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EUR/USD Forex Market Prices Prediction Using MLP
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Copious test by countless professionals have proven technical
analysis to be, at best, break-even tools, even with the finest money
management techniques. Technical analysis is not designed to deal with
non-uniform periodic, and discontinuous functions. To manage these
inadequacies, one employs a neural network. The simple network described
in this paper predicts technical indicators, and generates trading
signals before regular technical indicators do. This gives one the
opportunity to enter, and exit trades before the crowd. Tests, and
actual trades, have shown that for most of the time, one or two days, is
all the advantage one needs
In recent years, the increasing influence of machine learning in different industries had inspired many traders to benefit from it in the world of finance, stock trading is one of the most important activities. Predicting the direction of stock prices is a widely studied subject in many fields including trading, finance, statistics and computer science. The main concern for Investors is to maximize their profit if they determine when to buy/sell an investment they apply Analytical methods that makes use of different sources ranging from news to price data, all aiming at predicting the company's future stock price ML applications have presented investors with something new. A combination of technologies that could entirely reshape the way they make investment decisions. The purpose of this thesis is to leverage the aggregation of technical, fundamental, and sentiment analysis with stacked machine learning models capable of predicting profitable actions to be executed.
Amongmanyexcitingdevelopmentsinstatisticsoverthelasttwodecades, nonlineartimeseriesanddata-analyticnonparametricmethodshavegreatly advanced along seemingly unrelated paths. In spite of the fact that the - plication of nonparametric techniques in time series can be traced back to the 1940s at least, there still exists healthy and justi?ed skepticism about the capability of nonparametric methods in time series analysis. As - thusiastic explorers of the modern nonparametric toolkit, we feel obliged to assemble together in one place the newly developed relevant techniques. Theaimofthisbookistoadvocatethosemodernnonparametrictechniques that have proven useful for analyzing real time series data, and to provoke further research in both methodology and theory for nonparametric time series analysis. Modern computers and the information age bring us opportunities with challenges. Technological inventions have led to the explosion in data c- lection (e.g., daily grocery sales, stock market trading, microarray data). The Internet makes big data warehouses readily accessible. Although cl- sic parametric models, which postulate global structures for underlying systems, are still very useful, large data sets prompt the search for more re?nedstructures,whichleadstobetterunderstandingandapproximations of the real world. Beyond postulated parametric models, there are in?nite other possibilities. Nonparametric techniques provide useful exploratory tools for this venture, including the suggestion of new parametric models and the validation of existing ones.
This empirical paper compares the accuracy of 12 time series methods for short-term (day-ahead) spot price forecasting in auction-type electricity markets. The methods considered include standard autoregression (AR) models and their extensions -- spike preprocessed, threshold and semiparametric autoregressions (i.e., AR models with nonparametric innovations) -- as well as mean-reverting jump diffusions. The methods are compared using a time series of hourly spot prices and system-wide loads for California, and a series of hourly spot prices and air temperatures for the Nordic market. We find evidence that (i) models with system load as the exogenous variable generally perform better than pure price models, but that this is not necessarily the case when air temperature is considered as the exogenous variable; and (ii) semiparametric models generally lead to better point and interval forecasts than their competitors, and more importantly, they have the potential to perform well under diverse market conditions.
Double moving averages are commonly used to identify trends within
capital markets. In this paper, a novel analysis technique is presented
which is based upon plotting the returns associated with thousands of
different double moving average trading rules for a time series. The
resulting plots are useful for gaining an understanding of the trends
contained within the time series. The analysis technique, which is
referred to as trend visualization, is also useful for selecting the
appropriate parameters for double moving average trading systems. In
this paper, the technique is used to investigate trends within the
foreign currency spot market
Reports on the feasibility of applying neural networks to the
problem of forecasting the Canada/US spot exchange rate. The inputs to
the network consist of the short-term trend in the spot rate
(Monday-Thursday) and the change in interest rate spread between the two
countries (Wednesday-Thursday). The output is a prediction of the
exchange rate on Friday. The model is able to explain 6.6% of the
variance in the data between November 1991 and June 1993 (out of the
sample), yielding a success rate of 59% on directional forecasts
New methods for training multi-layer perceptron network for forecasting problems are presented. The first method exploits spectral characteristics of time series to get faster learning and improved prediction accuracy. A neural network scheme for real time implementation of this method is also presented. The second method suggests the use of two new weight initialization schemes which give very fast convergence besides giving better prediction. The foreign exchange time series is used to illustrate the efficacy of the proposed methods
Multilayer Perceptron explained with a real-life example and python code: Sentiment Analysis
- C Bento