Technical Analysis Indicators Research Articles

Stock price trend prediction with long short-term memory neural networks

Stock market is an immensely complex, chaotic and dynamic environment. Thus, the task of predicting changes in such an environment becomes challenging with regards to its accuracy. A number of approaches have been adopted to take on that challenge and machine learning has been at the crux in many of them. There are plenty of examples of algorithms based on machine learning yielding satisfactory results for such type of prediction. This paper presents the usage of long short-term memory (LSTM) networks in this scenario, to predict future trends of stock market prices based on the patterns from price history, paired with technical analysis indicators. To achieve this, a model has been built, and a series of experiments have been conducted through a number of parameters and the results were analysed against predefined metrics to assess if this algorithm presents any improvements in front of other machine learning methods and strategies. Also, a comparative study is presented which analyses popularly used optimisers and error schemes to check which given optimiser yields the best results. The results obtained are promising and presented a reasonably accurate prediction for the rise or fall of a particular stock in the near future.

An Agent-Based Artificial Market Model for Studying the Bitcoin Trading

The objective of this paper is to simulate the trading of the currency pair BTC/USD, investigating through the theory of the genetic algorithms the best sets of trading strategies, simulating through a realistic order book the bitcoin price formation, and reproducing a bitcoin price series that exhibits some stylized facts found in real-time price series. In this artificial market model two kinds of agents, Chartists and Random traders, perform trading. Chartists trade through the application of trading rules. Specifically, a part of Chartists trades applying the best sets of trading rules selected by a genetic algorithm that simulates a trading system, based on four technical analysis indicators, searching for parameters of each indicator that guarantee the highest profits in the training period; the remaining part trades applying trading rules choosing their parameters in a random way. On the contrary random trader's trade without applying any trading strategy, issuing in a random way sell or buy orders. Results show that the best sets of rules found to guarantee the highest profits both in the training and in the testing periods, and perform well also in the artificial market model where the Chartists who adopt the best sets of trading rules are able to achieve higher profits.

The Role of Technical Analysis Indicators over Equity Market (NOMU) with R Programing Language

The stock market is a potent, fickle and fast-changing domain. Unanticipated market occurrences and unstructured financial information complicate predicting future market responses. A tool that continues to be advantageous when forecasting future market trends in a global aspect is correlation analysis to significant market events. Data analysis can be used for the difficult task of making stock market forecasts in case the stock price rises or fall. A high number of automated exchanges in the stock market are done with advanced prognostic software. Data analysis is centered on the main idea that previously recorded data is used to predict future patterns. This advancement is aimed speculators in pinpointing hidden data in real evidence that would give them some financial foresight when considering their ventures of choice. Data analysis can be applied in order to predict the rises and falls of stocks in the future. This paper aims to critically investigate, develop and judge the different systems that predict and assess future stock trades as these systems have their own various process to foretell the fluctuations in the costs of stocks. Several different technical analysis indicators have been applied in this study including; Chaikin Money Flow (CMF), Stochastic Momentum Index (SMI), Relative Strength Index (RSI), Bollinger Bands (BBands), and Aroon (Aroon) indicator. The experiments have been conducted using R programing language over two companies’ real-world datasets obtained for two years from Saudi stock market (NOMU) which is a parallel stock market with lighter listing requirements that serves as an alternative platform for companies to go public in the main market. To the best of our knowledge, this is the first work to be conducted in NOMU stock market.

Stock price trend prediction with long short-term memory neural networks

Stock market is an immensely complex, chaotic and dynamic environment. Thus, the task of predicting changes in such an environment becomes challenging with regards to its accuracy. A number of approaches have been adopted to take on that challenge and machine learning has been at the crux in many of them. There are plenty of examples of algorithms based on machine learning yielding satisfactory results for such type of prediction. This paper presents the usage of long short-term memory (LSTM) networks in this scenario, to predict future trends of stock market prices based on the patterns from price history, paired with technical analysis indicators. To achieve this, a model has been built, and a series of experiments have been conducted through a number of parameters and the results were analysed against predefined metrics to assess if this algorithm presents any improvements in front of other machine learning methods and strategies. Also, a comparative study is presented which analyses popularly used optimisers and error schemes to check which given optimiser yields the best results. The results obtained are promising and presented a reasonably accurate prediction for the rise or fall of a particular stock in the near future.

Indicators of Technical Analysis on the Basis of Moving Averages as Prognostic Methods in the Food Industry

Indicators of Technical Analysis on the Basis of Moving Averages as Prognostic Methods in the Food Industry

Fundamental Analysis VS Technical analysis: The Comparison of Two Analysis in Malaysia Stock Market

This study is conducted to analyse the credibility of the fundamental analysis and technical analysis on predicting the stock return and compare both models to determine which model is more credible to be used as a good trading strategy by investors. The study is based on 80 companies selected from Bursa Malaysia in the food manufacturing industry in the main market from the year of 2012 to 2016. The stock return is used to measure whether both analyses are able to forecast and generate the positive return. Net profit margin, price earnings ratio and total asset turnover are used as fundamental indicators while moving average convergence divergence is used as technical analysis indicators. In order to test the significance of both model on stock return, panel regression models are applied in this study. The result shows that although both model can be used to generate positive return, technical analysis did not outperform fundamental analysis in the food manufacturing industry in Bursa Malaysia.

Hybrid Models Combining Technical and Fractal Analysis with ANN for Short-Term Prediction of Close Values on the Warsaw Stock Exchange

This paper presents new methods and models for forecasting stock prices and computing hybrid models, combining analytical and neural approaches. First, technical and fractal analyses are conducted and selected stock market indices calculated, such as moving averages and oscillators. Next, on the basis of these indices, an artificial neural network (ANN) provides predictions one day ahead of the closing prices of the assets. New technical analysis indicators using fractal modeling are also proposed. Three kinds of hybrid model with different degrees of fractal analysis were considered. The new hybrid modeling approach was compared to previous ANN-based prediction methods. The results showed that the hybrid model with fractal analysis outperforms other models and is more robust over longer periods of time.

Dynamically Adjustable Moving Average (AMA’) technical analysis indicator to forecast Asian Tigers’ futures markets

To determine the predictability of futures time series, the daily stock market indices’ futures returns from the Asia Tigers countries, namely Hong Kong’s Hang Seng Futures (HSF), South Korea’s KOSPI Futures (KOSPIF), Singapore’s SiMSCI Futures (SiMSCIF) and Taiwan’s TAIEX Futures (TAIEXF) from 2006 to 2013 are examined for profitability results using technical analysis indicators. A dynamic volatility indicator, named Adjustable Moving Average (AMA’) is used as a trade timing devise to seek returns above the threshold buy and hold strategy. Using the information learnt during this in-sample period, further tests are conducted on an out-of-sample period, 2014–2015 to validate the viability of AMA’. AMA’ adjusts to the prevailing market conditions, to avoid some whipsaws (trading losses) in range trading and capture a larger portion of profit by entering into the new trends early. Using the trading signals from AMA’ and the other moving averages rules, evidence of returns after transaction costs above those of the threshold passive buy and hold strategy are found in these time series’ returns, especially more so for AMA’. The results here suggest that it is worthwhile to investigate the profitability of moving averages trading rules, especially the more adjustable ones.

Analysis and classification of technical analysis indicators by support vector machines

The search for models which can accurately forecast the market trend has developed over the past decades. Technical indicators and oscillators are the most usually employed inputs in the prediction models. These inputs basically rely on prices and the evolution of the index itself, which may cause some problems like multicolinearity and autocorrelation, in the case of linear models, or overoptimization and noise, in the case of neural networks. This paper proposes filtering the inputs to be employed in the models. To this end, their impact on the forecast will be analysed. A support vector machine will be used to this end, in order to characterize both inputs (indicators and oscillators) and output (market trend). Doing this, it can be assessed whether the relationship between the different inputs and the market trend offers relevant information regarding the contribution of the inputs in the prediction process and whether this contribution remains constant over time. Those inputs will be selected, which obtain more stable forecasts in order to obtain more consistent predictions.

An Ensemble of LSTM Neural Networks for High-Frequency Stock Market Classification

We propose an ensemble of Long-Short Term Memory (LSTM) Neural Networks for intraday stock predictions, using a large variety of Technical Analysis indicators as network inputs. The proposed ensemble operates in an online way, weighting the individual models proportionally to their recent performance, which allows us to deal with possible non-stationarities in an innovative way. The performance of the models is measured by Area Under the Curve of the Receiver Operating Characteristic. We evaluate the predictive power of our model on several US large-cap stocks and benchmark it against Lasso and Ridge logistic classifiers. The proposed model is found to perform better than the benchmark models or equally weighted ensembles.

Returns from neural network enhanced technical analysis indicator: A study on crude oil futures

Returns from neural network enhanced technical analysis indicator: A study on crude oil futures

A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting

In this paper we propose and validate a trading rule based on flag pattern recognition, incorporating important innovations with respect to the previous research. Firstly, we propose a dynamic window scheme that allows the stop loss and take profit to be updated on a quarterly basis. In addition, since the flag pattern is a trend-following pattern, we have added the EMA indicator to filter trades. This technical analysis indicator is calculated both for 15-min and 1-day timeframes, which enables short and medium terms to be considered simultaneously. We also filter the flags according to the price range on which they are developed and have limited the maximum loss of each trade to 100 points. The proposed methodology was applied to 91,309 intraday observations of the DJIA index, considerably improving the results obtained in the previous proposals and those obtained by the buy & hold strategy, both for profitability and risk, and also after taking into account the transaction costs. These results seem to challenge market efficiency in line with other similar studies, in the specific analysis carried out on the DJIA index and is also limited to the setup considered.

Mean-semivariance portfolio optimization with multiobjective evolutionary algorithms and technical analysis rules

Recent work has been devoted to study the use of multiobjective evolutionary algorithms (MOEAs) in stock portfolio optimization, within a common mean-variance framework. This article proposes the use of a more appropriate framework, mean-semivariance framework, which takes into account only adverse return variations instead of overall variations. It also proposes the use and comparison of established technical analysis (TA) indicators in pursuing better outcomes within the risk-return relation. Results show there is some difference in the performance of the two selected MOEAs – non-dominated sorting genetic algorithm II (NSGA II) and strength pareto evolutionary algorithm 2 (SPEA 2) – within portfolio optimization. In addition, when used with four TA based strategies – relative strength index (RSI), moving average convergence/divergence (MACD), contrarian bollinger bands (CBB) and bollinger bands (BB), the two selected MOEAs achieve solutions with interesting in-sample and out-of-sample outcomes for the BB strategy.

Fundamental and technical analysis as a means of efficient portfolio management of securities

The subject of the research is to analyze the methods of fundamental and technical analysis of HoV on the companies quoted in the Belex index 15. The aim of the research is to create a pool of specific information, tested in practice, on the possibilities of improving efficient portfolio management with a special focus on optimal selection of techniques and the method of fundamental and technical analysis of HoV. The fundamental analysis of securities is a method of examining economic, political and social indicators of national economy in an attempt to forecast the future trends on the financial market. Differently from the fundamental method, the technical analysis examines historical and actual data on prices and the volume aimed at forecasting the market activities in the future period. Therefore, historical data on prices make the basis and are most frequently used in technical analysis. There are numerous methods of both fundamental analysis and technical analysis and this paper suggests that the use of solely one technique cannot provide results that are satisfactory and sufficient for managing a portfolio of securities efficiently. Methodology used in the research comprises methods of analysis, synthesis, explorative and inductive- deductive techniques. Due to numerous information that could be obtained by using all of the methods and techniques, this paper presents an optimal combination of indicators of technical and fundamental analysis, as a means to efficient portfolio management. In order to apply an optimal set of fundamental and technical analysis indicators, in this paper we have used a sample of companies extracted from the Index basket that comprises 15 most liquid shares traded on the Belgrade Stock Exchange (BSE) in addition to the Belex15 Index, which describes price movements of the most liquid Serbian shares, and which is positioned as the leading index of the Belgrade Stock Exchange and calculated in real time. The research results will be useful to the academic community for further research in the field, as well as to institutional and individual investors in the function of creating a set of instruments geared towards efficient trading with HoV.

Optimized Indicators of Technical Analysis on the New York Stock Exchange

The article is focused on the use of technical analysis and it’s indicators. The main aim is the evaluation of technical analysis for selected index instruments which are traded on NYSE. The secondary objective is the optimization of indicator’s parameters of technical analysis and subsequent comparison of profitability of business strategies based on these optimized parameters. The empirical analysis includes the backtesting of optimized indicators and comparison with the default settings of these indicators. The optimization and backtesting were performed on cyclical stocks, represented by stock index S&P 500 Financial from 11/1/2014 to 10/31/2015.

Elliott wave principle and the corresponding fractional Brownian motion in stock markets: Evidence from Nikkei 225 index

This paper examines one of the vital technical analysis indicators known as the Elliott wave principle. Since these waves have a fractal nature with patterns that are not exact, we first determine the dimension of them. Our second aim is to find a linkage between Elliott wave principle and fractional Brownian motion via comparing their Hausdorff dimensions. Thirdly, we consider the Nikkei 225 index during Japan asset price bubble, which is a perfect example of an Elliott wave.

Study on Stock Selection Strategy Based on SPSS Regression Mean Quantization

In the stock market, the main three kinds of state, rise, decline and the range of volatility, in the technical analysis indicators are MACD, KDJ, and other combinations. In the specific investment, technical analysis often has limitations such as delay or delay. In this paper, through the analysis of the various indicators of the combination, a new focus on the filter method, selection of different industries, a comprehensive comparison of the effectiveness of indicators. Based on the 30 day moving average, the extreme values of different technical indicators are selected, and the corresponding effective indexes are selected for clustering analysis.

Assessing the Efficacy of Adjustable Moving Averages Using ASEAN-5 Currencies.

The objective of this research is to examine the trends in the exchange rate markets of the ASEAN-5 countries (Indonesia (IDR), Malaysia (MYR), the Philippines (PHP), Singapore (SGD), and Thailand (THB)) through the application of dynamic moving average trading systems. This research offers evidence of the usefulness of the time-varying volatility technical analysis indicator, Adjustable Moving Average (AMA′) in deciphering trends in these ASEAN-5 exchange rate markets. This time-varying volatility factor, referred to as the Efficacy Ratio in this paper, is embedded in AMA′. The Efficacy Ratio adjusts the AMA′ to the prevailing market conditions by avoiding whipsaws (losses due, in part, to acting on wrong trading signals, which generally occur when there is no general direction in the market) in range trading and by entering early into new trends in trend trading. The efficacy of AMA′ is assessed against other popular moving-average rules. Based on the January 2005 to December 2014 dataset, our findings show that the moving averages and AMA′ are superior to the passive buy-and-hold strategy. Specifically, AMA′ outperforms the other models for the United States Dollar against PHP (USD/PHP) and USD/THB currency pairs. The results show that different length moving averages perform better in different periods for the five currencies. This is consistent with our hypothesis that a dynamic adjustable technical indicator is needed to cater for different periods in different markets.

IS TECHNICAL ANALYSIS PROFITABLE AND CAPABLE FOR STOCK PRICE PREDICTION? EVIDENCE FROM MALAYSIAN STOCK MARKET

This study aims to test the ability of technical analysis in predicting the stock price and generating profits. This study employed two of the technical analysis indicators, which are (i) Variable Moving Average (VMA) rules and (ii) Elliot Wave Principle incorporated with Fibonacci numbers. Besides that, this study also examines the relationship between the signals emitted by VMA rules and the stock return by applying Ordinary Least Square (OLS) regression analysis. Among the 42 VMA rules tested, there were only 10 VMA rules shown that the mean returns generated from buy signals are significant higher than the unconditional return. While, the mean returns from sell signals are significant lesser than the unconditional return for all the VMA rules tested. As for Elliot Wave Principle incorporated with Fibonacci numbers indicator, the findings shows that impulsive wave is predictable, meanwhile the corrective wave is less predictable. Lastly, only the signals of 14 VMA rules had shown a significant relationship with the daily stock return. In conclusion, the VMA rules only able to generate profits for certain term of moving average, whereas the Elliot Wave Principle incorporated with Fibonacci numbers tools is useful in predicting the stock market trend.

Forecasting East Asian Indices Futures via a Novel Hybrid of Wavelet-PCA Denoising and Artificial Neural Network Models.

The motivation behind this research is to innovatively combine new methods like wavelet, principal component analysis (PCA), and artificial neural network (ANN) approaches to analyze trade in today’s increasingly difficult and volatile financial futures markets. The main focus of this study is to facilitate forecasting by using an enhanced denoising process on market data, taken as a multivariate signal, in order to deduct the same noise from the open-high-low-close signal of a market. This research offers evidence on the predictive ability and the profitability of abnormal returns of a new hybrid forecasting model using Wavelet-PCA denoising and ANN (named WPCA-NN) on futures contracts of Hong Kong’s Hang Seng futures, Japan’s NIKKEI 225 futures, Singapore’s MSCI futures, South Korea’s KOSPI 200 futures, and Taiwan’s TAIEX futures from 2005 to 2014. Using a host of technical analysis indicators consisting of RSI, MACD, MACD Signal, Stochastic Fast %K, Stochastic Slow %K, Stochastic %D, and Ultimate Oscillator, empirical results show that the annual mean returns of WPCA-NN are more than the threshold buy-and-hold for the validation, test, and evaluation periods; this is inconsistent with the traditional random walk hypothesis, which insists that mechanical rules cannot outperform the threshold buy-and-hold. The findings, however, are consistent with literature that advocates technical analysis.