List of articles (by subject) Economic and Financial Time Series
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Open Access Article
1 - Improved NARX-ANFIS Network structure with Genetic Algorithm to optimizing Cash Flow of ATM Model
Neda kiani Ghasem Tohidi Shabnam Razavyan Nosratallah Shadnoosh Masood SaneiNowadays, the rapid growth of data in organizations has caused managers to look for a way to analyze them. Extracting useful knowledge from aggregation data can lead to appropriate strategic decision-making for the organization. This paper suggests an application of hyb MoreNowadays, the rapid growth of data in organizations has caused managers to look for a way to analyze them. Extracting useful knowledge from aggregation data can lead to appropriate strategic decision-making for the organization. This paper suggests an application of hybrid network based on amount month demand in every ATM device based on transaction mean of 9 months for 1377 devices to obtain customer behavior patterns, to do so, first designed a basic model based on an auto-regressive with exogenous input network (NARX) then, the optimization of the weight and bias of the designed network is made by the genetic algorithm (GA). As a result, finding the weights of the network represents a nonlinear optimization problem that is solved by the genetic algorithm. Paper results show that the NARX-ANFIS Hybrid network using GA for the learning of rules and to optimize the network weights and weights of the network and the fixed threshold can improve the accuracy of the prediction model. Also, classic models are more efficient and increased benefits and lower financing costs and more rational inventory cash control. As well, the designed model can lead to increase benefits and decrease costs in the bank so that, exact forecast and optimal cash upload in ATMs will lead to increase funds on the bank and rise customers and popularity the brand of the bank. Manuscript profile -
Open Access Article
2 - The role of aggregate cost stickiness in unemployment rate prediction
Naser Riyahinasab Babak Jamshidinavid Alireza Moradi Mehrdad GhanbariPredicting macroeconomic indicators is very important for policymakers and economists. Unemployment is one of the key indicators of macroeconomics that has adverse economic and social consequences. So far, many models have been proposed to predict this variable, but mod MorePredicting macroeconomic indicators is very important for policymakers and economists. Unemployment is one of the key indicators of macroeconomics that has adverse economic and social consequences. So far, many models have been proposed to predict this variable, but models in which accounting information was used to predict unemployment rate were ignored. The purpose of this paper is to investigate the relationship between aggregate cost stickiness, as one of the known variables in accounting, and unemployment rate. To this end, seasonal macro level time series data of Tehran Stock Exchange (TSE) and macroeconomic data are analyzed in two stages from 2008:2 to 2018:1. In the first stage, the relationship between these two variables is determined by specifying a linear regression model that is estimated using the OLS method. To investigate the predictive power of this model, the RMSE criterion was estimated in two scenarios with and without aggregate cost stickiness. Secondly, the reaction of the unemployment rate in response to a shock from aggregate cost stickiness is estimated by a Vector Autoregressive (VAR) model and the share of this variable is measured in the fluctuations of unemployment rate. The results show that aggregate cost stickiness improves the forecast of unemployment rate in the horizon previous. Also, the shock of aggregate cost stickiness explains about 6.5 percent of unemployment rate fluctuations. Manuscript profile -
Open Access Article
3 - 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
4 - Applying the GARCH and COPULA Models to Examine the Relationship Between Trading Volume and the Value of Trading with the Bubble Pricing
Jalil Beytary Hossein PanahianGiven the importance of the securities market in each country's economy and the adverse effects of the price bubble on the irrational fluctuations of the stock market, it is clear that it must be prevented; therefore, with reference to the ambiguity of the factors causi MoreGiven the importance of the securities market in each country's economy and the adverse effects of the price bubble on the irrational fluctuations of the stock market, it is clear that it must be prevented; therefore, with reference to the ambiguity of the factors causing the price bubble, research is underway. Investigating the relationship between the volume of transactions and the value of transactions with the price bubble in different industries of the Stock Exchange during the years 2006 to 2016 is a step towards recognizing this phenomenon.To investigate these communications, we used DCC-GJR-GARCH, diagonal BEKK and COPULA models. The results of the study of the relationship between the volume of exchanges and the value of exchanges with price bubbles suggest that there is a negative and complete correlation between them. In relation to the study of the relationship between price bubbles and research variables, we found that oil prices have a reverse and significant relationship with bubble prices. Other variables are not meaningful relationships with price bubbles. Also, in the study between variables of research with volume of transactions, it was determined that changes in tax volume and oil price variables have a reverse and significant relationship with the volume of transactions and the value of transactions with the volume of transactions has a direct and significant relationship. Manuscript profile -
Open Access Article
5 - A new two-phase approach to the portfolio optimization problem based on the prediction of stock price trends
Hamid Reza Yousefzade Amin Karrabi Aghileh HeydariForming a portfolio of different stocks instead of buying a particular type of stock can reduce the potential loss of investing in the stock market. Although forming a portfolio based solely on past data is the main theme of various researches in this field, considering MoreForming a portfolio of different stocks instead of buying a particular type of stock can reduce the potential loss of investing in the stock market. Although forming a portfolio based solely on past data is the main theme of various researches in this field, considering a portfolio of different stocks regardless of their future return can reduce the profits of investment. The aim of this paper is to introduce a new two-phase approach to forming an optimal portfolio using the predicted stock trend pat-tern. In the first phase, we use the Hurst exponent as a filter to identify stable stocks and then, we use a meta-heuristic algorithm such as the support vector regression algorithm to predict stable stock price trends. In the next phase, according to the predicted price trend of each stock having a positive return, we start arranging the portfolio based on the type of stock and the percentage of allocated capacity of the total portfolio to that stock. To this end, we use the multi-objective particle swarm optimization algorithm to determine the optimal portfolios as well as the optimal weights corresponding to each stock. The sample, which was selected using the systematic removal method, consists of active firms listed on the Tehran Stock Ex-change from 2018 to 2020. Experimental results, obtained from a portfolio based on the prediction of stock price trends, indicate that our suggested approach outperforms the retrospective approaches in approximating the actual efficient frontier of the problem, in terms of both diversity and convergence. Manuscript profile -
Open Access Article
6 - Provide an improved factor pricing model using neural networks and the gray wolf optimization algorithm
Reza Tehrani Ali Souri Ardeshir Zohrabi Seyyed Jalal Sadeghi SharifThe issue of asset pricing in the market is one of the most important and old issues in the financial world. Factor pricing models seek to be able to determine a significant relationship between return on assets based on the risk parameters of that asset. A wide range o MoreThe issue of asset pricing in the market is one of the most important and old issues in the financial world. Factor pricing models seek to be able to determine a significant relationship between return on assets based on the risk parameters of that asset. A wide range of factors can be found in the literature that can be an element for measuring the risk of an asset, but the big question is which of these models will work better. The factors studied in this research include factors that cover market risk, valuation risk, psychological (technical) market risk, profit quality risk, profitability, investment, etc. In this study, we have tried to Using machine learning techniques and optimization tools is a way to derive adaptive-robust nonlinear models that can reduce the risk of model error as much as possible. In this research, two models have been developed. In the first model, using the feature extraction technique and optimization of models based on neural network, a non-linear and adaptable model has been developed for each asset. In the second approach, a portfolio of improved neural network-based models is used in the first stage, which can be used to minimize the risk of model error and achieve a model that is resistant to different market conditions. Finally, it can be seen that the development of these models can significantly improve the risk of error and average error of the model compared to traditional CAPM approaches and the Fama and French three-factor model. Manuscript profile -
Open Access Article
7 - Investigating Randomness By Walsh-Hadamard Transform in Financial Series
Seyed Jalal TabatabaeiThe objective of the ongoing research is to introduce the initial, substantial, and practical implementation of the Walsh-Hadamard Transform in the realm of quantitative finance. It is worth noting that this particular tool, which has limited utility in the domain of di MoreThe objective of the ongoing research is to introduce the initial, substantial, and practical implementation of the Walsh-Hadamard Transform in the realm of quantitative finance. It is worth noting that this particular tool, which has limited utility in the domain of digital signal processing, has demonstrated its effectiveness in evaluating the statistical significance of any binary sequence. Therefore, employing this approach in financial series would be exceptionally noteworthy. By employing five primary tests to assess the randomness of the series, including those pertaining to the Tehran Stock Exchange, as well as copper and gold, the outcomes reveal the presence of randomness in the transformed series in all aspects. Naturally, this random-ness could be examined to identify any underlying trends. Manuscript profile