Development of Neurofuzzy Architectures for Electricity Price Forecasting

In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision‐making process as well as strategic planning. In this study, a prototype asymmetric‐based neuro‐fuzzy network (AGFINN) architecture has been implemented for short‐term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well‐established learning‐based models

Stock Market Prediction Using Evolutionary Support Vector Machines: An Application To The ASE20 Index

The main motivation for this paper is to introduce a novel hybrid method for the prediction of the directional movement of financial assets with an application to the ASE20 Greek stock index. Specifically, we use an alternative computational methodology named evolutionary support vector machine (ESVM) stock predictor for modeling and trading the ASE20 Greek stock index extending the universe of the examined inputs to include autoregressive inputs and moving averages of the ASE20 index and other four financial indices. The proposed hybrid method consists of a combination of genetic algorithms with support vector machines modified to uncover effective short-term trading models and overcome the limitations of existing methods. For comparison purposes, the trading performance of the ESVM stock predictor is benchmarked with four traditional strategies (a naïve strategy, a buy and hold strategy, a moving average convergence/divergence and an autoregressive moving average model), and a multilayer perceptron neural network model. As it turns out, the proposed methodology produces a higher trading performance, even during the financial crisis period, in terms of annualized return and information ratio, while providing information about the relationship between the ASE20 index and DAX30, NIKKEI225, FTSE100 and S&P500 indices

An empirical study on the various stock market prediction methods

Investment in the stock market is one of the much-admired investment actions. However, prediction of the stock market has remained a hard task because of the non-linearity exhibited. The non-linearity is due to multiple affecting factors such as global economy, political situations, sector performance, economic numbers, foreign institution investment, domestic institution investment, and so on. A proper set of such representative factors must be analyzed to make an efficient prediction model. Marginal improvement of prediction accuracy can be gainful for investors. This review provides a detailed analysis of research papers presenting stock market prediction techniques. These techniques are assessed in the time series analysis and sentiment analysis section. A detailed discussion on research gaps and issues is presented. The reviewed articles are analyzed based on the use of prediction techniques, optimization algorithms, feature selection methods, datasets, toolset, evaluation matrices, and input parameters. The techniques are further investigated to analyze relations of prediction methods with feature selection algorithm, datasets, feature selection methods, and input parameters. In addition, major problems raised in the present techniques are also discussed. This survey will provide researchers with deeper insight into various aspects of current stock market prediction methods

Soft Computing Approaches to Stock Forecasting: A Survey

Soft computing techniques has been effectively applied in business, engineering, medical domain to solve problems in the past decade. However, this paper focuses on censoring the application of soft computing techniques for stock market prediction in the last decade (2010 - todate). Over a hundred published articles on stock price prediction were reviewed. The survey is done by grouping these published articles into: the stock market surveyed, input variable choices, summary of modelling technique applied, comparative studies, and summary of performance measures. This survey aptly shows that soft computing techniques are widely used and it has demonstrated widely acceptability to accurately use for predicting stock price and stock index behavior worldwide

Using international diversification to enhance predicted equity index performance: a South African perspective

In the weak form, the Efficient Market Hypothesis (EMH) states that it is not possible to forecast the future price of an asset based on the information contained in the historical prices of that same asset. Under this assumption, the market behaves as a random walk and as a result, price forecasting is impossible. Furthermore, financial forecasting is a difficult task due to the intrinsic complexities of any financial system. The purpose of this study is to examine the potential of developing an international investment strategy using future index price predictions and offsetting predicted price declines by investing in negatively correlated international markets. Therefore, the first objective of this study was to examine the feasibility and accuracy of using a machine learning technique to model and predict the future price of stock market indices of South Africa (All Share Index) and a variety of other developed and developing international markets, which included South Africa, Brazil, Russia, India and China of the BRIC countries and Italy, France, Netherlands, Switzerland, Germany, Nigeria, Australia, Hong Kong, Saudi Arabia, Japan, the U.S., Turkey and the U.K., which were identified as South Africa’s major trading partners. Secondly, an analysis of market correlation between each country’s equity index and South Africa’s ALSI was conducted to determine which of these international indices were positively and negatively correlated to the South African ALSI. This allowed an extrapolation of potential international diversification opportunities. By using machine learning to predict future price trends of the South African All Share Index (ALSI) within a specified time period, the market correlation aspect of this study was able to suggest possible negatively correlated safe haven markets to invest in to offset predicted losses in an expected declining local market. The study’s major limitations include a single method for regression analysis (GARCH(1, 1)) and a limited number of variables in the feature space when predicting future prices. Additional parameters could prove a more robust modelling technique. The data used was a series of past closing prices of each country’s major index. The data was split into five periods, where each period was assigned an overarching theme based on the prevailing market conditions at the time. The ALSI data set was subjected to a unit root test and found to be non-stationary. The analysis thereafter followed a two-step test, with the first being the determination of market correlation of the South African equity market with other markets, using a generalised autoregressive conditional heteroskedasticity (GARCH (1: 1)) approach given the non-stationary nature of the ALSI historic data. The results showed strong positive market correlations between South Africa and China, India, Nigeria, Russia and Saudi Arabia, and strong negative correlation between South Africa and Australia, Germany, the Netherlands, and the United Kingdom. Secondly, the specific area of machine learning employed in this study was support vector machines, as implemented using Python programming. The results compare the actual index price with those predicted by the model and showed that this technique has the ability to predict the future price of the Index within an acceptable accuracy. The accuracy measure used was the mean relative error which in most cases was calculated to be between 95 and 98 which is considered relatively high. However, the results of the investment approach described above was considered to be too inconsistent to consider this diversification strategy viable. From a South African perspective, this approach has not been documented previously

Competitive Models to Detect Stock Manipulation

In this paper, data from the Indian stock market is used to study the prediction accuracy of various classification techniques that can be used to identify market manipulation. The data contains information regarding price, volume and volatility of various stocks. Techniques like discriminant analysis, a composite model based on artificial neural network – genetic algorithm (ann-ga) and support vector machine (svm) have been used for classifying stocks into manipulated and non manipulated categories. It is observed that the support vector machine based technique gives the best classification accuracy among the three techniques

Portfolio optimization: from markowitz to machine learning

Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementIn the past few decades, substantial progress has been made in portfolio optimization, especially with the emergence of machine learning. Therefore, it is essential to find the models that not only achieve the best results but also simplify the process. This project aims to demonstrate that to achieve optimal portfolios cannot be based only on traditional statistical methods. Therefore the Random Forest regression model, a machine learning model, was chosen to predict stock prices to complement the Markowitz model, a classical portfolio selection model. To evaluate the efficacy of the modified model compared to the classical model the following methodology was adopted: data was collected (from 2012 to 2019 from 10 companies and it was divided in 15 periods) and treated; some common technical indicators were extracted; one stock price was predicted per period; expected returns and partially estimated volatility were derived from the predictions and introduced in the classical model; 15 portfolios were constructed by each model; and finally, a performance analysis was conducted. The results obtained show that the 1-day predictions were quite accurate, almost 90%, and the modified model’s portfolios’ outperformed the classical model’s portfolios for most periods analyzed