Price limit changes, order decisions, and stock price movements: an empirical analysis of the Taiwan Stock Exchange

Lien, Donald; Hung, Pi‐Hsia; Pan, Chiu-Ting

doi:10.1007/s11156-019-00842-3

Cited by 4 publications

(2 citation statements)

References 67 publications

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“…Relative to individuals, institutional investors conduct more informed trades (Lee et al ., 2004; Binay, 2005; Wang and Chiao, 2008; Barber et al ., 2009; Lien et al ., 2020a,b). In terms of trading performance, we further propose hypotheses H 3A and H 3B : H 3A .…”

Section: Research Hypotheses and Data Descriptionmentioning

confidence: 99%

Analyst Recommendations, Herding Intensity, and Trading Performance*

Hung

Lien²,

Chen³

2020

Asia-Pac J of Fin Stud

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This paper examines how analyst recommendations and investors’ herding influence stock returns and trading performance. Different types of investors display distinct herding intensity. Institutional investors herd significantly more than individuals do and investment trusts exhibit the highest herding intensity. Foreign investors tend to herd in buying (selling) stocks when analysts issue better (worse) recommendations. Investment trusts are inclined to herd in the sell side when analysts issue “sell” recommendations. Institutional buy (sell) herding positively (negatively) correlates to current‐day returns. Individual herding shows the exact opposite pattern. Finally, institutional investors’ higher buy intensity has better trading performance following analysts’ buy‐oriented recommendations.

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Section: Research Hypotheses and Data Descriptionmentioning

confidence: 99%

Analyst Recommendations, Herding Intensity, and Trading Performance*

Hung

Lien²,

Chen³

2020

Asia-Pac J of Fin Stud

Self Cite

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“…The US Dollar Index, which is measured as a value of the United States currency against a number of its trade partners' currencies, strengthens when the value of the USD increases compared to other countries, hence, leading it to be a good benchmark for the value of the USD and a crucial measure for commodities that whose prices are dominated by the dollar (Sun et al, 2016). A study was conducted by (Lien et al, 2020) regarding the Dollar Index, US Stock market, and currency exchange on the Taiwanese stock market during the previous era of the financial crisis. This study found that the foreign exchange rate drastically affects the stock markets of both US and Taiwan.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Gold Price and Us Dollar Index: The Volatile Case of Shanghai Stock Exchange and Bombay Stock Exchange During the Crisis of Covid-19

Kumar

Robiyanto²

2021

J. Keuang. dan Perbank.

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This literature aims to analyze the impact of the Dollar Index and Gold Price returns and volatility on stock market volatility of India and China, viz., Shanghai Stock Exchange and Bombay Stock Exchange Sensex, during the period of Covid-19. This study employs daily time-series data from January up to August for 2019, 2020, and a merged data of 2019-2020, i.e., Pre-Pandemic, Mid-Pandemic and Pre through Mid-Pandemic periods, respectively; to avoid possible abnormalities and heteroscedasticity, the GARCH (1,1) model is utilized to scrutinize the data depending on which distribution is more acceptable, GED or Gaussian, which is decided based on the Unit-Root and normality test results. The findings of this study prove that Gold Price mostly does have a significant effect on both markets, especially during times of financial crisis like the Covid-19 epidemic. Whereas Dollar Index has a significant impact on emerging markets such as India and China though significant effects persist in some cases, it is not valid in most cases.DOI: 10.26905/jkdp.v25i3.5142

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Noise traders in an agent-based artificial stock market

2023

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This paper investigates whether noise traders can survive in the long run and how they influence financial markets by proposing an agent-based artificial stock market, as one simulation model of computational economics. This market contains noise traders, informed and uninformed traders. Informed and uninformed traders can learn from information by using Genetic Programming, while noise traders cannot. The system is first calibrated to real financial markets by replicating several stylized facts. We find that noise traders cannot survive or they just transform to other kind of traders in the long run, and they increase market volatility, price distortion, noise trader risk, and trading volume in the market. However, regulation intervention, e.g., price limits, transaction tax and longer settlement cycle, can affect noise trader’s surviving period and their influence on markets.

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Price limit changes, order decisions, and stock price movements: an empirical analysis of the Taiwan Stock Exchange

Cited by 4 publications

References 67 publications

Analyst Recommendations, Herding Intensity, and Trading Performance*

Analyst Recommendations, Herding Intensity, and Trading Performance*

The Impact of Gold Price and Us Dollar Index: The Volatile Case of Shanghai Stock Exchange and Bombay Stock Exchange During the Crisis of Covid-19

Noise traders in an agent-based artificial stock market

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