25 research outputs found

    The development of hybrid intelligent systems for technical analysis based equivolume charting

    Get PDF
    This dissertation proposes the development of a hybrid intelligent system applied to technical analysis based equivolume charting for stock trading. A Neuro-Fuzzy based Genetic Algorithms (NF-GA) system of the Volume Adjusted Moving Average (VAMA) membership functions is introduced to evaluate the effectiveness of using a hybrid intelligent system that integrates neural networks, fuzzy logic, and genetic algorithms techniques for increasing the efficiency of technical analysis based equivolume charting for trading stocks --Introduction, page 1

    An Intelligent technical analysis using neural network

    Get PDF
    Technical analysis has been one of the most popular methods for stock market predictions for the past few decades. There have been enormous technical analysis methods to study the behavior of stock market for different kinds of trading markets such as currency, commodity or stock. In this paper, we propose two different methods based on volume adjusted moving average and ease of movement for stock trading. These methods are used with and without generalized regression neural network methods and the results are compared with each other. The preliminary results on historical stock price of 20 firms indicate that there is no meaningful difference between various proposed models of this paper

    Can Deep Learning Techniques Improve the Risk Adjusted Returns from Enhanced Indexing Investment Strategies

    Get PDF
    Deep learning techniques have been widely applied in the field of stock market prediction particularly with respect to the implementation of active trading strategies. However, the area of portfolio management and passive portfolio management in particular has been much less well served by research to date. This research project conducts an investigation into the science underlying the implementation of portfolio management strategies in practice focusing on enhanced indexing strategies. Enhanced indexing is a passive management approach which introduces an element of active management with the aim of achieving a level of active return through small adjustments to the portfolio weights. It then proceeds to investigate current applications of deep learning techniques in the field of financial market predictions and also in the specific area of portfolio management. A series of successively deeper neural network models were then developed and assessed in terms of their ability to accurately predict whether a sample of stocks would either outperform or underperform the selected benchmark index. The predictions generated by these models were then used to guide the adjustment of portfolio weightings to implement and forward test an enhanced indexing strategy on a hypothetical stock portfolio

    Applying GMDH-Type Neural Network and Genetic Algorithm for Stock Price Prediction of Iranian Cement Sector

    Get PDF
    The cement industry is one of the most important and profitable industries in Iran and great content of financial resources are investing in this sector yearly. In this paper a GMDH-type neural network and genetic algorithm is developed for stock price prediction of cement sector. For stocks price prediction by GMDH type-neural network, we are using earnings per share (EPS), Prediction Earnings Per Share (PEPS), Dividend per share (DPS), Price-earnings ratio (P/E), Earnings-price ratio (E/P) as input data and stock price as output data. For this work, data of ten cement companies is gathering from Tehran stock exchange (TSE) in decennial range (1999-2008). GMDH type neural network is designed by 80% of the experimental data. For testing the appropriateness of the modeling, reminder of primary data were entered into the GMDH network. The results are very encouraging and congruent with the experimental result

    Study on the Stock Price Index Forecasting Model Based on Optimized Neural Network

    Get PDF
    从19世纪建立以后,股票市场的涨跌已经成为反映一个国家国民经济的重要指标。它的作用不仅被政府部门所重视,也成为了广大投资者的关注对象。股票价格的变化和未来发展趋势对投资者来说是非常重要的。对股价预测越准确,投资者就能在风险较小的情况下获得较大的利润。股票市场的变化对于国家的国民经济发展和现代化建设也具有举足轻重的作用。因此对股票价格的预测研究,不仅有利于投资者掌握科学的投资方法,帮助投资者进行科学理性的投资,使投资者在风险最小的情况下获得最大的收益,而且在宏观层面上也具有重大的理论意义和诱人的应用前景。 本文以股票价格预测分析为研究的出发点,以广泛应用于预测实践的BP神经网络技术为基础,探讨...Since it was established in the 19th century, the stock market has become an important index of national economy. Its effect is not only valued by the government, but also by the majority of the investors. For stock investors, the more accurate of the trend of the future stock price forecasts, the safer of access to the profit and risk aversion; as for the national economic development and financi...学位:管理学硕士院系专业:管理学院管理科学系_管理科学与工程学号:1772009115093

    Integrated computational intelligence and Japanese candlestick method for short-term financial forecasting

    Get PDF
    This research presents a study of intelligent stock price forecasting systems using interval type-2 fuzzy logic for analyzing Japanese candlestick techniques. Many intelligent financial forecasting models have been developed to predict stock prices, but many of them do not perform well under unstable market conditions. One reason for poor performance is that stock price forecasting is very complex, and many factors are involved in stock price movement. In this environment, two kinds of information exist, including quantitative data, such as actual stock prices, and qualitative data, such as stock traders\u27 opinions and expertise. Japanese candlestick techniques have been proven to be effective methods for describing the market psychology. This study is motivated by the challenges of implementing Japanese candlestick techniques to computational intelligent systems to forecast stock prices. The quantitative information, Japanese candlestick definitions, is managed by type-2 fuzzy logic systems. The qualitative data sets for the stock market are handled by a hybrid type of dynamic committee machine architecture. Inside this committee machine, generalized regression neural network-based experts handle actual stock prices for monitoring price movements. Neural network architecture is an effective tool for function approximation problems such as forecasting. Few studies have explored integrating intelligent systems and Japanese candlestick methods for stock price forecasting. The proposed model shows promising results. This research, derived from the interval type-2 fuzzy logic system, contributes to the understanding of Japanese candlestick techniques and becomes a potential resource for future financial market forecasting studies --Abstract, page iii

    New Neural Network Based on Ant Colony Algorithm for Financial Data

    Get PDF
    Abstrac

    FAST: Fundamental Analysis Support for Financial Statements: using semantics for trading recommendations

    Get PDF
    Trading systems are tools to aid financial analysts in the investment process in companies. This process is highly complex because a big number of variables take part in it. Furthermore, huge sets of data must be taken into account to perform a grounded investment, making the process even more complicated. In this paper we present a real trading system that has been developed using semantic technologies. These cutting-edge technologies are very useful in this context because they enable the definition of schemes that can be used for storing financial information, which, in turn, can be easily accessed and queried. Additionally, the inference capabilities of the existing reasoning engines enable the generation of a set of rules supporting this investment analysis process.This work is supported by the Spanish Ministry of Science and Innovation under the project TRAZAMED (IPT 090000 2010 007)Publicad

    Buy and sell signals on Bucharest Stock Exchange

    Get PDF
    Trading rules of the technical analysis are widely used in investing on the capital markets. However, prediction of the financial markets movements based on their past evolutions is in contradiction with the principles of the Efficient Market Hypothesis. In case of the emerging markets, the impact of the development markets evolutions could also be taken into consideration in establishing the trading rules. In this paper we investigate the efficiency of three simple trading rules on Romanian capital market. Two of them, Variable-Length Moving Average and Bollinger Bands, belong to the technical analysis methods, while the third is based on the impact of the shocks from New York Stock Exchange. The results indicate some significant differences between these methods of shocks’ identification
    corecore