scholarly journals Dynamically Trading Frequency for Complexity in a GALS Microprocessor

2005 ◽  
Author(s):  
S. Dropsho ◽  
G. Semeraro ◽  
D.H. Albonesi ◽  
G. Magklis ◽  
M.L. Scott
Keyword(s):  
Trading Frequency

PENGARUH FREKUENSI PERDAGANGAN PADA SEKTOR PROPERTY DAN REAL ESTATE TERHADAP HARGA SAHAM

2019 ◽  
Vol 7 (02) ◽  
pp. 51
Author(s):  
Adri Wihananto
Keyword(s):  
Real Estate ◽  
Stock Price ◽  
Stock Exchange ◽  
Stock Trading ◽  
Trading Frequency ◽  
Important Thing ◽  
Know How ◽  
Stock Value ◽  
Measuring Frequency ◽  
Case Based

Trading frequency can be said as the implementation from trader of commerce. This case based on positive or negative trader reaction given by trader information.  Stock trading in BEI always fluctuate with price of volume value and frequency particularly. Frequency itself shows the company  involved or not. In trading frequency, if the indicator frequency it self shown the higher point, it means better. In spite of the most important thing is how the fluctuation or value conversion itself. On the frequencies we also could see which stocks is interested by the investor. When trading frequency high, it  may be create sense of interest from investors.The aim of this research, in order to know how far the effect of trading frequency (X) with stock value (Y) using cover stock value. The information used is begin 2008 with sample from twelve property and real estate companies. According to the research can be conclude from twelve companies in Indonesia Stock Exchange in 2008, 75 % of trading frequency samples doesn’t have signification degree between trading frequency and stock value. This case can be explained count on smaller than t tableEvaluation of this research is the trading measuring frequency at property sector and real estate not influence to stock priceKeywords : Trading Frequency, Stock Price 

Uji Komparasi Abnormal Return, Trading Volume, Trading Frequency, Dan Bid-Ask Spread Sebelum Dan Sesudah Share Split

2016 ◽  
Vol 8 (2) ◽  
pp. 24-45
Author(s):  
Tania Hayu Safira ◽  
Febryanti Simon
Keyword(s):  
Abnormal Return ◽  
Trading Volume ◽  
Stock Exchange ◽  
Distributed Data ◽  
Trading Frequency ◽  
Bid Ask Spread ◽  
Analysis Technique ◽  
Before And After ◽  
Test Of Hypothesis ◽  
Kolmogorov Smirnov

This study is event study that was conduct to examine the differences of abnormal return, trading volume, trading frequency and bid-ask spread before and after the events of share split. The object of this research is the companies that did share split and listed in Indonesia Stock Exchange in 2008 - 2015. The samples are 30 companies chosen by purposive sampling method. The criteria are the company did not do corporate action right issue, pre-emptive rights, a share dividend and bonus shares in the same year with share split. Event window used in this study was 30 days consisting of 15 days before and 15 days after the share split. Data analysis technique begins with a test of normality using Kolmogorov – Smirnov and transform for unnormally distributed data. Then, test of hypothesis using Paired t – test to compare the differences before and after share split. The results of this study showed that volume trading activity and trading frequency had significant differences before and after the share split. While, variable abnormal return and bid-ask spread had not significant differences before and after the share split. Keywords: Abnormal return, bid-ask spread, share split, trading frequency, trading volume.

Trading Frequency and Fund Performance

2016 ◽  
Author(s):  
Jeffrey A. Busse ◽  
Lin Tong ◽  
Qing Tong ◽  
Zhe Zhang
Keyword(s):  
Fund Performance ◽  
Trading Frequency

scholarly journals Day Of The Week dan Monday Effect: Fenomena yang Terbuktikan Tidak Konsisten Di Pasar Modal Indonesia

2008 ◽  
Vol 1 (1) ◽  
Author(s):  
Sumiyana Sumiyana
Keyword(s):  
Regression Analysis ◽  
Stock Exchange ◽  
Short Interval ◽  
Return Periods ◽  
Trading Frequency ◽  
Monday Effect ◽  
Methodological Research ◽  
Intraday Data ◽  
Day Of The Week ◽  
Observation Period

This research critiques Sumiyana (2007a) that is actually weak methodological research design. Sumiyana (2007a) investigates trading and nontrading periods return only, or it doesn’t split intra-day return into short interval period. Although Sumiyana (2007a) found strongly the phenomenon of the Monday effect, but it could not capture the inside occurrence in the intra-trading periods. This study examines the day of the week and Monday effect phenomena in the Indonesian Stock Exchange using intraday data in every 30 minutes interval. Samples of the data are the firms listed in LQ45. Sequentially, samples are filtered to stocks that actively traded in the Indonesian Stock Exchange based on trading frequency in observation period from January to December 2006. This study uses regression analysis with multiple dummies constructed by separating trading periods in every day into 12 return periods. This research finds that day of the week phenomena occur consistently in Indonesian Stock Exchange, but the occurrence are not evenly in the same day. In addition, this study concludes that Monday effect exists partially and incidentally only.

scholarly journals Trading frequency and volatility clustering

2012 ◽  
Vol 36 (3) ◽  
pp. 760-773 ◽  
Author(s):  
Yi Xue ◽  
Ramazan Gençay
Keyword(s):  
Volatility Clustering ◽  
Trading Frequency

Does investor personality moderate the relationship between information sources and trading behavior? Evidence from Chinese stock market

2017 ◽  
Vol 43 (5) ◽  
pp. 545-566 ◽  
Author(s):  
Muhammad Zubair Tauni ◽  
Zia-ur-Rehman Rao ◽  
Hong-Xing Fang ◽  
Minghao Gao
Keyword(s):  
Stock Market ◽  
Personality Traits ◽  
Chinese Stock Market ◽  
Sources Of Information ◽  
Trading Behavior ◽  
Content Type ◽  
Trading Frequency ◽  
Word Of Mouth Communication ◽  
The Impact ◽  
The Relationship

Purpose The purpose of this paper is to investigate the impact of the key sources of information, namely, financial advice, word-of-mouth communication and specialized press, on trading behavior of Chinese stock investors. The study also analyzed if the association between the key sources of information and trading behavior is influenced by investor personality. Design/methodology/approach The authors adopted the Big Five personality framework and examined the survey results of individual stock investors (n=541) in China. Personality traits of investors were measured by the NEO-Five Factor Inventory (Costa and McCrae, 1989). The authors performed probit regression analysis to evaluate the moderating influence of investor personality traits on the association between sources of information and stock trading behavior. Findings The results of the study confirm the previous findings that the key sources of information used by investors as a foundation of their financial choices have a significant influence on their trading behavior. The study also provides empirical evidence that investor personality traits moderate the relationship between the key sources of information and trading behavior. Financial advisors tend to increase the frequency of trading in investors with openness, extraversion, neuroticism and agreeableness personality traits, and tend to decrease the intensity of trading in investors with conscientiousness trait. On the other hand, financial information acquired from word-of-mouth communication is more likely to enhance trading frequency in extraverted and agreeable investors, and is more likely to reduce trading frequency in investors with openness, conscientiousness and neuroticism traits. Finally, the use of specialized press leads to more adjustment in portfolios of the investors with openness and conscientiousness traits than those with other personality traits. An alternative mediated model was not supported. Originality/value This research contributes to information search literature and behavioral finance literature and provides empirical evidence that the psychological characteristics of investors are significant predictors of the variations in information-trading link. The study offers new theoretical insights of investors’ behavior due to the characteristics of Chinese stock market which are unique from other stock markets in the world. To the authors’ best knowledge, no previous study has been conducted so far in Chinese stock market to explore variations with regards to the impact of the key sources of information on trading behavior by the Big Five investor personality and this paper seeks to fill this gap.

Trading frequency and noise

2005 ◽  
Vol 1 (4) ◽  
pp. 243-247 ◽  
Author(s):  
Shing-yang Hu * ◽  
Chang Chan
Keyword(s):  
Trading Frequency

scholarly journals Mechanisms for Repeated Trade

2015 ◽  
Vol 7 (4) ◽  
pp. 252-293 ◽  
Author(s):  
Andrzej Skrzypacz ◽  
Juuso Toikka
Keyword(s):  
Private Information ◽  
Collective Choice ◽  
Sufficient Condition ◽  
Necessary And Sufficient Condition ◽  
Trading Frequency ◽  
Second Best ◽  
Necessary And Sufficient ◽  
General Dynamic

How does feasibility of efficient repeated trade depend on the features of the environment such as persistence of values, private information about their evolution, or trading frequency? We derive a necessary and sufficient condition for efficient, unsubsidized, and voluntary trade, which implies that efficient contracting requires sufficient congruence of expectations. This translates to bounds on persistence of values and on private information about their evolution, and distinguishes increasing patience from more frequent interaction; the latter need not facilitate efficiency even when the former does. We also discuss second-best mechanisms and extend the characterization to general dynamic collective choice problems. (JEL C73, D82, D86)

scholarly journals Investor memory of past performance is positively biased and predicts overconfidence

2021 ◽  
Vol 118 (36) ◽  
pp. e2026680118 ◽  
Author(s):  
Daniel J. Walters ◽  
Philip M. Fernbach
Keyword(s):  
Recent Past ◽  
Past Performance ◽  
Memory Biases ◽  
Trading Frequency ◽  
Incentive Compatible

We document a memory-based mechanism associated with investor overconfidence. In Studies 1 and 2, investors were asked to recall their most important trades in the recent past and then reported investing confidence and trading frequency. After the study, they looked up and reported the actual returns of these trades. In both studies, investors were biased to recall returns as higher than achieved, and larger memory biases were associated with greater overconfidence and trading frequency. The design of Study 2 allowed us to separately investigate the effects of two types of memory biases: distortion and selective forgetting. Both types of bias were present and were independently associated with overconfidence and trading frequency. Study 3 was an incentive-compatible experiment in which overconfidence and trading frequency were reduced when participants looked up previous consequential trades compared to when they reported them from memory.

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