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The human mind is not as good at processing large amounts of information as we might like. Psychologists have shown that human beings are only able to juggle small numbers of related and often conflicting pieces of information without making judgment errors. As a result, individuals faced with the vast amounts of information available to support investment decisions often find themselves swamped by the enormity of the task; unable to see the wood from the trees. Technical analysis is a field of financial markets research that works to address the above problem by focusing on a single, commonly available, data source that reflects all known information and activity relating to all monetary securities- Price history. Technical analysts argue that as markets are efficient, prices reflect all known information and that they move over time as participants react to new information and changing needs. As a result, the technical analysis of these price changes can provide real insight into the market dynamics and be used to develop trade strategies that exhibit superior risk/reward characteristics. While technical analysis approaches have developed significantly over the past few decades, some techniques are far more ancient. While their real origins are anonymous, Japanese candlestick charts have been recorded as being employed in the rice markets as far back as the 1600s. What is particularly interesting is that various of these ancient approaches continue to provide highly effective trading signals when applied to modern markets and securities. Crude oil price volatility is in the midst of the largest business risk that oil and gas companies face. This is followed by unstable policy regime, managing costs and risks emerging from technological advancements. The high levels and rapid fluctuations of petroleum prices have become a great concern to individual consumers, firms, policy makers and society. Technical Analysis is the forecasting of future financial price movements based on an examination of past price movements. Like weather forecasting, technical analysis does not result in absolute predictions about the future. Instead, technical analysis can help investors anticipate what is "likely" to happen to prices over time. Technical analysis uses a wide variety of charts that show price over time. Hence, to mitigate the negative impacts of price volatility and to predict about the future price movement of crude oil and natural gas we can use technical analysis. Technical analysis is the study of market action, primarily through the use of charts, for the purpose of forecasting price trends. The term “market action” includes the three principal source of action available to the technician-price, volume and open interest. This research paper highlights fundamental factor which affects the Brent price and analysed the factor which are highly correlated with Brent price and on the basis of the results forecasted the Brent price for next five years. Fundamental analysis of Brent oil, price pattern & movement of crude oil has also been carried out using candlestick technical tool.

Technical analysts believe that by studying past price action, it is possible to forecast future prices of specific securities. Technical analysts rely on the use of charts, which graphically show the past fluctuations of prices. This study focuses on the chart style known as Japanese candlesticks, which relies on four pieces of information for every session: Open, High, Low and Close prices. It is believed that some candle patterns possess predictive capabilities that can alert investors of imminent price tops, bottoms, or price trend continuations. For this study, we performed a statistical analysis, using historical prices of the S&P 500 index, of the effectiveness of Bullish Engulfing and Bearish Engulfing patterns, which are believed to forecast bottoms and tops respectively. Results indicate that the Bearish Engulfing provide strong short-term forecasting power when using the Open and High criteria but not the Close criterion. Likewise, the Bullish Engulfing offered strong short-term forecasting power when using the Open and Low criteria but not the Close criterion.

Stock market forecasting is a knotty challenging task due to the highly noisy, nonparametric, complex and chaotic nature of the stock price time series. With a simple eight-trigram feature engineering scheme of the inter-day candlestick patterns, we construct a novel ensemble machine learning framework for daily stock pattern prediction, combining traditional candlestick charting with the latest artificial intelligence methods. Several machine learning techniques, including deep learning methods, are applied to stock data to predict the direction of the closing price. This framework can give a suitable machine learning prediction method for each pattern based on the trained results. The investment strategy is constructed according to the ensemble machine learning techniques. Empirical results from 2000 to 2017 of China’s stock market confirm that our feature engineering has effective predictive power, with a prediction accuracy of more than 60% for some trend patterns. Various measures such as big data, feature standardization, and elimination of abnormal data can effectively solve data noise. An investment strategy based on our forecasting framework excels in both individual stock and portfolio performance theoretically. However, transaction costs have a significant impact on investment. Additional technical indicators can improve the forecast accuracy to varying degrees. Technical indicators, especially momentum indicators, can improve forecasting accuracy in most cases.

There are two types of analysis done for a stock market. One is fundamental analysis, where an investor looks at an intrinsic value of the stock, and another is technical analysis, where investors determine the future trend of the market looking at the current pattern or trend of the market. This paper is focused on one of the technical analysis tools, i.e., Moving Average Convergence-Divergence. It is a tool based on the three exponential moving average (9-12-26 EMA Rule). The MACD analysis, with the help of a single line, was helpful to find out the exact bullish and the bearish trend of the Nepse. A signal line is a benchmark to determine the stock market moving either to a bullish or bearish trend. It can help an investor, where the market is going in a direction. A market convergence, divergence, and crossover were better identified with the help of the MACD histogram. The paper found that the Nepse return was stable for a very minimal period from 1998-99 to 2019-20. The shift from the bullish to bearish or vice-verse were seen easily identified with the help of a MACD histogram. Finally, a better-combined knowledge of moving average and candlestick chart analysis will help an investor, to put a clear picture of a market trend with the help of MACD analysis.

A Japanese candlestick chart consists of not only the closing price but also the high, low and opening price information. Using the Japanese candlestick, this paper investigates the forecasting power of the shadow in Japanese candlestick chart. Empirical studies performed with the US stock market show that 1) there is a significant Halloween effect in the shadow; 2) shadow is valuable for predicting the stock market returns in both statistical and economic sense; 3) the predictability reported by the shadow can not be explained by either the CAPM model or the Fama-French three-factor model. This paper confirms that predictability of the stock market can be improved if more price information is used.

The aim of the paper was to present health care systems and assess the recent trend in health care expenditure in the European Union countries. The data source was the World Bank and European Statistical Office (Eurostat). The adopted research period covered the years 2000–2016, due to data availability. The methodology of the study was based on an analysis of data indicator series related to health care expenditure, which evaluate the national health care system performance. The research results were presented using primarily Japanese candlestick charting. The study showed that health care expenditure represented an ever-increasing burden for all the EU economies, both in absolute values and in relation to GDP. However, substantial differentiations in the amount and structure of health care expenditure were observed at the country level, having roots in the level of a country’s economic development and diverse post-war economic and political evolution. The analysis of health care expenditure structure confirmed that all three types of health care systems (Beveridge, Bismarck and mixed one) were observed in the EU, but the last one had a marginal importance. The form of system did not determine its effectiveness. On average, more than three-fourths of health care expenses were financed by general government expenditure. Out-of-pocket spending varied widely among the analysed EU member states.

Earning via real-time predictions with the experience in the visible trend directions of an investment instrument in the past requires a different perspective on charts. Indicators and formations within the scope of technical analysis constitute the most significant basis of this perspective. Those who can generate a high income in financial markets and even be more successful than large companies are actually the ones interpreting the data in a different way. In this study, a model which had never been encountered in the literature before, was designed through a different perspective on the same data, enabling the movements of an investment element over the 2D candlestick chart to be recognized as a “Buy-Sell” object respectively and to decide on the trend direction as a result. The model is trained by state-of-the-art, real-time object detection system (You Only Look Once) YOLO; for the training, one-year candlestick charts belonging to the stocks traded on Borsa Istanbul (BIST) between 2000-2018 were used. The model, which can make a “Buy-Sell” decision without the need for an additional time series except for the views on the visual candlestick charts, is promising in terms of its successful predictions. Its ultimate aim is to provide a foresight strengthening the “Buy-Sell” decisions to be made in the decision-making process following the other basic and technical analyses in addition to its stand-alone use in making investment decisions. The effect of this foresight on the success can clearly be seen on the test results received. In the results, the model was found to be successful by 85% while a 100% profit was generated. Besides, the model can be used for all the time series for which candlestick charts can be created.

Determining the price movement of stocks is a challenging problem to solve because of factors such as industry performance, economic variables, investor sentiment, company news, company performance, and social media sentiment. People can predict the price movement of stocks by applying machine learning algorithms on information contained in historical data, stock candlestick-chart data, and social-media data. However, it is hard to predict stock movement based on a single classifier. In this study, we proposed a multichannel collaborative network by incorporating candlestick-chart and social-media data for stock trend predictions. We first extracted the social media sentiment features using the Natural Language Toolkit and sentiment analysis data from Twitter. We then transformed the stock’s historical time series data into a candlestick chart to elucidate patterns in the stock’s movement. Finally, we integrated the stock’s sentiment features and its candlestick chart to predict the stock price movement over 4-, 6-, 8-, and 10-day time periods. Our collaborative network consisted of two branches: the first branch contained a one-dimensional convolutional neural network (CNN) performing sentiment classification. The second branch included a two-dimensional (2D) CNN performing image classifications based on 2D candlestick chart data. We evaluated our model for five high-demand stocks (Apple, Tesla, IBM, Amazon, and Google) and determined that our collaborative network achieved promising results and compared favorably against single-network models using either sentiment data or candlestick charts alone. The proposed method obtained the most favorable performance with 75.38% accuracy for Apple stock. We also found that the stock price prediction achieved more favorable performance over longer periods of time compared with shorter periods of time.

Machine learning methods, which have shown great success in financial applications, have also been effective in portfolio construction recently. Although raw time series data are generally used in these studies, recent studies have shown that features extracted from time series data increase the capacity of the models. In this study, we propose a portfolio creation pipeline that utilizes features extracted from images generated from stock data. First, we generate a candlestick chart from the raw stock data and extract semantic information from them with the help of an autoencoder. Next, we train a long short-term memory (LSTM) model that predicts stock prices with enhanced features which are the combination of extracted features with raw stock data. Portfolios that are estimated to bring the highest return are constructed by using the estimated price information. We evaluate the performance of the portfolio construction method with stock data in Standard & Poor 500 index in terms of daily mean return, Sharpe ratio, and compound return. We compare with three baselines portfolio construction methods: 1/N rule, portfolio construction by Sharpe ratio and portfolio construction by LSTM that takes raw time series as input. Our model outperforms 1/N, Sharpe ratio, and LSTM that uses raw time series.

So far, all countries around the world have to deal with the Covid-19 challenge. As imperative, it includes providing food security subject to pandemic restrictions and consequences. The study purpose took focus on exploring price dynamics observed for basic crop and animal products. The research hypothesis supposed different volatility of their prices linked with a certain similarity across the considered Eastern European countries. The utilized methods involved candlestick charting and analysis of variance ANOVA to compare the food price indices and changes in times of the Covid-19 between February and December 2020 with regard to the corresponding preCovid-19 period. The research findings revealed stable pricing for bread and cheese with fluctuations below 4%. The study outcomes indicated the most disruptive Covid-19 impact on potato and pork. The latter largely associated with Hungary, Poland, Romania, and Slovakia. Prices for sugar and eggs in Ukraine gained by 46.2% to 30.4% and illuminated unhealthy shifts in dietary patterns. The basic research conclusions are as follows. Firstly, food chains in animal husbandry need special enhancements to face the Covid19 crisis. Secondly, the evidenced price volatility coupled with the decreasing purchasing power are the reasons to concern on providing balanced nutrition and healthy diets in Ukraine and the neighboring EU countries.

This article proposes a novel fuzzy recommendation system for stock market investors. This intelligent decision tool uses fuzzy Japanese candlesticks and includes the effect of currency devaluation on the forecasting. To do so, first the next market session is obtained by a new designed fuzzy forecasting trading system. Then, it is compared to the one obtained by a non-parametric system based on the k-nearest neighbor technique. Finally, an amount of money to be invested is considered using a new capital management fuzzy strategy. The results have been compared to an analogous fuzzy trading system that has the same all-or-nothing investment strategy with risk control, but where this capitalization is not included. Both intelligent decision systems have been applied to two very different stock markets, the American Nasdaq100 and the Spanish Ibex35 markets, using the Buy and Hold investment strategy as benchmark. Results prove that the proposed fuzzy system with capitalization is profitable and presents high stability, and could be a good support system for investors.

Technical analysis is an important part of financial industry, research, and teaching. The methodology has two parts: i) calculation of the individual tools and ii) visual representations. In this article, I provide a community-contributed command, candlechart, to draw the most common technical analysis charts. My intent is to draw these charts similarly to industry examples. The popular candle price chart is combined with charts for volume, moving-average convergence divergence, relative strength index, and Bollinger bands.

Financial markets, such as the stock market, bond market and foreign exchange market, are important channels for fund transfer. As a graphical analysis tool, candlestick charts use graphs to display the open, high, low, and close prices in a specific period. In the past, there have been attempts to identify the characteristics of candlesticks based on Gramian Angular Field (GAF) images, but they are not perfect. In this study, we implemented Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), AdaBoost, Random Forest (RF) and XGBoost models, we found that the use of deep learning models is not the best choice for the recognition of candlestick features based on GAF images. Comparing these models, MLP and CNN are better than AdaBoost and RF, but worse than XGBoost. Our results show that for the candlestick pattern classification problem based on GAF images, it is unnecessary to use complex CNNs and traditional machine learning models can also achieve satisfactory results with much less computation resources.

Technical Analysis is useful for analyzing the behavior of securities based on different plots with the purpose of determining the upward or downward trends. In the present chapter the discussion will center on returns, volumes, candlestick charts, line charts, simple moving average, MACD, RSI and other technical analysis tools.

In this study, we are developing a reinforcement learning-based strategy for the intraday trading of stocks. This study’s primary goals include developing an environment that can be used as a simulator for the day trading stock market and, train an agent to trade in this environment by performing actions and finding the optimal policy to maximize its reward. This study also focuses on experimentation with different state representations to understand how data representation affects the learning system. We have experimented with three different state representations. All the representations focus on presenting the intraday stock data as images. We have chosen images rather than numerical data as our state representative to make it human interpretable. We want the agent to learn the insights from patterns and trends and apply that knowledge to make a profit, just like a seasoned human trader. We have also experimented with two different learning algorithms, the effects of exploration rate decay and the effects of reward penalty.

In this work, we propose a framework called TABot, a distributed deep learning (DL) framework for classifying price chart images. The deep learning engine we outline consists of an ensemble of convolutional neural networks employed for classifying price chart images. We present three workflows that compose our framework. The first is the data sourcing workflow: a distributed asynchronous pipeline to collect price data and programmatically generate candlestick charts. We measure the processing times of our distributed solution relative to a synchronous analog to identify a nominal processing time differential between the two solutions. The second is the model training workflow. We again leverage a distributed asynchronous pipeline to train each convolutional neural network in a parallel fashion. We measure the processing time of our parallel solution and compare to a synchronous analog. The third is the prediction workflow. We introduce a simple scheme for collecting the prediction output of each component model in an ensemble network model. Our results support the viability of convolutional neural networks to classify price chart images. The TAbot architecture additionally highlights the benefit of utilizing elastic computing environments to manage computational and data persistence costs incurred by deep learning frameworks.

This paper analyzes the set of trading algorithms A. An element of this set is a(I, C s , C p ) where I is a set of indicators of which behavior is taken into account for a given trading algorithm. Indicators are tools that perform specific computations using specified formulas based on price chart values, and then automatically produce a result. Most often, the indicators look like graphical constructions (lines, histograms, etc.) that are plotted on the chart in automatic mode. C s is the set of conditions for the behavior of the indicators of the set I under which the sale is made, C p is the purchase, respectively. For each under investigation algorithm a(I, C s , C p ), the expected profit E i is calculated at the end of the set operating time of the algorithm. Based on these data, the most efficient trading algorithms are defined. In algorithmic trading, exchange trading using trading algorithms, there is a significant correlation between the quantity/quality of the data available for analysis and the success of the trading system. For this reason, we are using a free QUIK demo account. In this way, we can get large datasets for experiments and analysis without real investments. For our case, we get real-time and historical data for the drawn set of instruments. It is a date of candlesticks for all time for the 1-minute interval. candlestick chart - chart of interval type. It is popular in technical analysis, it consists of "candles" corresponding to a time interval of a preassigned value. Data of candlestick have a date, time, open price, high price, low price, close price, the volume of transactions.

Traditional portfolio theory divides stocks into different categories using indicators such as industry, market value, and liquidity, and then selects representative stocks according to them. In this paper, we propose a novel portfolio learning approach based on deep learning and apply it to China&rsquo, s stock market. Specifically, this method is based on the similarity of deep features extracted from candlestick charts. First, we obtained whole stock information from Tushare, a professional financial data interface. These raw time series data are then plotted into candlestick charts to make an image dataset for studying the stock market. Next, the method extracts high-dimensional features from candlestick charts through an autoencoder. After that, K-means is used to cluster these high-dimensional features. Finally, we choose one stock from each category according to the Sharpe ratio and a low-risk, high-return portfolio is obtained. Extensive experiments are conducted on stocks in the Chinese stock market for evaluation. The results demonstrate that the proposed portfolio outperforms the market&rsquo, s leading funds and the Shanghai Stock Exchange Composite Index (SSE Index) in a number of metrics.

In this paper, the trends and the timings of stock market of Japanese Candlestick are predicted and analyzed empirically by developing the hybrid model that uses the three prominent techniques of artificial intelligence, i.e., SVM, ICA and genetic algorithms. In order to conduct the effective technical analysis of stock market—support vector machines (SVM) are used with genetic algorithms and imperialist competition algorithms (ICA). ICA is used to indicate the stock market timing and to optimize the SVM parameters, whereas GA is used to select the best features in addition to SVM parameters optimization. The input data of a model is generated using the two very important approaches—raw-based and signal-based. The results of the paper indicate that the performance of SVM-IC-GA is far better than the existing feed forward static neural network techniques of the existing literature.

Forecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. The use of technical analysis for financial forecasting has been successfully employed by many researchers. The existing qualitative based methods developed based on fuzzy reasoning techniques cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. Extended fuzzy sets (e.g., fuzzy probabilistic set) study the fuzziness of the membership grade to a concept. The cloud model, based on probability measure space, automatically produces random membership grades of a concept through a cloud generator. In this paper, a cloud model-based approach was proposed to confirm accurate stock based on Japanese candlestick. By incorporating probability statistics and fuzzy set theories, the cloud model can aid the required transformation between the qualitative concepts and quantitative data. The degree of certainty associated with candlestick patterns can be calculated through repeated assessments by employing the normal cloud model. The hybrid weighting method comprising the fuzzy time series, and Heikin&ndash, Ashi candlestick was employed for determining the weights of the indicators in the multi-criteria decision-making process. Fuzzy membership functions are constructed by the cloud model to deal effectively with uncertainty and vagueness of the stock historical data with the aim to predict the next open, high, low, and close prices for the stock. The experimental results prove the feasibility and high forecasting accuracy of the proposed model.

Match outcome prediction is a challenging problem that has led to the recent rise in machine learning being adopted and receiving significant interest from researchers in data science and sports. This study explores predictability in match outcomes using machine learning and candlestick charts, which have been used for stock market technical analysis. We compile candlestick charts based on betting market data and consider the character of the candlestick charts as features in our predictive model rather than the performance indicators used in the technical and tactical analysis in most studies. The predictions are investigated as two types of problems, namely, the classification of wins and losses and the regression of the winning/losing margin. Both are examined using various methods of machine learning, such as ensemble learning, support vector machines and neural networks. The effectiveness of our proposed approach is evaluated with a dataset of 13261 instances over 32 seasons in the National Football League. The results reveal that the random subspace method for regression achieves the best accuracy rate of 68.4%. The candlestick charts of betting market data can enable promising results of match outcome prediction based on pattern recognition by machine learning, without limitations regarding the specific knowledge required for various kinds of sports.

Candlestick charts display the high, low, opening, and closing prices in a specific period. Candlestick patterns emerge because human actions and reactions are patterned and continuously replicate. These patterns capture information on the candles. According to Thomas Bulkowski’s Encyclopedia of Candlestick Charts, there are 103 candlestick patterns. Traders use these patterns to determine when to enter and exit. Candlestick pattern classification approaches take the hard work out of visually identifying these patterns. To highlight its capabilities, we propose a two-steps approach to recognize candlestick patterns automatically. The first step uses the Gramian Angular Field (GAF) to encode the time series as different types of images. The second step uses the Convolutional Neural Network (CNN) with the GAF images to learn eight critical kinds of candlestick patterns. In this paper, we call the approach GAF-CNN. In the experiments, our approach can identify the eight types of candlestick patterns with 90.7% average accuracy automatically in real-world data, outperforming the LSTM model.

The Japanese candlesticks&rsquo, technique is one of the well-known graphic methods of dynamic analysis of securities. If we apply Japanese candlesticks for the analysis of high-frequency financial data, then we need a numerical representation of any Japanese candlestick. Kacprzak et al. have proposed to represent Japanese candlesticks by ordered fuzzy numbers introduced by Kosiński and his cooperators. For some formal reasons, Kosiński&rsquo, s theory of ordered fuzzy numbers has been revised. The main goal of our paper is to propose a universal method of representation of Japanese candlesticks by revised ordered fuzzy numbers. The discussion also justifies the need for such revision of a numerical model of the Japanese candlesticks. There are considered the following main kinds of Japanese candlestick: White Candle (White Spinning), Black Candle (Black Spinning), Doji Star, Dragonfly Doji, Gravestone Doji, and Four Price Doji. For example, we apply numerical model of Japanese candlesticks for financial portfolio analysis.

This paper is to evaluate two investment strategies of periodic versus active for the Yuanta Taiwan Dividend Plus ETF. The purpose of the research is to identify a better investment strategy of the above two strategies for long-term investors and pension-purpose funds. Periodic strategy, which is passive and straightforward, will invest the same amount of money in the same day of each month. Otherwise, active strategy, which needs a little bit intense care, will invest various amount of money according to the criteria based on the technical analysis of the candlestick chart. This research concludes that the performance of active investment strategy is better than the periodic investment strategty.

Cloud model covers the randomness gap in fuzzy logic model and represents the uncertainty transformation between two different concepts. First concept is the linguistic term that represent the qualitative mean. While the second is the crisp term which represent the quantitative mean. The proposed work presents promising model which combines cloud model, fuzzy time series, and Heikin-Ashi candlestick to predict and confirm accurate stock trend. The model solves several challenging such as: nonlinearity, uncertainty and noises in stock market trend. Heikin-Ashi Candlesticks are an extended branch of Japanese candlesticks, such candlestick filters out stock noise and effort to highlight the trend. Heikin-Ashi Candlestick is constructed by calculating averages of the previous and current period prices. Cloud model handle the ambiguous and uncertainty in the Japanese candlestick definitions (qualitative information) and actual stock prices (quantitative data). It is applied to build membership functions by handling the uncertainty and vagueness of the stock historical data. Then the suggested model constructs dynamic weighted fuzzy logical relationships based on the membership functions to predict the next open and close prices of the stock as well as the high and low values. Finally it constructs the next Heikin-Ashi Japanese candlestick pattern that clarify the trend direction based on the patterns sequence. The imperial evaluation proves that the proposed model has high forecasting accuracy and is feasible to be implemented.

The present phase of the IoT (Internet of Things) networks has no limits to perform loaded data transmissions and computational requirements, which is made possible by Fog Computing technology. However, excessive energy resources and considerable loss of quality in loaded data transmission remains a significant challenge to the Fog Computing comprised IoT networks. This manuscript endeavors to portray a novel scheduling algorithm that optimizes edge node and fog node scheduling processes. The proposed approach is a supervised learning approach, titled "Quality-aware Energy Efficient Scheduling Model for Fog computing comprised IoT networks. The statistical measures moving averages and Heiken-Ashi patterns (Heiken-Ashi has been used in conjunction with candlestick charts to predict the present scheduling opportunities easily) have been used as a base for the proposed scheduling model. The experimental study signifies the performance of the proposed model and is scaled against the contemporary models.

Electricity consumption data are collected more frequently by high-quality meters in smart grids. Therefore, the load data volume and length increase dramatically. On the other hand, for advanced market-based applications, e.g. demand response, load service entities hope to identify or classify users better. In this study, a trend-based load characterising approach is proposed. Firstly, the concept of the candlestick chart is utilised as an innovative tool for load description. In addition, electricity trend indexes, e.g. stochastic oscillator and moving average convergence/divergence, are introduced as parameters for load characterising. Secondly, the stacked auto-encoders are utilised to forecast the future load based on the input historical trend indexes. Case studies in Guangdong province demonstrate that the proposed trend-based method is more applicable than existing approaches both in physical significance and accuracy.

In this paper, two hybrid models are used for timing of the stock markets on the basis of the technical analysis of Japanese Candlestick by Support Vector Machine (SVM) and Heuristic Algorithms of Imperialist Competition and Genetic. In the first model, SVM and Imperialist Competition Algorithm (ICA) are developed for stock market timing in which ICA is used to optimize the SVM parameters. In the second model, SVM is used with Genetic Algorithm (GA) where GA is used for feature selection in addition to SVM parameters optimization. Here the two approaches, Raw-based and Signal-based are devised on the basis of the literature to generate the input data of the model. For a comparison, the Hit Rate is considered as the percentage of correct predictions for periods of 1–6 day. The results show that SVM-ICA performance is better than SVM-GA and most importantly the feed-forward static neural network of the literature as the standard one.

Despite the enormous amount of financial data stored as candlestick charts, their high and low dimensions are often neglected in econophysics research. In this contribution, stylized facts are computed for open, close, high and low price series: Power-law decay in return distribution tails, the Hurst exponent, and multifractal scaling are investigated, as well as the accuracy of a forecast model based on systems of differential equations. Two groups of stocks were chosen for the study, one belonging to a developed market and another from an emerging market. The hypothesis that the outcomes from high and low series and the ones from open and close data come from the same distribution was rejected at the 99% confidence level in the vast majority of cases analyzed. Taking high and low candlestick dimensions into account in econophysics can improve our understanding of market dynamics. It can also be useful for trading models.

One of the main issues in IFS theory are generalizations of intuitionistic fuzzy set (IFS) definition as well as IFS operations. In this paper, we present the LBIFS-IBA approach by applying operations based on interpolative Boolean algebra (IBA) on generalized IFS. Namely, LBIFS are defined as a special case of Liu’s generalized IFS with the maximal interpretational surface. By extending the interpretational surface, the descriptive power of the approach is enhanced, and therefore the problematic situations when μA+νA>1 can be modeled. In addition, IBA-based algebra secures Boolean properties of the proposed approach. Considerable attention is given to comprehension of uncertainty within LBIFS-IBA, i.e., we propose a novel manner of uncertainty interpretation by treating values from [−1,1] interval. In order to prove its importance, we compare LBIFS-IBA with several well-known IFS generalizations, showing that only our approach offers meaningful uncertainty interpretation is all selected cases. Additionally, we illustrate the practical benefits of LBIFS-IBA by applying it to an example of modeling Japanese candlesticks for price charting and paying special attention to uncertainty interpretation.

Information retrieval systems are generally used to find documents that are most appropriate according to some query that comes dynamically from users. In this paper a novel Fuzzy Document based Information Retrieval Model (FDIRM) is proposed for the purpose of Stock Market Index forecasting. The novelty of proposed approach is a modified tf-idf scoring scheme to predict the future trend of the stock market index. The contribution of this paper has two dimensions, 1) In the proposed system the simple time series is converted to an enriched fuzzy linguistic time series with a unique approach of incorporating market sentiment related information along with the price and 2) A unique approach is followed while modeling the information retrieval (IR) system which converts a simple IR system into a forecasting system. From the performance comparison of FDIRM with standard benchmark models it can be affirmed that the proposed model has a potential of becoming a good forecasting model. The stock market data provided by Standard & Poor’s CRISIL NSE Index 50 (CNX NIFTY-50 index) of National Stock Exchange of India (NSE) is used to experiment and validate the proposed model. The authentic data for validation and experimentation is obtained from http://www.nseindia.com which is the official website of NSE. A java program is under construction to implement the model in real-time with graphical users’ interface.