin Rahavard software. Data analysis was performed in MATLAB software, and the obtained experimental evidence was described using t-test. According to the results, the research strategy had a higher performance in terms of returns and risks compared to the market. Manuscript profile -
Open Access Article
5 - Selecting The Optimal Multi-Period Stock Portfolio with Different Time Horizons in the Credibility Theory Framework
Younes Nozarpour Sayyed Mohammad Reza Davoodi Mahdi FadaeeAfter closing, the multi-period portfolio can be corrected and revised at regular intervals. The philosophy behind using multi-period portfolio models is that investors often have a multi-period view of future changes in assets, which can be the result of technical and MoreAfter closing, the multi-period portfolio can be corrected and revised at regular intervals. The philosophy behind using multi-period portfolio models is that investors often have a multi-period view of future changes in assets, which can be the result of technical and fundamental analysis or statistical model analysis. In conventional multi-period portfolio models, it is assumed that the forecast and correction time horizons are the same for all assets. However, one asset may be forecasted over a one-month horizon while another may be forecasted over a two-month horizon, and both may be revised in the future. The purpose of this study is to present a multi-period portfolio model in which assets have different time horizons for corrections or an asset may not be traded for the first few periods and then enter the correction stage. In this model, fuzzy variables defined in a credibility space are used to describe the return, and the credibility measure controls the risk. The model's objective function is to maximize the portfolio's ultimate wealth, and a constraint is used to control portfolio risk, in which the validity of the portfolio's ultimate wealth below a certain threshold is controlled at a certain level of confidence. A combination of particle swarm optimization and simulation is used to find the best solution. Finally, using a numerical example, the model is implemented on a portfolio with 6 assets and 4 monthly time steps on the Tehran Stock Exchange. Manuscript profile
List of Articles Sayyed Mohammad Reza Davoodi
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