Nigerian Stock Market Investment using a Fuzzy Strategy

The Nigerian Capital Market though an emerging market, has in recent times been adjudged to be one of the most resilient in the world even in the heat of the global economic meltdown. It offers high returns on investment as compensation for its high risk. In this research, we have investigated the predictive capability of the fuzzy inference system (FIS) on stocks listed on the Nigerian Stock Exchange, within a two-month window. For each selected stock, the technical indicator-based fuzzy expert system developed in Matlab 7.0 provides the buy, sell or hold decision for each trading day. A web-based user interface enables the investor to access the trade forecast for each day. Using the Netbeans IDE, we implemented the user interface with Sun Java. Our results show that the FIS can reliably serve as a decision support workbench for intelligent investments. Keywords: fuzzy logic, stock market, Forecasting, Decision making, technical indicato

Twitter mood predicts the stock market

Behavioral economics tells us that emotions can profoundly affect individual behavior and decision-making. Does this also apply to societies at large, i.e., can societies experience mood states that affect their collective decision making? By extension is the public mood correlated or even predictive of economic indicators? Here we investigate whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time. We analyze the text content of daily Twitter feeds by two mood tracking tools, namely OpinionFinder that measures positive vs. negative mood and Google-Profile of Mood States (GPOMS) that measures mood in terms of 6 dimensions (Calm, Alert, Sure, Vital, Kind, and Happy). We cross-validate the resulting mood time series by comparing their ability to detect the public's response to the presidential election and Thanksgiving day in 2008. A Granger causality analysis and a Self-Organizing Fuzzy Neural Network are then used to investigate the hypothesis that public mood states, as measured by the OpinionFinder and GPOMS mood time series, are predictive of changes in DJIA closing values. Our results indicate that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others. We find an accuracy of 87.6% in predicting the daily up and down changes in the closing values of the DJIA and a reduction of the Mean Average Percentage Error by more than 6%

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

APPLICATION OF NEURO FUZZY MODEL FOR FORECASTING CONSUMER PRICE INDEX IN YOGYAKARTA

The aim of this research is to predict the consumer price index (CPI) in Yogyakarta using neuro fuzzy model. The rule bases of neuro fuzzy model is constructed by tipe III, i.e. the consequent of fuzzy rules is linear combination of input variables. We apply the proposed method to predict CPI in Yogyakarta and the MAPE values of the model for training and testing data are 4.23% and 9.59%, respectively

A refined approach for forecasting based on neutrosophic time series

This research introduces a neutrosophic forecasting approach based on neutrosophic time series (NTS). Historical data can be transformed into neutrosophic time series data to determine their truth, indeterminacy and falsity functions. The basis for the neutrosophication process is the score and accuracy functions of historical data. In addition, neutrosophic logical relationship groups (NLRGs) are determined and a deneutrosophication method for NTS is presented. The objective of this research is to suggest an idea of first-and high-order NTS. By comparing our approach with other approaches, we conclude that the suggested approach of forecasting gets better results compared to the other existing approaches of fuzzy, intuitionistic fuzzy, and neutrosophic time series

No news in business cycles

This paper uses a structural, large dimensional factor model to evaluate the role of `news' shocks (shocks with a delayed effect on productivity) in generating the business cycle. We find that (i) existing small-scale VECM models are affected by `non-fundamentalness' and therefore fail to recover the correct shock and impulse response functions; (ii) news shocks have a limited role in explaining the business cycle; (iii) their effects are in line with what predicted by standard neoclassical theory; (iv) the bulk of business cycle flucuations is explained by shocks unrelated to technology.structural factor model; news shocks; invertibility; fundamentalness

Triangular Fuzzy Time Series for Two Factors High-order based on Interval Variations

Fuzzy time series (FTS) firstly introduced by Song and Chissom has been developed to forecast such as enrollment data, stock index, air pollution, etc. In forecasting FTS data several authors define universe of discourse using coefficient values with any integer or real number as a substitute. This study focuses on interval variation in order to get better evaluation. Coefficient values analyzed and compared in unequal partition intervals and equal partition intervals with base and triangular fuzzy membership functions applied in two factors high-order. The study implemented in the Shen-hu stock index data. The models evaluated by average forecasting error rate (AFER) and compared with existing methods. AFER value 0.28% for Shen-hu stock index daily data. Based on the result, this research can be used as a reference to determine the better interval and degree membership value in the fuzzy time series.