An intense Nigerian stock exchange market prediction using logistic with back-propagation ANN model

This paper is a continuation of our research work on the Nigerian Stock Exchange Market (NSEM) uncertainties, In our previous work (Magaji et al, 2013) we presented the Naive Bayes and SVM-SMO algorithms as a tools for predicting the Nigerian Stock Exchange Market; subsequently we used the same transformed data of the NSEM and explored the implementation of the Logistic function on Back-propagation algorithm on the WEKA platform, and results obtained, made us to also conclude that the Back-propagation model of Artificial Neural Network (ANN) performed very well and thus it is another algorithm that can effectively and efficiently be used for predicting the Nigerian Stock Exchange Market.Keywords: Nigerian Stock Market, Prediction, Data Mining, Machine Learning, Artificial Neural Network, Back-propagatio

Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction

Over the past decades, the Least Squares Support Vector Machines (LSSVM) has been widely utilized in prediction task of various application domains. Nevertheless, existing literature showed that the capability of LSSVM is highly dependent on the value of its hyper-parameters, namely regularization parameter and kernel parameter, where this would greatly affect the generalization of LSSVM in prediction task. This study proposed a hybrid algorithm, based on Artificial Bee Colony (ABC) and LSSVM, that consists of three algorithms; ABC-LSSVM, lvABC-LSSVM and cmABC-LSSVM. The lvABC algorithm is introduced to overcome the local optima problem by enriching the searching behaviour using Levy mutation. On the other hand, the cmABC algorithm that incorporates conventional mutation addresses the over- fitting or under-fitting problem. The combination of lvABC and cmABC algorithm, which is later introduced as Enhanced Artificial Bee Colony–Least Squares Support Vector Machine (eABC-LSSVM), is realized in prediction of non renewable natural resources commodity price. Upon the completion of data collection and data pre processing, the eABC-LSSVM algorithm is designed and developed. The predictability of eABC-LSSVM is measured based on five statistical metrics which include Mean Absolute Percentage Error (MAPE), prediction accuracy, symmetric MAPE (sMAPE), Root Mean Square Percentage Error (RMSPE) and Theils’ U. Results showed that the eABC-LSSVM possess lower prediction error rate as compared to eight hybridization models of LSSVM and Evolutionary Computation (EC) algorithms. In addition, the proposed algorithm is compared to single prediction techniques, namely, Support Vector Machines (SVM) and Back Propagation Neural Network (BPNN). In general, the eABC-LSSVM produced more than 90% prediction accuracy. This indicates that the proposed eABC-LSSVM is capable of solving optimization problem, specifically in the prediction task. The eABC-LSSVM is hoped to be useful to investors and commodities traders in planning their investment and projecting their profit

Storage Capacity Estimation of Commercial Scale Injection and Storage of CO2 in the Jacksonburg-Stringtown Oil Field, West Virginia

Geological capture, utilization and storage (CCUS) of carbon dioxide (CO2) in depleted oil and gas reservoirs is one method to reduce greenhouse gas emissions with enhanced oil recovery (EOR) and extending the life of the field. Therefore CCUS coupled with EOR is considered to be an economic approach to demonstration of commercial-scale injection and storage of anthropogenic CO2. Several critical issues should be taken into account prior to injecting large volumes of CO2, such as storage capacity, project duration and long-term containment. Reservoir characterization and 3D geological modeling are the best way to estimate the theoretical CO 2 storage capacity in mature oil fields. The Jacksonburg-Stringtown field, located in northwestern West Virginia, has produced over 22 million barrels of oil (MMBO) since 1895. The sandstone of the Late Devonian Gordon Stray is the primary reservoir.;The Upper Devonian fluvial sandstone reservoirs in Jacksonburg-Stringtown oil field, which has produced over 22 million barrels of oil since 1895, are an ideal candidate for CO2 sequestration coupled with EOR. Supercritical depth (\u3e2500 ft.), minimum miscible pressure (941 psi), favorable API gravity (46.5°) and good water flood response are indicators that facilitate CO 2-EOR operations. Moreover, Jacksonburg-Stringtown oil field is adjacent to a large concentration of CO2 sources located along the Ohio River that could potentially supply enough CO2 for sequestration and EOR without constructing new pipeline facilities.;Permeability evaluation is a critical parameter to understand the subsurface fluid flow and reservoir management for primary and enhanced hydrocarbon recovery and efficient carbon storage. In this study, a rapid, robust and cost-effective artificial neural network (ANN) model is constructed to predict permeability using the model\u27s strong ability to recognize the possible interrelationships between input and output variables. Two commonly available conventional well logs, gamma ray and bulk density, and three logs derived variables, the slope of GR, the slope of bulk density and Vsh were selected as input parameters and permeability was selected as desired output parameter to train and test an artificial neural network. The results indicate that the ANN model can be applied effectively in permeability prediction.;Porosity is another fundamental property that characterizes the storage capability of fluid and gas bearing formations in a reservoir. In this study, a support vector machine (SVM) with mixed kernels function (MKF) is utilized to construct the relationship between limited conventional well log suites and sparse core data. The input parameters for SVM model consist of core porosity values and the same log suite as ANN\u27s input parameters, and porosity is the desired output. Compared with results from the SVM model with a single kernel function, mixed kernel function based SVM model provide more accurate porosity prediction values.;Base on the well log analysis, four reservoir subunits within a marine-dominated estuarine depositional system are defined: barrier sand, central bay shale, tidal channels and fluvial channel subunits. A 3-D geological model, which is used to estimate theoretical CO2 sequestration capacity, is constructed with the integration of core data, wireline log data and geological background knowledge. Depending on the proposed 3-D geological model, the best regions for coupled CCUS-EOR are located in southern portions of the field, and the estimated CO2 theoretical storage capacity for Jacksonburg-Stringtown oil field vary between 24 to 383 million metric tons. The estimation results of CO2 sequestration and EOR potential indicate that the Jacksonburg-Stringtown oilfield has significant potential for CO2 storage and value-added EOR

Financial Analysis with Artificial Neural Networks Short-term Stock Market Forecasting

Excerpt from Introduction Seldom reward is absent from risk, and stock markets are a prime example. Stock markets across the world are viewed as profitable and risky at the same time. Companies have made a business out of forecasting these markets. Quantitative analysis companies use mathematicians, financial analysts, and computer scientists to compete in the stock market. The old days of floor trading have progressed towards high-frequency trading with supercomputers housed within the exchange. For example, the New York Stock exchange has created regulations for these companies so that there’s competitive equality. The computer’s power, length of cable to the exchange, and more has been standardized so that no single company will have an advantage with the exception to algorithms. Computers are delegated the buying and selling of stocks in the New York Stock exchange. A computer receives information from the market, decides an action in microseconds, and that decision gets sent to the exchange in milliseconds. From the computer’s perspective, the difference between microseconds and millisecond is significant. The company’s trading algorithms are secretive and protected, but their performance depends on time series analysis and machine learning theory

A system to predict the S&P 500 using a bio-inspired algorithm

The goal of this research was to develop an algorithmic system capable of predicting the directional trend of the S&P 500 financial index. The approach I have taken was inspired by the biology of the human retina. Extensive research has been published attempting to predict different financial markets using historical data, testing on an in-sample and trend basis with many employing sophisticated mathematical techniques. In reviewing and evaluating these in-sample methodologies, it became evident that this approach was unable to achieve sufficiently reliable prediction performance for commercial exploitation. For these reasons, I moved to an out-of-sample strategy and am able to predict tomorrow’s (t+1) directional trend of the S&P 500 at 55.1%. The key elements that underpin my bio-inspired out-of-sample system are: Identification of 51 financial market data (FMD) inputs, including other indices, currency pairs, swap rates, that affect the 500 component companies of the S&P 500. The use of an extensive historical data set, comprising the actual daily closing prices of the chosen 51 FMD inputs and S&P 500. The ability to compute this large data set in a time frame of less than 24 hours. The data set was fed into a linear regression algorithm to determine the predicted value of tomorrow’s (t+1) S&P 500 closing price. This process was initially carried out in MatLab which proved the concept of my approach, but (3) above was not met. In order to successfully meet the requirement of handling such a large data set to complete the prediction target on time, I decided to adopt a novel graphics processing unit (GPU) based computational architecture. Through extensive optimisation of my GPU engine, I was able to achieve a sufficient speed up of 150x to meet (3). In achieving my optimum directional trend of 55.1%, an extensive range of tests exploring a number of trade offs were carried out using an 8 year data set. The results I have obtained will form the basis of a commercial investment fund. It should be noted that my algorithm uses financial data of the past 60-days, and as such would not be able to predict rapid market changes such as a stock market crash

Predicting the Future

Due to the increased capabilities of microprocessors and the advent of graphics processing units (GPUs) in recent decades, the use of machine learning methodologies has become popular in many fields of science and technology. This fact, together with the availability of large amounts of information, has meant that machine learning and Big Data have an important presence in the field of Energy. This Special Issue entitled “Predicting the Future—Big Data and Machine Learning” is focused on applications of machine learning methodologies in the field of energy. Topics include but are not limited to the following: big data architectures of power supply systems, energy-saving and efficiency models, environmental effects of energy consumption, prediction of occupational health and safety outcomes in the energy industry, price forecast prediction of raw materials, and energy management of smart buildings

Four Classifiers Used in Data Mining and Knowledge Discovery for Petroleum Exploration and Development

The application of data mining and knowledge discovery in databases for petroleum exploration and development (PE&D) is becoming promising, though still at an early stage. Up to now, the data mining tools usually used in PE&D are four classifiers: multiple regression analysis (MRA), Bayesian discrimination (BAYD), back-propagation neural network (BPNN), and support vector machine (SVM). Each of the four classifiers has its advantages and disadvantages. A question, however, has been raised in applications is: which classifier is the most applicable to a specified application? This paper has given an answer to the question through two case studies: 1) trap quality evaluation of the Northern Kuqa Depression of the Tarim Basin in western China, and 2) oil identification of the Xiefengqiao anticlinal structure of the Jianghan Basin in central China. Case 1 shows that the results of BAYD, BPNN and SVM are same and can have zero residuals, while MRA has unallowable residuals; but Case 2 shows that the results of only SVM have zero residuals, while BAYD, BPNN and MRA have unallowable residuals. The reasons are: a) since the two cases are nonlinear problems, the linear MRA is not applicable; b) since the nonlinearity of Case 1 is weak, the nonlinear BAYD, BPNN and SVM are applicable; and c) since the nonlinearity of Case 2 is strong, only nonlinear SVM is applicable. Therefore, it is proposed that: we can adopt MRA when a problem is linear; adopt BAYD, BPNN, or SVM when a problem is weakly nonlinear; and adopt only SVM when a problem is strongly nonlinear. In addition, the predictions of the applicable classifiers coincide with real exploration results, and a commercial gas trap was discovered after the forecast in Case 1 and SVM can correct some erroneous well-log interpretations in Case 2.Key words: Multiple regression analysis; Bayesian discrimination; Back-propagation neural network; Support vector machine; Trap quality evaluation; Oil identificatio

Four essays on quantitative economics applications to volatility analysis in Emerging Markets and renewable energy projects

[ES]Las decisiones financieras se pueden dividir en decisiones de inversión y decisiones de financiación. En lo que respecta a las decisiones de inversión, la incertidumbre acerca de la dinámica futura de las variables económicas y de las financieras tiene un rol fundamental. Eso, se explica porque los retornos esperados por las empresas y por los inversionistas se pueden ver afectados por los movimientos adversos en los mercados financieros y por los altos niveles de volatilidad. Como consecuencia, resulta crucial realizar un adecuado análisis y modelación de la volatilidad para el proceso de toma de decisiones financieras, por parte de las empresas y el diseño de estrategias de inversión y cobertura por parte de los inversionistas. En este sentido, el estudio de la volatilidad se ha convertido en uno de los temas más interesantes de la investigación en finanzas. Lo anterior ha cobrado mayor relevancia en los últimos años, teniendo en cuenta el escenario de alta volatilidad e incertidumbre que afrontan los mercados a nivel global. Este documento tiene como objetivo abordar cuatro cuestiones centrales, las cuales están relacionadas con la volatilidad financiera como campo de investigación. Esas cuestiones son, la transmisión y spillovers de volatilidad en mercados emergentes, la calibración de la superficie de volatilidad para proyectos de energía renovable y el pronóstico de los rendimientos de activos energéticos y spillovers de volatilidad a través de técnicas de machine learning. En el primer capítulo del documento, se examinan los efectos de transmisión de volatilidad entre un índice de energía y un índice financiero para los Mercados Emergentes. En consecuencia, mediante el uso de un modelo DCC, se muestra que los efectos de transmisión de volatilidad entre los índices empleados para la crisis subprime y la crisis del COVID-19 fueron diferentes. Lo anteriormente dicho, considerando que la primera crisis se originó en el sector financiero y luego se extendió al resto de la economía, mientras que la segunda se originó en el sector real y posteriormente afectó al resto de la economía. Teniendo en cuenta que la relación entre la volatilidad de los mercados es cambiante en el tiempo, en el segundo capítulo se llevó a cabo un análisis dinámico de los spillovers de volatilidad entre materias primas, Bitcoin y un índice de Mercados Emergentes. Así, empleando la metodología propuesta por Diebold y Yilmaz (2012), se concluyó que los efectos de los spillovers de volatilidad entre los activos analizados no son constantes en dirección e intensidad a través del tiempo. En particular, para períodos de crisis como el de la pandemia del COVID-19, hay reversiones en la dirección de los spillovers de volatilidad debido al sector en el que se originó la crisis. Además, en este capítulo se explota la naturaleza dinámica de los spillovers de volatilidad. Por lo tanto, se planteó que el índice de spillovers de volatilidad propuesto por Diebold y Yilmaz puede ser usado como una medida para pronosticar periodos de alta turbulencia. Lo anterior se desarrolló a través de modelos econométricos tradicionales y de técnicas de machine learning. En el tercer capítulo del documento, se propone un modelo que predice los retornos de los precios del carbono y del petróleo. En este sentido, se desarrolló un modelo híbrido, el cual combina las proyecciones obtenidas a partir de diferentes técnicas de machine learning y modelos econométricos tradicionales, obteniéndose resultados los cuales muestran las ventajas de emplear modelos híbridos que incorporan técnicas de machine learning, exclusivamente, para pronosticar variables financieras. Finalmente, en el capítulo cuatro, se presenta una metodología para la estimación de la volatilidad en la valoración de proyectos de energías renovables mediante opciones reales. En esta metodología, la cual es una extensión del enfoque de volatilidad implícita empleada para las opciones financieras, la volatilidad de un proyecto es la volatilidad implícita obtenida a partir de la superficie de la volatilidad de empresas comparables, según una determinada fecha de valoración y dada la relación deuda-capital de un proyecto de energía renovable. En este análisis, se utilizó el modelo estocástico 'alfa-beta-rho' para calibrar la superficie de la volatilidad para la valoración mediante opciones reales. Por último, al final del documento se presentan las conclusiones derivadas de los capítulos mencionados, así como algunas recomendaciones para las futuras investigaciones. [EN]Financial decisions can be divided in investment and financing decisions. Concerning investment decisions, the uncertainty about the future dynamics of financial and economic variables has a central role, considering that the returns expected by firms and investors can be affected by the adverse movements in financial markets and their high volatility. In consequence, the adequate volatility analysis and modeling is crucial for the firm’s financial decision-making process and the design of investing and hedging strategies by investors. In this regard, the study of volatility has become one of the most interesting topics in finance research. The foregoing has become more relevant in recent years considering the scenario of high volatility and uncertainty faced by markets globally. This document aims to address four central issues related to financial volatility as a research area. These are, volatility transmission and spillovers in Emerging Markets, the calibration of the volatility surface for renewable energy projects and the forecast of energy assets returns and volatility spillovers through machine learning techniques. In the first chapter of the document, the volatility transmission effects between an energy index and a financial index for Emerging Markets are examined. Then, by using a DCC model, it is shown that the volatility transmission effects between the employed indices for the subprime crisis and the COVID-19 pandemic were different. This, considering that the former crisis originated in the financial sector and spread to the rest of the economy, while the second originated in the real sector and trasmitted to the rest of the economy posteriorly. Considering that the relationship between markets volatility is time-varying, in the second chapter, a dynamic analysis of volatility spillovers between commodities, Bitcoin and an Emerging Markets index is developed. Employing the methodology proposed by Diebold and Yilmaz (2012), it is concluded that the volatility spillovers effects between the analyzed assets is not constant in direction and intensity over time. In particular, for periods of crisis such as the COVID-19 pandemics, there are reversals in the direction of volatility spillovers due to the sector in which the crises originate. In addition, in this chapter the dynamic nature of volatility spillovers is exploited. Hence, the volatility spillover index proposed by Diebold and Yilmaz is forecasted to be used as a measure to anticipate high turbulence periods. This, through both traditional econometric models and machine learning techniques. In the third chapter, a model for the prediction of carbon and oil prices is proposed. In this sense, a hybrid model that ensembles the forecasts obtained from different machine learning techniques and traditional econometric models is developed, obtaining results that show the advantages of employing hybrid models which combine machine learning techniques, exclusively, to forecast financial variables. In Chapter four, a methodology for the estimation of volatility in renewable energy projects valuation through real options is presented. In this methodology, which is an extension of the implied volatility approach employed for financial options, the volatility of the project is the implied volatility obtained from the volatility surface of comparable firms for a certain valuation date and given debt-to-equity relation of a renewable energy project. In this analysis, the stochastic ‘alpha-beta-rho’ model is utilized to calibrate the volatility surface for real option valuation purposes. Finally, the conclusions derived from the mentioned chapters are presented at the end of the document as well as some recommendations for future research