scholarly journals Universal Features of Price Formation in Financial Markets: Perspectives From Deep Learning

2018 ◽  
Author(s):  
Justin Sirignano ◽  
Rama Cont
Keyword(s):  
Deep Learning ◽  
Financial Markets ◽  
Price Formation

A guide on empirical tests of the EMH

2019 ◽  
Vol 18 (2) ◽  
pp. 268-295
Author(s):  
David Peón ◽  
Manel Antelo ◽  
Anxo Calvo
Keyword(s):  
Financial Markets ◽  
Design Methodology ◽  
Recent Literature ◽  
Asset Price ◽  
Efficient Market Hypothesis ◽  
Price Formation ◽  
Content Type ◽  
Economic Fundamentals ◽  
Empirical Tests ◽  
Available Information

Purpose The efficient market hypothesis (EMH) states that asset prices in financial markets always reflect all available information about economic fundamentals. The purpose of this paper is to provide a guide as to which predictions of the EMH seem to be borne out by empirical evidence. Design/methodology/approach Rather than following the classic three groups of tests for the different forms of EMH that are common in the literature, the authors consider how the two alternative definitions of the EMH and the joint hypothesis problem impact on the tests and leave the controversy unsolved. The authors briefly report the antecedents, the main theoretical and empirical contributions and recent literature on each type of tests. Findings Eventually, as a summary for each type of tests, the authors provide a critical view on the main sources of acrimony between the alternative schools of thought in understanding asset price formation. Originality/value The paper may be seen as an up-to-date introductory review for researchers on the different tests of the EMH performed, and for newcomers to understand the key sources of acrimony between rationalists and behaviorists.

scholarly journals Using deep learning to detect price change indications in financial markets

2017 ◽  
Author(s):  
Avraam Tsantekidis ◽  
Nikolaos Passalis ◽  
Anastasios Tefas ◽  
Juho Kanniainen ◽  
Moncef Gabbouj ◽  
...  
Keyword(s):  
Deep Learning ◽  
Financial Markets ◽  
Price Change

scholarly journals INVESTIGATION OF THE HERDING AND CONTRARIAN BEHAVIOUR OF INVESTORS

2021 ◽  
Author(s):  
Nijolė MAKNICKIENĖ ◽  
Jovita MASĖNAITĖ ◽  
Viktorija STASYTYTĖ ◽  
Raimonda MARTINKUTĖ-KAULIENĖ
Keyword(s):  
Deep Learning ◽  
Financial Markets ◽  
Negative Impact ◽  
Investment Decision ◽  
Investor Sentiment ◽  
Technical Analysis ◽  
Financial Instruments ◽  
Decision Strategy ◽  
Financial Decisions ◽  
Investor Behaviour

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.

Research on Intelligent Forecasting of Financial Market Parameter Time Series Based on Deep Learning

2021 ◽  
Vol 7 (5) ◽  
pp. 4596-4607
Author(s):  
Enyang Zhu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Financial Markets ◽  
Intelligent System ◽  
System Modeling ◽  
Layer By Layer ◽  
Healthy Development ◽  
The Neural Network ◽  
Deep Integration

Objectives: Deep learning has become the most representative and potential intelligent system modeling technology in artificial intelligence. However, the complexity of financial markets goes far beyond all economic games. Methods: This paper is devoted to the feasibility and efficiency of the deep-integration neural network model as one of the main paradigms of in-depth learning in the intelligent prediction of financial time. A prediction model of stack self-coding neural network composed of bottom stack self-coding and top regression neurons is proposed. Results: Firstly, the self-encoder unsupervised learning mechanism is used to identify and learn the time series, and the layers of the neural network are learned greedy layer by layer. Then the stack self-encoder is extended to the SAEP model with supervised mechanism, and the parameters learned by SAE are used. Used to initialize the neural network, and finally use the supervised learning to fine-tune the weights. Conclusion: The research results show that the model provides effective financial planning and decision-making basis for financial forecasting, maintains the healthy development of financial markets, and maximizes the benefits of profit-making institutions.

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