Adaptive Technical Analysis in the Financial Markets Using Machine Learning: a Statistical View

2004 ◽  
pp. 1-16 ◽  
Author(s):  
David Edelman ◽  
Pam Davy
Keyword(s):  
Machine Learning ◽  
Financial Markets ◽  
Technical Analysis

Ensemble of Technical Analysis and Machine Learning for Market Trend Prediction

2018 ◽  
Author(s):  
Andrea Picasso Ratto ◽  
Simone Merello ◽  
Luca Oneto ◽  
Yukun Ma ◽  
Lorenzo Malandri ◽  
...  
Keyword(s):  
Machine Learning ◽  
Technical Analysis ◽  
Trend Prediction

scholarly journals Machine learning applied in the stock market through the Moving Average Convergence Divergence (MACD) indicator

2020 ◽  
Vol 17 (4) ◽  
pp. 44-60
Author(s):  
Alberto Antonio Agudelo Aguirre ◽  
Ricardo Alfredo Rojas Medina ◽  
Néstor Darío Duque Méndez
Keyword(s):  
Machine Learning ◽  
Genetic Algorithms ◽  
Stock Market ◽  
Moving Average ◽  
Asset Price ◽  
Technical Analysis ◽  
Investment Strategies ◽  
Historical Data Analysis ◽  
Financial Asset Returns ◽  
Technical Indicator

The implementation of tools such as Genetic Algorithms has not been exploited for asset price prediction despite their power, robustness, and potential application in the stock market. This paper aims to fill the gap existing in the literature on the use of Genetic Algorithms for predicting asset pricing of investment strategies into stock markets and investigate its advantages over its peers Buy & Hold and traditional technical analysis. The Genetic Algorithms strategy applied to the MACD was carried out in two different validation periods and sought to optimize the parameters that generate the buy-sell signals. The performance between the machine learning-based approach, technical analysis with the MACD and B&H was compared. The results suggest that it is possible to find optimal values of the technical indicator parameters that result in a higher return on investment through Genetic Algorithms, beating the traditional technical analysis and B&H by around 4%. This study offers a new insight for practitioners, traders, and finance researchers to take advantage of Genetic Algorithms for trading rules application in forecasting financial asset returns under a more efficient and robust methodology based on historical data analysis.

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.

scholarly journals Applications of Machine Learning to Estimating the Sizes and Market Impact of Hidden Orders in the BRICS Financial Markets

2020 ◽  
Vol 11 (1) ◽  
pp. 28
Author(s):  
Witness MAAKE ◽  
Terence VAN ZYL
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Financial Markets ◽  
Random Forests ◽  
Price Impact ◽  
Average Price ◽  
Market Impact ◽  
Far Right ◽  
Hidden Orders

The research aims to investigate the role of hidden orders on the structure of the average market impact curves in the five BRICS financial markets. The concept of market impact is central to the implementation of cost-effective trading strategies during financial order executions. The literature is replicated using the data of visible orders from the five BRICS financial markets. We repeat the implementation of the literature to investigate the effect of hidden orders. We subsequently study the dynamics of hidden orders. The research applies machine learning to estimate the sizes of hidden orders. We revisit the methodology of the literature to compare the average market impact curves in which true hidden orders are added to visible orders to the average market impact curves in which hidden orders sizes are estimated via machine learning. The study discovers that: (1) hidden orders sizes could be uncovered via machine learning techniques such as Generalized Linear Models (GLM), Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forests (RF); and (2) there exist no set of market features that are consistently predictive of the sizes of hidden orders across different stocks. Artificial Neural Networks produce large R2 and small Mean Squared Error on the prediction of hidden orders of individual stocks across the five studied markets. Random Forests produce the most appropriate average price impact curves of visible and estimated hidden orders that are closest to the average market impact curves of visible and true hidden orders. In some markets, hidden orders produce a convex power-law far-right tail in contrast to visible orders which produce a concave power-law far-right tail. Hidden orders may affect the average price impact curves for orders of size less than the average order size; meanwhile, hidden orders may not affect the structure of the average price impact curves in other markets. The research implies ANN and RF as the recommended tools to uncover hidden orders.

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