New Techniques Applied In Economics. Artificial Neural Network (original) (raw)
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Using Neural Network for forecasting in the Financial Sector
International Journal of Advanced Trends in Computer Science and Engineering, 2020
Most areas of human activity, including the economy, require constant improvement. Every year, the volume of information and the speed of its change are rapidly increasing. Processing and managing so much human intelligence is inefficient, and using traditional computing becomes a time-consuming process. Therefore, modern information technologies come to the rescue. Today, various intelligent methods are widely used for data analysis, in particular, neural networks. In order for an enterprise to function more effectively, many statistical methods and models are created, as well as specialized software. However, most methods lack Multi-linearity, it is possible to describe most processions and uniqueness of the stationary solution in systems equations, which makes it not accurate enough. In such cases, the use of neural networks as a method of modeling economic processes is Crucial. The purpose of this study was to highlight the concept of a piece neural network and the principles of its functioning. To investigate the use of neural networks as a method of forecasting and modeling economic refinancing processes, as well as to highlight the main types of software for working with neural networks.
Analysis of Artificial Neural Network for Financial Time Series Forecasting
International Journal of Computer Applications, 2010
Financial forecasting has been challenging problem due to its high non-linearity and high volatility. An Artificial Neural Network (ANN) can model flexible linear or non-linear relationship among variables. ANN can be configured to produce desired set of output based on set of given input. In this paper we attempt at analyzing the usefulness of artificial neural network for forecasting financial data series with use of different algorithms such as backpropagation, radial basis function etc. With their ability of adapting non-linear and chaotic patterns, ANN is the current technique being used which offers the ability of predicting financial data more accurately. "A x-y-1 network topology is adopted because of x input variables in which variable y was determined by the number of hidden neurons during network selection with single output." Both x and y were changed.
The study of Artificial Neural Networks derives from first trials to translate in mathe- matical models the principles of biological "processing". An Artificial Neural Network deals with generating, in the fastest times, an implicit and predictive model of the evolution of a system. In particular, it derives from experience its ability to be able to recognize some behaviours or situa- tions and to "suggest" how to take them into account. This work illustrates an approach to the use of Artificial Neural Networks for Financial Modelling; we aim to explore the structural differences (and implications) between one- and multi- agent and population models. In one-population mod- els, ANNs are involved as forecasting devices with wealth-maximizing agents (in which agents make decisions so as to achieve an utility maximization following non-linear models to do fore- casting), while in multi-population models agents do not follow predetermined rules, but tend to create th...
International Journal of Engineering & Technology, 2018
The involvement of big populace in the quantitative trading has been increased remarkably since the wired and wireless systems have become quite ubiquitous in the fields of finance and economics. Statistical, mathematical and technical analysis in parallel with machine learning and artificial intelligence are frequently being applied to perceive prices moving pattern and forecasting. However stock price do not follow any deterministic regulatory function, factor or circumstances rather than many considerations such as economy and finance, political environments, demand and supply, buying and selling tendency, trading and investment, etc. Historical data assist remarkably for prices forecasting as an important option for mathematicians and researchers. In this paper, we have followed backpropagation and radial basis function neural network for predicting future prices by modifying these techniques as per requirements. We have also performed a comparative analysis of the two ANN techn...
Effectiveness of Artificial Neural Networks in Solving Financial Time Series
International journal of engineering & technology, 2018
This research aims to study and analyze which type of Artificial Neural Network (ANN) is more efficient and suitable in handling nonhomogenous variance for financial series. Apart from addressing the behavior and efficiency of ANN, the paper also aims to present an advanced methodology for ANN, as a replacement of GARCH and ARCH models in crisis management decision makers. The application part was applied to the Egyptian exchange market, to study the local currency exchange rate volatility (1/1/2009-4/6/2013) in order to develop a model describing those changes in the exchange rate. The research concludes that the best network type to represent such financial series is the Back Propagation. Moreover, comparing the result with general regression and probabilistic networks rendered the later two inefficient at handling such series.
Neural Networks in Economics: Background, Applications and New Developments
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Neural Networks have been developed in the sixties as a device for classificationand pattern recognition. While the approach has been inspiredfrom Neuroscience its attractiveness lies in the ability to "learn", i.e. togeneralize as to yet unseen observations. One aim of this paper is to givean introduction into the technique of Neural Networks and an overviewon the most current implementations. To
Neural Networks Architectures for Modeling and Simulation of the Economy System Dynamics
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This research work investigates the possibility to apply several neural network architectures for simulation and prediction of the dynamic behavior of the complex economic processes. Therefore we will explore different neural networks architectures to build several neural models of the complex dynamic economy system. In future work we will use these architectures to be trained by well-known training algorithms, such
Prediction of Financial Crisis with Artificial Neural Network: An Empirical Analysis on Turkey
International Journal of Financial Research, 2015
Prediction of economic crisis, financial distress or bankruptcy has attracted great deal of attention in financial literature and in many other fields among the researchers over the past few decades. Although there are a variety of different methods that can be used to predict the future financial crisis, due to the complexity of the existing factors, prediction of financial crisis is a very difficult case. With the advent of Artificial Neural Networks (ANNs), researchers had the chance to solve various problems in finance. ANN approach is the application of artificial intelligence, which has been improved by the simulation of cognitive learning process of human brain. ANNs are commonly used in recent years, due to major advantages that they offer such as their ability to perform nonlinear statistical modeling that provides new alternative to other statistical methods and to learn directly from examples without needing or providing an analytical solution to the problem. In this study, a monthly dataset covering the period of 1990 and 2014 that belong to the Turkish economy will be used. The purpose of this study is to develop an early-warning system to predict financial crisis. To realize this aim, multi-layered feedforward neural networks (MLFNs) will be used. By using monthly data of 7 key macroeconomic and financial indicators of Turkish economy during 1990 and 2014, we find that predictive power of ANN is quite striking. Our out-of-sample forecasts indicate that the Turkish economy remains at high risk due to major negative developments and potential political instability between 2014 and 2016.
Indian Journal of Research in Capital Markets, 2019
This paper presents a computational approach for predicting the S&P CNX Nifty 50 Index. A neural network based model has been used in predicting the direction of the movement of the closing value of the index. The model presented in the paper also confirms that it can be used to predict price index value of the stock market. After studying the various features of the network model, an optimal model is proposed for the purpose of forecasting. The model has used the preprocessed data set of closing value of S&P CNX Nifty 50 Index. The data set encompassed the trading days from 1 st January, 2000 to 31st December, 2009. In the paper, the model has been validated across 4 years of the trading days. Accuracy of the performance of the neural network is compared using various out of sample performance measures. The highest performance of the network in terms of accuracy in predicting the direction of the closing value of the index is reported at 89.65% and with an average accuracy of 69.72% over a period of 4 years.
Applications of artificial neural networks in emerging financial markets
Cement concrete is widely used throughout the world as a key construction material in civil engineering projects. Being a complex compound comprising of cement, sand, coarse aggregate, admixture and water, its compressive strength is a highly nonlinear function of its constituents, thereby making its modeling and prediction a difficult task. Nature inspired computational techniques, provide an efficient and easy approach for modeling complex, nonlinear or difficult to establish relationships between the independent and dependent variables. Artificial Neural Networks inspired by the learning ability of a human brain, can be regarded as an engineering counterpart of a biological neuron and its highly interconnected and parallel nature, gives them immense ability to learn from past examples capturing unknown relationships, making them a versatile tool for modeling the real world problems. The review paper is an attempt to provide an introduction to artificial neural networks, highlighting its applications as a computational tool for modeling complex functional relationships of various constituents influencing the compressive strength of concrete.