scholarly journals Predicting Financial Extremes Based on Weighted Visual Graph of Major Stock Indices

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Dong-Rui Chen ◽  
Chuang Liu ◽  
Yi-Cheng Zhang ◽  
Zi-Ke Zhang
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Financial Market ◽  
Extreme Events ◽  
Time Series Data ◽  
Graph Algorithm ◽  
Series Data ◽  
Financial Bubbles ◽  
Stock Indices ◽  
Visual Graph

Understanding and predicting extreme turning points in the financial market, such as financial bubbles and crashes, has attracted much attention in recent years. Experimental observations of the superexponential increase of prices before crashes indicate the predictability of financial extremes. In this study, we aim to forecast extreme events in the stock market using 19-year time-series data (January 2000–December 2018) of the financial market, covering 12 kinds of worldwide stock indices. In addition, we propose an extremes indicator through the network, which is constructed from the price time series using a weighted visual graph algorithm. Experimental results on 12 stock indices show that the proposed indicators can predict financial extremes very well.

A new approach to detect extreme events: a case study using remotely-sensed precipitation time-series data

2021 ◽  
Vol 24 ◽  
pp. 100618
Author(s):  
Philipe Riskalla Leal ◽  
Ricardo José de Paula Souza e Guimarães ◽  
Fábio Dall Cortivo ◽  
Rayana Santos Araújo Palharini ◽  
Milton Kampel
Keyword(s):  
Time Series ◽  
Extreme Events ◽  
Time Series Data ◽  
Remotely Sensed ◽  
Series Data ◽  
Precipitation Time Series ◽  
New Approach

scholarly journals A Predictive Meta Model for Forecasting Stock Price using Time Series Data

2021 ◽  
Vol 10 (1) ◽  
pp. 345-356
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Price ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
Meta Model ◽  
Price Range ◽  
Stock Traders ◽  
The Right

Stock market prediction through time series is a challenging as well as an interesting research areafor the finance domain, through which stock traders and investors can find the right time to buy/sell stocks. However, various algorithms have been developed based on the statistical approach to forecast the time series for stock data, but due to the volatile nature and different price ranges of the stock price one particular algorithm is not enough to visualize the prediction. This study aims to propose a model that will choose the preeminent algorithm for that particular company’s stock that can forecastthe time series with minimal error. This model can assist a trader/investor with or without expertise in the stock market to achieve profitable investments. We have used the Stock data from Stock Exchange Bangladesh, which covers 300+ companies to train and test our system. We have classified those companies based on the stock price range and then applied our model to identify which algorithm suites most for a particular range of stock price. Comparative forecasting results of all algorithms in diverse price ranges have been presented to show the usefulness of this Predictive Meta Model

scholarly journals Impact of Stock Market Development on Economic Growth in BRICS

2018 ◽  
Vol 10 (1) ◽  
pp. 23
Author(s):  
Godfrey Osaseri ◽  
Ifuero Osad Osamwonyi
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Stock Market ◽  
World Bank ◽  
Time Series Data ◽  
Market Development ◽  
Series Data ◽  
Stock Market Development ◽  
Development Indicators ◽  
Effect Estimation

The study examines Stock Market development and economic growth in BRICS, Quarterly time series data for the period 1994QI to 2015Q4 were sourced from World Bank Indicator. The Panel Least Squares based on the fixed effect estimation was employed to determine how stock market development impacts on the economic growth of BRICS. Diagnostics tests were conducted to ascertain the robustness and stability of the regression results. The findings reveal that stock market development exerts significant impact on the economic growth. The study revealed that there is a positive correlation between stock market development indicators and BRICS’s economic growth. The study recommends that the weakness of each of the BRICS member country should be taken as policy focus and strategies necessary to strengthen them should be swiftly applied by the governments.

scholarly journals Volatility Similarity and Spillover Effects in G20 Stock Market Comovements: An ICA-Based ARMA-APARCH-M Approach

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Shanglei Chai ◽  
Zhen Zhang ◽  
Mo Du ◽  
Lei Jiang
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Financial Markets ◽  
Stock Markets ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Spillover Effects ◽  
Series Data ◽  
Dominant Source ◽  
Financial Internationalization

Financial internationalization leads to similar fluctuations and spillover effects in financial markets around the world, resulting in cross-border financial risks. This study examines comovements across G20 international stock markets while considering the volatility similarity and spillover effects. We provide a new approach using an ICA- (independent component analysis-) based ARMA-APARCH-M model to shed light on whether there are spillover effects among G20 stock markets with similar dynamics. Specifically, we first identify which G20 stock markets have similar volatility features using a fuzzy C-means time series clustering method and then investigate the dominant source of volatility spillovers using the ICA-based ARMA-APARCH-M model. The evidence has shown that the ICA method can more accurately capture market comovements with nonnormal distributions of the financial time series data by transforming the multivariate time series into statistically independent components (ICs). Our findings indicate that the G20 stock markets are clustered into three categories according to volatility similarity. There are spillover effects in stock market comovements of each group and the dominant source can be identified. This study has important implications for investors in international financial markets and for policymakers in G20 countries.

scholarly journals GARCH(1,1) Model of the Financial Market with the Minkowski Metric

2018 ◽  
Vol 73 (8) ◽  
pp. 669-684
Author(s):  
Richard Pincak ◽  
Kabin Kanjamapornkul
Keyword(s):  
Time Series ◽  
Financial Market ◽  
Time Series Data ◽  
Minkowski Spacetime ◽  
Relativity Theory ◽  
Series Data ◽  
Minkowski Metric ◽  
Yang Mills ◽  
Long Memory Process ◽  
Market Crashes

AbstractWe solve a stylised fact on a long memory process of the volatility cluster phenomena by using the Minkowski metric for GARCH(1,1) (generalised autoregressive conditional heteroskedasticity) under the assumption that price and time cannot be separated. We provide a Yang-Mills equation in financial market and an anomaly on superspace of time series data as a consequence of the proof from the general relativity theory. We use an original idea in the Minkowski spacetime embedded in Kolmogorov space in time series data with the behaviour of traders. The result of this work is equivalent to the dark volatility or the hidden risk fear field induced by the interaction of the behaviour of the trader in the financial market panic when the market crashes.

scholarly journals Anticipated Volatility, Unanticipated Volatility Shocks, Excess Monthly Returns and Stock Market Behaviour of Pakistan

2021 ◽  
Vol 7 (1) ◽  
pp. 77-91
Author(s):  
Muhammad Ramzan Sheikh ◽  
Sahrish Zameer ◽  
Sulaman Hafeez Siddiqui
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Portfolio Choice ◽  
Risk Premium ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
Financial Assets ◽  
Exchange Market ◽  
Risk Return

An investor considers various factors to choose the financial assets. The portfolio theory suggests that risk, return, taxes, information and liquidity are vital factors in portfolio choice. The study is based on risk premium, uncertainty, shocks and volatility of Pakistan stock exchange market. The study has used monthly time series data of returns of ten sectors of Pakistan stock market ranging from 2006 to 2014 to measure the anticipated and unanticipated factors of risk, return and uncertainty. Using CAPM, it is pointed out that volatility factor is present and high in overall stock market and the level of volatility in different sectors of the market moves in the same direction which suggest that speculative activities are widely spread in every sector and in overall market as well.

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