scholarly journals Trading Volume and Stock Indices: A Test of Technical Analysis

2010 ◽  
Vol 2 (3) ◽  
pp. 287-292 ◽  
Author(s):  
Paul Abbondante
Keyword(s):  
Trading Volume ◽  
Technical Analysis ◽  
Stock Indices

scholarly journals PENGARUH TOTAL ASSET TURN OVER, RETURN ON ASSET DAN RETURN ON EQUITY TERHADAP ACCUMULATION DISTRIBUTION LINE

2017 ◽  
Vol 18 (3) ◽  
Author(s):  
Luna Haningsih ◽  
Zulkifli Zulkifli ◽  
Caturida Meiwanto Doktoralina
Keyword(s):  
Stock Price ◽  
Trading Volume ◽  
Technical Analysis ◽  
Total Asset ◽  
Fundamental Analysis ◽  
Return On Assets ◽  
Return On Equity ◽  
Asset Turnover ◽  
Distribution Line ◽  
Price Movements

Fundamental and technical analysis is used by analysis to predict the trend ofstock price and trading volume. Studies conducted aimed to determine the effect of fundamental analysis to technical analysis. Combining two forms of analysis can produce a more accurate prediction of the stock price movement of listed cement companies in Indonesia Stock Exchange. Research experts indicate that the fundamental and technical analysis can be used independently with the ability to predict stock price movements. This study combines both analysis in a model that can provide a more robust predictive capability in the Company's share price movements of cement. Fundamental analysis is the economy wide scope, one of the predictions of financial performance. In this study the total asset turnover, return on assets and return on equityto determine which stocks are pretty good. While technical analysis is usedaccumulation distribution line that has a better ability to predict future stock prices because the data contained technical stock price and trading volume to determine when to buy and sell momentum. These results indicate that the total asset turnover, return on assets and return on equity significantly influence the accumulation distribution line. While the individual that the return on equity has no significant effect. The results of this study are expected to improve knowledge for the readers, especially investors in order to obtain optimal benefits.

scholarly journals ВИКОРИСТАННЯ СИСТЕМ ШТУЧНОГО ІНТЕЛЕКТУ У ЗАДАЧАХ ПРОГНОЗУВАННЯ ФІНАНСОВИХ ІНДЕКСІВ: ОГЛЯД НАУКОВИХ ДЖЕРЕЛ

2020 ◽  
pp. 108-117
Author(s):  
Николай Ярославович Кушнир ◽  
Катерина Токарева
Keyword(s):  
Artificial Intelligence ◽  
Time Series ◽  
Large Scale ◽  
Scientific Literature ◽  
Financial Time Series ◽  
Technical Analysis ◽  
Soft Data ◽  
Scientific Publications ◽  
Financial Time ◽  
Stock Indices

The paper investigates methods of artificial intelligence in the prognostication and analysis of financial data time series. The usage of well-known methods of artificial intelligence in forecasting and analysis of time series is investigated. Financial time series are inherently highly dispersed, complex, dynamic, nonlinear, nonparametric, and chaotic nature, so large-scale and soft data mining techniques should be used to predict future values. As the scientific literature superficially describes the numerous artificial intelligence algorithms to be used in forecasting financial time series, a detailed analysis of the relevant scientific literature was conducted in scientometric databases Scopus, Science Direct, Google Scholar, IEEExplore, and Springer. It is revealed that the existing scientific publications do not contain a comprehensive analysis of literature sources devoted to the use of artificial intelligence methods in forecasting stock indices. Besides, the analyzed works, which are related in detail to the object of our study, have a limited scope because they focus on only one family of artificial intelligence algorithms, namely artificial neural networks. It was found that the analysis of the use of artificial intelligence systems should be based on two well-known approaches to predicting the behavior of financial markets: fundamental and technical analysis. The first approach is based on the study of economic factors that have a possible impact on market dynamics and more common in long-term planning. Representatives of technical analysis, on the other hand, argue that the price already contains all the fundamental factors that affect it. In this regard, technical analysis involves forecasting the dynamics of price changes based on the analysis of their change in the past, ie time series. Although today there are many developed models for forecasting stock indices using artificial intelligence algorithms, in the scientific literature there is no established methodology that defines the main elements and stages of the algorithm for forecasting financial time series. Therefore, this study has improved the methodology for forecasting financial time series.

scholarly journals Analisis Fundamental dan Teknikal terhadap Harga Saham pada Perusahaan Utilitas dan Transportasi

2021 ◽  
Vol 31 (2) ◽  
pp. 499
Author(s):  
Christina Christina ◽  
Sulastri Halim ◽  
Valentina Angrensia ◽  
Arie Pratania Putri
Keyword(s):  
Stock Price ◽  
Trading Volume ◽  
Technical Analysis ◽  
Fundamental Analysis ◽  
Current Ratio ◽  
Book Value ◽  
Asset Turnover ◽  
Equity Ratio

The purpose of this research is to known if fundamental and technical analysis could affect the price of stock. Furthermore each of analysis have several factor to determine the stock price, as for fundamental analysis we will use current ratio, DER, and ROA while for the technical analysis we will use IHSG and trading volume. From the population of 69 company we pick 18 sample that suit the analysis, for the regression it will use multiply 5 because of the independent variabel. According to the partial research, there are only two factor that have significant impact to the price of stock, it is DER And ROA, as for the rest of the factor it don’t really affect the price. Simultaneously the five variabel shown the effect to the stock price according to result of adjused r square amount of 29.1% . From the variation of stock price according to the variabel such as CR, DER , ROA. IHSG and trading volume, while the rest 70.9% affected by other factor such as company asset turnover, price earning ratio and price book value. Keywords: Current Ratio; Debt to Equity Ratio; IDX Composite; Return On Asset; Stock Price; Trading Volume.

scholarly journals Analyzing Performance of Technical Analysis on Stock Markets

2020 ◽  
pp. 53-62
Author(s):  
A. Stavytskyy ◽  
V. Taraba
Keyword(s):  
Hong Kong ◽  
Historical Data ◽  
Arima Model ◽  
Technical Analysis ◽  
Optimal Parameters ◽  
Arima Models ◽  
Optimal Model ◽  
Stock Indices ◽  
Analysis Methods ◽  
The Stability

The article analyzes the profitability of technical analysis methods for the seven stock indices during the last ten years. According to the analysis, the profitability of technical analysis has increased recently due to changes in market conditions. However, the efficiency of technical analysis methods was much lower during 2010-2018. The analysis showed that technical analysis methods demonstrated best results on the Chinese, Indian, and Hong Kong stock indices, the worst – on the American, European, and Japanese stock indices. However, the stability of these methods is quite low: their profitability varies greatly with the change of the sample. The issue of aggregation of technical analysis signals and ARIMA-model signals is also considered in this paper. The optimal parameters for the technical analysis methods were selected by testing on historical data; optimal ARIMA models were selected for each index. For 3 out of 7 indices the optimal model is WN (white noise). Most technical analysis methods showed poor results on the American (S&P 500) and European (Euronext 100) stock indices (except for the last two years). The results can be used to develop trading strategies.The analysis showed that technical analysis methods demonstrated best results on the Chinese, Indian, and Hong Kong stock indices, the worst – on the American, European, and Japanese stock indices. However, the stability of these methods is quite low: their profitability varies greatly with the change of the sample. The issue of aggregation of technical analysis signals and ARIMA-model signals is also considered in this paper. The optimal parameters for the technical analysis methods were selected by testing on historical data; optimal ARIMA models were selected for each index. For 3 out of 7 indices the optimal model is WN (white noise). Most technical analysis methods showed poor results on the American (S&P 500) and European (Euronext 100) stock indices (except for the last two years). The results can be used to develop trading strategies.

scholarly journals Stock market prediction using technical analysis

2006 ◽  
Vol 51 (170) ◽  
pp. 125-146 ◽  
Author(s):  
Aleksandra Bradic-Martinovic
Keyword(s):  
Stock Market ◽  
Trading Volume ◽  
Moving Average ◽  
Supply And Demand ◽  
Wave Theory ◽  
Technical Analysis ◽  
Ease Of Use ◽  
Future Price ◽  
Mathematical Methods ◽  
Price Movements

Technical analysis (TA) is a form of analyzing market encompassing supply and demand of securities according to the study of their prices and trading volume. Using the appropriate methods, TA aims to identify price movements in the stock market, futures or currencies. In short, TA analysis is the process by which "future price movements are formulated according to the price history". TA originates from the work of Charles Dow and his conclusions about the global behavior of the market, as well as from Elliot Wave Theory. Dow did not regard its theory as a tool for stock market movement prediction, nor as a guide for investors, but as a kind of barometer of general market movements. The term TA methods encompasses all the methods used in tracking prices aiming to clearly predict future events. Many different methods, mainly statistical, are used in technical analysis, the most popular ones being: establishing and following trends using moving average, recognizing price momentum, calculating indicators and oscillators, as well as cycle analysis (structure indicators). It is also necessary to point out that TA is not a science in the true meaning of the term, and that methods it uses frequently deviate from the conventional manner of their use. The main advantage of these methods is their relative ease of use, aiming to give as clear picture as possible of price movements, while at the same time avoiding the use of complicated and complex mathematical methods. The reason for this is simple and is reflected in the dynamics of financial markets, where changes occur during short periods of time and where prompt decision-making is of vital importance.

Technical analysis, trading volume and market efficiency: evidence from an emerging market

1997 ◽  
Vol 7 (4) ◽  
pp. 361-365 ◽  
Author(s):  
A. Antoniou ◽  
N. Ergul ◽  
P. Holmes ◽  
R. Priestley
Keyword(s):  
Market Efficiency ◽  
Trading Volume ◽  
Emerging Market ◽  
Technical Analysis

scholarly journals Stock market activity and Google Trends: the case of a developing economy

2019 ◽  
Vol 21 (2) ◽  
pp. 191-212
Author(s):  
Vinh Xuan Bui ◽  
Hang Thu Nguyen
Keyword(s):  
Stock Market ◽  
Trading Volume ◽  
Design Methodology ◽  
Investor Attention ◽  
Content Type ◽  
Market Activity ◽  
Search Terms ◽  
Search Volume ◽  
Stock Indices ◽  
Google Search

Purpose The purpose of this paper is to investigate the impacts of investor attention on stock market activity. Design/methodology/approach The authors employed the Google Search Volume (GSV) Index, a direct and non-traditional proxy for investor attention. Findings The results indicate a strong correlation between GSV and trading volume – a traditional measure of attention – proving the new measure’s reliability. In addition, market-wide attention increases both stock illiquidity and volatility, whereas company-level attention shows mixed results, driving illiquidity and volatility in both directions. Originality/value To the best of the authors’ knowledge, Nguyen and Pham’s (2018) study has been the only previous study identifying investor attention in Vietnam by using GSV as a proxy and examining the impacts of broad search terms about the macroeconomy on the stock market as a whole – on stock indices’ movements. The paper will contribute to this by quantifying GSV impacts on each stock individually.

Back to the Future. A Behavioural Perspective on Technical Analysis into PIGS Countries

2015 ◽  
Vol 4 (1) ◽  
pp. 67-87
Author(s):  
Papathanasiou Spyros ◽  
Vasiliou Dimitrios ◽  
Eriotis Nikolaos
Keyword(s):  
Technical Analysis ◽  
The Future
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