Are foreign currency markets interdependent? evidence from data mining technologies

This study uses two data mining methodologies: Classification and Regression Trees (C&RT) and Generalized Rule Induction (GRI) to uncover patterns among daily cash closing prices of eight currency markets. Data from 2000 through 2009 is used, with the last year held out to test the robustness of the rules found in the previous nine years. Results from the two methodologies are contrasted. A number of rules which perform well in both the training and testing years are discussed as empirical evidence of interdependence among foreign currency markets. The mechanical rules identified in this paper can usefully supplement other types of financial modeling of foreign currencies.Foreign Currency Markets

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

What Microeconomic Fundamentals Drove Global Oil Prices during 1986–2020?

The global financial crisis of 2007–2009 caused major economic disturbances in the oil market. In this paper, we consider five variables that describe the microeconomics of the supply of and demand for oil, and evaluate their importance before, during and after the global financial crisis. We consider five dissimilar regimes during the period of January 1986 to the end of 2020: two regimes prior to the global financial crisis, the regime during the crisis, and two regimes after the crisis. The main hypothesis tested is that oil fundamentals of supply and demand remained important, even though the five regimes were dissimilar. We built five boosted and over-fitted neural networks to capture the exact relationships between spot oil prices and oil data related to these prices. This analysis shows that, while the inputs into an accurate neural network can remain the same, the impact of each variable can change considerably during different regimes

Investment Principles for Individual Retirement Accounts

The phenomenal growth of individual retirement accounts in the US, and globally, challenges both individuals and their advisors to rationally manage these investments. The two essential differences between an individual retirement account and an institutional portfolio are the length of the investment horizon and the regularity of monthly contributions. The purpose of this paper is to contrast principles of institutional investing with the management of individual retirement accounts. Using monthly historical data from 1926 to 2005 we evaluate the suitability for managing individual retirement portfolios of seven principles employed in institutional investing. We discover that some of these guidelines can be beneficially applied to the investment management of individual retirement accounts while others need to be reconsidered

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

Are Foreign Currency Markets Interdependent? Evidence from Data Mining Technologies

This study uses two data mining methodologies: Classification and Regression Trees (C&RT) and Generalized Rule Induction (GRI) to uncover patterns among daily cash closing prices of eight currency markets. Data from 2000 through 2009 is used, with the last year held out to test the robustness of the rules found in the previous nine years. Results from the two methodologies are contrasted. A number of rules which perform well in both the training and testing years are discussed as empirical evidence of interdependence among foreign currency markets. The mechanical rules identified in this paper can usefully supplement other types of financial modeling of foreign currencies

Modeling Federal Funds Rates: A Comparison of Four Methodologies

Monthly Federal Fund interest rate values, set by the Federal Open Market Committee, have been the subject of much speculation prior to the announcement of their new values each period. In this study we use four competing methodologies to model and forecast the behavior of these short term Federal Fund interest rates. These methodologies are: time series, Taylor, econometric and neural network. The time series forecasts use only past values of Federal Funds rates. The celebrated Taylor rule methodology theorizes that the Federal Fund rate values are influenced solely by deviations from a desired level of inflation and from potential output. The econometric and neural network models have inputs used by both the time series and Taylor rule. Using monthly data from 1958 to the end of 2005 we distinguish between sample and out-of-sample sets to train, evaluate, and compare the models’ effectiveness. Our results indicate that the econometric modeling performs better than the other approaches when the data are divided into two sets of pre-Greenspan and Greenspan periods. However, when the data sample is divided into three groups of low, medium and high Federal Funds, the neural network approach does best

N-Tuple S&P 500 Index Patterns Across Decades, 1950s to 2011

Numerous studies have analyzed the movements of the S&P 500 Index using several methodologies such as technical analysis, econometric modeling, time series techniques and theories from behavioral finance. In this paper we take a novel approach. We use daily closing prices for the S&P 500 Index for a very long period from 1/3/1950 to 7/19/2011 for a total of 15,488 daily observations. We then investigate the up and down movements and their combinations for 1 to 7 days giving us multiple possible patterns for over six decades. Some patterns of each type are more dominant across decades. We split the data into training and validation sets and then select the dominant patterns to build conditional forecasts in several ways, including using a decision tree methodology. The best model is correct 51% of the time on the validation set when forecasting a down day, and 61% when forecasting an up day. We show that certain conditional forecasts outperform the unconditional random walk model