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.

scholarly journals Relationships among the Fossil Fuel and Financial Markets during the COVID-19 Pandemic: Evidence from Bayesian DCC-MGARCH Models

2021 ◽  
Vol 14 (1) ◽  
pp. 51
Author(s):  
Chaofeng Tang ◽  
Kentaka Aruga
Keyword(s):  
Financial Markets ◽  
Fossil Fuel ◽  
Price Change ◽  
Clean Energy ◽  
Multivariate Garch ◽  
Conditional Correlation ◽  
Daily Data ◽  
Oil And Natural Gas ◽  
Negative Relationships

This study examined how the relationships among the fossil fuel, clean energy stock, gold, and Bitcoin markets have changed since the COVID-19 pandemic took place for hedging the price change risks in the fossil fuel markets. We applied the Bayesian Dynamic Conditional Correlation-Multivariate GARCH (DCC-MGARCH) models using US daily data from 2 January 2019 to 26 February 2021. Our results suggest that the fossil fuel (WTI crude oil and natural gas) and financial markets (clean energy stock, gold, and Bitcoin) generally had negative relationships in 2019 before the pandemic prevailed, but they became positive for a while in mid-2020, alternating between positive (0.8) and negative values (−0.8). As it is known that negative relationships are required among assets to hedge the risk of price changes, this implies that stakeholders need to be cautious in hedging the risk across the fossil fuel and financial markets when a crisis like COVID-19 occurs. However, our study also revealed that such negative relationships only lasted for three to six months, suggesting that the effects of the pandemic were short term and that stakeholders in the fossil fuel markets could cross hedge with the financial markets in the long term.

scholarly journals Artificial Intelligence Implementations on the Blockchain. Use Cases and Future Applications

2019 ◽  
Vol 11 (8) ◽  
pp. 170 ◽  
Author(s):  
Konstantinos Sgantzos ◽  
Ian Grigg
Keyword(s):  
Deep Learning ◽  
Financial Markets ◽  
Smart Cities ◽  
Proper Selection ◽  
Small Communities ◽  
Secure Storage ◽  
Quantum Leap ◽  
Learning Data ◽  
Selection Of

An exemplary paradigm of how an AI can be a disruptive technological paragon via the utilization of blockchain comes straight from the world of deep learning. Data scientists have long struggled to maintain the quality of a dataset for machine learning by an AI entity. Datasets can be very expensive to purchase, as, depending on both the proper selection of the elements and the homogeneity of the data contained within, constructing and maintaining the integrity of a dataset is difficult. Blockchain as a highly secure storage medium presents a technological quantum leap in maintaining data integrity. Furthermore, blockchain’s immutability constructs a fruitful environment for creating high quality, permanent and growing datasets for deep learning. The combination of AI and blockchain could impact fields like Internet of things (IoT), identity, financial markets, civil governance, smart cities, small communities, supply chains, personalized medicine and other fields, and thereby deliver benefits to many people.

scholarly journals Investigating the Effect of Noise Elimination on LSTM Models for Financial Markets Prediction Using Kalman Filter and Wavelet Transform

2022 ◽  
Vol 19 ◽  
pp. 432-441
Author(s):  
Amin Karimi Dastgerdi ◽  
Paolo Mercorelli
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Kalman Filter ◽  
Wavelet Transform ◽  
Financial Markets ◽  
Financial Time Series ◽  
Considerable Effect ◽  
Noise Elimination ◽  
Financial Time ◽  
Learning Techniques

Predicting financial markets is of particular importance for investors who intend to make the most profit. Analysing reasonable and precise strategies for predicting financial markets has a long history. Deep learning techniques include analyses and predictions that can assist scientists in discovering unknown patterns of data. In this project, application of noise elimination techniques such as Wavelet transform and Kalman filter in combination of deep learning methods were discussed for predicting financial time series. The results show employing noise elimination techniques such as Wavelet transform and Kalman filter, have considerable effect on performance of LSTM neural network in extracting hidden patterns in the financial time series and can precisely predict future actions in these markets.

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