Are oil, gold and the euro inter-related? time series and neural network analysis

This paper investigates inter-relationships among the price behavior of oil, gold and the euro using time series and neural network methodologies. Traditionally gold is a leading indicator of future inflation. Both the demand and supply of oil as a key global commodity are impacted by inflationary expectations and such expectations determine current spot prices. Inflation influences both short and long-term interest rates that in turn influence the value of the dollar measured in terms of the euro. Certain hypotheses are formulated in this paper and time series and neural network methodologies are employed to test these hypotheses. We find that the markets for oil, gold and the euro are efficient but have limited inter-relationships among themselves.Oil, Gold, the Euro, Relationships, Time-series Analysis, Neural Network Methodology

Are Oil, Gold and the Euro Inter-Related? Time Series and Neural Network Analysis

This paper investigates inter-relationships among the price behavior of oil, gold and the euro using time series and neural network methodologies. Traditionally gold is a leading indicator of future inflation. Both the demand and supply of oil as a key global commodity are impacted by inflationary expectations and such expectations determine current spot prices. Inflation influences both short and long-term interest rates that in turn influence the value of the dollar measured in terms of the euro. Certain hypotheses are formulated in this paper and time series and neural network methodologies are employed to test these hypotheses. We find that the markets for oil, gold and the euro are efficient but have limited inter-relationships among themselves

What Drives Gold Returns? A Decision Tree Analysis

The behavior of gold as an investment asset has been researched extensively. For the very long run, that is several decades, gold does not outperform equities. However, for shorter periods, gold responds to fears of inflation, stock market corrections, currency crises, and financial instabilities very vigorously. In this paper we follow a decision tree methodology to investigate the behavior of gold prices using both traditional financial variables such as equity returns, equity volatility, oil prices, and the euro. We also use the new Cleveland Financial Stress Index to investigate its effectiveness in explaining changes in gold prices. We find that gold returns depend on different determinants across various regimes

Comparing deep learning models for volatility prediction using multivariate data

This study aims at comparing several deep learning-based forecasters in the task of volatility prediction using multivariate data, proceeding from simpler or shallower to deeper and more complex models and compare them to the naive prediction and variations of classical GARCH models. Specifically, the volatility of five assets (i.e., S\&P500, NASDAQ100, gold, silver, and oil) was predicted with the GARCH models, Multi-Layer Perceptrons, recurrent neural networks, Temporal Convolutional Networks, and the Temporal Fusion Transformer. In most cases the Temporal Fusion Transformer followed by variants of Temporal Convolutional Network outperformed classical approaches and shallow networks. These experiments were repeated, and the difference between competing models was shown to be statistically significant, therefore encouraging their use in practice

Forecasting foreign exchange rates with adaptive neural networks using radial basis functions and particle swarm optimization

The motivation for this paper is to introduce a hybrid Neural Network architecture of Particle Swarm Optimization and Adaptive Radial Basis Function (ARBF-PSO), a time varying leverage trading strategy based on Glosten, Jagannathan and Runkle (GJR) volatility forecasts and a Neural Network fitness function for financial forecasting purposes. This is done by benchmarking the ARBF-PSO results with those of three different Neural Networks architectures, a Nearest Neighbors algorithm (k-NN), an autoregressive moving average model (ARMA), a moving average convergence/divergence model (MACD) plus a naïve strategy. More specifically, the trading and statistical performance of all models is investigated in a forecast simulation of the EUR/USD, EUR/GBP and EUR/JPY ECB exchange rate fixing time series over the period January 1999 to March 2011 using the last two years for out-of-sample testing

Forecasting Daily Returns: A Comparison Of Neural Networks With Parametric Regression Analysis

Since the seminal work of Fama (1965), many researchers have found that the actual distribution of stock returns, for the USA market, is significantly non-normal. Our study is focusing on the examining stock returns predictability for the Hellenic market given some macroeconomic variables. The objective is to use the given information set to reach an optimal way for forecasting. Hence, two basic models for forecasting are examined; a multivariable OLS regression approach and a non-parametric neural network approach and we compare them, based on the minimum forecasted error. Then, the approach that gives the minimum forecasting error is selected. The results indicated that better forecasting approach between the selected two ones is the neural network regression, since it has the smaller mean absolute percent error

Modifying Hidden Layer in Neural Network Models to Improve Prediction Accuracy: A Combined Model for Estimating Stock Price

Investment experts, who deal with stock price estimation, commonly look for the most accurate and appropriate statistical techniques to make decisions on investment. The aim of this study is to improve the accuracy of stock price prediction models through modifying the structure of a combined neural network model with time-series data, in which the main contribution is to insert the time-series analysis prediction into the hidden layer of the neural network. The proposed structure is made up of neural networks and time-series analysis, with variable reduction used to remove attributes with inter-correlations. Data has been collected over six years (72 months) from the Iranian stock market, including the number of trades, new-coin price, gold-18 price, US Dollar and Euro equivalent currencies, oil-index price, Brent-oil price, industry index, and balanced stock index, followed by developing the prediction models. Comparing the performance criteria of the proposed structure to the traditional ones in terms of the mean square and mean absolute errors revealed that inserting time-series estimated variables into hidden layers would improve the performance of neural network models to estimate stock prices for making investment decisions. Doi: 10.28991/HIJ-2022-03-01-05 Full Text: PD

Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction

Over the past decades, the Least Squares Support Vector Machines (LSSVM) has been widely utilized in prediction task of various application domains. Nevertheless, existing literature showed that the capability of LSSVM is highly dependent on the value of its hyper-parameters, namely regularization parameter and kernel parameter, where this would greatly affect the generalization of LSSVM in prediction task. This study proposed a hybrid algorithm, based on Artificial Bee Colony (ABC) and LSSVM, that consists of three algorithms; ABC-LSSVM, lvABC-LSSVM and cmABC-LSSVM. The lvABC algorithm is introduced to overcome the local optima problem by enriching the searching behaviour using Levy mutation. On the other hand, the cmABC algorithm that incorporates conventional mutation addresses the over- fitting or under-fitting problem. The combination of lvABC and cmABC algorithm, which is later introduced as Enhanced Artificial Bee Colony–Least Squares Support Vector Machine (eABC-LSSVM), is realized in prediction of non renewable natural resources commodity price. Upon the completion of data collection and data pre processing, the eABC-LSSVM algorithm is designed and developed. The predictability of eABC-LSSVM is measured based on five statistical metrics which include Mean Absolute Percentage Error (MAPE), prediction accuracy, symmetric MAPE (sMAPE), Root Mean Square Percentage Error (RMSPE) and Theils’ U. Results showed that the eABC-LSSVM possess lower prediction error rate as compared to eight hybridization models of LSSVM and Evolutionary Computation (EC) algorithms. In addition, the proposed algorithm is compared to single prediction techniques, namely, Support Vector Machines (SVM) and Back Propagation Neural Network (BPNN). In general, the eABC-LSSVM produced more than 90% prediction accuracy. This indicates that the proposed eABC-LSSVM is capable of solving optimization problem, specifically in the prediction task. The eABC-LSSVM is hoped to be useful to investors and commodities traders in planning their investment and projecting their profit

Temporal Evolution of Financial Market Correlations

We investigate financial market correlations using random matrix theory and principal component analysis. We use random matrix theory to demonstrate that correlation matrices of asset price changes contain structure that is incompatible with uncorrelated random price changes. We then identify the principal components of these correlation matrices and demonstrate that a small number of components accounts for a large proportion of the variability of the markets that we consider. We then characterize the time-evolving relationships between the different assets by investigating the correlations between the asset price time series and principal components. Using this approach, we uncover notable changes that occurred in financial markets and identify the assets that were significantly affected by these changes. We show in particular that there was an increase in the strength of the relationships between several different markets following the 2007--2008 credit and liquidity crisis.Comment: 15 pages, 10 figures, 1 table. Accepted for publication in Phys. Rev. E. v2 includes additional section