نتایج جستجو برای: gold price forecast
تعداد نتایج: 192924 فیلتر نتایج به سال:
Generally,some booms in housing prices are followed by busts. One common phenomenon relating these changes is that the house price cycle is generally believed to the product of the short-run deviations from the long-run upward trends. The long-term cyclical fluctuation in Iran’s housing market was periodically occurred about every 6 years. Furthermore, Movements in house prices have significant...
This paper presents a model based on multilayer feedforward neural network to forecast crude oil spot price direction in the short-term, up to three days ahead. A great deal of attention was paid on finding the optimal ANN model structure. In addition, several methods of data pre-processing were tested. Our approach is to create a benchmark based on lagged value of pre-processed spot price, the...
Market traders often buy and sell volatile assets to maximize total returns. We have developed an optimal trading strategy model using gold bitcion daily price streams meet this need. Based on a sliding window, we use the ARIMA predict prices of bitcoin, respectively. Meanwhile, Granger causality test results showed that they were not cointegrated in short term. construct multi-objective dynami...
In response to the problem of how optimally invest in gold and bitcoin, this paper establishes a daily trading strategy model, uses large amount price data conduct in-depth research on optimal transactions obtain maximum value. Based prices bitcoin within 5 years, we first build an ARIMA time series forecast use MATLAB programming solve results get next day's prices. Using prediction result, es...
Improving return forecasting is very important for both investors and researchers in financial markets. In this study we try to aim this object by two new methods. First, instead of using traditional variable, gold prices have been used as predictor and compare the results with Goyal's variables. Second, unlike previous researches new machine learning algorithm called Deep learning (DP) has bee...
Financial forecasting is a difficult task due to the intrinsic complexity of the financial system. A simplified approach in forecasting is given by “black box” methods like neural networks that assume little about the structure of the economy. In the present paper we relate our experience using neural nets as financial time series forecast method. In particular we show that a neural net able to...
This paper examines whether the gold coin futures prices in the Iran Mercantile Exchange can forecast accurately the gold coin spot prices at the maturity date. For this, it uses daily data of both futures and spot prices from Azar 1387 to Tir 1397. A cointegration analysis shows that in horizons shorter than 100 days, there is a significant one-to-one relation between these two prices which im...
This study aims to forecast Iran's electricity demand by using meta-heuristic algorithms, and based on economic and social indexes. To approach the goal, two strategies are considered. In the first strategy, genetic algorithm (GA), particle swarm optimization (PSO), and imperialist competitive algorithm (ICA) are used to determine equations of electricity demand based on economic and social ind...
The purpose of the present study is to examine the dynamic long run and the short run relationship between stock price and a set of macroeconomic variables for Indian economy using monthly data from April 2004 to July 2014. The long run relationship is examined by implementing the ARDL bounds testing approach to co-integration. VECM method is used to test the short and long run causality and Va...
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