Stock Return, Dividend Growth and Consumption Growth Predictability Across Markets and Time: Implications for Stock Price Movement

2013 ◽  
Author(s):  
David G. McMillan
Keyword(s):  
Stock Price ◽  
Stock Return ◽  
Consumption Growth ◽  
Price Movement ◽  
Dividend Growth

Stock Price Jumps and Cross-Sectional Return Predictability

2013 ◽  
Vol 48 (5) ◽  
pp. 1519-1544 ◽  
Author(s):  
George J. Jiang ◽  
Tong Yao
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Stock Return ◽  
Return Predictability ◽  
Value Premium ◽  
The Other ◽  
Stock Return Predictability ◽  
Cross Sectional ◽  
Return Dynamics

AbstractWe identify large discontinuous changes, known as jumps, in daily stock prices and explore the role of jumps in cross-sectional stock return predictability. Our results show that small and illiquid stocks have higher jump returns to the extent that cross-sectional differences in jumps fully account for the size and illiquidity effects. Based on value-weighted portfolios, jumps also account for the value premium. On the other hand, jumps are not the cause of momentum or net share issue effects. The findings of our study shed new light on stock return dynamics and present challenges to conventional explanations of stock return predictability.

scholarly journals FACTORS AFFECTING OPERATING CASH FLOW TO STOCK RETURN THROUGH STOCK PRICE

2018 ◽  
Vol 5 (2) ◽  
Author(s):  
Aprih . Santoso
Keyword(s):  
Stock Returns ◽  
Cash Flow ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Return ◽  
Stock Exchange ◽  
Sampling Technique ◽  
Financial Statements ◽  
Manufacturing Companies ◽  
Operating Cash Flow

Abstract : Companies need funds in order to carry out operations such as the financing of production activities, pay employees, pay other expenses related to the operation of the company. One way to obtain these funds is to attract investors to invest in companies in the form of stock, but in making this investment is certainly not easy for investors, because investors need consideration beforehand to find out how the company's performance. The purpose of this study was to examine and analyze the effect of operating cash flow to stock return through stock price at companies listed on the Stock Exchange Year 2012-2015. The data used in this study dala are secondary data from the financial statements of companies listed on the Indonesia Stock Exchange period 2012 - 2015. The data are in the form of financial statements can be obtained from the Indonesian Capital Market Directory (ICMD), the IDX website www.idx.co. id as well as from various other sources to support this research. The population in this research is manufacturing companies listed on the Stock Exchange the period 2012 - 2015. The samples taken by the sampling technique used purposive sampling.From the test results and analysis of the data it can be concluded that operating cash flow directly and indirectly has no effect on stock returns through stock prices showed no significant results. Keywords :  Operating Cash Flow, Stock Price, Stocks Return

scholarly journals The Geometric Brownian Motion of Indosat Telecommunications Daily Stock Price During the Covid-19 Pandemic in Indonesia

2021 ◽  
Vol 2084 (1) ◽  
pp. 012012
Author(s):  
Tiara Shofi Edriani ◽  
Udjianna Sekteria Pasaribu ◽  
Yuli Sri Afrianti ◽  
Ni Nyoman Wahyu Astute
Keyword(s):  
Brownian Motion ◽  
Stock Price ◽  
Service Providers ◽  
Main Idea ◽  
Geometric Brownian Motion ◽  
Government Policies ◽  
Percentage Error ◽  
Price Movement ◽  
Data Movement ◽  
Total Data

Abstract One of the major telecommunication and network service providers in Indonesia is PT Indosat Tbk. During the coronavirus (COVID-19) pandemic, the daily stock price of that company was influenced by government policies. This study addresses stock data movement from February 5, 2020 to February 5, 2021, resulted in 243 data, using the Geometric Brownian motion (GBM). The stochastic process realization of this stock price fluctuates and increases exponentially, especially in the 40 latest data. Because of this situation, the realization is transformed into log 10 and calculated its return. As a result, weak stationary in variance is obtained. Furthermore, only data from December 7, 2020 to February 5, 2021 fulfill the GBM assumption of stock price return, as R t 1 * , t 1 * = 1 , 2 , 3 , … , 40 . The main idea of this study is adding datum one by one as much as 10% – 15% of the total data R t 1 * , starting from December 4, 2020 backwards. Following this procedure, and based on the 3% < p-value < 10%, the study shows that its datum can be included in R t 1 * , so t 1 * = − 4. − 3 , − 2 , … , 40 and form five other data groups, R t 2 * , … , R t 6 * . Considering Mean Absolute Percentage Error (MAPE) and amount of data from each group, R t 6 * is selected for modelling. Thus, GBM succeeded in representing the stock price movement of the second most popular Indonesian telecommunication company during COVID-19 pandemic.

scholarly journals Forecasting stock price movement: new evidence from a novel hybrid deep learning model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yang Zhao ◽  
Zhonglu Chen
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Hybrid Model ◽  
Stock Price ◽  
Linear Time ◽  
Price Movement ◽  
Content Type ◽  
Non Linear ◽  
New Evidence ◽  
Deep Learning Model

PurposeThis study explores whether a new machine learning method can more accurately predict the movement of stock prices.Design/methodology/approachThis study presents a novel hybrid deep learning model, Residual-CNN-Seq2Seq (RCSNet), to predict the trend of stock price movement. RCSNet integrates the autoregressive integrated moving average (ARIMA) model, convolutional neural network (CNN) and the sequence-to-sequence (Seq2Seq) long–short-term memory (LSTM) model.FindingsThe hybrid model is able to forecast both linear and non-linear time-series component of stock dataset. CNN and Seq2Seq LSTMs can be effectively combined for dynamic modeling of short- and long-term-dependent patterns in non-linear time series forecast. Experimental results show that the proposed model outperforms baseline models on S&P 500 index stock dataset from January 2000 to August 2016.Originality/valueThis study develops the RCSNet hybrid model to tackle the challenge by combining both linear and non-linear models. New evidence has been obtained in predicting the movement of stock market prices.

Analisis Pengaruh Tingkat Harga Saham, Return Saham, dan Volume Perdagangan Saham terhadap Stock Split ISSI Periode 2015– 2020

2021 ◽  
Vol 4 (2) ◽  
pp. 234-245
Author(s):  
Farhan Maulana ◽  
Ahmad Mulyadi Kosim ◽  
Abrista Devi
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Trading Volume ◽  
Stock Return ◽  
Positive Impact ◽  
Secondary Data ◽  
Normality Test ◽  
Stock Split ◽  
Before And After ◽  
Sample Test

For companies that collect funds from the public through capital from capital market, it can be used to meet capital needs and finance the company’s operation. So that company is expected not to rely on commercial debt financing both from within the country and abroad. With stock split, it is hoped that it will increase investors’ interest in buying affordable shares. This study aims to determine whether the stock split has an effect on stock prices, trading volume, and stock return. The method used by the researcher uses quantitative secondary data methods by using descriptive statistical data test, then use the kolgomorov smirnov normality test, and using theaverage paired sample test. The results of this research is that: 1) stock price have a significant effect after the stock split occurs, 2) while the trading volume has no significant effect after the stock split occours, 3)  then stock return has a siginificant impact before and after the stock split because it is expected to have a positive impact for issuers and investors.

Effect of Stock Price Information on Timing of Share Repurchases

2019 ◽  
Vol 4 (1) ◽  
pp. 36-46
Author(s):  
Chong-Meng Chee ◽  
Nazrul Hisyam Bin Ab Razak
Keyword(s):  
Cost Saving ◽  
Private Information ◽  
Stock Price ◽  
Stock Return ◽  
Market Timing ◽  
Abnormal Returns ◽  
Jel Classification ◽  
Share Repurchases ◽  
Price Informativeness ◽  
Stock Price Informativeness

Objective - This study investigates whether private information newly incorporated into stock price enhances performance in timing share repurchases. Methodology/Technique - Cost saving gained in share repurchases is used a proxy for performance of market-timing in share repurchases and firm-specific stock return variation is used to gauge stock price informativeness. A sample of 334 U.S. repurchasing firms are tested using panel data regression. Findings - The paper concludes that managers possess better market timing skill by obtaining more cost saving from their share repurchases when private information is reflected in stock price. Stock price informativeness may be the tool for managers to improve their market timing skill to take advantage of the stock market. Furthermore, firms with smaller size and a higher market-to-book ratios, and firms with higher cash-to-assets ratios are found to achieve more cost saving in buying back their shares indicating that these firms are able to time the market in share repurchasing. Novelty – Despite numerous previous studies focusing solely on using share repurchases announcement for computing cumulative abnormal returns in testing managerial market timing, this study contributes to the literature in several ways: (i) providing evidence relating stock price informativeness and performance of market-timing in share repurchases; (ii) developing a better timing measure constructed using actual repurchasing data; (iii) adopting a cost saving measure as the timing measure instead of cumulative abnormal return. Type of Paper - Empirical. Keywords: Managerial Learning Hypothesis; Market Timing; Stock Repurchase; Stock Price Informativeness; Firm-specific Stock Return Variation. JEL Classification: G12, G13, G14. DOI: https://doi.org/10.35609/jfbr.2019.4.1(5)

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