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

The impact of macroeconomic leading indicators on inventory management

Forecasting tactical sales is important for long term decisions such as procurement and informing lower level inventory management decisions. Macroeconomic indicators have been shown to improve the forecast accuracy at tactical level, as these indicators can provide early warnings of changing markets while at the same time tactical sales are sufficiently aggregated to facilitate the identification of useful leading indicators. Past research has shown that we can achieve significant gains by incorporating such information. However, at lower levels, that inventory decisions are taken, this is often not feasible due to the level of noise in the data. To take advantage of macroeconomic leading indicators at this level we need to translate the tactical forecasts into operational level ones. In this research we investigate how to best assimilate top level forecasts that incorporate such exogenous information with bottom level (at Stock Keeping Unit level) extrapolative forecasts. The aim is to demonstrate whether incorporating these variables has a positive impact on bottom level planning and eventually inventory levels. We construct appropriate hierarchies of sales and use that structure to reconcile the forecasts, and in turn the different available information, across levels. We are interested both at the point forecast and the prediction intervals, as the latter inform safety stock decisions. Therefore the contribution of this research is twofold. We investigate the usefulness of macroeconomic leading indicators for SKU level forecasts and alternative ways to estimate the variance of hierarchically reconciled forecasts. We provide evidence using a real case study

How Europe can deliver: Optimising the division of competences among the EU and its member states

This study aims to give guidance for a better-performing EU through an improved allocation of competences between the European Union and its member states. The study analyses eight specific policies from a wide range of fields with respect to their preferable assignment. The analysis applies a unified quantified approach and is precise in its definition of ‘counterfactuals’. These counterfactuals are understood as conceptual alternatives to the allocation of competences under the status quo. As such, they either relate to a new European competence (if the policy is currently a national responsibility) or a new national competence (if the policy is currently assigned to the EU). The comprehensive, quantification-based assessments indicate that it would be preferable to have responsibility for higher education and providing farmers with income support at the national level. Conversely, a shift of competences to the EU level would be advantageous when it comes to asylum policies, defence, corporate taxation, development aid and a (complementary) unemployment insurance scheme in the euro area. For one policy – railway freight transport – the findings are indeterminate. Overall, the study recommends a differentiated integration strategy comprising both new European policies and a roll-back of EU competences in other fields

Virtual currency: a cointegration analysis between bitcoin prices and economic and financial data

The cryptocurrencies development around the world has been studied and published by the media, speculating on its continuity, applicability and security. The Bitcoin stands out as the virtual currency that has achieved the highest market value to date and for being in circulation for more than 5 years. This study intends to investigate the existence of a dynamic relationship between Bitcoin prices and economic and financial data whose relationship with physical currencies is known or it has been showed in previous studies. This data includes the Crude and Gold prices, the 6-month and 1-year U.S. Treasury Yields and the S&P 500 Index prices. The results of the study suggests that only the 6-month U.S. Treasury Yields presents a long-term relationship with the Bitcoin prices.A criação e crescimento de moedas virtuais pelo mundo têm sido alvo de vários estudos e notícias divulgadas pelos media, especulando-se quanto à sua continuidade, aplicabilidade e segurança. Dessas moedas, destaca-se a Bitcoin, a moeda virtual que apresentou até hoje o maior valor de mercado e que se tem mantido em circulação há mais de 5 anos. O presente estudo tem como objetivo investigar a existência de uma relação dinâmica entre os preços da Bitcoin e indicadores económico-financeiros cuja relação com as moedas físicas é conhecida ou foi demonstrada em estudos anteriores. Esses indicadores são os preços do petróleo e do ouro, as taxas de juro a 6 meses e a 1 ano das obrigações do Tesouro americanas e os valores de fecho do índice S&P 500. Os resultados deste estudo demonstram que apenas as taxas de juro a 6 meses de obrigações do Tesouro americanas apresentam uma relação de longo prazo com as cotações da Bitcoin

International Conference Management, Business and Economics

UBT Annual International Conference is the 9th international interdisciplinary peer reviewed conference which publishes works of the scientists as well as practitioners in the area where UBT is active in Education, Research and Development. The UBT aims to implement an integrated strategy to establish itself as an internationally competitive, research-intensive university, committed to the transfer of knowledge and the provision of a world-class education to the most talented students from all background. The main perspective of the conference is to connect the scientists and practitioners from different disciplines in the same place and make them be aware of the recent advancements in different research fields, and provide them with a unique forum to share their experiences. It is also the place to support the new academic staff for doing research and publish their work in international standard level. This conference consists of sub conferences in different fields like: Art and Digital Media Agriculture, Food Science and Technology Architecture and Spatial Planning Civil Engineering, Infrastructure and Environment Computer Science and Communication Engineering Dental Sciences Education and Development Energy Efficiency Engineering Integrated Design Information Systems and Security Journalism, Media and Communication Law Language and Culture Management, Business and Economics Modern Music, Digital Production and Management Medicine and Nursing Mechatronics, System Engineering and Robotics Pharmaceutical and Natural Sciences Political Science Psychology Sport, Health and Society Security Studies This conference is the major scientific event of the UBT. It is organizing annually and always in cooperation with the partner universities from the region and Europe. We have to thank all Authors, partners, sponsors and also the conference organizing team making this event a real international scientific event

Machine Learning of Lifestyle Data for Diabetes

Self-Monitoring of Blood Glucose (SMBG) for Type-2 Diabetes (T2D) remains highly challenging for both patients and doctors due to the complexities of diabetic lifestyle data logging and insufficient short-term and personalized recommendations/advice. The recent mobile diabetes management systems have been proved clinically effective to facilitate self-management. However, most such systems have poor usability and are limited in data analytic functionalities. These two challenges are connected and affected by each other. The ease of data recording brings better data for applicable data analytic algorithms. On the other hand, the irrelevant or inaccurate data input will certainly commit errors and noises. The output of data analysis, as potentially valuable patterns or knowledge, could be the incentives for users to contribute more data. We believe that the incorporation of machine learning technologies in mobile diabetes management could tackle these challenge simultaneously. In this thesis, we propose, build, and evaluate an intelligent mobile diabetes management system, called GlucoGuide for T2D patients. GlucoGuide conveniently aggregates varieties of lifestyle data collected via mobile devices, analyzes the data with machine learning models, and outputs recommendations. The most complicated part of SMBG is diet management. GlucoGuide aims to address this crucial issue using classification models and camera-based automatic data logging. The proposed model classifies each food item into three recommendation classes using its nutrient and textual features. Empirical studies show that the food classification task is effective. A lifestyle-data-driven recommendations framework in GlucoGuide can output short-term and personalized recommendations of lifestyle changes to help patients stabilize their blood glucose level. To evaluate performance and clinical effectiveness of this framework, we conduct a three-month clinical trial on human subjects, in collaboration with Dr. Petrella (MD). Due to the high cost and complexity of trials on humans, a small but representative subject group is involved. Two standard laboratory blood tests for diabetes are used before and after the trial. The results are quite remarkable. Generally speaking, GlucoGuide amounted to turning an early diabetic patient to be pre-diabetic, and pre-diabetic to non-diabetic, in only 3-months, depending on their before-trial diabetic conditions. cThis clinical dataset has also been expanded and enhanced to generate scientifically controlled artificial datasets. Such datasets can be used for varieties of machine learning empirical studies, as our on-going and future research works. GlucoGuide now is a university spin-off, allowing us to collect a large scale of practical diabetic lifestyle data and make potential impact on diabetes treatment and management

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace

Prediction of nonlinear nonstationary time series data using a digital filter and support vector regression

Volatility is a key parameter when measuring the size of the errors made in modelling returns and other nonlinear nonstationary time series data. The Autoregressive Integrated Moving- Average (ARIMA) model is a linear process in time series; whilst in the nonlinear system, the Generalised Autoregressive Conditional Heteroskedasticity (GARCH) and Markov Switching GARCH (MS-GARCH) models have been widely applied. In statistical learning theory, Support Vector Regression (SVR) plays an important role in predicting nonlinear and nonstationary time series data. We propose a new class model comprised of a combination of a novel derivative Empirical Mode Decomposition (EMD), averaging intrinsic mode function (aIMF) and a novel of multiclass SVR using mean reversion and coefficient of variance (CV) to predict financial data i.e. EUR-USD exchange rates. The proposed novel aIMF is capable of smoothing and reducing noise, whereas the novel of multiclass SVR model can predict exchange rates. Our simulation results show that our model significantly outperforms simulations by state-of-art ARIMA, GARCH, Markov Switching generalised Autoregressive conditional Heteroskedasticity (MS-GARCH), Markov Switching Regression (MSR) models and Markov chain Monte Carlo (MCMC) regression.Open Acces