scholarly journals Company Stock, Market Rationality, and Legal Reform

2004 ◽  
Author(s):  
Shlomo Benartzi ◽  
Richard H. Thaler ◽  
Stephen P. Utkus ◽  
Cass R. Sunstein
Keyword(s):  
Stock Market ◽  
Legal Reform ◽  
Company Stock ◽  
Market Rationality

Dividend Omissions and Stock Market Rationality

1991 ◽  
Vol 18 (3) ◽  
pp. 315-330 ◽  
Author(s):  
Chinmoy Ghosh ◽  
J. Randall Woolridge
Keyword(s):  
Stock Market ◽  
Market Rationality ◽  
Dividend Omissions

scholarly journals FACTORS DETERMINING STOCK MARKET RETURNS: CASE OF NAIROBI STOCK EXCHANGE

2016 ◽  
Vol 1 (1) ◽  
pp. 108
Author(s):  
Samson Okoth Ondiek ◽  
Dr. Ongoro
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Significant Relationship ◽  
Unique Contribution ◽  
Market Returns ◽  
Macroeconomic Factors ◽  
Stock Market Returns ◽  
Book Value ◽  
Company Stock ◽  
The Government

AbstractPurpose: The study attempts to establish if the changing macroeconomic factors and the industry variables can predict the variation on the Nairobi Security Exchange stocks return Methodology: It adopted a regression model that related stock returns to various selected macro and micro economic factors and used data of 20 companies that constitute the NSE index. The study used monthly data spanning the year 2006 to 2010.Results: The regression results indicate that, four of the variables i.e. market return (NSEI), exchange rate for US/KSH, market to book value ratio have a positive and significant relationship with an individual company stock market returns. Risk Free rate (91 Treasury bill rate) also had a positive and significant relationship while industrial growth opportunity and inflation were found to be negative and significant. leverage on the other hand was found to be insignificant and therefore does not influence individual company stock market returns. Unique contribution to theory, practice and policy: These findings will have significant effects on investors’ investment decisions making as well as the Government and the capital markets authority (CMA) in the formulation of polices and guidelines. Once factor betas are estimated, we can describe the expected change in security returns with respect to changes in a given factor and thus giving the investors, CMA and the Government a better understanding on the effect of a change in the fiscal and monetary policies in the stock market. This is crucial to the Government as it seeks to promote the capital market as a source of alternative funding for economic growth. Investors wishing to construct portfolios should also consider the trends of the inflation rates, exchange rates, market to book value ratio, industrial production and the stock market.  The rise of either of this micro and macroeconomic indicators may influence the returns positively or negatively and hence the investor may choose the best time to either buy or sell their securities

scholarly journals APPLYING MACHINE LEARNING MODELS IN STOCK MARKET PREDICTION

2020 ◽  
pp. 395-398
Author(s):  
Vignesh CK
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Support Vector ◽  
Company Stock ◽  
The Future ◽  
Future Value ◽  
Past Data

This paper deals with the techniques of attempting to calculate the future value of a company stock or any other financial instrument which is being traded in a stock exchange. This prediction plays a great role in many financing and investing decisions. This calculation can be done by Machine learning by training a model to identify the trend from past data in order to predict the future. The main topic of study here will be the comparative analysis of the SVM and LTSM algorithms. KEYWORDS: Machine learning, Stock price, Stock market, Support vector machine, neural network, long short term memory.

scholarly journals The Effect of Political Elections on Stock Market Volatility in Malaysia

2018 ◽  
Vol 7 (3.21) ◽  
pp. 114
Author(s):  
Ricky Chia Chee Jiun
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
General Election ◽  
Stock Index ◽  
Stock Market Volatility ◽  
Political Uncertainty ◽  
Stock Volatility ◽  
Volatility Risk ◽  
Company Stock ◽  
General Elections

During the past general elections held in Malaysia, empirical evidence showed a significant election effect in stock volatility. In this study, we investigate the influence of election on Malaysian stock market during the 12th and 13th general election where political tensions persisted due to the close fight between the two major parties. The findings indicate that the political uncertainty surrounding elections significantly affected investors respond. Results from statistical analysis uncover significant higher stock volatility in the pre-general election periods. Nevertheless, lower stock volatility is only found in two stock indices in the post-general election periods. By using the EGARCH model, a significant election effect is found in stock volatility but not in stock returns. Notably, political uncertainty showed up its significant role in influencing the stock volatility prior to the general elections in the year 2008 and 2013. Furthermore, lower stock volatility is found in the Shariah-compliant indices and stock index with greater market capitalization. Our findings have important implications for investors who are exposed to volatility risk. Investors may shift to large company stock and Shariah-compliant stock during the general election period. Investors should also be cautious because the high volatility is not compensated with a significant abnormal return. 

scholarly journals Optimizing Stock Market Prediction using Long Short Term Memory

2019 ◽  
Vol 9 (1) ◽  
pp. 2819-2823
Keyword(s):  
Stock Market ◽  
Financial Market ◽  
Stock Prices ◽  
Short Term Memory ◽  
Market Price ◽  
Data Set ◽  
Stock Market Prediction ◽  
Company Stock ◽  
Financial Gain ◽  
And Mathematics

Stock market prediction has been an important issue in the field of finance, engineering and mathematics due to its potential financial gain. Stock market prediction is a process of predicting the future value of a company stock or other financial instrument traded in financial market. Stock market prediction brings with it the challenge of proving whether the financial market is predictable or not, since stock market data is of high velocity. This project proposes a machine learning model to predict stock market price based on the data set available by using LSTM model for performing prediction by de-noising the data using wavelet transform and performing auto-encoding on the data. The process includes removal of noise, preprocessing, feature selection, data mining, analysis and derivations. This project focuses mainly on the use of LSTM algorithm along with a layer of neural network to forecast stock prices and allocate stocks to maximize the profit within the risk factor range of the stock buyers and sellers.

Doctoral Studies in Psychology in Spain

2003 ◽  
Vol 8 (1) ◽  
pp. 28-33
Author(s):  
Yolanda García Rodríguez
Keyword(s):  
Doctoral Programs ◽  
Legal Reform ◽  
The State ◽  
Teaching Program ◽  
Doctoral Studies ◽  
Public And Private ◽  
Teaching And Research ◽  
Doctoral Thesis ◽  
Critical Phase ◽  
The Individual

In Spain doctoral studies underwent a major legal reform in 1998. The new legislation has brought together the criteria, norms, rules, and study certificates in universities throughout the country, both public and private. A brief description is presented here of the planning and structuring of doctoral programs, which have two clearly differentiated periods: teaching and research. At the end of the 2-year teaching program, the individual and personal phase of preparing one's doctoral thesis commences. However, despite efforts by the state to regulate these studies and to achieve greater efficiency, critical judgment is in order as to whether the envisioned aims are being achieved, namely, that students successfully complete their doctoral studies. After this analysis, we make proposals for the future aimed mainly at the individual period during which the thesis is written, a critical phase in obtaining the doctor's degree. Not enough attention has been given to this in the existing legislation.

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