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

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

Fuzzy Decision Mechanism for Stock Market Trading

Investors utilize various methods to make buy/sell decisions depending on time-dependent stock market prices. In this study, a fuzzy decision mechanism that makes buy/sell decisions for stock market data is proposed. The proposed mechanism generates instant buy/sell decisions by evaluating three popular indicators which are the Moving Average Convergence/Divergence (MACD) Strategy, Chaikin Money Flow (CMF), and Stochastic Oscillator (SO). The fuzzy decision mechanism has three inputs and one output which are defined by using Gaussian membership functions. In the design of the decision mechanism, Mamdani inference method is used and the rule table is defined by nine rules. Therefore, the structure of the proposed fuzzy decision mechanism is simple and straightforward. The performance of the proposed fuzzy decision mechanism is compared with two classical decision mechanisms using MACD and CMF indicators separately. In the comparisons, the stock market data of Borsa Istanbul 100 Index (XU100), Dow Jones Industrial Average (^DJI), and S&P 500 (^GSPC) are used. The comparison results show that the proposed fuzzy decision mechanism provides significantly higher profit than the mechanisms using either MACD or CMF indicators for all stock market data.115988

Tendencias líderes de investigación sobre estrategias de trading

[EN] Trading strategies have attracted the attention of academic researchers and practitioners for a long time, but most specially in recent years due to the explosion of high-quality databases and computation capacity. Numerous studies are devoted to the analysis and proposal of trading strategies which cover aspects such as trend prediction, variables selection, technical analysis, pattern recognition etc. and apply many di erent methodologies. This paper conducts a meta-literature review which covers 1187 research articles from 1984 to 2020. The aim of this paper is to show the increasing importance of the topic and present a systematic study of the leading research areas, countries, institutions and authors contributing to this field. Moreover, a network analysis to identify the main research streams and future research opportunities is conducted.[ES] La creación de estrategias de inversión siempre ha atraído la atención de los académicos y de los inversores profesionales, pero, indudablemente, esta popularidad ha aumentado en los últimos años, con la aparición de bases de datos más completas y mayor potencia de cálculo de las computadoras. Son numerosos los estudios que analizan y proponen estrategias de inversión y que tratan aspectos como la predicción de la tendencia, la selección de variables, el análisis técnico, el reconocimiento de patrones etc. aplicando diferentes metodologías. En este trabajo se realiza un estudio bibliográfico que abarca 1187 artículos de investigación desde 1984 hasta 2020. El objetivo es mostrar la creciente importancia de este campo de investigación y presentar un análisis sistemático de los países, instituciones y autores que más están contribuyendo al avance del conocimiento. Además, se realiza un análisis de redes para identificar las principales áreas de investigación y las tendencias futuras.Oliver-Muncharaz, J.; García García, F. (2020). Leading research trends on trading strategies. Finance, Markets and Valuation. 6(2):27-54. https://doi.org/10.46503/LHTP1113S27546

Recent Advances in Theory and Methods for the Analysis of High Dimensional and High Frequency Financial Data

Recently, considerable attention has been placed on the development and application of tools useful for the analysis of the high-dimensional and/or high-frequency datasets that now dominate the landscape. The purpose of this Special Issue is to collect both methodological and empirical papers that develop and utilize state-of-the-art econometric techniques for the analysis of such data

Machine Learning and Finance: A Review using Latent Dirichlet Allocation Technique (LDA)

The aim of this paper is provide a first comprehensive structuring of the literature applying machine learning to finance. We use a probabilistic topic modelling approach to make sense of this diverse body of research spanning across the disciplines of finance, economics, computer sciences, and decision sciences. Through the topic modelling approach, a Latent Dirichlet Allocation Technique (LDA), we can extract the 14 coherent research topics that are the focus of the 6,148 academic articles during the years 1990-2019 analysed. We first describe and structure these topics, and then further show how the topic focus has evolved over the last two decades. Our study thus provides a structured topography for finance researchers seeking to integrate machine learning research approaches in their exploration of finance phenomena. We also showcase the benefits to finance researchers of the method of probabilistic modelling of topics for deep comprehension of a body of literature, especially when that literature has diverse multi-disciplinary actors

Stop Hunt Detection using Indicators and Expert Advisors in the Forex Market

Foreign exchange trading activities are one of the businesses that can generate big profits, and provide freedom for business people without the need to provide a large capital. Traders often suffer losses due to uncertainty in the market. One of them is market manipulation carried out by brokers or banks. For this reason, this research was conducted to detect any manipulation that occurred in the foreign exchange market. This research tries to combine trading systems, indicators and expert advisors that aim to help traders detect fake market price movements to minimize losses that occur due to errors in making transaction decisions. The results of the study produce an indicator that is able to detect the potential of certain patterns used by the market maker to reverse the direction of market prices, and is supported by the presence of expert advisors who are able to pinpoint potential market manipulation, so traders can avoid large losses

Data Science: Measuring Uncertainties

With the increase in data processing and storage capacity, a large amount of data is available. Data without analysis does not have much value. Thus, the demand for data analysis is increasing daily, and the consequence is the appearance of a large number of jobs and published articles. Data science has emerged as a multidisciplinary field to support data-driven activities, integrating and developing ideas, methods, and processes to extract information from data. This includes methods built from different knowledge areas: Statistics, Computer Science, Mathematics, Physics, Information Science, and Engineering. This mixture of areas has given rise to what we call Data Science. New solutions to the new problems are reproducing rapidly to generate large volumes of data. Current and future challenges require greater care in creating new solutions that satisfy the rationality for each type of problem. Labels such as Big Data, Data Science, Machine Learning, Statistical Learning, and Artificial Intelligence are demanding more sophistication in the foundations and how they are being applied. This point highlights the importance of building the foundations of Data Science. This book is dedicated to solutions and discussions of measuring uncertainties in data analysis problems

Machine Learning Methods to Exploit the Predictive Power of Open, High, Low, Close (OHLC) Data

Novel machine learning techniques are developed for the prediction of financial markets, with a combination of supervised, unsupervised and Bayesian optimisation machine learning methods shown able to give a predictive power rarely previously observed. A new data mining technique named Deep Candlestick Mining (DCM) is proposed that is able to discover highly predictive dataset specific candlestick patterns (arrangements of open, high, low, close (OHLC) aggregated price data structures) which significantly outperform traditional candlestick patterns. The power that OHLC features can provide is further investigated, using LSTM RNNs and XGBoost trees, in the prediction of a mid-price directional change, defined here as the mid-point between either the open and close or high and low of an OHLC bar. This target variable has been overlooked in the literature, which is surprising given the relative ease of predicting it, significantly in excess of noisier financial quantities. However, the true value of this quantity is only known upon the period's ending – i.e. it is an after-the-fact observation. To make use of and enhance the remarkable predictability of the mid-price directional change, multi-period predictions are investigated by training many LSTM RNNs (XGBoost trees being used to identify powerful OHLC input feature combinations), over different time horizons, to construct a Bayesian optimised trend prediction ensemble. This fusion of long-, medium- and short-term information results in a model capable of predicting market trend direction to greater than 70% better than random. A trading strategy is constructed to demonstrate how this predictive power can be used by exploiting an artefact of the LSTM RNN training process which allows the trading system to size and place trades in accordance with the ensemble's predictive certainty