Are oil, gold and the euro inter-related? time series and neural network analysis

This paper investigates inter-relationships among the price behavior of oil, gold and the euro using time series and neural network methodologies. Traditionally gold is a leading indicator of future inflation. Both the demand and supply of oil as a key global commodity are impacted by inflationary expectations and such expectations determine current spot prices. Inflation influences both short and long-term interest rates that in turn influence the value of the dollar measured in terms of the euro. Certain hypotheses are formulated in this paper and time series and neural network methodologies are employed to test these hypotheses. We find that the markets for oil, gold and the euro are efficient but have limited inter-relationships among themselves.Oil, Gold, the Euro, Relationships, Time-series Analysis, Neural Network Methodology

Limit order books in statistical arbitrage and anomaly detection

Cette thèse propose des méthodes exploitant la vaste information contenue dans les carnets d’ordres (LOBs). La première partie de cette thèse découvre des inefficacités dans les LOBs qui sont source d’arbitrage statistique pour les traders haute fréquence. Le chapitre 1 développe de nouvelles relations théoriques entre les actions intercotées afin que leurs prix soient exempts d’arbitrage. Toute déviation de prix est capturée par une stratégie novatrice qui est ensuite évaluée dans un nouvel environnement de backtesting permettant l’étude de la latence et de son importance pour les traders haute fréquence. Le chapitre 2 démontre empiriquement l’existence d’arbitrage lead-lag à haute fréquence. Les relations dites lead-lag ont été bien documentées par le passé, mais aucune étude n’a montré leur véritable potentiel économique. Un modèle économétrique original est proposé pour prédire les rendements de l’actif en retard, ce qu’il réalise de manière précise hors échantillon, conduisant à des opportunités d’arbitrage de courte durée. Dans ces deux chapitres, les inefficacités des LOBs découvertes sont démontrées comme étant rentables, fournissant ainsi une meilleure compréhension des activités des traders haute fréquence. La deuxième partie de cette thèse investigue les séquences anormales dans les LOBs. Le chapitre 3 évalue la performance de méthodes d’apprentissage automatique dans la détection d’ordres frauduleux. En raison de la grande quantité de données, les fraudes sont difficilement détectables et peu de cas sont disponibles pour ajuster les modèles de détection. Un nouveau cadre d’apprentissage profond non supervisé est proposé afin de discerner les comportements anormaux du LOB dans ce contexte ardu. Celui-ci est indépendant de l’actif et peut évoluer avec les marchés, offrant alors de meilleures capacités de détection pour les régulateurs financiers.This thesis proposes methods exploiting the vast informational content of limit order books (LOBs). The first part of this thesis discovers LOB inefficiencies that are sources of statistical arbitrage for high-frequency traders. Chapter 1 develops new theoretical relationships between cross-listed stocks, so their prices are arbitrage free. Price deviations are captured by a novel strategy that is then evaluated in a new backtesting environment enabling the study of latency and its importance for high-frequency traders. Chapter 2 empirically demonstrates the existence of lead-lag arbitrage at high-frequency. Lead-lag relationships have been well documented in the past, but no study has shown their true economic potential. An original econometric model is proposed to forecast returns on the lagging asset, and does so accurately out-of-sample, resulting in short-lived arbitrage opportunities. In both chapters, the discovered LOB inefficiencies are shown to be profitable, thus providing a better understanding of high-frequency traders’ activities. The second part of this thesis investigates anomalous patterns in LOBs. Chapter 3 studies the performance of machine learning methods in the detection of fraudulent orders. Because of the large amount of LOB data generated daily, trade frauds are challenging to catch, and very few cases are available to fit detection models. A novel unsupervised deep learning–based framework is proposed to discern abnormal LOB behavior in this difficult context. It is asset independent and can evolve alongside markets, providing better fraud detection capabilities to market regulators

Neural networks can detect model-free static arbitrage strategies

In this paper we demonstrate both theoretically as well as numerically that neural networks can detect model-free static arbitrage opportunities whenever the market admits some. Due to the use of neural networks, our method can be applied to financial markets with a high number of traded securities and ensures almost immediate execution of the corresponding trading strategies. To demonstrate its tractability, effectiveness, and robustness we provide examples using real financial data. From a technical point of view, we prove that a single neural network can approximately solve a class of convex semi-infinite programs, which is the key result in order to derive our theoretical results that neural networks can detect model-free static arbitrage strategies whenever the financial market admits such opportunities

Application of Stationary Wavelet Support Vector Machines for the Prediction of Economic Recessions

This paper examines the efficiency of various approaches on the classification and prediction of economic expansion and recession periods in United Kingdom. Four approaches are applied. The first is discrete choice models using Logit and Probit regressions, while the second approach is a Markov Switching Regime (MSR) Model with Time-Varying Transition Probabilities. The third approach refers on Support Vector Machines (SVM), while the fourth approach proposed in this study is a Stationary Wavelet SVM modelling. The findings show that SW-SVM and MSR present the best forecasting performance, in the out-of sample period. In addition, the forecasts for period 2012-2015 are provided using all approaches

Network communities and the foreign exchange market

Many systems studied in the biological, physical, and social sciences are composed of multiple interacting components. Often the number of components and interactions is so large that attaining an understanding of the system necessitates some form of simplication. A common representation that captures the key connection patterns is a network in which the nodes correspond to system components and the edges represent interactions. In this thesis we use network techniques and more traditional clustering methods to coarse-grain systems composed of many interacting components and to identify the most important interactions.\ud \ud This thesis focuses on two main themes: the analysis of financial systems and the study of network communities, an important mesoscopic feature of many networks. In the first part of the thesis, we discuss some of the issues associated with the analysis of financial data and investigate the potential for risk-free profit in the foreign exchange market. We then use principal component analysis (PCA) to identify common features in the correlation structure of different financial markets. In the second part of the thesis, we focus on network communities. We investigate the evolving structure of foreign exchange (FX) market correlations by representing the correlations as time-dependent networks and investigating the evolution of network communities. We employ a node-centric approach that allows us to track the effects of the community evolution on the functional roles of individual nodes and uncovers major trading changes that occurred in the market. Finally, we consider the community structure of networks from a wide variety of different disciplines. We introduce a framework for comparing network communities and use this technique to identify networks with similar mesoscopic structures. Based on this similarity, we create taxonomies of a large set of networks from different fields and individual families of networks from the same field

The History of the Quantitative Methods in Finance Conference Series. 1992-2007

This report charts the history of the Quantitative Methods in Finance (QMF) conference from its beginning in 1993 to the 15th conference in 2007. It lists alphabetically the 1037 speakers who presented at all 15 conferences and the titles of their papers.

Forecasting Foreign Exchange Rates Using Recurrent Neural Networks : The Role of Political Uncertainty

In June 2016, the majority of UK citizens voted to leave the EU (Brexit). The referendum outcome took both citizens and policymakers by surprise. No other member state has ever left the EU. As a result, the global stock and currency markets collapsed. The impact of uncertainty on financial markets has been studied for decades (Garfinkel, 1999). Studies show that political instability has a significant impact on economic performance. In addition to the market fluctuation, it has been found to increase the unemployment rate and decrease consumers’ and companies’ willingness to invest. Thus, prolonged political instability may lead to a scenario in which the capital moves less, the quality of public services decreases, and economic growth slows down. (Carmignani, 2003; Canes-Wrone et al., 2014). Exchange rate forecasting is an important area of financial research that has recently received more popularity due to its dynamic nonlinear features. In the past, exchange rates have been analyzed using traditional financial models. However, recently academics have started to use artificial learning approaches alongside the traditional ones. In particular, neural networks have been used in time series modeling, and thus exchange rates have been modeled with neural networks. Machine learning aims to improve efficiency and make financial forecasting more automated. The empirical part of this analysis is carried out using a recurrent neural network architecture known as the Long Short Term Memory (LSTM). The LSTM model enables the analysis of non-linear data as well as the detection of diverse cause-and-effect relations. Therefore, it is reasonable to believe that accurate results can be obtained using this approach. The results are analyzed by comparing two different error values - the Mean Squared Error and the Absolute Mean Error. The results prove that the LSTM model is capable of modeling exchange rate values even in times of high volatility. As the Brexit-related uncertainty is higher, the predictability of the Pound to Euro and Dollar decreases. This finding is consistent with previous studies that have shown that political instability reduces the predictability of exchange rates. On the contrary, as the uncertainty surrounding Brexit increased, the predictability of the Pound to Yen improved. This result can partly be explained by the Safe Haven effect, according to which the value of the Yen rises as the values of other developed countries’ currencies fall. Finally, it can be stated that exchange rates are complex financial instruments whose volatility is influenced by a variety of factors and this study is able to produce new perspectives for further research.Kesäkuussa 2016 enemmistö Iso-Britannian kansasta äänesti EU:sta eroamisen puolesta (Brexit). Kansanäänestyksen tulos yllätti niin kansalaiset kuin vallanpitäjätkin. Mikään muu jäsenvaltio ei ole aikaisemmin eronnut EU:sta. Tämän seurauksena valuutta- sekä osake-markkinat romahtivat globaalisti. Epävarmuuden vaikutusta rahoitusmarkkinoihin on tutkittu jo vuosikausien ajan (Garfinkel, 1999). Tutkimukset todistavat, että poliittisella epävakaudella on merkittävä vaikutus taloudelliseen suorituskykyyn. Rahoitusmarkkinoiden heilunnan lisäksi sen on todettu lisäävän työttömyyttä sekä vähentävän kuluttajien ja yritysten investointihalukkuutta. Täten pitkittynyt poliittinen epävakaus voi johtaa tilanteeseen, jossa pääoma liikkuu hitaammin, julkisten palvelujen laatu heikentyy sekä talouskasvu hidastuu. (Carmignani, 2003; Canes-Wrone ym., 2014). Valuuttakurssien ennustaminen on tärkeä rahoituksen tutkimusala, joka on kasvattanut suosiotaan sen haastavien ja epälineaaristen piirteiden vuoksi. Aikaisemmin valuuttakursseja on tutkittu perinteisillä rahoituksen menetelmillä, mutta lähivuosina tutkijat ovat alkaneet hyödyntämään yhä enemmän koneoppimista perinteisten mallien rinnalla. Erityisesti neuroverkkoja on hyödynnetty aikasarjojen mallintamisessa ja täten myös valuuttakursseja on mallinnettu neuroverkoilla. Koneoppimisen malleilla pyritään tekemään rahoitusmarkkinoiden ennustamisesta tehokkaampaa ja itseohjautuvampaa. Tämä tutkimus hyödyntää empiirisessä osuudessa takaisinkytketyn neuroverkon arkkitehtuuria nimeltä pitkäkestoinen lyhytkestomuisti (Long Short Term Memory, LSTM). LSTM-arkkitehtuuri mahdollistaa epälineaarisen datan analysoinnin sekä monipuolisten syy-seurausketjujen hahmottamisen. Näin ollen on perusteellista uskoa, että tällä metodilla on mahdollista saavuttaa tarkkoja tuloksia valuuttakursseja analysoitaessa. Tulosten analysointi toteutetaan vertailemalla eri valuutoilla saatavia virhearvoja (keskihajonta sekä absoluuttinen keskivirhe). Tulokset todistavat, että LSTM-malli on kykenevä mallintamaan valuuttakurssien arvoja myös epävakaina aikoina. Euron ja dollarin ennustettavuus heikentyy tutkituilla ajanjaksoilla, kun Brexitiin liittyvä epävarmuus lisääntyy. Tämä tutkimustulos on johdonmukainen aikaisemman tutkimuksen kanssa, jonka perusteella on todettu, että valuuttakurssien ennustettavuus heikentyy poliittisen epävarmuuden seurauksena. Jenin ennustettavuus taas päinvastoin paranee ajanjaksolla, kun Brexitiin liittyvä epävarmuus lisääntyy. Tämä tulos voidaan osittain perustella turvasatamailmiöllä, jonka mukaan jenin arvo nousee, kun muiden kurssien arvot laskevat. Lopuksi todetaan, että valuuttakurssit ovat monimutkaisia rahoitusinstrumentteja, joiden heilahteluun vaikuttaa useita eri tekijöitä. Tästä huolimatta, tämä työ onnistuu tarjoamaan uusia näkökulmia tulevaisuuden tutkimukselle