scholarly journals IPO Underpiricing, Konservatisme Akuntansi, dan Sentimen Investor

2021 ◽  
Vol 20 (2) ◽  
pp. 145-156
Author(s):  
Buddi Wibowo ◽  
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Investor Sentiment ◽  
Secondary Market ◽  
Accounting Conservatism ◽  
Fair Price ◽  
Ipo Underpricing ◽  
Accrual Quality ◽  
Holding Period ◽  
Lack Of Information

Abstract. The significant increase of stock price compared to its IPO price is difficult to explain. Companies which issue their stock are impossible to set underpriced IPO voluntarily, while investors who buy an overpriced stock are only they who are not rational or lack of information. Conservative financial statements and high accrual quality provide an opportunity for all investors to be able to estimate the fair price of IPO shares so that the disagreement among investors is not too wide and price fluctuations due to speculation are not too high in the secondary market. Using Indonesia Stock Exchanges, the results show that influence of accounting conservatism and accrual quality are stronger in the 30 holding period after the IPO because the uncertainty of financial statement information led to wider speculation opportunities in the long period. The rise in stock prices after IPO is also influenced by investor’s sentiment and market conditions Keywords: IPO, underpricing, accounting conservatism; accrual quality, investor sentiment

METHODS ASSESSMENT QUALITY OF ACCOUNTING REPORTING AND MODELS OF THEIR MEASUREMENT

2020 ◽  
Vol 3 (3) ◽  
pp. 95-99
Author(s):  
E. H. ABU ◽  
Keyword(s):  
Internal Control ◽  
Stock Prices ◽  
Control Method ◽  
Financial Statements ◽  
Accounting Conservatism ◽  
Standardized Assessment ◽  
Accrual Quality ◽  
Indirect Measures

The article explores individual approaches used to measure and evaluate the quality of financial state-ments: standardized assessment, accrual-based models (accrual quality), Beneise models (M-Score), in-dexes - the internal control method and the degree of accounting conservatism. The reason for the great dependence on the use of indirect measures (proxies for the quality of financial statements or stock prices) is that some of the qualities of financial statements are unobservable.

The real effects of primary and secondary equity markets on firm performance

2017 ◽  
Vol 13 (4) ◽  
pp. 397-418 ◽  
Author(s):  
Andriansyah Andriansyah
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Operating Performance ◽  
Equity Markets ◽  
Secondary Market ◽  
Price Informativeness ◽  
Content Type ◽  
Real Effects ◽  
Stock Price Informativeness

Purpose The purpose of this paper is to investigate the real effects of primary and secondary equity markets on the post-issue operating performance of initial public offering (IPO) firms. Design/methodology/approach The author utilizes the intended use of proceeds as a proxy variable for the primary market and the investment-to-price sensitivity and the informativeness of stock prices as alternative proxy variables for the secondary market. The compositional data, and non-parametric quantile regressions which are more robust to outliers than standard least square regressions, are employed for Indonesian equity market over the period of 1999-2013. Findings While confirming that firm operating performance can be explained by the firm’s motivation to go public, the author also shows that the operating performance is positively affected by investment-to-price sensitivity and negatively affected by stock price informativeness. The stock prices affect investment decisions by the way that the more liquid a stock is, the more informative its price is, and the more relevant stock prices are in investment decisions. These findings still hold after controlling for ownership structure. Originality/value Departing from the existing literature, the author investigates the role of primary and secondary equity markets for firm performance in an integrated framework because both markets interact closely in reality. The author shows that public listed firms can benefit both from the capital-raising function of the primary market and from the informational role of the stock prices of the secondary market. A measure of stock price informativeness, 1−R2, however, must be understood in the context of thin trading in the sense that the level of liquidity affects the level of stock price informativeness.

scholarly journals PENENTUAN HARGA KONTRAK OPSI TIPE ASIA MENGGUNAKAN MODEL SIMULASI NORMAL INVERSE GAUSSIAN (NIG)

2014 ◽  
Vol 3 (3) ◽  
pp. 123
Author(s):  
I PUTU OKA PARAMARTHA ◽  
KOMANG DHARMAWAN ◽  
DESAK PUTU EKA NILAKUSMAWATI
Keyword(s):  
Monte Carlo ◽  
Stock Prices ◽  
Stock Price ◽  
Monte Carlo Model ◽  
Asian Option ◽  
Inverse Gaussian ◽  
Fair Price ◽  
Simulation Accuracy ◽  
Two Parameters ◽  
Normal Inverse Gaussian

The aim to determine of the simulation results and to calculate the stock price of Asian Option with Normal Inverse Gaussian (NIG) method and Monte Carlo method using MATLAB program. Results of both models are compared and selected a fair price. Besides to determine simulation accuracy of the stock price, speed of program execution MATLAB is calculated for both models for time efficiency. The first part, set variabels used to calculate the trajectory of stock prices at time t to simulate the stock price at the time. The second part, simulate the stock price with NIG model. The third part, simulate the stock price with Monte Carlo model. After simulating the stock price, calculated the value of the pay-off of the Asian Option, and then estimate the price of Asian Option by averaging the entire value of pay-off from each iteration. The last part, compare result of both models. The results of this research is price of Asian Option calculated using Monte Carlo simulation and NIG. The rates were calculated using the NIG produce a fair price, because of the pricing contract NIG using four parameters ?, ?, ?, and ?, while Monte Carlo is using only two parameters ? and ?. For execution time of the program, the Monte Carlo model is better in all iterations.

scholarly journals FORECASTING HARGA SAHAM MENGGUNAKAN METODE SIMPLE MOVING AVERAGE DAN WEB SCRAPPING

2019 ◽  
Vol 21 (3) ◽  
pp. 234-241
Author(s):  
Dessy Tri Anggraeni
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Moving Average ◽  
Technical Analysis ◽  
Secondary Market ◽  
A Value ◽  
Price History ◽  
The Difference ◽  
Absolute Percent Error ◽  
Error Method

Abstract:  The fluctuative of stock prices in a secondary market provide the possibility for investors/traders to gain profits through the difference in stock prices (capital gain). In order to obtain these benefits, it is necessary to analyze before buying shares, through fundamental and technical analysis. One of several methods in Technical Analysis is Simple Moving Average Method. This method can be used to predict (forecast) stock prices by calculating moving average of the stock price history. Historical stock prices can be obtained in real time using the Web Scrapper technique, so the results is more quickly and accurately. Using the MAPE (Mean Absolute Percent Error) method, the level of accuracy of forecasting can be calculated. As a result, the program was able to run successfully and was able to display the value of forecasting and the level of accuracy for the entire data tested in LQ45. Besides forecasting with a value of N = 5 has the highest level of accuracy that reaches 97,6 % while the lowest one is using the value of N = 30 which is 95,0 %.

scholarly journals Stock Prediction Based on Optimized LSTM and GRU Models

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Ya Gao ◽  
Rong Wang ◽  
Enmin Zhou
Keyword(s):  
Stock Market ◽  
Dimension Reduction ◽  
Stock Prices ◽  
Stock Price ◽  
Investor Sentiment ◽  
Prediction Ability ◽  
Stock Market Forecasting ◽  
Important Research Topic ◽  
Reduction Methods ◽  
The Many

Stock market prediction has always been an important research topic in the financial field. In the past, inventors used traditional analysis methods such as K-line diagrams to predict stock trends, but with the progress of science and technology and the development of market economy, the price trend of a stock is disturbed by various factors. The traditional analysis method is far from being able to resolve the stock price fluctuations in the hidden important information. So, the prediction accuracy is greatly reduced. In this paper, we design a new model for optimizing stock forecasting. We incorporate a range of technical indicators, including investor sentiment indicators and financial data, and perform dimension reduction on the many influencing factors of the retrieved stock price using depth learning LASSO and PCA approaches. In addition, a comparison of the performances of LSTM and GRU for stock market forecasting under various parameters was performed. Our experiments show that (1) both LSTM and GRU models can predict stock prices efficiently, not one better than the other, and (2) for the two different dimension reduction methods, both the two neural models using LASSO reflect better prediction ability than the models using PCA.

scholarly journals Asymmetric Price Adjustment: Are IPO Prices too "Sticky"?

2011 ◽  
Vol 7 (5) ◽  
Author(s):  
Michael Adams ◽  
Barry Thornton ◽  
Russ Baker
Keyword(s):  
Market Efficiency ◽  
Stock Prices ◽  
Stock Price ◽  
Investor Sentiment ◽  
Relevant Information ◽  
Price Adjustment ◽  
Informational Efficiency ◽  
Long Run ◽  
New Information ◽  
Short Run

The study of IPO mispricing is salient because it raises important questions concerning market efficiency and the existence of systematic stock patterns that can be employed by investors to generate excess market returns. The purpose of this paper is to investigate the informational efficiency of IPO market prices with respect to the first 3 trading day’s return and to examine the effect of varying investor sentiment on this information efficiency.  Under traditional definitions of market efficiency, asset prices, including IPO prices should fully reflect all available and relevant information (Fama 1970).  An increasing body of empirical evidence, however, suggests that IPO prices are not efficient as evidenced both in the short run and the long run.  The speed of incorporation of new information into stock prices is critical to many central issues in financial research, such as market efficiency, arbitrage, and market structure. This paper analyzes the speed of price adjustment to information events for IPOs. The setting of the immediate aftermarket presents an opportunity to investigate the issue when little or no trading history exists. In such a setting, investors are more exposed to new information because they cannot observe the stock price behavior or the reactions to previous information signals.

Does intellectual capital have any influence on stock price crash risk?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Agung Nur Probohudono ◽  
Adelia Dyaning Pratiwi ◽  
Mahameru Rosy Rochmatullah
Keyword(s):  
Intellectual Capital ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
Investor Sentiment ◽  
Price Volatility ◽  
Crash Risk ◽  
Company Performance ◽  
Content Type ◽  
The Impact

PurposeThis paper explores the influence between intellectual capital (IC) and the risk of stock price crashes by using company performance as an intervening variable.Design/methodology/approachThis study empirically analyzes the impact of the efficiency of IC on stock price crash risk using a sample size of 152 companies listed on the Indonesia Stock Exchange (IDX) during 2018. To test the research hypotheses, regression analysis and path analysis were applied. In addition, the researchers added exploration to several studies to strengthen the results of this study.FindingsThis study’s findings indicate that investors' optimistic (pessimistic) sentiment regarding stock price volatility has obscured aspects of the financial performance of listed companies. This finding implies that investor sentiment has dominated influence on stock price crash risk so that the aspects of IC are obscured.Originality/valueThis research provides new information that IC disclosure in the stock market needs to include knowledge of the volatility of stock prices in order to reveal stock price crash risk.

scholarly journals How Does the Accounting Conservatism Affect the Stock Price Crash Risk in Pakistan: The Complementary Role of Managerial and Institutional Ownership

2021 ◽  
Vol 7 (3) ◽  
pp. 607-621
Author(s):  
Aon Waqas ◽  
Danish Ahmed Siddiqui
Keyword(s):  
Stock Prices ◽  
Information Disclosure ◽  
Stock Price ◽  
Panel Data Analysis ◽  
Institutional Ownership ◽  
Accounting Conservatism ◽  
Crash Risk ◽  
Agency Conflicts ◽  
Complementary Role

Purpose: The conservatism of accounting and robustness of accounting information disclosure may restrain the irrational behavior of investors and help to reduce the risk of stock price crashes. This study aims to explore this in the context of developing country Pakistan. More specifically, this study investigates the effect of accounting conservatism on stock price crash risk. We also examine the complementary role of managerial and institutional ownership in strengthening this effect. Design/Methodology/Approach: This study conducts the panel data analysis of 155 nonfinancial firms listed in PSX from 2007 to 2019. This study calculates the C-Score to measure accounting conservatism. This study measures the firm’s stock price crash risk by calculating the DUVOL of weekly share prices. Findings: This study finds that there is a significant negative effect of accounting conservatism on firms’ stock price crash risk. This study also finds that managerial ownership enhances the stock price crash risk of the sample firms significantly as a moderator while there is no significant moderating influence of institutional ownership. Implications/Originality/Value: The competent authorities of Pakistan should consider agency conflicts. They should direct the firms’ management to share equal information in time regardless of whether the information is good or bad for stock prices.

scholarly journals Investor Sentiment Combined with Multisource Information to Predict Stock Prices: An Analysis of China’s A-Share Market

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xin Huang ◽  
Huilin Song
Keyword(s):  
Social Media ◽  
Time Series ◽  
News Media ◽  
Multilayer Perceptron ◽  
Stock Prices ◽  
Stock Price ◽  
Investor Sentiment ◽  
Prediction Method ◽  
Stock Price Prediction ◽  
Price Prediction

Investor sentiment has been widely used in the research of the stock market, and how to accurately measure investor sentiment is still being explored. With the rise of social media, investor sentiment is no longer only influenced by macroeconomic data and news media, but also guided by We-Media and fragmented information. We take the data of China A-shares from January 2020 to December 2020 as the research object and propose a stock price prediction method that combines investor sentiment with multisource information. Firstly, the sentiment of macroeconomic data, brokerage research reports, news, and We-Media is calculated, respectively, and then the investor sentiment vector combining multisource information is obtained by the multilayer perceptron. Finally, the LSTM model is used to represent the stock time series characteristics. The results show that (1) the proposed algorithm is superior to the benchmark algorithm in terms of accuracy and F1-score, (2) investor sentiment vector can effectively measure the investment sentiment of stocks, and (3) compared with vector concatenation, multilayer perceptron can better represent investor sentiment.

scholarly journals Stock Price Prediction Using LSTM on Indian Share Market

10.29007/qgcz ◽  
2019 ◽  
Author(s):  
Achyut Ghosh ◽  
Soumik Bose ◽  
Giridhar Maji ◽  
Narayan Debnath ◽  
Soumya Sen
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Investor Sentiment ◽  
Spot Price ◽  
Physical Factors ◽  
Stock Price Prediction ◽  
The Impact ◽  
A Company

Predicting stock market is one of the most difficult tasks in the field of computation. There are many factors involved in the prediction – physical factors vs. physiological, rational and irrational behavior, investor sentiment, market rumors,etc. All these aspects combine to make stock prices volatile and very difficult to predict with a high degree of accuracy. We investigate data analysis as a game changer in this domain.As per efficient market theory when all information related to a company and stock market events are instantly available to all stakeholders/market investors, then the effects of those events already embed themselves in the stock price. So, it is said that only the historical spot price carries the impact of all other market events and can be employed to predict its future movement. Hence, considering the past stock price as the final manifestation of all impacting factors we employ Machine Learning (ML) techniques on historical stock price data to infer future trend. ML techniques have the potential to unearth patterns and insights we didn’t see before, and these can be used to make unerringly accurate predictions. We propose a framework using LSTM (Long Short- Term Memory) model and companies’ net growth calculation algorithm to analyze as well as prediction of future growth of a company.

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