An Examination of Alternative Trading Techniques Using Intraday EUR/USD Currency Prices

Global financial institutions provide a mechanism for multinational corporations to hedge against exchange rate risk via currency futures contracts and spot exchange rates. Currency managers working at these global financial institutions overseeing EUR/USD spot currency traders lack adequate data to determine if alternative trading tools could increase net gains for their respective firms. The purpose of this quantitative study is to examine the net gains from alternative trading techniques that can be utilized by currency managers working for international banks and hedge funds when trading the EUR/USD currency on an intraday basis. A buy and hold strategy, sell and hold strategy, and a Bollinger Band strategy was applied to tick level sample data gathered from 2009 to 2016 to determine net gains from each strategy. The results of an ANOVA test indicate that there is a statistically significant difference, however the Bollinger Band strategy produced an overall net loss from trading. The findings suggest that using an alternative trading strategy, Bollinger Bands, on an intraday basis does not increase net gains from trading activity

Financial crises and bank failures: a review of prediction methods

In this article we analyze financial and economic circumstances associated with the U.S. subprime mortgage crisis and the global financial turmoil that has led to severe crises in many countries. We suggest that the level of cross-border holdings of long-term securities between the United States and the rest of the world may indicate a direct link between the turmoil in the securitized market originated in the United States and that in other countries. We provide a summary of empirical results obtained in several Economics and Operations Research papers that attempt to explain, predict, or suggest remedies for financial crises or banking defaults; we also extensively outline the methodologies used in them. The intent of this article is to promote future empirical research for preventing financial crises.Subprime mortgage ; Financial crises

Machine Learning-Driven Decision Making based on Financial Time Series

L'abstract è presente nell'allegato / the abstract is in the attachmen

Stock portfolio selection using learning-to-rank algorithms with news sentiment

In this study, we apply learning-to-rank algorithms to design trading strategies using relative performance of a group of stocks based on investors' sentiment toward these stocks. We show that learning-to-rank algorithms are effective in producing reliable rankings of the best and the worst performing stocks based on investors' sentiment. More specifically, we use the sentiment shock and trend indicators introduced in the previous studies, and we design stock selection rules of holding long positions of the top 25% stocks and short positions of the bottom 25% stocks according to rankings produced by learning-to-rank algorithms. We then apply two learning-to-rank algorithms, ListNet and RankNet, in stock selection processes and test long-only and long-short portfolio selection strategies using 10 years of market and news sentiment data. Through backtesting of these strategies from 2006 to 2014, we demonstrate that our portfolio strategies produce risk-adjusted returns superior to the S&P500 index return, the hedge fund industry average performance - HFRIEMN, and some sentiment-based approaches without learning-to-rank algorithm during the same period

Essays in Microstructure Analysis in the Foreign Exchange Market

The aim of this thesis is to investigate the effects of foreign exchange order flows on exchange rate and stock market changes, in particular to examine the forecasting power of order flows and better understand the nature of the private information conveyed in order flows in the foreign exchange market. Chapter 1 investigates the performance of foreign exchange customer order flows (six major exchange rates over 3.5 years) as an additional explanatory variable to technical analysis to forecast exchange rate changes by applying genetic algorithm non-linear methodology. Using the interval permutations technique, we suggest that the improvement of order flows to the performance of technical analysis is not consistently present. Chapter 2 examines the role daily customer GBPUSD order flows play in explaining concurrent and future stock market changes in both UK and US, and discusses the heterogeneous effects from different groups of customers. The basic hypothesis tested is that if foreign exchange order flows have days-ahead effects on future stock market changes, it suggests that at least a part of the information carried by foreign exchange order flows is relevant for stock markets. Using daily GBPUSD order flows over 3.5 years from 2002 to 2006 provided by RBS, we find that: 1) impacts of order flows from corporate customers on stock markets are positive, while impacts of order flows from unleveraged financial institutions are negative; 2) impacts of corporate order flows are longer than those of financial order flows, especially for the US stock market, suggesting that the two groups of customers may hold different types of private price-relative information. We hypothesize that corporate customers of the bank are mainly based in the UK. When the world economy is doing well, multi-national companies are selling more goods in the US and repatriate more foreign currencies back to UK, during which more GBP or EUR are converted from US Dollars. More sales of US Dollars then reflect the good future prospects of the world economy and stocks listed in both US and UK will rise in value. For unleveraged financial institutions, when the world economy is going bad, clients of those mutual funds which are based in the UK will ask for redemptions of their funds. Assuming the bank services a client base that is UK oriented, this leads to the repatriation of money from abroad back to UK. The buying of GBP or EUR in exchange for US Dollars then takes place alongside sales of US and UK stocks. Foreign exchange flows into GBP or EUR from unleveraged funds forecast poor future stock market returns globally. Chapter 3 empirically tests the effects of EURUSD order flows from different groups of counterparties on the US stock market changes at high frequencies ranging from 1-minute to 30-minute, using a unique set of tick-by-tick order flows data obtained from a leading European commercial bank. We find that: 1) Order flows from “corporates” are positively related to exchange rate changes, while order flows from “financials” are negatively signed, which contradict many well-documented papers such as Evans and Lyons (2002a) (this high frequency forecasting power partly explain the failure of the trading strategy based on our daily order flows data in chapter); 2) The effects of order flows from “financials” are negative on stock market changes, while the effects of orders from “corporates” are positive on stock market changes, which further confirms our findings in chapter 2. Similar to chapter 2, the cross market effects documented in chapter 3 also suggest that there is information content in foreign exchange order flows and that it is likely to be macroeconomic in nature, relevant for stock markets. Chapter 4 concludes and suggests some directions of refinements and further research

Financial crises and bank failures: a review of prediction methods

In this article we provide a summary of empirical results obtained in several economics and operations research papers that attempt to explain, predict, or suggest remedies for financial crises or banking defaults, as well as outlines of the methodologies used. We analyze financial and economic circumstances associated with the US subprime mortgage crisis and the global financial turmoil that has led to severe crises in many countries. The intent of the article is to promote future empirical research that might help to prevent bank failures and financial crises.financial crises; banking failures; operations research; early warning methods; leading indicators; subprime markets

Machine Learning and Finance: A Review using Latent Dirichlet Allocation Technique (LDA)

The aim of this paper is provide a first comprehensive structuring of the literature applying machine learning to finance. We use a probabilistic topic modelling approach to make sense of this diverse body of research spanning across the disciplines of finance, economics, computer sciences, and decision sciences. Through the topic modelling approach, a Latent Dirichlet Allocation Technique (LDA), we can extract the 14 coherent research topics that are the focus of the 6,148 academic articles during the years 1990-2019 analysed. We first describe and structure these topics, and then further show how the topic focus has evolved over the last two decades. Our study thus provides a structured topography for finance researchers seeking to integrate machine learning research approaches in their exploration of finance phenomena. We also showcase the benefits to finance researchers of the method of probabilistic modelling of topics for deep comprehension of a body of literature, especially when that literature has diverse multi-disciplinary actors

Systematic Trading: Calibration Advances through Machine Learning

Systematic trading in finance uses computer models to define trade goals, risk controls and rules that can execute trade orders in a methodical way. This thesis investigates how performance in systematic trading can be crucially enhanced by both i) persistently reducing the bid-offer spread quoted by the trader through optimized and realistically backtested strategies and ii) improving the out-of-sample robustness of the strategy selected through the injection of theory into the typically data-driven calibration processes. While doing so it brings to the foreground sound scientific reasons that, for the first time to my knowledge, technically underpin popular academic observations about the recent nature of the financial markets. The thesis conducts consecutive experiments across strategies within the three important building blocks of systematic trading: a) execution, b) quoting and c) risk-reward allowing me to progressively generate more complex and accurate backtested scenarios as recently demanded in the literature (Cahan et al. (2010)). The three experiments conducted are: 1. Execution: an execution model based on support vector machines. The first experiment is deployed to improve the realism of the other two. It analyses a popular model of execution: the volume weighted average price (VWAP). The VWAP algorithm targets to split the size of an order along the trading session according to the expected intraday volume's profile since the activity in the markets typically resembles convex seasonality – with more activity around the open and the closing auctions than along the rest of the day. In doing so, the main challenge is to provide the model with a reasonable expected profile. After proving in my data sample that two simple static approaches to the profile overcome the PCA-ARMA from Bialkowski et al. (2008) (a popular two-fold model composed by a dynamic component around an unsupervised learning structure) a further combination of both through an index based on supervised learning is proposed. The Sample Sensitivity Index hence successfully allows estimating the expected volume's profile more accurately by selecting those ranges of time where the model shall be less sensitive to past data through the identification of patterns via support vector machines. Only once the intraday execution risk has been defined can the quoting policy of a mid-frequency (in general, up to a week) hedging strategy be accurately analysed. 2. Quoting: a quoting model built upon particle swarm optimization. The second experiment analyses for the first time to my knowledge how to achieve the disruptive 50% bid-offer spread discount observed in Menkveld (2013) without increasing the risk profile of a trading agent. The experiment depends crucially on a series of variables of which market impact and slippage are typically the most difficult to estimate. By adapting the market impact model in Almgren et al. (2005) to the VWAP developed in the previous experiment and by estimating its slippage through its errors' distribution a framework within which the bid-offer spread can be assessed is generated. First, a full-replication spread, (that set out following the strict definition of a product in order to hedge it completely) is calculated and fixed as a benchmark. Then, by allowing benefiting from a lower market impact at the cost of assuming deviation risk (tracking error and tail risk) a non-full-replication spread is calibrated through particle swarm optimization (PSO) as in Diez et al. (2012) and compared with the benchmark. Finally, it is shown that the latter can reach a discount of a 50% with respect to the benchmark if a certain number of trades is granted. This typically occurs on the most liquid securities. This result not only underpins Menkveld's observations but also points out that there is room for further reductions. When seeking additional performance, once the quoting policy has been defined, a further layer with a calibrated risk-reward policy shall be deployed. 3. Risk-Reward: a calibration model defined within a Q-learning framework. The third experiment analyses how the calibration process of a risk-reward policy can be enhanced to achieve a more robust out-of-sample performance – a cornerstone in quantitative trading. It successfully gives a response to the literature that recently focusses on the detrimental role of overfitting (Bailey et al. (2013a)). The experiment was motivated by the assumption that the techniques underpinned by financial theory shall show a better behaviour (a lower deviation between in-sample and out-of-sample performance) than the classical data-driven only processes. As such, both approaches are compared within a framework of active trading upon a novel indicator. The indicator, called the Expectations' Shift, is rooted on the expectations of the markets' evolution embedded in the dynamics of the prices. The crucial challenge of the experiment is the injection of theory within the calibration process. This is achieved through the usage of reinforcement learning (RL). RL is an area of ML inspired by behaviourist psychology concerned with how software agents take decisions in an specific environment incentivised by a policy of rewards. By analysing the Q-learning matrix that collects the set of state/actions learnt by the agent within the environment, defined by each combination of parameters considered within the calibration universe, the rationale that an autonomous agent would have learnt in terms of risk management can be generated. Finally, by then selecting the combination of parameters whose attached rationale is closest to that of the portfolio manager a data-driven solution that converges to the theory-driven solution can be found and this is shown to successfully outperform out-of-sample the classical approaches followed in Finance. The thesis contributes to science by addressing what techniques could underpin recent academic findings about the nature of the trading industry for which a scientific explanation was not yet given: • A novel agent-based approach that allows for a robust out-of-sampkle performance by crucially providing the trader with a way to inject financial insights into the generally data-driven only calibration processes. It this way benefits from surpassing the generic model limitations present in the literature (Bailey et al. (2013b), Schorfheid and Wolpin (2012), Van Belle and Kerr (2012) or Weiss and Kulikowski (1991)) by finding a point where theory-driven patterns (the trader's priors tend to enhance out-of-sample robustness) merge with data-driven ones (those that allow to exploit latent information). • The provision of a technique that, to the best of my knowledge, explains for the first time how to reduce the bid-offer spread quoted by a traditional trader without modifying her risk appetite. A reduction not previously addressed in the literature in spite of the fact that the increasing regulation against the assumption of risk by market makers (e.g. Dodd–Frank Wall Street Reform and Consumer Protection Act) does yet coincide with the aggressive discounts observed by Menkveld (2013). As a result, this thesis could further contribute to science by serving as a framework to conduct future analyses in the context of systematic trading. • The completion of a mid-frequency trading experiment with high frequency execution information. It is shown how the latter can have a significant effect on the former not only through the erosion of its performance but, more subtly, by changing its entire strategic design (both, optimal composition and parameterization). This tends to be highly disregarded by the financial literature. More importantly, the methodologies disclosed herein have been crucial to underpin the setup of a new unit in the industry, BBVA's Global Strategies & Data Science. This disruptive, global and cross-asset team gives an enhanced role to science by successfully becoming the main responsible for the risk management of the Bank's strategies both in electronic trading and electronic commerce. Other contributions include: the provision of a novel risk measure (flowVaR); the proposal of a novel trading indicator (Expectations’ Shift); and the definition of a novel index that allows to improve the estimation of the intraday volume’s profile (Sample Sensitivity Index)