scholarly journals THE INFLUENCE OF BIASED SIGNAL SET ON STOCK MARKET—BASED ON THE SHANGHAI COMPOSITE INDEX

2021 ◽  
Author(s):  
ZE-LIN LIU
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Time Series Analysis ◽  
Cross Section ◽  
Financial Market ◽  
High Weight ◽  
Composite Index ◽  
Signal Set ◽  
Low Weight ◽  
Section Analysis

Based on the two phenomena of over and underreaction, this paper introduces the characteristics of signal set into the model, which studies the price deviation of financial market caused by investors' signal set deviation. The text concludes that investors overreact to information of low weight and underreact to information of high weight, and uses cross-section analysis and time series analysis to verify the correctness of the results.

Predicting US recessions with stock market illiquidity

2016 ◽  
Vol 16 (1) ◽  
Author(s):  
Shiu-Sheng Chen ◽  
Yu-Hsi Chou ◽  
Chia-Yi Yen
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Returns ◽  
Cross Section ◽  
Market Liquidity ◽  
Credit Spreads ◽  
Stock Market Liquidity ◽  
Predictive Content ◽  
Dynamic Link

AbstractIn this paper, we investigate the dynamic link between recessions and stock market liquidity by examining the predictive content of illiquidity for US recessions. After controlling for other commonly featured recession predictors such as term spreads and credit spreads, we find that the illiquidity measure proposed by (Amihud, Y. 2002. “Illiquidity and Stock Returns: Cross-Section and Time-Series Effects.”

scholarly journals A Time Series Analysis of the Nexus Between Macroeconomic Fundamentals and Stock Prices in Nigeria

2018 ◽  
Vol 13 (2) ◽  
pp. 69-91
Author(s):  
Amassoma Ditimi ◽  
Bolarinwa Ifeoluwa
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Stock Market ◽  
Time Series Analysis ◽  
Stock Prices ◽  
Macroeconomic Factors ◽  
Series Analysis ◽  
Integration Test ◽  
Macroeconomic Fundamentals ◽  
The Government

AbstractSince macroeconomic fundamentals have been found to play a vital role for changes in the economy of a country. Consequently, the onus is on the appropriate regulatory authorities to take measures in making amendments in these policies to put the economy on the right development track. The aim of this study is to use time series analysis to empirically showcase the nexus between macroeconomic fundamentals and stock prices in Nigeria. The method used for this study was the Co-integration test and the EGARCH technique to estimate the possible influence of the selected macroeconomic fundamentals on stock prices. Volatility was captured by using quarterly data and estimated using GARCH (1,1) respectively. The study found there is a positive relationship between macroeconomic factors and stock prices in Nigeria. Therefore, the study recommends that the Federal authority should put in place policy measures that will enable the exchange rate to be relatively stabilized. This is because empirical evidence from studies has shown that exchange rate affects stock market prices. In addition, the government authority should ensure an enabling environment that would build the mindset of institutional investors in the Nigerian stock market due to the existence of information asymmetry problems among potential investors.

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.

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