The Effect of Stock Price Jumps on Analyst Recommendations: Information or Extrapolation?

2009 ◽  
Author(s):  
George J. Jiang ◽  
Woojin Kim
Keyword(s):  
Stock Price ◽  
Analyst Recommendations

scholarly journals How Do Analyst Recommendations Respond to Major News?

2006 ◽  
Vol 41 (1) ◽  
pp. 25-49 ◽  
Author(s):  
Jennifer Conrad ◽  
Bradford Cornell ◽  
Wayne R. Landsman ◽  
Brian R. Rountree
Keyword(s):  
Stock Price ◽  
Public Information ◽  
Investment Banking ◽  
Optimistic Bias ◽  
Analyst Recommendations ◽  
Herding Behavior ◽  
Stock Recommendations ◽  
Price Increases ◽  
Banking Relationships ◽  
Stock Price Movements

AbstractWe examine how analysts respond to public information when setting stock recommendations. We model the determinants of analysts' recommendation changes following large stock price movements. We find evidence of an asymmetry following large positive and negative returns. Following large stock price increases, analysts are equally likely to upgrade or downgrade. Following large stock price declines, analysts are more likely to downgrade. This asymmetry exists after accounting for investment banking relationships and herding behavior. This result suggests recommendation changes are “sticky” in one direction, with analysts reluctant to downgrade. Moreover, this result implies that analysts' optimistic bias may vary through time.

Analyst Recommendations and Stock Price Momentum

2012 ◽  
Author(s):  
Keming Li ◽  
Larry J. Lockwood ◽  
Mohammad Riaz Uddin
Keyword(s):  
Stock Price ◽  
Analyst Recommendations ◽  
Price Momentum

Analyst recommendations and stock price bubbles

2020 ◽  
Author(s):  
Karthik Balakrishnan ◽  
Catherine Schrand ◽  
Rahul Vashishtha
Keyword(s):  
Stock Price ◽  
Positive Association ◽  
Causal Factor ◽  
Analyst Recommendations ◽  
Positive Relation ◽  
Sample Analysis ◽  
Timely Manner ◽  
Price Bubbles ◽  
Out Of Sample ◽  
Future Return

This paper documents how analyst recommendations are related to periods of bubbles. We find a strong positive relation between the concentration in analyst buy recommendations and bubble continuation in two settings. First, we show a positive association between the concentration in buy recommendations and the tech bubble; the crash was associated with changes in buy recommendation concentration. Second, in an out-of-sample analysis of firms in multiple industries from 1994-2009, we show that analyst buy recommendation concentration predicts future return patterns that exhibit characteristics of a rational speculative bubble. While the evidence is not sufficient to conclude that analyst buy recommendations are the causal factor that perpetuates the mispricing, our findings suggest that, at a minimum, analysts do not act proactively to correct this form of mispricing in a timely manner.

Large and Small Firm Stock Price Response to “Heard on the Street” Recommendations

1992 ◽  
Vol 7 (1) ◽  
pp. 27-44 ◽  
Author(s):  
William L. Huth ◽  
Brian A. Maris
Keyword(s):  
Decision Making ◽  
Firm Size ◽  
Wall Street Journal ◽  
Stock Price ◽  
Analyst Recommendations ◽  
Short Term ◽  
Wall Street ◽  
Price Response ◽  
Negative Comments ◽  
The Wall Street Journal

Stock price response to analyst recommendations in the Wall Street Journal “Heard on the Street” column are examined for their usefulness in short-term trade decision making. The stock price response relation to firm size is also examined. Information from the column appears to produce statistically significant but economically insignificant stock price movements. Firm size is important only for negative comments in the column.

ANALISIS PENGARUH INFLASI, KURS DAN IHSG TERHADAP RETURN REKSA DANA SAHAM PT NIKKO SECURITIES INDONESIA PERIODE 2014-2017

2019 ◽  
Vol 2 (2) ◽  
pp. 103
Author(s):  
Muhammad Rois Rois ◽  
Manarotul Fatati Fatati ◽  
Winda Ihda Magfiroh
Keyword(s):  
Regression Analysis ◽  
Exchange Rate ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Stock Price ◽  
Price Index ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Equity Fund ◽  
Stock Price Index

This study aims to determine the effect of Inflation, Exchange Rate and Composite Stock Price Index (IHSG) to Return of PT Nikko Securities Indonesia Stock Fund period 2014-2017. The study used secondary data obtained through documentation in the form of PT Nikko Securities Indonesia Monthly Net Asset (NAB) report. Data analysis is used with quantitative analysis, multiple linear regression analysis using eviews 9. Population and sample in this research are PT Nikko Securities Indonesia. The result of multiple linear regression analysis was the coefficient of determination (R2) showed the result of 0.123819 or 12%. This means that the Inflation, Exchange Rate and Composite Stock Price Index (IHSG) variables can influence the return of PT Nikko Securities Indonesia's equity fund of 12% and 88% is influenced by other variables. Based on the result of the research, the variables of inflation and exchange rate have a negative and significant effect toward the return of PT Nikko Securities Indonesia's equity fund. While the variable of Composite Stock Price Index (IHSG) has a negative but not significant effect toward Return of Equity Fund of PT Nikko Securities Indonesia

Family Firms and Stock Price Crash Risk

2019 ◽  
Vol 10 (4) ◽  
pp. 77-86
Author(s):  
Hae-Young Ryu ◽  
Soo-Joon Chae
Keyword(s):  
Stock Price ◽  
Family Firms ◽  
Crash Risk

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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 

Analisis Kinerja Keuangan Perusahaan dengan Menggunakan Pendekatan Economic Value Added (EVA) dan Market Value Added (MVA) (Studi Kasus pada PT Astra International, Tbk. Periode Tahun 20082012)

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Abdul Hamid
Keyword(s):  
Financial Performance ◽  
Financial Services ◽  
Stock Price ◽  
Economic Value ◽  
Composite Index ◽  
Value Added ◽  
Secondary Data ◽  
Study Approach ◽  
Market Value Added ◽  
The Interest Rate

This study is a qualitative study using a case study approach to the PT. Astra International, Tbk. The object of this research is PT. Astra International, Tbk. PT. Astra International, Tbk is a company engaged in six business sectors, namely: automotive,financial services, heavy equipment, mining and energy, agribusiness, information technology, infrastructure and logistics. Researchers chose PT. Astra International, Tbk as research objects due in the year 2012, PT. Astra International, Tbk managed to rank first in the list of 100 Best Companies to Go Public by the 2011 financial performance of Fortune magazines Indonesia. The data used in this research is secondary data, the financial statements. Astra International, Tbk 20082012. Other secondary data used is the interest rate of Bank Indonesia Certificates (SBI), the Jakarta Composite Index (JCI), and thecompanys stock price began the year 20082012. This study aims to determine the companys financial performance by the use of EVA and MVA approach, therefore the data analysis technique used is the EVA and MVA. Based on the value EVA of the year 2008 2012, PT. Astra International, Tbk has good financial performance that managed to meet the expectations of the company and the investors. Based on the value of MVA during the years 20082012, PT. Astra International, Tbk managed to create wealth and prosperity for companies and investors. It concluded that financial performance. AstraInternational, Tbk for five years was satisfactory.

Sign in / Sign up

Export Citation Format

Share Document