A Hidden Markov Model Approach to Classify and Predict the Sign of Financial Local Trends

In the field of financial time series analysis it is widely accepted that the returns (price variations) are unpredictable in the long period [1]; nevertheless, this unappealing constraint could be somehow relaxed if sufficiently short time intervals are considered. In this paper this alternative scenario is investigated with a novel methodology, aimed at analyzing short (local) financial trends for predicting their sign (increase or decrease). This peculiar problem needs specific models – different from standard techniques used for estimating the volatility or the returns – able to capture the asymmetries between increase and decrease periods in the short time. This is achieved by modeling directly the signs of the local trends using two separate Hidden Markov models, one for positive and one for negative trends. The approach has been tested with different financial indexes, with encouraging results also in comparison with standard methods

Огляд сучасних розробок прогнозування часових рядів з використанням прихованих марківських моделей

The review of modern developments in the area of time series forecasting using Markov processes has been performed. The main problems arising in the process of using Markov models (HMM) in forecasting and algorithms of their solution are considered. The basic algorithm of time series forecasting using HMM has been presented. The review of basic algorithm modifications has been performed. The range of questions to investigate in this area of research has been discussed.Выполнен обзор современных разработок в области использования марковских процессов к решению задачи прогнозирования временных рядов. Рассмотрены основные проблемы, возникающие в процессе использования марковских моделей (СММ) в прогнозировании, и алгоритмы их решения. Представлен базовый алгоритм прогнозирования временных рядов с использованием СММ. Сделан обзор модификаций базового алгоритма. Рассмотрен круг открытых вопросов в этой области исследований.Виконано огляд сучасних розробок у сфері використання марківських процесів до вирішення задачі прогнозування часових рядів. Розглянуто основні проблеми, які виникають у процесі використання прихованих марківських моделей (ПММ) у прогнозуванні, та алгоритми їх розв’язку. Переставлено базовий алгоритм прогнозування часових рядів з використанням ПММ. Зроблено огляд модифікацій базового алгоритму. Розглянуто коло відкритих питань у цій сфері досліджень.

Predicting market direction with hidden Markov models

This paper develops the model of Bicego, Grosso, and Otranto (2008) and applies Hidden Markov Models to predict market direction. The paper draws an analogy between financial markets and speech recognition, seeking inspiration from the latter to solve common issues in quantitative investing. Whereas previous works focus mostly on very complex modifications of the original hidden markov model algorithm, the current paper provides an innovative methodology by drawing inspiration from thoroughly tested, yet simple, speech recognition methodologies. By grouping returns into sequences, Hidden Markov Models can then predict market direction the same way they are used to identify phonemes in speech recognition. The model proves highly successful in identifying market direction but fails to consistently identify whether a trend is in place. All in all, the current paper seeks to bridge the gap between speech recognition and quantitative finance and, even though the model is not fully successful, several refinements are suggested and the room for improvement is significant.UNL - NSB

Understanding musical genre preference evolution within a social network

Dissertation presented as partial requirement for obtaining the Master’s degree in Information Management, specialization in Knowledge Management and Business IntelligenceA música é um campo que simplesmente não pode ser desassociado dos aspetos sociais da vida. Durante a história da humanidade, a música mais popular consistiu sempre num reflexo dos diferentes aspetos da sociedade. Como tal, diferentes estudos foram feitos anteriormente que demonstram este reflexo e obtiveram diversas conclusões. Nesta tese, iremos contribuir para este campo através de uma análise da evolução das preferências de géneros musicais ao longo do tempo através de uma rede social. Usando dados obtidos através de uma experiência de evolução social com cerca de 80 participantes faremos uma análise dos dados existentes. De seguida, esta análise é tida em conta para definir os princípios necessários para representar e analisar a rede social existente. Após esta definição, iremos avaliar a homogeneização da rede social ao longo do tempo. Isto é, iremos avaliar a evolução das diferenças de preferências musicais entre indivíduos que estão ligados na rede social, de forma a perceber se existe alguma tendência de estas diminuírem ao longo do tempo. Um Sequential Algorithm, conhecido como Hidden Markov Model, é aplicado para prever mudanças nas preferências de géneros musicais, considerando as próprias preferências de cada individuo, bem como as preferências dos indivíduos com que este se encontra ligado na nossa rede social. O algoritmo Support Vector Machines é também utilizado para fazer o mesmo tipo de previsão que o modelo anterior servindo como comparação. Por último, discutimos o processo e as limitações que conduziram à definição final do nosso modelo e de forma a contextualizar os resultados que foram obtidos através deste. Em suma, esta tese procurar acrescentar ao trabalho existente em termos de preferências de géneros musicais através de uma avaliação destes dentro do contexto de uma rede social e tendo também em conta a evolução destas ao longo do tempo.Music is a field that simply cannot be disassociated with the social aspects of life. Throughout human history, popular music has always been a reflection of the different aspects of society. As such, there is an interesting amount of studies available that showcase this reflection and draw multiple types of insights. In this thesis, we will look to contribute to this field by assessing the evolution of musical genre preferences over time throughout a social network. Using data obtained through a social evolution experiment of around 80 different individuals we will make an initial assessment of our existing data. This evaluation is then taken into consideration in the next phase of our work where we define the principles necessary to represent and analyse the existing social network. Afterwards, we will showcase a representation of this network, as well as analyse it using various metrics and sub-structures commonly applied in Social Network Analysis. After this, we will evaluate the homogenisation of a network as time goes on. In other words, we will assess the evolution of differences in preferences between individuals that were connected in the social network, in order to understand if there is a trend of these differences diminishing over time. A Sequential-Based algorithm, more specifically, a Hidden Markov Model is used to predict the change in musical genre preferences. This was done by considering each individual’s own preferences as well as the preferences of his connections within the social network with the ultimate goal of assessing how influential the network is in the evolution of a person’s musical genre preferences. To tackle the same research question and provide an alternative approach, as well as a comparison model, we used a Support Vector Machine model. Finally, we discuss the results and limitations that led to our model definition. Overall, this thesis seeks to build upon previous work regarding musical genre preferences by assessing these within the context of a network and taking into account the evolution of these over time