Lessons from the evolution of foreign exchange trading strategies

The adaptive markets hypothesis posits that trading strategies evolve as traders adapt their behavior to changing circumstances. This paper studies the evolution of trading strategies for a hypothetical trader who chooses portfolios from foreign exchange (forex) technical rules in major and emerging markets, the carry trade, and U.S. equities. The results show that forex trading alone dramatically outperforms the S&P 500 but there is little gain to coordinating forex and equity strategies, which explains why practitioners consider these tools separately. In addition, a backtesting procedure to choose optimal portfolios does not select carry trade strategies until well into the 1990s, which helps to explain the relatively recent surge in interest in this strategy. Forex trading returns dip significantly in the 1990s but recover by the end of the decade and have greatly outperformed an equity position since 1998. Overall, trading rule returns still exist in forex markets—with substantial stability in the types of rules—though they have migrated to emerging markets to a considerable degree.Foreign exchange ; Trade

The cointegrating relationship in Asian markets with applications to stock prices, exchange rates and interest rates

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The aim of this research is to investigate the long-run co-integrating relationships in the Asian markets. Our research focuses on 4 areas; pair trading, out-of-sample forecasting, testing the unbiased forward exchange rate hypothesis and testing the expectation hypothesis of the term structure of interest rates. The introduction is provided in chapter one. In chapter two, we develop a pairs trading strategy using individual stocks listed in the Stock Exchange of Thailand. Engle and Granger approach is used to identify the potential pairs that are cointegrated. The results show that pairs trading strategy is profitable in this market. Chapter three examines the forecasting performance of the error correction model on daily share price series from the Stock Exchange of Thailand. The disequilibrium term is classified into “correct” and “mix” sign based on Alexander (2008)’s criterion; the results indicate that the error correction component can help to improve the predictability in the long run. Chapter four tests the unbiased forward rate hypothesis of 11 Asian exchange rates using linear conventional regression, ECM and logistic smooth transition regression with the forward premium as the transition variable. Out-of-sample forecasting results also suggest that inferior forecasting performance could be obtained as a result of using linear models. In chapter five, we investigate the expectation hypothesis of the term structure of interest rate for four Asian countries. We employ linear models and nonlinear approaches that allow to capture asymmetric and symmetric adjustments. The result also indicates that the term structure can be better modeled by means of LSTR models. The forecasting exercise also confirms these findings

Comparative Study of Pair Trading Techniques in Pakistan’s Financial and Non-Financial Sector

Purpose: This study attempts to empirically investigate the pair trading performance of financial and non-financial firms in Pakistan. Methodology: Daily data from 2008 to 2017 was collected for nine years. Cointegration and the distance approach were the two major analytical techniques used to evaluate the profitability of pair trading. The financial and non-financial sectors of the Pakistan Stock Exchange were used to build the pairs. Findings: Results showed that the top 5 pairs of portfolios exhibited the highest average excess returns of 0.0698, and Jensen's alpha is 0.0947 for the top 5 pairs. All pairs of firms showed significant and positive risk-adjusted performance. In the non-financial sector, the Top 10 pairs of portfolios had the highest average excess returns of 0.0789, and Jensen's alpha under the co-integration method for non-financial firms for all pairs 5, 10, 15, and 20 of the portfolios is also substantial and positive for risk-adjusted performance, with 0.0046, 0.0618, 0.0577, and 0.0493, respectively. Finally, pair trading under both techniques showed profitability. However, the co-integration technique exhibited better performance than the distance method. Conclusion: The study concluded that both pair trading techniques, particularly the co-integration technique, exhibited profitable pair trading performance that can assist investors and fund managers to earn positive returns on their investments regardless of market direction

Robust FOREX Trading with Deep Q Network (DQN)

Financial trading is one of the most attractive areas in finance. Trading systems development is not an easy task because it requires extensive knowledge in several areas such as quantitative analysis, financial skills, and computer programming. A trading systems expert, as a human, also brings in their own bias when developing the system. There should be another, more effective way to develop the system using artificial intelligence. The aim of this study was to compare the performance of AI agents to the performance of the buy-and-hold strategy and the expert trader. The tested market consisted of 15 years of the Forex data market, from two currency pairs (EURUSD, USDJPY) obtained from Dukascopy Bank SA Switzerland. Both hypotheses were tested with a paired t-Test at the 0.05 significance level. The findings showed that AI can beat the buy & hold strategy with significant superiority, in FOREX for both currency pairs (EURUSD, USDJPY), and that AI can also significantly outperform CTA (experienced trader) for trading in EURUSD. However, the AI could not significantly outperform CTA for USDJPY trading. Limitations, contributions, and further research were recommended

Cross-Autocorrelation of Dual-Listed Stock Portfolio Returns: Evidence from the Chinese Stock Market

In this paper, we apply a GARCH model to examine the cross-autocorrelation pattern between daily returns of portfolios composed of dual-listed stocks in Chinese stock market, before and after China opened its once foreign-exclusive B-share market. A lead-lag relationship between the A-share and B-share portfolio returns is identified during our sample periods, with the A-share portfolio leading the B-share portfolio. Upon the opening of B-share market, a change from underreaction to overreaction is found in the response pattern of B-share market, producing a rarely seen negative cross-autocorrelation. The results of two additional tests are reported. First, by decomposing the portfolio return into portfolio-specific and market-wide returns, we find that the market-wide information contained in A-share portfolio return is strongly associated with the cross-autocorrelation structure. Second, we document a directional asymmetry in which B-share portfolio shows either slow or over response to bad, but not good, news of A-share portfolio. We conclude that information asymmetry alone is not enough to explain the lead-lag relationship, and investor behavior must be taken into considerationCross-Autocorrelation, Segmented Stock Markets, Dual-Listed Stocks, Market-Wide and Portfolio-Specific Information.

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

Niche Markets and Their Lessons

Markets are full of nooks and crannies. Out of the glare of the big economies and their public exchanges, markets specializing by financial product, activity, or industry thrive, often attracting little by way of formal regulatory oversight. But there is another kind of specialized market, one which is geographically and politically determined albeit internationally focused. Luxembourg, Ireland, Dubai, Bahrain, Malaysia, Singapore, Switzerland, among others, these are some of the world’s niche markets.It is a hard business being a niche market, operating in a competitive and often unforgiving environment, engaging in constant repositioning and facing inherent limitations on growth. Surprisingly, perhaps, there are lots of niche markets and a very diverse grouping they are, deploying a variety of survival strategies. In all cases, state capitalism, in various guises, supports these markets. In earlier times, reputation, a friendly regulator, and good business practices might have sufficed. Now, there is a new dynamic. This chapter in a new book, International Capital Markets: Law and Institutions (Oxford University Press, 2014), examines the characteristics of niche markets, such as a high tolerance for legal pluralism and the role of state capitalism, the vulnerabilities of niche markets, especially to change, and the secrets of their success

Government Intervention and Arbitrage

We model and document the novel notion that direct government intervention in a market may induce violations of the law of one price (LOP) in other, arbitrage-related markets. We show that the introduction of a government pursuing a non-public, partially informative price target in a model of strategic trading and segmented dealership generates equilibrium price differentials among fundamentally identical (or linearly related) assets -- especially when markets are illiquid, LOP violations are small, speculators are heterogeneously informed, or policy uncertainty is high. We find supportive evidence in a sample of ADRs traded in U.S. exchanges and currency interventions by developed and emerging countries between 1980 and 2009.https://deepblue.lib.umich.edu/bitstream/2027.42/107444/13/1240_Pasquariello_May2017.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/107444/10/Pasquariello-March2017.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/107444/8/1240_Pasquariello_May2016.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/107444/6/1240_ Pasquariello_June2015_2.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/107444/4/1240_ Pasquariello_June2015.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/107444/1/1240_Pasquariello.pdfDescription of 1240_Pasquariello_May2017.pdf : May 2017 revisionDescription of 1240_Pasquariello_May2016.pdf : SUPERSEDED: May 2016 RevisionDescription of 1240_ Pasquariello_June2015_2.pdf : SUPERSEDED: June 2015 Revision (newer)Description of 1240_ Pasquariello_June2015.pdf : SUPERSEDED: June 2015 RevisionDescription of 1240_Pasquariello.pdf : SUPERSEDED: Original versionDescription of 1240_Pasquariello_May2017.pdf : May 2017 revision