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

A comparison of statistical machine learning methods in heartbeat detection and classification

In health care, patients with heart problems require quick responsiveness in a clinical setting or in the operating theatre. Towards that end, automated classification of heartbeats is vital as some heartbeat irregularities are time consuming to detect. Therefore, analysis of electro-cardiogram (ECG) signals is an active area of research. The methods proposed in the literature depend on the structure of a heartbeat cycle. In this paper, we use interval and amplitude based features together with a few samples from the ECG signal as a feature vector. We studied a variety of classification algorithms focused especially on a type of arrhythmia known as the ventricular ectopic fibrillation (VEB). We compare the performance of the classifiers against algorithms proposed in the literature and make recommendations regarding features, sampling rate, and choice of the classifier to apply in a real-time clinical setting. The extensive study is based on the MIT-BIH arrhythmia database. Our main contribution is the evaluation of existing classifiers over a range sampling rates, recommendation of a detection methodology to employ in a practical setting, and extend the notion of a mixture of experts to a larger class of algorithms

Defining and Designing a Model to Predict the Performance of Mutual Funds by Using Macroeconomic Variables in Tehran Stock Exchange

Given the importance of the mutual funds in capital market and the role ofthese funds in the capital market development, the expected return of funds is very important. In thisstudy, the macroeconomic variables were investigated to predict the performanceof mutual funds in Tehran Stock Exchange.  In this study theperformance of 67 mutual funds in Tehran Stock Exchange for the December 2008to 29 March 2013were reviewed. Macroeconomicvariables consisted of oil prices,inflation, liquidity, exchange rate, theprice of gold and the housing price indices.Data analysis was done by multiple linear regression models and artificial neuralnetwork (ANN). Results indicated asignificant relationship among the rate of inflation, liquidity, exchangerate and housing price indices and return of funds. A model was offered to predictthe performance of mutual funds

Will the US Economy Recover in 2010? A Minimal Spanning Tree Study

We calculated the cross correlations between the half-hourly times series of the ten Dow Jones US economic sectors over the period February 2000 to August 2008, the two-year intervals 2002--2003, 2004--2005, 2008--2009, and also over 11 segments within the present financial crisis, to construct minimal spanning trees (MSTs) of the US economy at the sector level. In all MSTs, a core-fringe structure is found, with consumer goods, consumer services, and the industrials consistently making up the core, and basic materials, oil and gas, healthcare, telecommunications, and utilities residing predominantly on the fringe. More importantly, we find that the MSTs can be classified into two distinct, statistically robust, topologies: (i) star-like, with the industrials at the center, associated with low-volatility economic growth; and (ii) chain-like, associated with high-volatility economic crisis. Finally, we present statistical evidence, based on the emergence of a star-like MST in Sep 2009, and the MST staying robustly star-like throughout the Greek Debt Crisis, that the US economy is on track to a recovery.Comment: elsarticle class, includes amsmath.sty, graphicx.sty and url.sty. 68 pages, 16 figures, 8 tables. Abridged version of the manuscript presented at the Econophysics Colloquim 2010, incorporating reviewer comment

Hierarchical representation of socio-economic complex systems according to minimal spanning trees and diagrams

We investigate hierarchical structure in various complex systems according to Minimum Spanning Tree methods. Firstly, we investigate stock markets where the graph is obtained from the matrix of correlation coefficients computed between all pairs of assets by considering the synchronous time evolution of the difference of the logarithm of daily stock price. The hierarchical tree provides information useful to investigate the number and nature of economic factors that are associated in a meaningful economic taxonomy. We extend this method on other financial markets – money exchange (FOREX) and commodity – phonographic market (where we have artists instead of stocks) and get information on which music genre is meaningful according to customers. We continue to use this method in social systems (sport, political parties and pharmacy) to investigate collective effects and detect how a single element of a system influences the other ones. The level of correlations and Minimum Spanning Trees in various complex systems is also discussed

Russia: Political and Institutional Determinants of Economic Reforms

The purpose of this study is to analyze the course, determinants and political economy of economic reforms in Russia conducted in the period 1985-2003. The year 1985 can be considered an important turning point in Soviet/Russian history, marked as it was by the election of Mikhail Gorbachev to the position of General Secretary of the Communist Party of Soviet Union (CPSU) and (de facto) leader of the USSR. This nomination brought an end to two decades of political consolidation of the communist regime connected with the name of General Secretary Leonid Brezhnev and his short living successors (Yurii Andropov and Konstantin Chernenko), often referred to ex post as 'the stagnation period' (vremya zastoya). Gorbachev initiated a series of important political and (to a lesser extent) economic reforms, which led eventually to the collapse of the communist regime and the disintegration of the Soviet empire in 1991. Thus, 1991 must be seen as another dramatic turning point in Russia's contemporary history. From the end of 1991 onwards political and economic reforms have been carried out by the new Russian state that emerged after the disintegration of the USSR. This paper aims to explain the political and institutional determinants of economic reforms in the Russian Federation. It has been carried out under the Global Research Project on 'Understanding Reforms' organized and financed by the Global Development Network (GDN)1 as one of 30 country studies covering a broad set of developing and transition economies. It presents the project's intermediate results and will be the subject of further discussion as well as analytical and editorial work in the near future. The case of Russia is very important and interesting from the point of view of GRP 'Understanding Reforms' goals and agenda, for many reasons. First, all transitions from communist regimes and centrally-planned economies to democratic capitalism represent a much more complex, complicated and difficult reform experience than policy reforms observed in developing countries, especially when they relate to just one or a few specific policy areas. Thus, learning the transition experience, particularly in its early phase, can provide an extremely valuable empirical input to 'understanding reform' and provide answers to the project's key questions: 'why reform?', 'what reform?', and 'how well did the reform perform?'economic reforms, transition, Russia, reform sequencing, political reforms, institutional reforms, political economy.

The 8th International Conference on Time Series and Forecasting

The aim of ITISE 2022 is to create a friendly environment that could lead to the establishment or strengthening of scientific collaborations and exchanges among attendees. Therefore, ITISE 2022 is soliciting high-quality original research papers (including significant works-in-progress) on any aspect time series analysis and forecasting, in order to motivating the generation and use of new knowledge, computational techniques and methods on forecasting in a wide range of fields

Approaches to monetary policy revisited - lessons from the crisis, 6th ECB Central Banking Conference, 18-19 November 2010

This volume contains a collection of papers, commentaries and speeches that review the strategic and operational decisions taken by the central banks to combat the crisis and that reflect on the lessons for the future. The contributions are grouped around five broad topics: monetary policy strategy, lessons from historical experiences, challenges for macroeconomic and finance theory, the international dimension of the crisis, and operational frameworks for monetary policy.monetary policy strategy, monetary policy operational framework