Advanced trading strategies - page 3

This paper studies predatory trading: Trading that induces and/or exploits other investors' need to reduce their positions. We show that if one trader needs to sell, others also sell and subsequently buy back the asset. This leads to price overshooting, and a reduced liquidation value for the distressed trader. Hence, the market is illiquid when liquidity is most needed. Further, a trader profits from triggering another trader's crisis, and the crisis can spill over across traders and across assets.

In the past decades, advanced probabilistic methods have had significant impact on the field of finance, both in academia and in the financial industry. Conversely, financial questions have stimulated new research directions in probability. In this survey paper, we review some of these developments and point to some areas that might deserve further investigation. We start by reviewing the basics of arbitrage pricing theory, with special emphasis on incomplete markets and on the different roles played by the 'real-world' probability measure and its equivalent martingale measures. We then focus on the issue of model ambiguity, also called Knightian uncertainty. We present two case studies in which it is possible to deal with Knightian uncertainty in mathematical terms. The first case study concerns the hedging of derivatives, such as variance swaps, in a strictly path wise sense. The second one deals with capital requirements and preferences specified by convex and coherent risk measures. In the final two sections we discuss mathematical issues arising from the dramatic increase of algorithmic trading in modern financial markets.

This paper examines the performance of momentum trading strategies in foreign exchange markets. We find the well-documented profitability of momentum strategies with equities to hold for currencies as well and to have continued throughout the 1980s and the 1990s. Our results indicate that the long/short strategy of buying the most attractive currency and shorting the least attractive currency obtains average excess returns that are significantly positive. Of particular note, the profitability to momentum strategies in foreign exchange markets has been particularly strong during the latter half of the 1990s. By examining 354 long/short moving average rules across eight currencies, we show the results are insensitive to the specification of the trading rule and the base currency for analysis. We also show that the correlations of the long/short momentum strategies using differing base currencies are very high - typically around 0.90. This would indicate that strong/weak momentum currencies relative to a base currency at a particular time are typically also strong/weak currencies relative to most other base currencies as well. Finally, using a bootstrap methodology we show that the performance is not due to a time-varying risk premium but depends on the underlying autocorrelation structure of the currency returns. In sum, the results lend further support to prior momentum studies on equities. The profitability to momentum-based strategies holds for currencies as well

We provide a broad empirical investigation of momentum strategies in the foreign exchange market. We find a significant cross-sectional spread in excess returns of up to 10% p.a. between past winner and loser currencies. This spread in excess returns is not explained by traditional risk factors, it is partially explained by transaction costs and shows behavior consistent with investor under- and over-reaction. Moreover, cross-sectional currency momentum has very different properties from the widely studied carry trade and is not highly correlated with returns of benchmark technical trading rules. However, there seem to be very effective limits to arbitrage which prevent momentum returns from being easily exploitable in currency markets.

Wang, Yu, Cheung, 2014a proposed complex trading strategy called Performance-based Reward Strategy (PRS). PRS combines component rules of 121 moving average (MA) rules and 19 trading range breakout (TRB) rules. To find the optimal set of parameters for PRS, the researchers used time variant particle swarm optimization (TVPSO) algorithm. The objective of the experiment was to measure annual net profit (ANP) of PRS for trading constituents of NASDAQ100. Wang, Yu, Cheung, 2014b conducted the same experiment but used seven classes of technical trading rules resulting in 1,059 component trading rules. For both of these studies, the researchers conducted the experiment only one time and PRS’s ANP was better than all component rules’. Thus both of these studies concluded that PRS outperforms all component rules. TVPSO algorithm is an approximation algorithm and running TVPSO only once may bias the conclusion. To improve reliability of the conclusion, we conduct both experiments 100 times. For both experiments, we find that mean ANP of PRS was lower than that of the best of the best component rules. Furthermore, when we replicate PRS, we find that weight updating equation proposed in both of these studies was wrong and we propose weight updating equation which we believe both of these studies used. Furthermore, to speed up the running time of TVPSO, a simple parallel programming idea for it is proposed.

This paper approaches risk management from three perspectives: firm-level risk measurement, governance and incentives, and systemic concerns. These are three essential dimensions of best practices in risk management; although we discuss each dimension separately, they are interrelated. The paper begins with a brief review of salient changes and unmet challenges in risk measurement in the wake of the financial crisis. It proceeds with a discussion of the interplay between volatility regimes and the potential for risk amplification at a system-wide level through simultaneous risk mitigation at the individual firm level. Quantitative risk measurement cannot be effective without a sound corporate risk culture, so the paper then develops a model of governance that recognizes cognitive biases in managers. The model allows a comparison of the incentive effects of compensation contracts and leads to recommendations for improving risk management through improved contract design. The last section takes a systemic perspective on risk management. Risk managers must recognize important ways in which market dynamics deviate from simple, idealized models of hedging an individual firm’s exposures. Firms’ collective hedging, funding, and collateral arrangements can channel through the financial system in ways that amplify shocks. Understanding these effects requires an appreciation for the organization of trading operations within firms. The article concludes with a summary and recommendations.

We model a large panel of time series as a VAR where the autoregressive matrices and the inverse covariance matrix of the system innovations are assumed to be sparse. The system has a network representation in terms of a directed graph representing predictive Granger relations and an undirected graph representing contemporaneous partial correlations. A LASSO algorithm called NETS is introduced to estimate the model. We apply the methodology to analyse a panel of volatility measures of ninety bluechips. The model captures an important fraction of the overall variability of the time series and improves out-of-sample forecasting.

A new factor model consisting of the market factor, an investment factor, and a return-on-equity factor is a good start to understanding the cross-section of expected stock returns. Firms will invest a lot when their profitability is high and the cost of capital is low. As such, controlling for profitability, investment should be negatively correlated with expected returns, and controlling for investment, profitability should be positively correlated with expected returns. The new three-factor model reduces the magnitude of the abnormal returns of a wide range of anomalies-based trading strategies, often to insignificance. The model's performance, combined with its economic intuition, suggests that it can be used to obtain expected return estimates in practice.

We provide a mathematical definition of fragility and antifragility as negative or positive sensitivity to a semi-measure of dispersion and volatility (a variant of negative or positive “vega”) and examine the link to nonlinear effects. We integrate model error (and biases) into the fragile or antifragile context. Unlike risk, which is linked to psychological notions such as subjective preferences (hence cannot apply to a coffee cup) we offer a measure that is universal and concerns any object that has a probability distribution (whether such distribution is known or, critically, unknown).

We propose a detection of fragility, robustness, and antifragility using a single “fast-and-frugal”, model-free, probability free heuristic that also picks up exposure to model error. The heuristic lends itself to immediate implementation, and uncovers hidden risks related to company size, forecasting problems, and bank tail exposures (it explains the forecasting biases). While simple to implement, it outperforms stress testing and other such methods such as Value-at-Risk.

In this paper, the authors explore the complex, long, and unique process of accession to the World Trade Organization, with its intertwined economic, legal, and political dimensions. Referring to country case studies and sector-specific issues, the paper organizes some of the current reflections on the topic around three main themes. First, it explores the rationale of accession to the World Trade Organization: Why would new members join the WTO? And why would incumbent members let new members in? Second, it analyzes the World Trade Organization accession process in detail: What are the main characteristics and challenges of the accession process? Has it evolved over time, and how? Third, the paper looks at the implementation of World Trade Organization accession deals: Is accession the end or the beginning of the story? What are the implications for the participating countries and the multilateral trading system?