Detecting Mean-Variance Shifts in a Financial Time Series: A Firm Level Case Analysis of Karachi Stock Exchange

We develop a random financial time series model of stock market by one of statistical physics systems, the stochastic contact interacting system. Contact process is a continuous time Markov process; one interpretation of this model is as a model for the spread of an infection, where the epidemic spreading mimics the interplay of local infections and recovery of individuals. From this financial model, we study the statistical behaviors of return time series, and the corresponding behaviors of returns for Shanghai Stock Exchange Composite Index (SSECI) and Hang Seng Index (HSI) are also comparatively studied. Further, we investigate the Zipf distribution and multifractal phenomenon of returns and price changes. Zipf analysis and MF-DFA analysis are applied to investigate the natures of fluctuations for the stock market.

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Predicting the Direction Movement of Financial Time Series Using Artificial Neural Network and Support Vector Machine

Complexity ◽

10.1155/2021/2906463 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Muhammad Ali ◽

Dost Muhammad Khan ◽

Muhammad Aamir ◽

Amjad Ali ◽

Zubair Ahmad

Keyword(s):

Neural Network ◽

Time Series ◽

Artificial Neural Network ◽

Support Vector Machine ◽

Stock Exchange ◽

Financial Time Series ◽

Composite Index ◽

Support Vector ◽

Financial Time ◽

Svm Model

Prediction of financial time series such as stock and stock indexes has remained the main focus of researchers because of its composite nature and instability in almost all of the developing and advanced countries. The main objective of this research work is to predict the direction movement of the daily stock prices index using the artificial neural network (ANN) and support vector machine (SVM). The datasets utilized in this study are the KSE-100 index of the Pakistan stock exchange, Korea composite stock price index (KOSPI), Nikkei 225 index of the Tokyo stock exchange, and Shenzhen stock exchange (SZSE) composite index for the last ten years that is from 2011 to 2020. To build the architect of a single layer ANN and SVM model with linear, radial basis function (RBF), and polynomial kernels, different technical indicators derived from the daily stock trading, such as closing, opening, daily high, and daily low prices and used as input layers. Since both the ANN and SVM models were used as classifiers; therefore, accuracy and F-score were used as performance metrics calculated from the confusion matrix. It can be concluded from the results that ANN performs better than SVM model in terms of accuracy and F-score to predict the direction movement of the KSE-100 index, KOSPI index, Nikkei 225 index, and SZSE composite index daily closing price movement.

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CLUSTERING FINANCIAL TIME SERIES BY NETWORK COMMUNITY ANALYSIS

International Journal of Modern Physics C ◽

10.1142/s012918311101604x ◽

2011 ◽

Vol 22 (01) ◽

pp. 35-50 ◽

Cited By ~ 16

Author(s):

CARLO PICCARDI ◽

LISA CALATRONI ◽

FABIO BERTONI

Keyword(s):

Time Series ◽

Stock Exchange ◽

Financial Time Series ◽

Community Analysis ◽

Network Graph ◽

Financial Time ◽

Network Communities ◽

The Us ◽

The Stability ◽

Symbolic Time Series Analysis

In this paper, we describe a method for clustering financial time series which is based on community analysis, a recently developed approach for partitioning the nodes of a network (graph). A network with N nodes is associated to the set of N time series. The weight of the link (i, j), which quantifies the similarity between the two corresponding time series, is defined according to a metric based on symbolic time series analysis, which has recently proved effective in the context of financial time series. Then, searching for network communities allows one to identify groups of nodes (and then time series) with strong similarity. A quantitative assessment of the significance of the obtained partition is also provided. The method is applied to two distinct case-studies concerning the US and Italy Stock Exchange, respectively. In the US case, the stability of the partitions over time is also thoroughly investigated. The results favorably compare with those obtained with the standard tools typically used for clustering financial time series, such as the minimal spanning tree and the hierarchical tree.

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Predicting Extreme Daily Regime Shifts in Financial Time Series Exchange/Johannesburg Stock Exchange—All Share Index

International Journal of Financial Studies ◽

10.3390/ijfs9020018 ◽

2021 ◽

Vol 9 (2) ◽

pp. 18

Author(s):

Katleho Makatjane ◽

Ntebogang Moroke

Keyword(s):

Time Series ◽

Stock Returns ◽

Linear Models ◽

Stock Exchange ◽

Financial Time Series ◽

Moving Average ◽

Autoregressive Integrated Moving Average ◽

Financial Time ◽

Johannesburg Stock Exchange ◽

Non Linear

During the past decades, seasonal autoregressive integrated moving average (SARIMA) had become one of a prevalent linear models in time series and forecasting. Empirical research advocated that forecasting with non-linear models can be an encouraging alternative to traditional linear models. Linear models are often compared to non-linear models with mixed conclusions in terms of superiority in forecasting performance. Therefore, the aim of this study is to build an early warning system (EWS) model for extreme daily losses for financial stock markets. A logistic model tree (LMT) is used in collaboration with a seasonal autoregressive integrated moving average-Markov-Switching exponential generalised autoregressive conditional heteroscedasticity-generalised extreme value distribution (SARIMA-MS-EGARCH-GEVD) estimates. A time series of the study is a five-day financial time series exchange/Johannesburg stock exchange-all share index (FTSE/JSE-ALSI) for the period of 4 January 2010 to 31 July 2020. The study is set into a two-stage framework. Firstly, SARIMA model is fitted to stock returns in order to obtain independently and identically distributed (i.i.d) residuals and fit the MS(k)-EGARCH(p,q)-GEVD to i.i.d residuals; while, in the second stage, we set-up an EWS model. The results of the estimated MS(2)-EGARCH(1,1) -GEVD revealed that the conditional distribution of returns is highly volatile giving the expected duration to approximately 36 months and 4 days in regime one and 58 months and 2 days in regime two. We further found that any degree losses above 25% implies that there will be no further losses. Using the seven statistical loss functions, the estimated SARIMA(2,1,0)×(2,1,0)240−MS(2)−EGARCH(1,1)−GEVD proved to be the most appropriate model for predicting extreme regimes losses as it was ranked at 71%. Finally, the results of EWS model exhibit reasonably an overall performance of 98%, sensitivity of 79.89% and specificity of 98.40% respectively. The model further indicated a success classification rate of 89% and a prediction rate of 95%. This is a promising technique for EWS. The findings also confirmed 63% and 51% of extreme losses for both training sample and validation sample to be correctly classified. The findings of this study are useful for decision makers and financial sector for future use and planning. Furthermore, a base for future researchers for conducting studies on emerging markets, have been contributed. These results are also important to risk managers and and investors.

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Estimating and Forecasting Volatility of Financial Time Series in Pakistan with GARCH-type Models

THE LAHORE JOURNAL OF ECONOMICS ◽

10.35536/lje.2007.v12.i2.a6 ◽

2007 ◽

Vol 12 (2) ◽

pp. 115-149

Author(s):

G.R. Pasha ◽

Tahira Qasim ◽

Muhammad Aslam

Keyword(s):

Time Series ◽

Stock Exchange ◽

Financial Time Series ◽

Garch Models ◽

Financial Time ◽

Daily Data ◽

Asymmetric Garch ◽

Gjr Model ◽

Forecasting Volatility ◽

Tail Distributions

In this paper we compare the performance of different GARCH models such as GARCH, EGARCH, GJR and APARCH models, to characterize and forecast financial time series volatility in Pakistan. The comparison is carried out by comparing symmetric and asymmetric GARCH models with normal and fat-tailed distributions for the innovations, over short and long forecast horizons. The forecasts are evaluated according to a set of statistical loss functions. Daily data on the Karachi Stock Exchange (KSE) 100 index are analyzed. The empirical results demonstrate that the use of asymmetry in the GARCH models and the assumption of fat-tail distributions for the innovations improve the volatility forecasts. Overall, EGARCH fits the best while the GJR model, with both normal and non-normal innovations, seems to provide superior forecasting ability over short and long horizons.

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A Regularized Vector Autoregressive Hidden Semi-Markov model, with Application to Multivariate Financial Data

The International FLAIRS Conference Proceedings ◽

10.32473/flairs.v34i1.128424 ◽

2021 ◽

Vol 34 (1) ◽

Author(s):

Zekun Xu ◽

Ye Liu

Keyword(s):

New York ◽

Time Series ◽

Markov Model ◽

Stock Exchange ◽

Financial Time Series ◽

The State ◽

Implicit Assumption ◽

Vector Autoregressive ◽

Financial Time ◽

State Duration

Hidden Markov model (HMM) has been a popular choice for financial time series modeling due to its advantage in capturing dynamic regimes. However, HMM's implicit assumption that the state duration follows a geometric distribution is too strong to hold in practice. In this work, we propose a regularized vector autoregressive hidden semi-Markov model to analyze multivariate financial time series. One challenge in such a model setting is that the number of parameters is too large to be reliably estimated unless the time series is extremely long. To address this issue, an augmented EM algorithm is developed for parameter estimation by using regularized estimators for the state-dependent covariance matrices and autoregression matrices in the M-step. The performance of the proposed model is evaluated in a simulation experiment, and demonstrated with the New York Stock Exchange financial portfolio data.

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QUANTILE CORRELATIONS: UNCOVERING TEMPORAL DEPENDENCIES IN FINANCIAL TIME SERIES

International Journal of Theoretical and Applied Finance ◽

10.1142/s0219024915500442 ◽

2015 ◽

Vol 18 (07) ◽

pp. 1550044

Author(s):

THILO A. SCHMITT ◽

RUDI SCHÄFER ◽

HOLGER DETTE ◽

THOMAS GUHR

Keyword(s):

New York ◽

Time Series ◽

Correlation Function ◽

Stochastic Processes ◽

Stock Exchange ◽

Financial Time Series ◽

Financial Time ◽

York Stock Exchange ◽

Intraday Data ◽

Temporal Dependencies

We conduct an empirical study using the quantile-based correlation function to uncover the temporal dependencies in financial time series. The study uses intraday data for the S&P500 stocks from the New York Stock Exchange (NYSE). After establishing an empirical overview, we compare the quantile-based correlation function to stochastic processes from the GARCH family and find striking differences. This motivates us to propose the quantile-based correlation function as a powerful tool to assess the agreements between stochastic processes and empirical data.

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A Hybrid Approach Integrating Multiple ICEEMDANs, WOA, and RVFL Networks for Economic and Financial Time Series Forecasting

Complexity ◽

10.1155/2020/9318308 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17

Author(s):

Jiang Wu ◽

Tengfei Zhou ◽

Taiyong Li

Keyword(s):

Time Series ◽

Stock Exchange ◽

Financial Time Series ◽

Hybrid Approach ◽

Composite Index ◽

Ensemble Prediction ◽

Functional Link ◽

Financial Time ◽

Whale Optimization ◽

Original Time

The fluctuations of economic and financial time series are influenced by various kinds of factors and usually demonstrate strong nonstationary and high complexity. Therefore, accurately forecasting economic and financial time series is always a challenging research topic. In this study, a novel multidecomposition and self-optimizing hybrid approach integrating multiple improved complete ensemble empirical mode decompositions with adaptive noise (ICEEMDANs), whale optimization algorithm (WOA), and random vector functional link (RVFL) neural networks, namely, MICEEMDAN-WOA-RVFL, is developed to predict economic and financial time series. First, we employ ICEEMDAN with random parameters to separate the original time series into a group of comparatively simple subseries multiple times. Second, we construct RVFL networks to individually forecast each subseries. Considering the complex parameter settings of RVFL networks, we utilize WOA to search the optimal parameters for RVFL networks simultaneously. Then, we aggregate the prediction results of individual decomposed subseries as the prediction results of each decomposition, respectively, and finally integrate these prediction results of all the decompositions as the final ensemble prediction results. The proposed MICEEMDAN-WOA-RVFL remarkably outperforms the compared single and ensemble benchmark models in terms of forecasting accuracy and stability, as demonstrated by the experiments conducted using various economic and financial time series, including West Texas Intermediate (WTI) crude oil prices, US dollar/Euro foreign exchange rate (USD/EUR), US industrial production (IP), and Shanghai stock exchange composite index (SSEC).

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A Hybrid Algorithm for Forecasting Financial Time Series Data Based on DBSCAN and SVR

Information ◽

10.3390/info10030103 ◽

2019 ◽

Vol 10 (3) ◽

pp. 103 ◽

Cited By ~ 2

Author(s):

Mengxing Huang ◽

Qili Bao ◽

Yu Zhang ◽

Wenlong Feng

Keyword(s):

Time Series ◽

Hybrid Algorithm ◽

Prediction Models ◽

Time Series Data ◽

Stock Exchange ◽

Financial Time Series ◽

Variable Parameter ◽

Series Data ◽

Support Vector ◽

Financial Time

Financial prediction is an important research field in financial data time series mining. There has always been a problem of clustering massive financial time series data. Conventional clustering algorithms are not practical for time series data because they are essentially designed for static data. This impracticality results in poor clustering accuracy in several financial forecasting models. In this paper, a new hybrid algorithm is proposed based on Optimization of Initial Points and Variable-Parameter Density-Based Spatial Clustering of Applications with Noise (OVDBCSAN) and support vector regression (SVR). At the initial point of optimization, ε and MinPts, which are global parameters in DBSCAN, mainly deal with datasets of different densities. According to different densities, appropriate parameters are selected for clustering through optimization. This algorithm can find a large number of similar classes and then establish regression prediction models. It was tested extensively using real-world time series datasets from Ping An Bank, the Shanghai Stock Exchange, and the Shenzhen Stock Exchange to evaluate accuracy. The evaluation showed that our approach has major potential in clustering massive financial time series data, therefore improving the accuracy of the prediction of stock prices and financial indexes.

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