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1 Richard Axa Surname: Axa First Name: Richard MSc in: Global Finance Student ID number: 220007203 Module Code: SMM728 Module Title: International Finance Lecturer: Professor Ian Marsh Submission Date: June 20th, 2022 Declaration: By submitting this work, I declare that this work is entirely my own except those parts duly identified and referenced in my submission. It complies with any specified word limits and the requirements and regulations detailed in the coursework instructions and any other relevant programme and module documentation. In submitting this work I acknowledge that I have read and understood the regulations and code regarding academic misconduct, including that relating to plagiarism, as specified in the Programme Handbook. I also acknowledge that this work will be subject to a variety of checks for academic misconduct. We acknowledge that work submitted late without a granted extension will be subject to penalties, as outlined in the Programme Handbook. Penalties will be applied for a maximum of five days lateness, after which a mark of zero will be awarded. Marker’s Comments (if not being marked on-line): Deduction for Late Submission: Final Mark: % Individual Coursework Submission Form Specialist Masters Programme 2 Richard Axa (1 page of text) As of 2019, FX global markets have reached a daily trading volume of $6.6 trillion per day (BIS, 2019), being roughly ten times the size of the equity marketplace (Bradfield, 2019). Since its inception, Bayes Capital (hedge fund) has only focused on equity market strategies. Given its recent initiative to expand and to explore FX markets, this report’s main purpose will be to introduce the three following FX trading strategies: (1) carry trading, (2) value trading, and (3) trading on order flow. For each strategy, the following will be discussed: definition and application, main risks, historical performance on a standalone basis, comparison with the performance of the S&P 500 index and that of alternative FX strategies, whether the strategy can benefit from diversification and leverage, and finally, whether the presented results are congruent with the literature. A basic understanding of financial concepts and terminology is assumed. All exchange rates used in the computations are quoted as US$/Foreign currency unit. Carry Trading In the context of FX, a carry trade is a cross-border trade that consists of borrowing funds in a low interest rate currency and investing these funds in a higher interest rate currency (Burnside, 2011). A carry trade is attractive in its nature because it is a leveraged transaction and therefore can increase return by allowing an investor to invest borrowed funds rather than their own money (Schweser, 2020b, p.69). To better understand the rationale by which the carry trade is derived, it is crucial to understand the uncovered interest parity (UIP) condition which states that changes in exchange rates must be equal to the difference in interest rates between the foreign and the domestic currency (Schweser, 2020a, p.61). Evidently, if UIP were to hold true, the higher interest rate currency’s value would decline by the same magnitude as the interest rate differential between both currencies, and vice-versa for the lower interest rate currency; in which case, a carry trade would yield no return (Schweser, 2020a, p.267). With that being said, the carry trade is based on a breach of UIP and it is synonymous with the concept of trading the forward bias ((Schweser, 2020a, p.268). In simpler terms, if we expect the higher interest rate currency to depreciate in the future by less than projected by UIP, the carry trade will then be profitable. Value Trading As part of the continuous attempts to develop a solid model to forecast exchange rates, value trading in currency markets was introduced and mainly focuses on purchasing power parity (PPP) as a fundamental (Melvin et al., 2013). PPP assumes a theoretical exchange rate that would allow one to buy an identical basket of goods for the same amount in different currencies (Melvin et al., 2013). Evidently, it is unlikely that PPP is of relevance between two countries with significant macroeconomic differences (Melvin et al., 2013). On the other hand, when two countries are similar in terms of productivity levels, trade barriers, level of economic development, and technological improvement to name a few, it is more likely that exchange rates are closer to PPP, and if variations arise, it is fair to anticipate a potential and more justifiable reversion to the PPP exchange rate (Melvin et al., 2013). That said, a trading strategy can be implemented to capitalize on this fundamental by using figures of PPP exchange rates published by the IMF and/or the OECD as a benchmark for our “fair value”. Comparing current spot exchange rates with equilibrium exchange rates would allow us to determine which currencies are over- or undervalued. If the deviation between the two is positive (negative), the subject currency is considered overvalued (undervalued), and we should take a short (long) position (Melvin et al., 2013). 3 Richard Axa (0.5 pages of text) Trading on Order Flow Unlike carry trading and value trading, trading on order flow requires the investor to apply a bottom-up approach within FX markets’ microstructure; rather than focusing on macroeconomic variables and pinpointing deviations from theoretical parity relationships such as UIP or PPP, one can monitor buying/selling pressure from orders being placed for given currencies and translate these trades into valuable information (Evans and Lyons, 2002). Such information can alert order flow traders of many things like sharp fluctuations in hedging/currency/liquidity demands or an important news announcement pertaining to monetary policy or a country’s political climate (Evans and Lyons, 2002). Order flow is an important dissemination tool which serves to transmit non-public information from one entity to another and it can be used to assess market sentiment for a given currency (Evans and Lyons, 2002). In an investment setting, intuitively, a currency with large positive inflows (negative outflows) will be deemed valuable and in demand (worthless and unwanted) and we will want to buy (sell) it. All it takes is one considerable order placed by an informed reputable entity (e.g., a hedge fund, a bank, or a world-renowned investor) and the information transmission mechanism will begin – this entity is said to hold an information advantage at first, and every subsequent counterparty with whom the currency is traded will play a role in this mechanism which will in turn impact the currency’s value (Marsh and O’Rourke, 2005). *See performance data for all three trading strategies and the S&P 500 Index in the following pages over the last 20 years. 4 Richard Axa Figure 1: Cumulative Excess Returns of FX Trading Strategies and the S&P 500 Index. Source: Own calculations. Figure 2: Performance data of Figure 1. Source: Own calculations. Note 1: The FX trade returns presented in Figures 1-2 were constructed with two currencies and equal weights of -1 and 1. Note 2: Performance data for the carry trade was computed using provided spot exchange rates, one- month forward rates, and forward returns (excess returns) earned next month from being long each currency against the dollar. Performance data for the value trading strategy was computed using provided spot exchange rates and PPP exchange rates generated by the IMF and the OECD. Performance data for the order flow trading strategy was computed using provided futures scaled non-commercial order flow into or out of each available currency. Performance data for the S&P 500 Index was computed using index returns from Yahoo! Finance (log differences) from which we subtracted the 3-month T-bill rates (risk-free rate divided by 3) from Fred (fred.stlouisfed.org) to generate the excess returns. Note 3: As seen in Figure 2, in addition to the Sharpe ratio, the Sortino ratio is also being used as a more relevant risk- adjusted measure for comparison purposes. The reason the Sortino ratio may be more appropriate than the Sharpe ratio in this case is because one of the Sharpe ratio’s caveats is that it assumes that returns are normally distributed (CFA Institute, 2021), which really isn’t the case for FX returns; their return distribution is in fact very skewed and heavily tailed (Corlu and Corlu, 2015). The Sharpe ratio penalizes performance appraisal measures for both upside and downside volatility, although downside volatility is the only one considered unfavorable (CFA Institute, 2021). That said, the Sortino ratio will serve to divide our excess return figure by only downside risk/volatility, also called target semideviation or downside risk (CFA Institute, 2021). The higher the ratio, the better the strategy’s performance in terms of returns/unit of downside risk taken. Carry Value IMF Value OECD Order Flow S&P 500 Average monthly excess return 0.3906% 0.5782% 0.3545% -0.2975% -0.1023% Monthly standard deviation 3.7291% 2.7452% 2.8230% 3.4549% 4.4617% Monthly downside deviation 3.2126% 1.7849% 1.8603% 2.1175% 3.4734% Annualised excess return 4.6878% 6.9389% 4.2537% -3.5702% -1.2271% Annualised standard deviation 12.9180% 9.5097% 9.7791% 11.9680% 15.4558% Annualised downside deviation 11.1287% 6.1832% 6.4443% 7.3351% 12.0321% Sharpe Ratio 0.362885 0.729661 0.434978 -0.298312 -0.079394 Sortino Ratio 0.421233 1.122214 0.660072 -0.486730 -0.101985 Minimum -20.6567% -8.0231% -9.0439% -11.3885% -18.7885% Maximum 9.6168% 11.1165% 11.1165% 24.9113% 11.8961% 5 Richard Axa Figure 3: Performance data for different combinations of diversification and/or leverage for the carry trade (Trial & Error). Source: Own calculations. Figure 4: Performance data for different combinations of diversification and/or leverage for the value trading strategy based on OECD (Trial & Error). Source: Own calculations. Figure 5: Performance data for different positions, combinations of diversification and/or leverage for the order flow trading strategy (Trial & Error). Source: Own calculations. Borrow 1 Long