Reinforcement Learning for Systematic FX Trading

We explore online inductive transfer learning, with a feature representation transfer from a radial basis function network formed of Gaussian mixture model hidden processing units to a direct, recurrent reinforcement learning agent. This agent is put to work in an experiment, trading the major spot market currency pairs, where we accurately account for transaction and funding costs. These sources of profit and loss, including the price trends that occur in the currency markets, are made available to the agent via a quadratic utility, who learns to target a position directly. We improve upon earlier work by learning to target a risk position in an online transfer learning context. Our agent achieves an annualised portfolio information ratio of 0.52 with a compound return of 9.3%, net of execution and funding cost, over a 7-year test set; this is despite forcing the model to trade at the close of the trading day at 5 pm EST when trading costs are statistically the most expensive.<br>

Implications of the Digital Economy on Merger Control in Pakistan and China: Policy Implications for Pakistan

The rise of the digital economy has challenged the foundation of competition law frameworks the world over. Today, the antitrust doctrine finds itself confronting a new economy; an econo-my wherein data acts as a currency, markets are without prices, market collisions are based on algorithms, and the market is &lsquo;infinite&rsquo;. Several jurisdictions such as Germany, Austria, and China have developed new regulations or amended existing legislations to confront the chal-lenges presented by the digital economy. A dearth of theoretical and empirical literature has evaluated whether digital markets are so fundamentally different as to require a different set of rules. Of specific interest to this paper is whether current competition rules are sufficient to deal with mergers and acquisitions (M&amp;As) in digital markets. This paper assesses M&amp;A regulations in China and Pakistan in light of the new digital economy. Expert interviews were conducted using semi-structured interviews to investigate the comparisons between Pakistan&rsquo;s and China&rsquo;s merger control regimes. The findings indicate that China&rsquo;s merger control regulations are better adopted for the digital economy than Pakistani&rsquo;s. It also sets out the policy implications for competition policy makers in Pakistan.

Economic Policy Uncertainty and the Co-Movement between REITs and Exchange Rate

The REITs market has attracted a lot of interest among the academic, policymakers, and market participants. The linkages between REITs and macroeconomic and financial variables have been adequately explored in the literature, with more emphasis on linear models. This study expands the frontier of knowledge by examining the role of uncertainty in the comovement/spillover between REITs and the currency markets. Some interesting results were observed. First, using the Diebold and Yilmaz (2012) spillover test, we find that there is strong connectedness between the REITs and currency markets. Second, the BDS test shows that nonlinearity is a very crucial factor to be put into consideration when examining the role of EPU in affecting the interactions between REITs and exchange rate markets. Third, the non-parametric causality-in-quantile test confirms that the connectedness between the markets and EPU is stronger around the lower and middle quantiles. These results have important policy implications for policymakers and market participants. The study also offers suggestions for future research.

Evolution of bitcoin as a Financial Asset

The cryptocurrency market debate resumed in 2020 with renewed vigour as the price of Bitcoin surpassed late 2017 highs. This study aims to analyse possible factors of Bitcoin’s pricing at various cryptocurrency market development stages — before the 2017 price bubble, after and during the COVID-19 pandemic. The main method of analysis is a generalized autoregressive conditional heteroskedasticity model with conditional generalized error distribution (GARCHGED). Two groups of indicators are used as possible factors related to the Bitcoin dynamics. The first group consists of various quantitative indicators directly related to Bitcoin (the so-called internal factors) — the volume of exchange trade, the volume of transactions in the Bitcoin blockchain, the number of new and active wallets, hash rate, the sum of fees paid in the blockchain, as well as the dynamics of Google Trends search queries. The second group is the return on various financial assets — stock and bond indexes, commodities, and currency markets. The results of the analysis demonstrate the absence of a stable correlation between any of the factors under consideration and Bitcoin returns in all the periods that we focus on. In the period before the 2017 price bubble, the internal factors and Bitcoin returns showed generally co-directional dynamics, but the situation changed in 2018. In early 2021, the correlation between Bitcoin and traditional financial assets returns has increased significantly. We can conclude that Bitcoin is becoming a popular means of diversification as a high-risk asset, which, however, follows the pattern of a speculative bubble at the beginning of 2021. The increased demand for the need to invest in Bitcoin using various exchange-traded instruments (ETFs for cryptocurrencies) may soon lead to a further increase in the price of this cryptocurrency if such instruments are registered on the exchange.

Policy Signaling and Foreign Electoral Uncertainty: Implications for Currency Markets

This paper examines the effects of foreign electoral shocks on currency markets. I develop a theory of signaling and uncertainty to explain why elections in countries with close economic ties should affect exchange rates. Methodologically, this paper focuses on several case studies, with the 2016 US election as a central case. I utilize an event analysis framework to measure the impact of the election on the Mexican peso by exploiting the plausible exogeneity of Donald Trump's tweets. I also measure changes in the peso using Trump's predicted chance of winning the election and show that the peso is weakest when Trump has the highest chance of winning the election. In addition, I include a series of robustness checks and analyses of other notable recent cases when electoral uncertainty affected currencies in other countries, including the 2018 Brazilian election. The results quantify the effect of foreign elections on exchange rates, building on the existing literature that focuses on how domestic elections shape currency markets. I conclude with a discussion of the external validity of the phenomenon demonstrated by the cases in the paper, charting future research on the topic and outlining ways to extend the findings.

The Effect of “Brexit” Uncertainty on The FTSE 100 Index and The UK Pound

We investigate the impact of the uncertainty surrounding the United Kingdom’s proposed departure from the European Community (“Brexit”) on financial assets. We conduct an event study around the November 14th 2018 draft withdrawal agreement. Our motivation was that the economic impact of the various political permutations that persisted throughout the negotiation period were both measurable and distinct. The probability of each Brexit scenario that was discussed varied over the political discourse. Using opinion poll data we investigate the event impact on both the FTSE 100 and the UK Pound. We found that, in accordance with existing academic evidence, asset prices discounted the weighted probabilistic economic impact of likely outcomes. We observe, however, that this impact was not as immediate as theory suggests. Interestingly, currency markets had the greater sensitivity. Our conclusions have important implications for the pricing of country risk premia in general and the European Union in particular. Key takeaways: 1) Asset prices were slow to discount the weighted probabilistic economic impact of Brexit risk. 2) Currency markets had the greater sensitivity to changes in Brexit risk. 3) Country risk premia can be impacted by perceived changes in custom union.

Foreign Exchange Volume

We investigate the information contained in foreign exchange (FX) volume using a novel data set from the over-the-counter market. We find volume helps predict next-day currency returns and is economically valuable for currency investors. Predictability implies a stronger return reversal for currency pairs with abnormally low volume and is driven by the component of volume unrelated to volatility, liquidity, and order flow. We rationalize these findings via a simple model, in which FX volume helps reveal the degree of asymmetric information in currency markets. Testing this prediction shows that asymmetric information is uniform across currency pairs but varies across instruments.