City Research Online

Abstract

Chapter 1: Momentum Effects: G10 Currency Return Survivals
The chapter analyses momentum effects in G10 currencies. For each of the currency crosses within the G10 universe the chapter models the “survival” probabilities of trading signals obtained from a wide set of dual crossover moving average combinations. The application of statistical tools that stem from survival time analysis sheds light on the subject of market efficiency within the currency market. Empirical momentum signals from shorter-term trading rules outlive respective benchmark signals, while longer-term moving average crossover signals have lower life expectancy than theory would suggest. Furthermore, a trading strategy constructed from a subset of short-term moving average signals exhibits clear outperformance over a trading strategy that is generically composed from all moving average crossover signals. This outperformance persists over time.

Chapter 2: Momentum Effects: G10 Currency Return Survivals, Implications for Trading Rules
The chapter models survival probabilities of positive and negative momentum signals that are obtained from a wide set of dual crossover moving average combinations for all G10 cross currency pairs. The results of this survival analysis are used to create trading rule enhancements that aim to outperform generic dual crossover moving average trading signals. The trading rule enhancements are assessed, by applying White’s (1999) “data snooper”. The results suggest that there is scope for trading rule enhancements to outperform generic trading rules. Moreover, results present strong evidence for Lo’s (2004) Adaptive Market Hypothesis.

Chapter 3: Momentum effects: Dissecting Generic G10 Trading Rule Returns
The chapter builds on the work of Pojarliev and Levich (2008, 2010), who dissect the returns of active currency managers by applying a multiple ordinary least squares (OLS) regression to currency fund returns. Where the chapter differs is in the specification of the dependent variable, which is in the context of the present chapter a set of trading rule parameterisations that are applied to a broad range of currency pairs. The results of this chapter suggest that there is some alpha embedded in the returns of technical trading rules. The chapter also establishes a comparatively strong positive, statistically significant link between the risk factors Trend, Momentum, Risk Aversion. The results of the chapter clearly indicate that shorter-term moving averages exhibit less systematic exposure than longer term moving averages. Other factors such as Carry, Value and Volatility have a considerably less pronounced relationship; only few factor sensitivities are statistically significant. Moreover, the results also indicate that systematic risk exposures of trend following trading strategies change with small adjustments in the design of trading rules.

Publication Type:	Thesis (Doctoral)
Subjects:	H Social Sciences > HG Finance
Departments:	Bayes Business School > Finance Doctoral Theses Bayes Business School > Bayes Business School Doctoral Theses

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