Stock market prices, 'causality' and efficiency: evidence from the Athens stock exchange

1998 ◽  
Vol 8 (2) ◽  
pp. 167-174 ◽  
Author(s):  
Nikitas A. Niarchos ◽  
Christos A. Alexakis
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Market Prices ◽  
Athens Stock Exchange

scholarly journals Differences In Intrinsic Value With Stock Market Prices Using The Price Earning Ratio (Per) Approach As An Investment Decision Making Indicator (Case Study Of Manufacturing Companies In Indonesia Period 2016 - 2017)

2019 ◽  
Vol 1 (1) ◽  
pp. 82-92
Author(s):  
Ardy Indra Lekso Wibowo Putra ◽  
Aditya Dwiansyah Putra ◽  
Murni Sari Dewi ◽  
Denny Oktavina Radianto
Keyword(s):  
Decision Making ◽  
Stock Market ◽  
Stock Price ◽  
Stock Exchange ◽  
Intrinsic Value ◽  
Investment Decision ◽  
Manufacturing Companies ◽  
Market Prices ◽  
Investment Decision Making

An investor must be able to consider all kinds of steps that will be taken or that will be carried out, assessing stocks - shares that will provide optimal benefits in making an investment decision. By analyzing the intrinsic value of the price of a company's stock, investors can assess the fairness of the stock price. The method used to analize intrinsic value is fundamental analysis using the Price Earning Ratio (PER) approach. The samples to be taken in this research are manufacturing companies in Indonesia which are listed on the Indonesia Stock Exchange (IDX) for the period 2016 - 2017 with certain criteria. The results of this research will show that the shares of companies listed are in overvalued, undervalued or correctly valued conditions. So investors can decide to buy, hold or sell their shares.

scholarly journals Stock Market Prices Prediction using Random Forest and Extra Tree Regression

2019 ◽  
Vol 8 (3) ◽  
pp. 1224-1228
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
Research Area ◽  
Support Vector ◽  
Market Prices ◽  
Machine Learning Approach ◽  
Innovative Work

Prediction of Stock price is now a day’s an existing and interesting research area in financial and academic sectors to know the scale of economies. There did not exists any significant set of rules to estimate and predict the scale of share in the stock exchange. Many evolutionary technologies are existing such as technical, fundamental, time, statistical and series analysis which help us to attempt the prediction process, but none of the methods are proved as reliable and accurate tool to the society in the estimation of stock exchange or share market scales. Here in this paper we attempted to do innovative work through Machine Learning approach to predict or sense the behaviour tracking of the stock market sensex. Linear regression, Support Vector regression, Decision Tree, Ramdom Forest Regressor and Extra Tree Regressor are the Machine Learning models implemented effectively in predicting the stock prices and define the activity between the exchanges the securities between the buyers and sellers. We predicted the price of the stock based on the closing value and stock price. An algorithm with high accuracy we do the process of comparison for the accuracy of each of the model and finally is considered as better algorithm for predicting stock price. As share market is a vague domain we cannot predict the conditions occur, and also share market can never be predicted, this job can be done easily and technically through this work and the main aim of this paper is to apply algorithms in Machine Learning in predicting the stock prices.

THE BEHAVIOR OF A THRESHOLD MODEL OF MARKET PRICE IN STOCK EXCHANGE

Fractals ◽  
1993 ◽  
Vol 01 (01) ◽  
pp. 29-40 ◽  
Author(s):  
TADASHI HIRABAYASHI ◽  
HIDEKI TAKAYASU ◽  
HITOSHI MIURA ◽  
KOICHI HAMADA
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Threshold Model ◽  
Market Price ◽  
Observation Time ◽  
Threshold Dynamics ◽  
Maximum Lyapunov Exponent ◽  
Stochastic Fluctuation ◽  
Market Prices ◽  
Global Trends

We analyze the behavior of deterministic threshold dynamics in a model of stock market. We observe global trends in the virtual market prices and find a kind of phase transition. At the critical region, the macroscopic variable of stock market price shows seemingly stochastic fluctuation with f-2 power spectrum consistent with real economic fluctuations. The maximum Lyapunov exponent is estimated to be slightly positive in short time steps (5 or 10 steps) and, as the observation time becomes longer, it converges to zero. This result indicates that the system is at the edge of chaos.

scholarly journals How Does Stock Prices Respond to Various Macroeconomic Factors? A Case Study of Pakistan

2014 ◽  
Vol 1 (4) ◽  
pp. 25-30
Author(s):  
Ayaz Khan
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
Correction Term ◽  
Macroeconomic Factors ◽  
Market Prices ◽  
Distributed Lag ◽  
Distributed Lag Model ◽  
Long Run

Over the time everything flourished, at the same token the interrelationship among the stock market prices, returns and macroeconomic factors got attendance of the researchers in the field of finance and economics around the world. In this respect current study is an attempt to investigate the response of various macroeconomic factors (GDP, Money Supply, inflation, exchange rate and Size of firm) toward stock market prices in case of Karachi stock exchange over a period of 1971 to 2012. The study utilizes Autoregressive Distributed lag model (ARDL) technique. The results shows that in long run each factor significantly contribute to the stock price while in shot run some factors were significant while some were not but the error correction term shows significant convergence toward equilibrium. The findings of study suggest that for smoothness of stock market the current factors must be targeted.

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