TECHNICAL ANALYSIS OF FINANCIAL MARKETS USING MARKET INDICATORS ON THE EXAMPLE OF SAMSUNG SHARES

2021 ◽  
pp. 34-34
Author(s):  
K. V. Ketova ◽  
A.V. Shishova ◽  
S.V. Dorofeeva
Author(s):  
Nijolė MAKNICKIENĖ ◽  
Jovita MASĖNAITĖ ◽  
Viktorija STASYTYTĖ ◽  
Raimonda MARTINKUTĖ-KAULIENĖ

Purpose – The paper analyses two different paradigms of investor behaviour that exist in the financial mar-ket – the herding and contrarian behaviour. The main objective of the paper is to determine which pattern of investor behaviour better reflects the real changes in the prices of financial instruments in the financial markets. Research methodology – Algorithms of technical analysis, deep learning and classification of sentiments were used for the research; data of positions held by investors were analysed. Data mining was performed using “Tweet Sentiment Visualization” tool. Findings – The performed analysis of investor behaviour has revealed that it is more useful to ground financial decisions on the opinion of the investors contradicting the majority. The analysis of the data on the positions held by investors helped to make sure that the herding behaviour could have a negative impact on investment results, as the opinion of the majority of investors is less in line with changes in the prices of financial instruments in the market. Research limitations – The study was conducted using a limited number of investment instruments. In the future, more investment instruments can be analysed and additional forecasting methods, as well as more records in social networks can be used. Practical implications – Identifying which paradigm of investor behaviour is more beneficial to rely on can offer ap-propriate practical guidance for investors in order to invest more effectively in financial markets. Investors could use investor sentiment data to make practical investment decisions. All the methods used complement each other and can be combined into one investment decision strategy. Originality/Value – The study compared the ratio of open positions not only with real price changes but also with data obtained from the known technical analysis, deep learning and sentiment classification algorithms, which has not been done in previous studies. The applied methods allowed to achieve reliable and original results.


2017 ◽  
Vol 10 (6) ◽  
pp. 1
Author(s):  
Eugenio D’Angelo ◽  
Giulio Grimaldi

The purpose of this paper is to investigate the capability of a technical analysis to be used as a valuable tool in forecasting financial markets. After discussing the primary theoretical and methodological differences that oppose the fundamental analysis and technical analysis and introducing the Elliott waves theory, the paper focuses on the results obtained after applying this method to the currency market. The results show that during the period from 2009-2015, the exchange rate between the U.S. dollar and euro could be forecasted with great accuracy. A potential future pattern is also proposed for the exchange rate beginning in March 2017. The research confirmed the usefulness of Elliott’s model for predicting currency markets, and the effectiveness of the fundamental analysis theories generally adopted for academic studies was evaluated.


2018 ◽  
Vol 33 (1) ◽  
pp. 9-18 ◽  
Author(s):  
Christophe Schinckus

This article deals with the increasing computerization of the financial markets and the consequences of such process on our ability to collect information about financial prices. The concept of information is at the heart of financial economics simply because this notion is a precondition for all investments. Since financial prices characterize an agreement on a transaction between two counterparties, they understandably became a key informational indicator for decision. This article will analyse the increasing computerization of financial sphere by discussing the recent emergence of what is called a “flash crash” and its impact on the traditional ways of collecting information in finance (technical analysis, fundamental analysis and statistical approach). I argue that the growing computerization of financial markets generated a “hyper-reality” in which financial prices do not refer to “something” anymore implying a revision of our usual way of defining/using the notion of information.


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