scholarly journals Towards the Ensemble: IPCBR Model in Investigating Financial Bubbles

2020 ◽  
Vol 4 (4) ◽  
Author(s):  
Francis Ekpenyong ◽  
Georgios Samakovitis ◽  
Stelios Kapetanakis ◽  
Miltos Petridis
Keyword(s):  
Inverse Problem ◽  
Stock Market ◽  
Research Work ◽  
Problem Formulation ◽  
Forward Problem ◽  
Research Trend ◽  
Case Based Reasoning ◽  
Market Prices ◽  
Asset Bubbles ◽  
Pattern Description

Asset value predictability remains a major research concern in financial market especially when considering the effect of unprecedented market fluctuations on the behaviour of market participants. This paper presents preliminary results toward the building a reliable forward problem on ensemble approach IPCBR model, that leverages the capabilities of Case based Reasoning(CBR) and Inverse Problem Techniques (IPTs) to describe and model abnormal stock market fluctuations (often associated with asset bubbles) using datasets from historical stock market prices. The framework uses a rich set of past observations and geometric pattern description and then applies a CBR to formulate the forward problem, Inverse Problem formulation is then applied to identify a set of parameters that can statistically be associated with the occurrence of the observed patterns. This research work presents a formative strategy aimed to determine the causes of behaviour, rather than predict future time series points which brings a novel perspective to the problem of asset bubbles predictability, and a deviation from the existing research trend. The results depict the stock dynamics and statistical fluctuating evidence associated with the envisaged bubble problem.

Bayesian Algorithm Based Localization of EEG Recorded Electromagnetic Brain Activity

2019 ◽  
Vol 15 (2) ◽  
pp. 184-193
Author(s):  
Munsif Ali Jatoi ◽  
Nidal Kamel ◽  
Sayed Hyder Abbas Musavi ◽  
José David López
Keyword(s):  
Inverse Problem ◽  
Source Localization ◽  
Brain Activity ◽  
Research Work ◽  
Bayesian Framework ◽  
Forward Problem ◽  
Source Reconstruction ◽  
Eeg Data ◽  
Physical Task ◽  
Ill Posed

Background: Electrical signals are generated inside human brain due to any mental or physical task. This causes activation of several sources inside brain which are localized using various optimization algorithms. Methods: Such activity is recorded through various neuroimaging techniques like fMRI, EEG, MEG etc. EEG signals based localization is termed as EEG source localization. The source localization problem is defined by two complementary problems; the forward problem and the inverse problem. The forward problem involves the modeling how the electromagnetic sources cause measurement in sensor space, while the inverse problem refers to the estimation of the sources (causes) from observed data (consequences). Usually, this inverse problem is ill-posed. In other words, there are many solutions to the inverse problem that explains the same data. This ill-posed problem can be finessed by using prior information within a Bayesian framework. This research work discusses source reconstruction for EEG data using a Bayesian framework. In particular, MSP, LORETA and MNE are compared. Results: The results are compared in terms of variational free energy approximation to model evidence and in terms of variance accounted for in the sensor space. The results are taken for real time EEG data and synthetically generated EEG data at an SNR level of 10dB. Conclusion: In brief, it was seen that MSP has the highest evidence and lowest localization error when compared to classical models. Furthermore, the plausibility and consistency of the source reconstruction speaks to the ability of MSP technique to localize active brain sources.

Macroeconomic Determinants of Stock Market Prices in Nepal

2020 ◽  
Vol 2 (1) ◽  
pp. 56-65
Author(s):  
Bhim Prasad Panta
Keyword(s):  
Exchange Rate ◽  
Interest Rate ◽  
Stock Market ◽  
Money Supply ◽  
Developing Economies ◽  
Annual Data ◽  
Economic Variables ◽  
Market Prices ◽  
Ardl Model ◽  
Long Run

Background: Stock market plays a crucial role in the financial system of a country. It can be viewed as a channel through which resources are properly channelized. It enables the governments and industry to raise long-term capital for financing new projects. The stock markets of developing economies are likely to be sensitive to various macro-economic factors such as GDP, imports, exports, exchange rates etc., when there is high demand on financial products, as a constituent of financial market, ultimately stock market needs to develop. Many factors can be a signal to stock market participants to expect a higher or lower return when investing in stock and one of these factors are macroeconomic variables and thus, macro-economic variables tend to effect on stock market development. Objective: This study examines the linkage between stock market prices (NEPSE index) and five macro-economic variables, namely; real GDP, broad money supply, interest rate, inflation, and exchange rate using ARDL model and to explain the behavior of the Nepal Stock Exchange Index. Methods: The ECM which is delivered from ARDL model through simple linear transformation to integrate short run adjustments with long run equilibrium without losing long run information. The analysis has been done by using 25 years' annual data from 1994 to 2019. Findings: The result suggests that the fluctuation of Nepse Index in long run is strongly associated with broad money supply, interest rate, inflation, and exchange rate. Conclusion: Though Nepalese stock market is in primitive stage, broad money supply, interest rate, inflation and exchange rate are major factors affecting stock market price of Nepal. So, policies and strategies should be made and directed taking these in to consideration. Implication: The findings of research can be helpful to understand the behavior of Nepalese stock market and develop policies for market stabilization.

Stock Market Prices and Financial System Stability

2019 ◽  
Author(s):  
Zaäfri A. Husodo ◽  
Muhammad Budi Prasetyo ◽  
Rizky Luxianto ◽  
Theresia Silitonga ◽  
Januar Hafidz ◽  
...  
Keyword(s):  
Stock Market ◽  
Financial System ◽  
System Stability ◽  
Market Prices

Framework for Flaw Detection: Application to Dynamic Crack Detection in Flat Membranes

2008 ◽  
Author(s):  
Daniel Rabinovich ◽  
Dan Givoli ◽  
Shmuel Vigdergauz
Keyword(s):  
Inverse Problem ◽  
Crack Detection ◽  
Flexible Structures ◽  
Flaw Detection ◽  
Forward Problem ◽  
Dynamic Crack ◽  
Computational Framework ◽  
Time Harmonic ◽  
Present Context ◽  
Flat Membranes

A computational framework is developed for the detection of flaws in flexible structures. The framework is based on posing the detection problem as an inverse problem, which requires the solution of many forward problems. Each forward problem is associated with a known flaw; an appropriate cost functional evaluates the quality of each candidate flaw based on the solution of the corresponding forward problem. On the higher level, the inverse problem is solved by a global optimization algorithm. The performance of the computational framework is evaluated by considering the detectability of various types of flaws. In the present context detectability is defined by introducing a measure of the distance between the sought flaw and trial flaws in the space of the parameters characterizing the configuration of the flaw. The framework is applied to crack detection in flat membranes subjected to time-harmonic and transient excitations. The detectability of cracks is compared for these two cases.

Spectral analysis of stock market prices

1979 ◽  
Vol 3 (2) ◽  
pp. 201-208 ◽  
Author(s):  
Marc L. Bertoneche
Keyword(s):  
Spectral Analysis ◽  
Stock Market ◽  
Market Prices

Interoperability in Cloud Manufacturing and Practice on Private Cloud Structure for SMEs

2017 ◽  
Author(s):  
Xi Vincent Wang ◽  
Lihui Wang
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing System ◽  
Research Work ◽  
Cloud Manufacturing ◽  
Cloud Services ◽  
Security Requirements ◽  
Research Trend ◽  
Safety Regulations ◽  
Multiple Challenges ◽  
New Research

In recent years, Cloud manufacturing has become a new research trend in manufacturing systems leading to the next generation of production paradigm. However, the interoperability issue still requires more research due to the heterogeneous environment caused by multiple Cloud services and applications developed in different platforms and languages. Therefore, this research aims to combat the interoperability issue in Cloud Manufacturing System. During implementation, the industrial users, especially Small- and Medium-sized Enterprises (SMEs), are normally short of budget for hardware and software investment due to financial stresses, but they are facing multiple challenges required by customers at the same time including security requirements, safety regulations. Therefore in this research work, the proposed Cloud manufacturing system is specifically tailored for SMEs.

scholarly journals Fractional differencing in stock market price and online presence of global tourist corporations

2019 ◽  
Vol 24 (48) ◽  
pp. 194-204 ◽  
Author(s):  
Francisco Flores-Muñoz ◽  
Alberto Javier Báez-García ◽  
Josué Gutiérrez-Barroso
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Market Price ◽  
Google Trends ◽  
Data Sets ◽  
Market Prices ◽  
Content Type ◽  
Search Results ◽  
Online Presence ◽  
Fractional Differencing

Purpose This work aims to explore the behavior of stock market prices according to the autoregressive fractional differencing integrated moving average model. This behavior will be compared with a measure of online presence, search engine results as measured by Google Trends. Design/methodology/approach The study sample is comprised by the companies listed at the STOXX® Global 3000 Travel and Leisure. Google Finance and Yahoo Finance, along with Google Trends, were used, respectively, to obtain the data of stock prices and search results, for a period of five years (October 2012 to October 2017). To guarantee certain comparability between the two data sets, weekly observations were collected, with a total figure of 118 firms, two time series each (price and search results), around 61,000 observations. Findings Relationships between the two data sets are explored, with theoretical implications for the fields of economics, finance and management. Tourist corporations were analyzed owing to their growing economic impact. The estimations are initially consistent with long memory; so, they suggest that both stock market prices and online search trends deserve further exploration for modeling and forecasting. Significant differences owing to country and sector effects are also shown. Originality/value This research contributes in two different ways: it demonstrate the potential of a new tool for the analysis of relevant time series to monitor the behavior of firms and markets, and it suggests several theoretical pathways for further research in the specific topics of asymmetry of information and corporate transparency, proposing pertinent bridges between the two fields.

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