A reality check on trading rule performance in the cryptocurrency market: Machine learning vs. technical analysis

2020 ◽  
pp. 101655
Author(s):  
Dan-Gabriel ANGHEL
Keyword(s):  
Machine Learning ◽  
Technical Analysis ◽  
Trading Rule ◽  
Reality Check

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.

Data Snooping on Technical Analysis: Evidence from the Taiwan Stock Market

2011 ◽  
Vol 14 (02) ◽  
pp. 195-212 ◽  
Author(s):  
Cheng-Wei Chen ◽  
Chin-Sheng Huang ◽  
Hung-Wei Lai
Keyword(s):  
Stock Market ◽  
Transaction Costs ◽  
Market Efficiency ◽  
Technical Analysis ◽  
The Other ◽  
Trading Rule ◽  
Data Snooping ◽  
Empirical Results

The main purpose of this paper is to investigate the validity and predictability of technical analysis in the Taiwan stock market. Bootstrapped tests of White (2000) and of Hansen (2005) are employed to ascertain whether there exists a superior trading rule among two broadly used sets of technical analysis. One coming from Brock et al. (1992) and the other from Sullivan et al. (1999). Moreover, this study brings together powerful bootstrapped tests along with two institutional adjustments to ascertain the efficacy of technical analysis: (1) non-synchronous trading and (2) transaction costs. The empirical results indicate that this triad-data snooping, non-synchronous trading and transaction costs, has a great impact on the performance of technical analysis. In fact, the Taiwan stock market stands for market efficiency, and economical profits cannot be rendered from technical analysis in this market.

Stock Market Prediction Using Machine Learning

2020 ◽  
Vol 17 (4) ◽  
pp. 1584-1589
Author(s):  
J. Shiva Nandhini ◽  
Chitrak Bari ◽  
Gareja Pradip
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Learning Algorithm ◽  
Market Price ◽  
Technical Analysis ◽  
Weak Form ◽  
Efficient Market Hypothesis ◽  
Machine Learning Techniques ◽  
Fundamental Analysis ◽  
Stock Market Prediction

The basic tool aimed at increasing the rate of investor’s interest in stock markets is by developing a vibrant application for analyzing and predicting stock market prices. In this report we explain, the development and implementation of a stock market price prediction application using machine learning algorithm. In this report, we try to analyze existing and new methods of stock market prediction. We take three different approaches for solving the problem: Fundamental analysis, Technical Analysis and The application of Machine Learning. We found evidence in support of the weak form of the Efficient Market Hypothesis. We can use Fundamental Analysis and Machine Learning to guide an investor’s decisions. We demonstrate a common flaw in Technical Analysis methodology to show that it produces limited useful information. Based on our findings, algorithmic trading programs are developed and simulated using Quant. During the past few decades, various machine learning techniques have been applied to study the highly theoretical and speculative nature of stock market by capturing and using repetitive patterns. Different companies use different types of analysis tools for forecasting and the main aim is the accuracy, with which they predict which set of stocks would yield the maximum amount of profit.

scholarly journals Development of a Stock Price Prediction Framework for Intelligent Media and Technical Analysis

2022 ◽  
Vol 12 (2) ◽  
pp. 719
Author(s):  
Sibusiso T. Mndawe ◽  
Babu Sena Paul ◽  
Wesley Doorsamy
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Technical Analysis ◽  
Memory Model ◽  
Short Term ◽  
Term Memory ◽  
Linear Discriminant ◽  
Stock Price Prediction ◽  
Closing Price ◽  
Long Short Term Memory

Equity traders are always looking for tools that will help them maximise returns and minimise risk, be it fundamental or technical analysis techniques. This research integrates tools used by equity traders and uses them together with machine learning and deep learning techniques. The presented work introduces a South African-based sentiment classifier to extract sentiment from new headlines and tweets. The experimental work uses four machine learning models for fundamental analysis and six long short-term memory model architectures, including a developed encoder-decoder long short-term memory model for technical analysis. Data used in the experiments is mined and collected from news sites, tweets from Twitter and Yahoo Finance. The results from 2 experiments show an accuracy of 96% in predicting one of the major telecommunication companies listed on the JSE closing price movement while using the linear discriminant analysis model and an RMSE of 0.023 in predicting a significant telecommunication company closing price using encoder-decoder long short-term memory. These findings reveal that the sentiment feature contains an essential fundamental value, and technical indicators also help move closer to predicting the closing price.

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