scholarly journals Mixture of Poisson distributions to model discrete stock price changes.

2013 ◽  
Author(s):  
Rasitha Dona
Keyword(s):  
Stock Price ◽  
Price Changes ◽  
Poisson Distributions

Modeling discrete stock price changes using a mixture of Poisson distributions

2016 ◽  
Vol 45 (3) ◽  
pp. 409-421
Author(s):  
Rasitha R. Jayasekare ◽  
Ryan Gill ◽  
Kiseop Lee
Keyword(s):  
Stock Price ◽  
Price Changes ◽  
Poisson Distributions

scholarly journals PENGARUH VARIABEL AKUNTANSI TERHADAPPERUBAHAN HARGA SAHAM PERUSAHAAN YANG GO PUBLIC DI BURSA EFEK JAKARTA

2016 ◽  
Vol 5 (1) ◽  
pp. 1
Author(s):  
Setyaningsih Setyaningsih
Keyword(s):  
Stock Prices ◽  
Random Sampling ◽  
Stock Price ◽  
Stock Exchange ◽  
Financial Accounting ◽  
Study Analysis ◽  
Price Changes ◽  
Leverage Ratio ◽  
Independent Variables ◽  
The Relationship

The objective of this study is to investigate the relationship between accounting variables and stock price changes in Jakarta Stock Exchange (JSX). Some accounting variables in this study are devidend payout  ratio, assets size, assets growth , leverage ratio, variability in earning and covariability in earning as independent variables, the independent variables are stock  price changes. The study analysis 80 cases of active firms  in  the period of 1994 to 1997.  Data is collected by means of purpo sive random sampling. Regression analysis is used to analyse the data.The  result  of  the study  shows  that  there  is significant  affect  of  the  sevent financial accounting informations in the model as predictor of stock price changes (Y); there are two variables to be dropped because there is multicolinierity among variables. Those variables are leverage ratio (X5) and covariability in earning (X7) . There are five other independent variables affect significantly to stock prices changes (Y), which their contribution is 49%.

scholarly journals The Effect of Volatility Expectations on Large Stock Price Changes

2018 ◽  
Vol 1 (2) ◽  
pp. 94
Author(s):  
Andrey Kudryavtsev
Keyword(s):  
Stock Returns ◽  
Stock Price ◽  
Trading Volume ◽  
Opposite Sign ◽  
Company Size ◽  
Price Changes ◽  
Price Reversal ◽  
Future Outcomes

<p><em>My study explores the effect of future volatility expectations, embedded in VIX index, on large daily stock price changes and on subsequent stock returns. Following both psychological and financial literature claiming that good (bad) mood may cause people to perceive positive (negative) future outcomes as more probable and that the changes in the value of VIX may be negatively correlated with contemporaneous investors’ mood, I hypothesize that if a major positive (negative) stock price move takes place on a day when the value of VIX falls (rises), then its magnitude may be amplified by positive (negative) investors’ mood, creating price overreaction to the initial company-specific shock, which may result in subsequent price reversal. In line with my hypothesis, I document that both positive and negative large price moves accompanied by the opposite-sign contemporaneous changes in VIX are followed by significant reversals on the next two trading days and over five- and twenty-day intervals following the event, the magnitude of the reversals increasing over longer post-event windows, while large stock price changes taking place on the days when the value of VIX moves in the same direction are followed by non-significant price drifts. The results remain robust after accounting for additional company (size, beta, historical volatility) and event-specific (stock’s return and trading volume on the event day) factors, and are stronger for small and volatile stocks.</em></p>

A quantitative model for intraday stock price changes based on order flows

2014 ◽  
Vol 27 (1) ◽  
pp. 208-224 ◽  
Author(s):  
Meng Li ◽  
Xiaofeng Hui ◽  
Misao Endo ◽  
Kazuo Kishimoto
Keyword(s):  
Stock Price ◽  
Quantitative Model ◽  
Price Changes

Testing for Overreaction and Return Continuations in Stock Price Index Returns

2010 ◽  
Vol 1 (2) ◽  
pp. 93-112 ◽  
Author(s):  
Nathan Lael Joseph ◽  
Khelifa Mazouz
Keyword(s):  
Market Efficiency ◽  
Stock Markets ◽  
Stock Price ◽  
Price Index ◽  
Abnormal Returns ◽  
Market Size ◽  
Price Shocks ◽  
Price Changes ◽  
Stock Indices ◽  
Initial Results

In this paper, the authors examine the impacts of large price changes (or shocks) on the abnormal returns (ARs) of a set of 39 national stock indices. Their initial results support returns continuations for both positive and negative shocks in line with prior results. After controlling for market size, their findings provide support for over-reaction, return continuations and market efficiency, but these result depend on the magnitude of the price shocks. Whilst the market is efficient when the positive shocks are large, the market also over-reacts when negative shocks are large. To illustrate, for large stock markets that are more liquid, positive shocks of more than 5% generate an insignificant day one CAR of -0.004%, whilst negative shocks of more than 5% generate a positive and significant day one CAR of 0.662%. In contrast, positive (negative) shocks of less than 5% generate a significant one day CAR of 0.119% (-0.174%) for these same (large) stock markets.

The Speed of Adjustment to the Target Market Value Leverage Is Slower Than You Think

2019 ◽  
Vol 55 (6) ◽  
pp. 1946-1977 ◽  
Author(s):  
Qie Ellie Yin ◽  
Jay R. Ritter
Keyword(s):  
Capital Structure ◽  
Empirical Evidence ◽  
Stock Price ◽  
Market Value ◽  
Target Market ◽  
Price Changes ◽  
Trade Off ◽  
Price Fluctuations ◽  
Speed Of Adjustment ◽  
The Capital Structure

In the capital structure literature, speed of adjustment (SOA) estimates are similar whether book or market leverage is used. This robustness is suspect, given the survey evidence that firms target their book leverage and the empirical evidence that they don’t issue securities to offset market leverage changes caused by stock price changes. We show that existing market SOA estimates are substantially upward biased due to the passive influence of stock price fluctuations. Controlling for this bias, the SOA estimate is 16% for book leverage and 10% for market leverage, implying that the trade-off theory is less important than previously thought.

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