Adaptive Algorithms For Classification On High-Frequency Data Streams: Application To Finance

Mención Internacional en el título de doctorIn recent years, the problem of concept drift has gained importance in the financial domain. The succession of manias, panics and crashes have stressed the nonstationary nature and the likelihood of drastic structural changes in financial markets. The most recent literature suggests the use of conventional machine learning and statistical approaches for this. However, these techniques are unable or slow to adapt to non-stationarities and may require re-training over time, which is computationally expensive and brings financial risks. This thesis proposes a set of adaptive algorithms to deal with high-frequency data streams and applies these to the financial domain. We present approaches to handle different types of concept drifts and perform predictions using up-to-date models. These mechanisms are designed to provide fast reaction times and are thus applicable to high-frequency data. The core experiments of this thesis are based on the prediction of the price movement direction at different intraday resolutions in the SPDR S&P 500 exchange-traded fund. The proposed algorithms are benchmarked against other popular methods from the data stream mining literature and achieve competitive results. We believe that this thesis opens good research prospects for financial forecasting during market instability and structural breaks. Results have shown that our proposed methods can improve prediction accuracy in many of these scenarios. Indeed, the results obtained are compatible with ideas against the efficient market hypothesis. However, we cannot claim that we can beat consistently buy and hold; therefore, we cannot reject it.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Gustavo Recio Isasi.- Secretario: Pedro Isasi Viñuela.- Vocal: Sandra García Rodrígue

A survey on machine learning for recurring concept drifting data streams

The problem of concept drift has gained a lot of attention in recent years. This aspect is key in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks affecting their generative processes. In this survey, we review the relevant literature to deal with regime changes in the behaviour of continuous data streams. The study starts with a general introduction to the field of data stream learning, describing recent works on passive or active mechanisms to adapt or detect concept drifts, frequent challenges in this area, and related performance metrics. Then, different supervised and non-supervised approaches such as online ensembles, meta-learning and model-based clustering that can be used to deal with seasonalities in a data stream are covered. The aim is to point out new research trends and give future research directions on the usage of machine learning techniques for data streams which can help in the event of shifts and recurrences in continuous learning scenarios in near real-time

Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic Review

Recent crises, recessions and bubbles have stressed the non-stationary nature and the presence of drastic structural changes in the financial domain. The most recent literature suggests the use of conventional machine learning and statistical approaches in this context. Unfortunately, several of these techniques are unable or slow to adapt to changes in the price-generation process. This study aims to survey the relevant literature on Machine Learning for financial prediction under regime change employing a systematic approach. It reviews key papers with a special emphasis on technical analysis. The study discusses the growing number of contributions that are bridging the gap between two separate communities, one focused on data stream learning and the other on economic research. However, it also makes apparent that we are still in an early stage. The range of machine learning algorithms that have been tested in this domain is very wide, but the results of the study do not suggest that currently there is a specific technique that is clearly dominant

Incremental Market Behavior Classification in Presence of Recurring Concepts

In recent years, the problem of concept drift has gained importance in the financial domain. The succession of manias, panics and crashes have stressed the non-stationary nature and the likelihood of drastic structural or concept changes in the markets. Traditional systems are unable or slow to adapt to these changes. Ensemble-based systems are widely known for their good results predicting both cyclic and non-stationary data such as stock prices. In this work, we propose RCARF (Recurring Concepts Adaptive Random Forests), an ensemble tree-based online classifier that handles recurring concepts explicitly. The algorithm extends the capabilities of a version of Random Forest for evolving data streams, adding on top a mechanism to store and handle a shared collection of inactive trees, called concept history, which holds memories of the way market operators reacted in similar circumstances. This works in conjunction with a decision strategy that reacts to drift by replacing active trees with the best available alternative: either a previously stored tree from the concept history or a newly trained background tree. Both mechanisms are designed to provide fast reaction times and are thus applicable to high-frequency data. The experimental validation of the algorithm is based on the prediction of price movement directions one second ahead in the SPDR (Standard & Poor's Depositary Receipts) S&P 500 Exchange-Traded Fund. RCARF is benchmarked against other popular methods from the incremental online machine learning literature and is able to achieve competitive results.This research was funded by the Spanish Ministry of Economy and Competitiveness under grant number ENE2014-56126-C2-2-R

Mechanistic versatility at Ir(PSiP) pincer catalysts: triflate proton shuttling from 2-Butyne to Diene and [3]Dendralene motifs

The five-coordinate hydrido complex [IrH(OTf)(PSiP)] (1) catalytically transforms 2-butyne into a mixture of its isomer 1,3-butadiene, and [3]dendralene and linear hexatriene dimerization products: (E)-4-methyl-3-methylene-1,4-hexadiene and (3Z)-3,4-dimethyl-1,3,5-hexatriene, respectively. Under the conditions of the catalytic reaction, benzene, and 363 K, the hexatriene further undergoes thermal electrocyclization into 2,3-dimethyl-1,3-cyclohexadiene. The reactions between 1 and the alkyne substrate allow isolation or nuclear magnetic resonance (NMR) observation of catalyst resting states and possible reaction intermediates, including complexes with the former PSiP pincer ligands disassembled into PSi and PC chelates, and species coordinating allyl or carbene fragments en route to products. The density functional theory (DFT) calculations guided by these experimental observations disclose competing mechanisms for C–H bond elaboration that move H atoms either classically, as hydrides, or as protons transported by the triflate. This latter role of triflate, previously recognized only for more basic anions such as carboxylates, is discussed to result from combining the unfavorable charge separation in the nonpolar solvent and the low electronic demand from the metal to the anion at coordination positions trans to silicon. Triflate deprotonation of methyl groups is key to release highly coordinating diene products from stable allyl intermediates, thus enabling catalytic cycling

CNO behaviour in planet-harbouring stars. II. Carbon abundances in stars with and without planets using the CH band

Context. Carbon, oxygen and nitrogen (CNO) are key elements in stellar formation and evolution, and their abundances should also have a significant impact on planetary formation and evolution. Aims. We present a detailed spectroscopic analysis of 1110 solar-type stars, 143 of which are known to have planetary companions. We have determined the carbon abundances of these stars and investigate a possible connection between C and the presence of planetary companions. Methods. We used the HARPS spectrograph to obtain high-resolution optical spectra of our targets. Spectral synthesis of the CH band at 4300\AA was performed with the spectral synthesis codes MOOG and FITTING. Results. We have studied carbon in several reliable spectral windows and have obtained abundances and distributions that show that planet host stars are carbon rich when compared to single stars, a signature caused by the known metal-rich nature of stars with planets. We find no different behaviour when separating the stars by the mass of the planetary companion. Conclusions. We conclude that reliable carbon abundances can be derived for solar-type stars from the CH band at 4300\AA. We confirm two different slope trends for [C/Fe] with [Fe/H] because the behaviour is opposite for stars above and below solar values. We observe a flat distribution of the [C/Fe] ratio for all planetary masses, a finding that apparently excludes any clear connection between the [C/Fe] abundance ratio and planetary mass.Comment: 10 pages, 10 figures. Accepted to A&

C/O vs Mg/Si ratios in solar type stars: The HARPS sample

Aims. We present a detailed study of the Mg/Si and C/O ratios and their importance in determining the mineralogy of planetary companions. Methods. Using 499 solar-like stars from the HARPS sample, we determine C/O and Mg/Si elemental abundance ratios to study the nature of the possible planets formed. We separated the planetary population in low-mass planets ( < 30 $\rm M_{\odot}$) and high-mass planets ( > 30 $\rm M_{\odot}$) to test for possible relation with the mass. Results. We find a diversity of mineralogical ratios that reveal the different kinds of planetary systems that can be formed, most of them dissimilar to our solar system. The different values of the Mg/Si and C/O ratios can determine different composition of planets formed. We found that 100\% of our planetary sample present C/O < 0.8. 86\% of stars with high-mass companions present 0.8 > C/O > 0.4, while 14\% present C/O values lower than 0.4. Regarding Mg/Si, all stars with low-mass planetary companion showed values between 1 and 2, while 85% of the high-mass companion sample does. The other 15\% showed Mg/Si values below 1. No stars with planets were found with Mg/Si > 2. Planet hosts with low-mass companions present C/O and Mg/Si ratios similar to those found in the Sun, whereas stars with high-mass companions have lower C/O.Comment: 9 pages, 12 figues. Accepted in A&

Pon un fósil en tu vida ¡y sácale partido! (Propuesta de recurso para el aprovechamiento didáctico de los fósiles)

p. 138-144En este taller se presenta una propuesta de recurso docente centrado en el reconocimiento y análisis de organismos fósiles. Con él se pretende dar a conocer el método de trabajo empleado en Paleontología así como la forma de obtener algunos de las datos que el estudio de los fósiles puede aportar a la Geología. Con este fin se ha ideado un juego en el que se simula la metodología empleada en Paleontología, mediante el establecimiento de especialistas en diferentes grupos fósiles que obtienen sus conocimientos de la información contenida en fichas. A partir de este punto, la colaboración entre especialistas de distintos grupos ha de permitir resolver diferentes problemas centrados en la datación, correlación, reconocimiento de estructuras tectónicas y determinación genética de diversas secuencias de rocas sedimentarias.S