Using Gleaned Computing Power to Forecast Emerging-market Equity Returns with Machine Learning

This paper examines developing machine learning and statistic models to build forecast models for equity returns in an emergent market, with an emphasis on computing. Distributed systems were pared with random search and Bayesian optimization to find good hyperparameters for neural networks. No significant results were found

Risk Visualization: A Mechanism for Supporting Unstructured Decision Making Processes

The premise of this paper is that risk visualization has the potential to reduce the seemingly irrational behavior of decision makers. In this context, we present a model that enhances our understanding of visualization and how it can be used to support risk based decision making. The contribution of our research stems from the fact that decision making scenarios in business are characterized by uncertainty and a lack of structure. The complexity inherent in such scenarios is manifested in the form of unavailability of information, too many alternatives, inability to quantify alternatives, or lack of knowledge of the payoff matrix. This is particularly prevalent in domains such as investment decision making. Rational decision making in such domains requires a careful assessment of the risk reward payoff matrix. However, individuals cope with such uncertainty by resorting to a variety of heuristics. Prior decision support models have been unsuccessful in dealing with complexity and nuances that have come to typify such heuristic based decision making

Web Mining For Financial Market Prediction Based On Online Sentiments

Financial market prediction is a critically important research topic in financial data mining because of its potential commerce application and attractive profits. Previous studies in financial market prediction mainly focus on financial and economic indicators. Web information, as an information repository, has been used in customer relationship management and recommendation, but it is rarely considered to be useful in financial market prediction. In this paper, a combined web mining and sentiment analysis method is proposed to forecast financial markets using web information. In the proposed method, a spider is firstly employed to crawl tweets from Twitter. Secondly, Opinion Finder is offered to mining the online sentiments hidden in tweets. Thirdly, some new sentiment indicators are suggested and a stochastic time effective function (STEF) is introduced to integrate everyday sentiments. Fourthly, support vector regressions (SVRs) are used to model the relationship between online sentiments and financial market prices. Finally, the selective model can be serviced for financial market prediction. To validate the proposed method, Standard and Poor’s 500 Index (S&P 500) is used for evaluation. The empirical results show that our proposed forecasting method outperforms the traditional forecasting methods, and meanwhile, the proposed method can also capture individual behavior in financial market quickly and easily. These findings imply that the proposed method is a promising approach for financial market prediction

Application of Seasonal and Multivariable Grey Prediction Models for Short-Term Load Forecasting

Short-term electricity load forecasting is one of the most important operations in electricity markets. The success in the operations of electricity market participants partially depends on the accuracy of load forecasts. In this paper, three grey prediction models, which are seasonal grey model (SGM), multivariable grey model (GM (1,N)) and genetic algorithm based multivariable grey model (GAGM (1,N)), are proposed for short-term load forecasting problem in day-ahead market. The effectiveness of these models is illustrated with two real-world data sets. Numerical results show that the genetic algorithm based multivariable grey model (GAGM (1,N)) is the most efficient grey forecasting model through its better forecast accuracy

FLANN Based Model to Predict Stock Price Movements of Stock Indices

Financial Forecasting or specifically Stock Market prediction is one of the hottest fields of research lately due to its commercial applications owing to the high stakes and the kinds of attractive benefits that it has to offer. Forecasting the price movements in stock markets has been a major challenge for common investors, businesses, brokers and speculators. As more and more money is being invested the investors get anxious of the future trends of the stock prices in the market. The primary area of concern is to determine the appropriate time to buy, hold or sell. In their quest to forecast, the investors assume that the future trends in the stock market are based at least in part on present and past events and data [1]. However financial time-series is one of the most ‘noisiest’ and ‘non-stationary’ signals present and hence very difficult to forecas

Prediction of CO2 Emissions in Iran using Grey and ARIMA Models

The examination of economic aspects of gas emissions and its consequences is very important, especially in terms of its volume at the current increasing trend. Therefore, the prediction of air pollution emissions of carbon dioxide can give the correct direction to policies adopted.  Hence, studying and forecasting of gas emissions is necessary. The purpose of this paper is the prediction of CO2 emissions based on Grey System and Autoregressive Integrated Moving Average and comparison of these two methods by RMSE, MAE and MAPE metrics. The results show the more accuracy of Grey system forecasting rather than other methods of prediction.  Also, based on the estimated results, the amount of carbon dioxide emissions will reach up to 925.68 million tons in 2020 which shows an increase of 66 percent growth compared to 2010 which is highly significant. Keywords: Carbon Dioxide Emissions; Forecasting; Grey system; Iran JEL Classifications: C22; C53; Q5

Grey relation performance correlations among economics, energy use and carbon dioxide emission in Taiwan

Abstract This study explores the inter-relationships among economy, energy and CO 2 emissions of 37 industrial sectors in Taiwan in order to provide insight regarding sustainable development policy making. Grey relation analysis was used to analyse the productivity, aggregate energy consumption, and the use of fuel mix (electricity, coal, oil and gas) in relation to CO 2 emission changes. An innovative evaluative index system was devised to explore grey relation grades among economics, energy and environmental quality. Results indicate that a rapid increase in electricity generation during the past 10 years is the main reason for CO 2 emission increase in Taiwan. The largest CO 2 emitting sectors include iron and steel, transportation, petrochemical materials, commerce and other services. Therefore, it is important to reduce the energy intensity of these sectors by energy conservation, efficiency improvement and adjustment of industrial structure towards high value-added products and services. Economic growth for all industries has a more significant influence, than does total energy consumption, on CO 2 emission increase in Taiwan. It is also important to decouple the energy consumption and production to reduce the impacts of CO 2 on economic growth. Furthermore, most of the sectors examined had increased CO 2 emissions, except for machinery and road transportation. For high energy intensive and CO 2 intensive industries, governmental policies for CO 2 mitigation should be directed towards low carbon fuels as well as towards enhancement of the demand side management mechanism, without loss of the nation's competitiveness.

Predicting the Daily Return Direction of the Stock Market using Hybrid Machine Learning Algorithms

Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields, including stock market investment. However, few studies have focused on forecasting daily stock market returns, especially when using powerful machine learning techniques, such as deep neural networks (DNNs), to perform the analyses. DNNs employ various deep learning algorithms based on the combination of network structure, activation function, and model parameters, with their performance depending on the format of the data representation. This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF (ticker symbol: SPY) based on 60 financial and economic features. DNNs and traditional artificial neural networks (ANNs) are then deployed over the entire preprocessed but untransformed dataset, along with two datasets transformed via principal component analysis (PCA), to predict the daily direction of future stock market index returns. While controlling for overfitting, a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000. Moreover, a set of hypothesis testing procedures are implemented on the classification, and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset, as well as several other hybrid machine learning algorithms. In addition, the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested, including in a comparison against two standard benchmarks