Ensemble Committees for Stock Return Classification and Prediction

This paper considers a portfolio trading strategy formulated by algorithms in the field of machine learning. The profitability of the strategy is measured by the algorithm's capability to consistently and accurately identify stock indices with positive or negative returns, and to generate a preferred portfolio allocation on the basis of a learned model. Stocks are characterized by time series data sets consisting of technical variables that reflect market conditions in a previous time interval, which are utilized produce binary classification decisions in subsequent intervals. The learned model is constructed as a committee of random forest classifiers, a non-linear support vector machine classifier, a relevance vector machine classifier, and a constituent ensemble of k-nearest neighbors classifiers. The Global Industry Classification Standard (GICS) is used to explore the ensemble model's efficacy within the context of various fields of investment including Energy, Materials, Financials, and Information Technology. Data from 2006 to 2012, inclusive, are considered, which are chosen for providing a range of market circumstances for evaluating the model. The model is observed to achieve an accuracy of approximately 70% when predicting stock price returns three months in advance.Comment: 15 pages, 4 figures, Neukom Institute Computational Undergraduate Research prize - second plac

Theoretical vs. Empirical Classification and Prediction of Congested Traffic States

Starting from the instability diagram of a traffic flow model, we derive conditions for the occurrence of congested traffic states, their appearance, their spreading in space and time, and the related increase in travel times. We discuss the terminology of traffic phases and give empirical evidence for the existence of a phase diagram of traffic states. In contrast to previously presented phase diagrams, it is shown that "widening synchronized patterns" are possible, if the maximum flow is located inside of a metastable density regime. Moreover, for various kinds of traffic models with different instability diagrams it is discussed, how the related phase diagrams are expected to approximately look like. Apart from this, it is pointed out that combinations of on- and off-ramps create different patterns than a single, isolated on-ramp.Comment: See http://www.helbing.org for related wor

The classification and prediction of macroinvertebrate communities in British rivers

This article describes the progress of the River Communities Project which commenced in 1977. This project aimed to develop a sensitive and practical system for river site classification using macroinvertebrates as an objective means of appraising the status of British rivers. The relationship between physical and chemical features of sites and their biological communities were examined. Sampling was undertaken on 41 British rivers. Ordination techniques were used to analyze data and the sites were classified into 16 groups using multiple discrimination analysis. The potential for using the environmental data to predict to which group a site belonged and the fauna likely to be present was investigated

A discrete choice model of dividend reinvestment plans: classification and prediction

We study 852 companies with dividend reinvestment plans in 1999 matched by total assets to 852 companies without such plans. We use discrete choice methods to predict the classification of these companies. We interpret the misclassified companies as being likely to switch their plan status. That is, if a firm's financial data suggest that a company should have had a dividend reinvestment plan in 1999 but did not, then we expect that it would be more likely to institute a plan than the other companies in the sample. Conversely, if it did have a plan but the financial data suggest that it should not, then we expect that the company would be more likely to drop the plan. We use data from 2004 to explore this conjecture and find evidence supporting it. Our model is an economically and statistically reliable predictor of changes in plan status. We also identify which variables have the most influence on a company's decision whether or not to offer a plan.

CLASSIFICATION AND PREDICTION OF PORT VARIABLES

[EN] Many variables are included in planning and management of port terminals. They can be economic, social, environmental and institutional. Agent needs to know relationship between these variables to modify planning conditions. Use of Bayesian Networks allows for classifying, predicting and diagnosing these variables. Bayesian Networks allow for estimating subsequent probability of unknown variables, basing on know variables. In planning level, it means that it is not necessary to know all variables because their relationships are known. Agent can know interesting information about how port variables are connected. It can be interpreted as cause-effect relationship. Bayesian Networks can be used to make optimal decisions by introduction of possible actions and utility of their results. In proposed methodology, a data base has been generated with more than 40 port variables. They have been classified in economic, social, environmental and institutional variables, in the same way that smart port studies in Spanish Port System make. From this data base, a network has been generated using a non-cyclic conducted grafo which allows for knowing port variable relationships - parents-children relationships-. Obtained network exhibits that economic variables are – in cause-effect terms- cause of rest of variable typologies. Economic variables represent parent role in the most of cases. Moreover, when environmental variables are known, obtained network allows for estimating subsequent probability of social variables. It has been concluded that Bayesian Networks allow for modeling uncertainty in a probabilistic way, even when number of variables is high as occurs in planning and management of port terminals.Molina Serrano, B.; González Cancelas, MN.; Soler Flores, F.; Camarero Orive, A. (2016). CLASSIFICATION AND PREDICTION OF PORT VARIABLES. En XII Congreso de ingeniería del transporte. 7, 8 y 9 de Junio, Valencia (España). Editorial Universitat Politècnica de València. 1437-1444. https://doi.org/10.4995/CIT2016.2015.3226OCS1437144

The role of emotional variables in the classification and prediction of collective social dynamics

We demonstrate the power of data mining techniques for the analysis of collective social dynamics within British Tweets during the Olympic Games 2012. The classification accuracy of online activities related to the successes of British athletes significantly improved when emotional components of tweets were taken into account, but employing emotional variables for activity prediction decreased the classifiers' quality. The approach could be easily adopted for any prediction or classification study with a set of problem-specific variables.Comment: 16 pages, 9 figures, 2 tables and 1 appendi

Modelling Identity Rules with Neural Networks

In this paper, we show that standard feed-forward and recurrent neural networks fail to learn abstract patterns based on identity rules. We propose Repetition Based Pattern (RBP) extensions to neural network structures that solve this problem and answer, as well as raise, questions about integrating structures for inductive bias into neural networks. Examples of abstract patterns are the sequence patterns ABA and ABB where A or B can be any object. These were introduced by Marcus et al (1999) who also found that 7 month old infants recognise these patterns in sequences that use an unfamiliar vocabulary while simple recurrent neural networks do not. This result has been contested in the literature but it is confirmed by our experiments. We also show that the inability to generalise extends to different, previously untested, settings. We propose a new approach to modify standard neural network architectures, called Repetition Based Patterns (RBP) with different variants for classification and prediction. Our experiments show that neural networks with the appropriate RBP structure achieve perfect classification and prediction performance on synthetic data, including mixed concrete and abstract patterns. RBP also improves neural network performance in experiments with real-world sequence prediction tasks. We discuss these finding in terms of challenges for neural network models and identify consequences from this result in terms of developing inductive biases for neural network learning