Are hedge funds active market liquidity timers?

Journal Pre-proof Are hedge funds active market liquidity timers?

Chenlu Li, Baibing Li, Kai-Hong Tee PII:

S1057-5219(18)30664-1

DOI:

https://doi.org/10.1016/j.irfa.2019.101415

Reference:

FINANA 101415

To appear in:

International Review of Financial Analysis

Received date:

11 September 2018

Revised date:

24 June 2019

Accepted date:

6 November 2019

Please cite this article as: C. Li, B. Li and K.-H. Tee, Are hedge funds active market liquidity timers?, International Review of Financial Analysis(2019), https://doi.org/ 10.1016/j.irfa.2019.101415

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© 2019 Published by Elsevier.

Journal Pre-proof

Are hedge funds active market liquidity timers? Chenlu Li School of Business & Economics Loughborough University, Loughborough LE11 3TU, U.K. Email: [email protected]

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Baibing Li (corresponding author)

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School of Business & Economics

Loughborough University, Loughborough LE11 3TU, U.K.

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Email: [email protected]

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Tel: 01509 228841 Kai-Hong Tee

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School of Business & Economics Loughborough University, Loughborough LE11 3TU, U.K.

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Email: [email protected]

Journal Pre-proof

Are hedge funds active market liquidity timers?

Abstract This paper investigates liquidity timing behaviour of hedge funds that invest globally in foreign investment assets. We expect these hedge funds to manage currencies exposure differently, depending

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on the extent they treat them as an asset class. In this paper, we investigate if actively timing foreign

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exchange (FX) liquidity adds value to hedge funds’ investments. Unlike the existing studies where fund managers are assumed to either time or not time the market over the entire study period, we

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argue that fund managers may strategically choose to be active market liquidity timers based on the

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market condition at the time. To test this hypothesis, we develop a state-dependent liquidity timing

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model embedded with a Markov regime switching process and identify changes in the FX liquidity timing behaviour among the Global Derivatives hedge funds over an eighteen-year period. Our

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findings reveal that such regime changes in timing behaviour are driven by the underlying FX liquidity condition. A further analysis to compare the changes in the timing behaviour over time

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shows that hedge funds that are active market liquidity timers outperform those that engage in liquidity timing less frequently in all strategies categories. Keywords: Foreign exchange market, Hedge fund, Liquidity, Markov regime switching, Timing behaviour. JEL classification: G1; F3

Journal Pre-proof 1. Introduction Since the financial crisis of 2008, there has been a considerable attention on the research for timing market liquidity condition by hedge funds. Following Chen and Liang (2007) who consider the return timing and volatility timing abilities of hedge funds respectively, Cao, Chen, Liang, and Lo (2013) investigate equity-oriented hedge funds’ ability to time the liquidity of the equity market to manage the exposure. The effect has been investigated from the asset pricing perspective to evaluate whether such timing ability explains hedge funds’ returns in the equity market. Li, Luo, and Tee

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(2017) and Luo, Tee, and Li (2017) subsequently extend the hedge funds’ focus market from the

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equity market to the fixed-income market and foreign exchange (FX) market respectively and investigate hedge fund managers’ liquidity timing abilities in these two markets. Market liquidity has

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also been explored in relation to its impact on other areas. Chelley-Steeley, Lambertides and Savva

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(2013), for example, explore market liquidity in relation to the effects on market co-movement. This

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shows the importance of the market liquidity topic in the literature. One underlying implicit assumption in the literature on such timing studies is that fund

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managers either time or not time the market across the entire time span under investigation. In fact, since the pioneering research on market timing by Treynor and Mazuy (1966), almost all the studies in

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the literature on timing effect implicitly follow this assumption, e.g., French and Ko (2007), Jiang, Yao, and Yu (2007), Kazemi and Li (2009), Cao, Simin, and Wang (2013), among many others. This assumption, we argue, may not be realistic as fund managers may deliberately and strategically choose to be an active market timer over different time periods, depending on the market status and their priorities at the time. Statistically, the various timing models in the literature all use ordinary multiple regression to test the relevant research hypothesis about managers’ time ability. With this approach, the average timing effect of fund managers across the entire time span under study is measured. Consequently, when fund managers time a market strategically and intermittently, rather than continuously over the time span of study, arising from market conditions and/or their investment priority, the evidence of the potential timing effect may be averaged out over the entire time span, leading to statistically

Journal Pre-proof insignificant results for the hypothesis testing about fund managers’ timing ability. Market condition and hedge funds’ performances have also been researched in the literature in relation to the effects on portfolio allocation (Harris and Mazibas, 2010). Therefore, it is an area not to be ignored, which also explained one of the motivations of this paper. This paper follows Luo, Tee, and Li (2017) and hypothesises that hedge fund managers, who take positions in the FX market, forecast the FX market beta by using information on the forecasted future FX liquidity. This paper aims to provide better evidences on the liquidity timing ability by

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adopting an embedded Markov regime switching model. Doing so makes it possible in the evaluation in the changes of timing behaviour, together with further analysis for performance outcomes. Regime

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switching technique is common in the hedge funds literature such as those in Harris and Mazibas

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(2010) who apply the technique to evaluate effect of market condition changes to hedge funds

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portfolio allocation.

Existing studies on the timing effect have shed light on this issue. For example, Luo, Tee, and

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Li (2017) compare and contrast hedge fund managers’ timing ability before and after the recent financial crisis. They show that the hedge funds’ strength of timing ability differed in these two time

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periods. Siegmann and Stefanova (2017) take one step further in their study: unlike Luo, Tee, and Li

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(2017) where the analysis is carried out based on two pre-determined time periods (i.e. before and after the recent financial crisis as defined to be pre and post July 2007), they use a changepoint approach to identify the time point at which hedge funds shift their timing behaviour. This paper re-examines the liquidity timing behaviour of hedge funds. We argue that hedge funds may strategically switch their timing behaviour from time to time, depending on the market conditions and/or their investment strategy at the time. Consequently, the methods used in Luo, Tee, and Li (2017) and Siegmann and Stefanova (2017) may not be a reasonable choice since they assume that the hedge funds’ timing behaviour shifts only once. In this paper, we develop a state-dependent approach to detect hedge funds’ timing behaviour, termed as the state-dependent liquidity timing (SDLT) model. We incorporate a Markov regime switching model to describe the hedge funds’ timing behaviour under different regimes, and to accommodate for likely frequent changes in liquidity timing behaviours. Within each regime, the conventional timing model is used to investigate timing skills.

Journal Pre-proof We focus on the Global Derivatives hedge funds where investment strategies cover the international financial assets’ markets, and with known FX exposures. Overall, the currency market is known to be highly liquid (Mancini, Ranaldo and Wrampelmeyer, 2013); it is reported to have an average trading volume of $5.1 trillion per day in April 2016 (Bank for International Settlements, 2016). Research by Nasypbek and Rehman (2011) shows that the participants in the FX market increasingly involve different types of investment funds, from hedge funds to mutual funds. Indeed, the existing academic literature that focuses on hedge funds’ investment strategies in the FX market

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shows that the currency hedge funds do heavily involved in the money market and exploit on carry trades to generate returns (e.g., Nucera and Valente, 2013). From time to time and particularly during

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a financial crisis, however, the liquidity condition may change. We use the proposed SDLT model to

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detect the shifts of timing behaviour for the Global Derivatives hedge funds. We hypothesize

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currencies to be treated as an asset class to different degrees among the Global Derivatives hedge funds. This then affects the extent they exploit currencies for profit and manage its exposure. We

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investigate whether and when the Global Derivatives hedge funds time the FX market liquidity condition when making investment decisions.

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In summary, this paper contributes to the literature in two folds. First, it makes

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methodological development by developing a state-dependent approach to investigate the changes in the liquidity timing behaviour of hedge funds. Secondly, it investigates the regime switching in the timing behaviour over the period of the study among the Global Derivatives hedge funds and the potential drivers behind such changes. It also compares the changes in the timing behaviour among the hedge funds and investigates the implication to hedge funds’ performances. This paper is structured as follows. Section 2 discusses the hedge fund data and the FX liquidity measure used in this study. In Section 3, we develop an econometric model, i.e. the SDLT model. This is followed by Section 4 which provides an empirical analysis at the hedge fund strategy level. Section 5 focuses on further analysis on the individual hedge funds. Finally, Section 6 concludes the paper.

Journal Pre-proof 2. Data In this section, we briefly discuss the hedge fund data and liquidity measure in the FX market used in this paper.

2.1. Hedge funds Hedge funds are investments using pooled funds, which employ various strategies constructed to take advantages of certain identifiable market opportunities such as timing opportunities; see El

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Kalak (2016) for a review on hedge funds and hedge funds’ managerial characteristics. These strategies and styles are often classified into different strategy categories. We source the hedge funds

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data in this paper from Morningstar1 which classifies hedge funds into six broad strategy categories:

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‘Directional Equity’, ‘Directional Debt’, ‘Event’, ‘Global Derivatives’, ‘Multi-strategy’, and ‘Relative

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Value’. Each of these is further broken down into several sub-categories. To investigate hedge funds’ liquidity timing ability in the FX market, this paper only uses

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those hedge funds that invest globally that need to manage risks associated with the foreign asset classes, as well as the underlying FX exposure. Unlike other strategies in the Morningstar database,

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the hedge funds in the Global Derivatives category invest mainly in the global markets with optimal

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global asset-allocations where the FX market plays an important role for aggressive strategies aiming at huge profitability. We therefore focus on this hedge fund strategy. The Global Derivatives category includes four sub-categories of hedge funds strategies, i.e., ‘Currency’, ‘Global Macro’, ‘Systematic Futures’, and ‘Volatility’2.

1 Other data vendors are also used in the academic research on hedge funds, such as Lipper TASS and HFR. Each differs in terms of the extent of survivorship bias, which is addressed in this paper. It is beyond the scope of this paper to investigate and discuss the differences among these various hedge fund databases. 2 Different data vendors use different ways to separate the strategy and style of hedge funds. According to Morningstar (2014), funds in the ‘Systematic Futures’ sub-category trade liquid global futures, options, and foreign-exchange contracts largely according to trend-following strategies (such as linking greater than 50% of fund's exposure to such strategies). These strategies are price-driven (technical) and systematic (automated) rather than fundamental or discretionary. Trend-followers typically trade in diversified global markets, including commodities, currencies, government bonds, interest rates, and equity indexes. However, some trend followers may concentrate in certain markets, such as interest rates. These strategies prosper when markets demonstrate sustained directional trends, either bullish or bearish. Some ‘Systematic Futures’ strategies involve mean-reversion or counter-trend strategies rather than momentum or trend-following strategies. At least 60% of the funds’ exposure is obtained through derivatives. Funds in the ‘Currency’ sub-category invest in portfolios of multiple currencies through the use of short-term money market instruments; derivative instruments, including and not limited to, forward currency contracts, index swaps and options, and cash deposits. These funds include both systematic currency traders and discretionary traders. Funds in the “Global Macro” sub-category base investment decisions on an assessment of the broad macroeconomic environment. They look for investment opportunities by studying such factors as the global economy, government policies, interest rates, inflation, and market trends. As opportunists, these funds are not restricted by asset class and may invest across such disparate assets as global equities, bonds, currencies, derivatives, and commodities. These funds primarily invest through derivatives markets. They typically make discretionary trading decisions rather than using a systematic strategy. At least 60% of the funds’ exposure is obtained through derivatives. Funds in the “Volatility” sub-category trade volatility as an asset class. Directional volatility strategies aim to profit from the trend in the implied

Journal Pre-proof Following the literature (e.g., Aggarwal and Jorion, 2010; Cao, Chen, Liang, and Lo, 2013; Siegmann and Stefanova, 2017), we restrict our analysis to those funds such that they have the value of assets under management at least US$10 million. We also require each fund to have at least 24 monthly returns to obtain meaningful results, following Eling and Faust (2010) and Siegmann & Stefanova (2017) amongst others. Panel A of Table 1 shows the average monthly returns (net-of-fee) of the Global

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Derivatives hedge funds.

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(Table 1 is here)

2.2. Factors

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In this paper, we follow Boyson, Stahel and Stulz (2010) and use a FX market factor to

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examine hedge fund returns in the Global Derivatives category, where the FX market factor is the change in the trade-weighted U.S. dollar exchange rate index3.

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To control for other factors associated with hedge funds’ performances, the seven-factor model proposed by Fung and Hsieh (2004)4 is used in the paper with the following factors included:

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the equity market factor (EMF), size factor (size spread), bond market factor (∆Term), credit spread factor (∆Credit), and the three Trend-Following Risk Factors (B-LBS, FX-LBS, COM-LBS).

Panel B displays the factor data in Fung and Hsieh’s seven-factor model, as well as the FX market factor. The sample period is from September 2000 to August 2017.

volatility embedded in derivatives referencing other asset classes. Volatility arbitrage seeks to profit from the implied volatility discrepancies between related securities. 3 as published by the Board of Governors of the U.S. Federal Reserve System. 4 The seven factors include both linear and option-like factors and have been shown to explain variation in hedge fund returns well. Specifically, these factors include an equity market factor, a size factor, changes in the constant maturity yield on 10-year Treasury bonds, change in the spread between Moody’s Baa and 10-year Treasury bonds, and three trend-following factors for bonds, currency, and commodities. These are available from http://faculty.fuqua.duke.edu/~dah7/HFRFData.htm. We thank Fung and Hsieh for providing these data.

Journal Pre-proof 2.3. FX Liquidity We use a basket of currencies to represent the FX market, including the United States Dollar (USD), Australian Dollar (AUD), Canadian Dollar (CAD), Swiss Franc (CHF), Euro (EUR), United Kingdom Pound (GBP), Japanese Yen (JPY), Swedish Krona (SEK), and Singapore Dollar (SGD). These currencies are the most heavily traded currencies globally. Similar currency baskets are also used by, for example, Mancini, Ranaldo, and Wrampelmeyer (2013) and Karnaukh, Ranaldo, and Sӧderlind (2015). Table 2 reports the distribution of over-the-counter (OCT) FX turnover for the

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currencies from 2001 to 2016, as reported in Bank for International Settlements (2016).

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(Table 2 is here)

The FX market liquidity measure used in this paper is the proportionally quoted spread 5 ,

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which is the most commonly used measure for currency liquidities (Kessler and Scherer, 2011;

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Mancini, Ranaldo and Wrampelmeyer, 2013; Karnaukh, Ranaldo and Söderlind, 2015; Luo, Tee, Li, 2017). The proportionally quoted spread for the FX market illiquidity is calculated as follows:

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𝑠𝑝𝑟𝑒𝑎𝑑(𝐴𝐵) = (𝑃𝐴 − 𝑃𝐵 )/𝑃𝑀

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where 𝑠𝑝𝑟𝑒𝑎𝑑(𝐴𝐵) denotes the proportionally quoted spread, 𝑃𝐴 and 𝑃𝐵 are the quoted ask price and bid price, and 𝑃𝑀 is the midpoint of the quoted bid and ask prices. The data of bid and ask prices used in this paper are sourced from DataStream directly from market transactions. The sample period covers from September 2000 to August 2017. The monthly market spread 𝑠𝑝𝑟𝑒𝑎𝑑(𝐴𝐵) is calculated based on the equally weighted average of all the currency spreads. A market is regarded as liquid (or illiquid) if the proportional quoted bid-ask spread is low (or high) (Mancini, Ranaldo, and Wrampelmeyer, 2013). We therefore define the FX liquidity measure at each time period 𝑡 to be6:

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In the literature, there are several other liquidity measures for the FX market, including measures based on price impact and return reversal, effective costs, and price dispersion. Evidence shows, however, proportionally quoted spread is highly correlated with these FX liquidity measures. For example, the correlation in Mancini et al. (2013) is 0.853 for the proportionally quoted spread and price impact, 0.890 for return reversal, 0.954 for effective costs, and 0.949 for price dispersion. Due to data availability, we focus on proportionally quoted spread to measure FX liquidity in this paper. 6 Following Mancini, Ranaldo, and Wrampelmeyer (2013), we define the FX liquidity index to be negative proportionally quoted spread in the empirical analysis so that the higher the liquidity index, the more liquid the FX market. In addition, because currency spreads are small

Journal Pre-proof 𝐿𝐹𝑋,𝑡 = −𝑚𝑜𝑛𝑡ℎ𝑙𝑦 𝑠𝑝𝑟𝑒𝑎𝑑(𝐴𝐵) 𝑎𝑡 𝑒𝑎𝑐ℎ 𝑚𝑜𝑛𝑡ℎ 𝑡𝑖𝑚𝑒 𝑡. Panel C of Table 1 provides a statistical summary of the calculated proportional spread as a measure of FX illiquidity.

3.

State-dependent FX Liquidity Timing (SDLT) Model In this section, we first briefly discuss the liquidity timing models used in the literature. Then

we develop a state-dependent timing model for liquidity timing in the FX market.

A brief overview on liquidity timing models

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3.1.

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In the literature, market timing models are constructed to describe the performances of

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investment funds based on the forecasts of the fund managers about future market conditions. The attitude of market timers (fund managers) to risk has been shown to affect their funds’ market

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exposure, resulting in dynamic asset allocation (Admati, Bhattacharya and Pfleiderer, 1986). The risk

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aversion theory of liquidity preference proven by Tobin (1958) and Hawawini (1983) further justifies the examination of timing ability from the perspective of liquidity.

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Analysis for fund managers’ timing ability can be traced back to the pioneering work in

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Treynor and Mazuy (1966). To examine the timing ability of a fund manager, the return of a fund (or fund strategy) 𝑖 is modelled based on the capital asset pricing model (CAPM): 𝑟𝑖,𝑡+1 = 𝛼𝑖 + 𝛽𝑖,𝑡 𝑀𝐾𝑇𝑡+1 + 𝑒𝑖,𝑡+1 , where 𝑟𝑖,𝑡+1 is the return in excess of risk-free rate for fund 𝑖, 𝑀𝐾𝑇𝑡+1 is the excess market return, and 𝑒𝑖,𝑡+1 is the error term in month 𝑡 + 1. 𝛼𝑖 is the fund’s alpha. It is argued in the existing literature (e.g. Admati, Bhattacharya and Pfleiderer, 1986; Shanken, 1990; Cao, Chen, Liang, and Lo, 2013) that the fund’s beta 𝛽𝑖,𝑡 in the above model varies over time 𝑡 and the fund manager may forecast the fund’s beta 𝛽𝑖,𝑡 using the information on the market condition: 𝛽𝑖,𝑡 = 𝛽𝑖 + 𝜆𝑖 𝐸(𝑚𝑎𝑟𝑘𝑒𝑡 𝑐𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛𝑡+1 |𝐼𝑡 ), (1)

in magnitude, we further rescale 𝐿𝐹𝑋,𝑡 by multiplying 1,000 to improve numerical stability during the numerical computation in the empirical analysis in Sections 4 and 5.

Journal Pre-proof where 𝛽𝑖 captures the fund’s average beta without timing and 𝐼𝑡 is the information set available to the fund manager in time period 𝑡. The coefficient 𝜆𝑖 describes the timing ability of the fund manager of fund 𝑖. Hence, the above equation characterises how the fund beta 𝛽𝑖,𝑡 is affected by the manager in month 𝑡 based on her/his forecast about the market condition in month 𝑡 + 1. In the literature, when analysing hedge fund performances, the CAPM-based model is usually extended to the Fung and Hsieh (2004)’s seven-factor model (see, e.g., Cao, Chen, Liang, and Lo, 2013):

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𝑟𝑖,𝑡+1 = 𝛼𝑖 + ∑7𝑗=1 𝛾𝑖𝑗 𝑓𝑗,𝑡+1 + 𝑒𝑖,𝑡+1 ,

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where 𝑓𝑗,𝑡+1 (j=1,…,7) stand for the seven factors in Fung and Hsieh (2004)’s seven-factor model

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and 𝛾𝑖𝑗 (j=1,…,7) are the corresponding coefficients. 𝑒𝑖,𝑡+1 denotes the error term in month 𝑡 + 1. A model for assessing hedge fund manager’s timing ability can be obtained by combining equation (1)

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and the above seven-factor model; see Cao, Chen, Liang, and Lo (2013) for details.

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Luo, Tee, and Li (2017) focus on the FX market. They follow Boyson et al. (2010) and Fung

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and Hsieh (2004) to add the eighth factor, an FX market factor, into the seven-factor model: 𝑟𝑖,𝑡+1 = 𝛼𝑖 + 𝛽𝑖,𝑡 𝐹𝑋𝐹𝑡+1 + ∑7𝑗=1 𝛾𝑖𝑗 𝑓𝑗,𝑡+1 + 𝑒𝑖,𝑡+1 ,

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where 𝐹𝑋𝐹𝑡 is defined to be the negative monthly change in month 𝑡 in the trade-weighted U.S. dollar exchange rate index.

Based on equation (1), Luo, Tee, and Li (2017) investigate the problem that the market liquidity condition is employed by a fund manager to forecast the beta in the liquidity timing problem: 𝛽𝑖,𝑡 = 𝛽𝑖 + 𝜆𝑖 (𝐿𝐹𝑋,𝑡+1 − 𝐿̅𝐹𝑋 + 𝜐𝑝,𝑡+1 ), where 𝐿𝐹𝑋,𝑡+1 denotes the liquidity level in the FX market in month 𝑡 + 1 and 𝐿̅𝐹𝑋 is the average FX market liquidity level. 𝜐𝑝,𝑡+1 is the forecast error. Note that in the above equation, the mean of the market liquidity is removed for ease of interpretation, following the existing studies (e.g., Ferson and Schadt,1996; Busse, 1999; Cao, Chen, Liang, and Lo, 2013). Consequently, by combining the above two equations, Luo, Tee, and Li (2017) use the following model to analyse liquidity timing ability in the FX market:

Journal Pre-proof 𝑟𝑖,𝑡+1 = 𝛼𝑖 + 𝛽𝑖 𝐹𝑋𝐹𝑡+1 + 𝜆𝑖 𝐹𝑋𝐹𝑡+1 (𝐿𝐹𝑋,𝑡+1 − 𝐿̅𝐹𝑋 ) + ∑7𝑗=1 𝛾𝑖𝑗 𝑓𝑗,𝑡+1 + 𝜀𝑖,𝑡+1 ,

(2)

where 𝜀𝑖,𝑡+1 denotes the error term in month 𝑡 + 1 in equation (2).

3.2.

State-dependent Timing Model In equation (2) developed in Luo, Tee, and Li (2017), the timing ability coefficient 𝜆𝑖 is

assumed to be a constant value over the entire time span under investigation. Recent studies in the literature, however, suggest the time-varying nature in the market timing behaviour of hedge fund

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managers. For instance, Luo, Tee, and Li (2017), and Siegmann and Stefanova (2017) find that the strength of liquidity timing skill differs in different time periods for hedge funds. To accommodate

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that the liquidity timing behaviour may change over time, we develop a new timing model with an

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embedded Markov regime switching model. In the literature, the regime switching technique has been

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shown on hedge funds data to reveal changes over times and may have impact on subsequent performances. For example, Harris and Mazibas (2010) provide evidence of this effect on the use of

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dynamic management in hedge funds’ portfolio allocation. Specifically, we follow Luo, Tee, and Li (2017) and hypothesise that hedge fund managers

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who take positions in the FX market forecast the FX market beta using information on the forecasted

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future FX liquidity. We extend the liquidity timing model (2) to allow the regime of funds’ FX liquidity-timing behaviour to switch based on a first-order Markov process: 7 ̅ 𝑟𝑖,𝑡+1 = 𝛼𝑖 + 𝛽𝑖 𝐹𝑋𝐹𝑡+1 + 𝜆𝐹𝑋 𝑖,𝑆𝑡 (𝐿𝐹𝑋,𝑡+1 − 𝐿𝐹𝑋 ) ∗ 𝐹𝑋𝐹𝑡+1 + ∑𝑗=1 𝛾𝑖𝑗 𝑓𝑗,𝑡+1 + 𝜀𝑖,𝑡+1 ,

(3)

where, similar to Luo, Tee, and Li (2017), 𝑟𝑖,𝑡+1 is the excess return for fund (or fund strategy) 𝑖 in month 𝑡 + 1. 𝐹𝑋𝐹𝑡+1 is the negative monthly change in month 𝑡 + 1 in the trade-weighted U.S. dollar exchange rate index in month 𝑡 + 1 and 𝛽𝑖 is the corresponding coefficient. In addition, 𝐿𝐹𝑋,𝑡+1 is an FX liquidity measure at time 𝑡 + 1 which is de-meaned by subtracting the mean 𝐿̅𝐹𝑋 for interpretation purposes. 𝑓𝑗,𝑡+1 (𝑗 = 1,2, … ,7) are the seven risk factors of Fung and Hsieh’s (2004) that benchmark the hedge fund returns and 𝛾𝑖𝑗 are the corresponding coefficients. 𝜀𝑖,𝑡+1 is the noise assumed to be independent of each other, with a zero mean and variance 𝜎 2 , i.e. 𝜀𝑖,𝑡+1 ~𝑁(0, 𝜎 2 ). 𝜆𝐹𝑋 𝑖,𝑆𝑡 measures the FX liquidity timing effect.

Journal Pre-proof Unlike the assumption of a constant timing coefficient in the existing studies (e.g., Cao, Chen, Liang, and Lo 2013; Luo, Tee, and Li, 2017), however, the effect 𝜆𝐹𝑋 𝑖,𝑆𝑡 for the FX liquidity timing is assumed to be time-varying and it follows a Markov regime switching process, shifting between two states, i.e. 𝑆𝑡 = 0,1. In other words, the timing coefficient 𝜆𝐹𝑋 𝑖,𝑆𝑡 takes either 𝜆𝑖,0 or 𝜆𝑖,1 (with 𝜆𝑖,0 ≤ 𝜆𝑖,1 ), depending on the current state 𝑆𝑡 : 𝜆𝐹𝑋 𝑖,𝑆𝑡 = 𝜆𝑖,0 (1 − 𝑆𝑡 ) + 𝜆𝑖,1 𝑆𝑡 .

(4)

We treat the state 𝑆𝑡 = 0 as the “non-timing” (baseline) status where hedge funds’ investment

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activities are not associated with liquidity timing, and 𝑆𝑡 = 1 with a larger timing coefficient 𝜆𝑖,1 to be

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the “timing” (termed “treatment” in the literature) status where the hedge fund managers time the

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market liquidity when making their investment decisions.

Next, we specify a Markov regime switching model for the stochastic process 𝑆𝑡 . Markov

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processes are initially introduced into time-series econometrics by Hamilton (1989, 1990) and Kim

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(1994) to investigate regime switching of an economy. Applications to the finance literature include Guidolin and Hyde (2012) who analyse whether switches in regimes imply dynamic strategic asset

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allocation choices in a portfolio with stocks, bonds and T-bills. Unlike the changepoint approach, the regimes in Markov regime switching processes can switch any times and at any time points during the

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study period, and hence this approach is less restrictive in its applications. To avoid heavy notation, we suppress the subscript 𝑖 for the rest of this section. The Markov stochastic process {𝑆𝑡 } in (4) is characterised by the following transition probability matrix: 𝑝00,𝑡 𝐏𝑡 = [𝑝 01,𝑡

𝑞10,𝑡 𝑞11,𝑡 ],

(5)

where Pr[𝑆𝑡 = 𝑗|𝑆𝑡−1 = 0] = 𝑝0𝑗,𝑡 ≥ 0 is the probability that when the regime process is in state 0 at time 𝑡 − 1, it will be in state 𝑗 (𝑗 = 0,1) at time 𝑡. Similarly, Pr[𝑆𝑡 = 𝑗|𝑆𝑡−1 = 1] = 𝑞1𝑗,𝑡 ≥ 0 denotes the probability that when the regime process is in state 1 at time 𝑡 − 1, it will be in state 𝑗 (𝑗 = 0,1) at time 𝑡. Note we assume that in general the transition matrix 𝐏𝑡 in (5) is time-varying, instead of imposing a fixed transition probability (FTP) matrix. The Markov-switching model with a time-varying transition probability (TVTP) matrix identifies systematic variations in the transition

Journal Pre-proof probabilities both before and after the turning points. The flexibility of TVTP matrices may capture more complex temporal persistence than the FTP model. Clearly, a difference in the timing coefficients between the two states is an indication of switches of timing behaviour. We therefore define the difference to be ∆𝜆 = 𝜆1 − 𝜆0 and consider the following null hypothesis: (6)

𝐻0 : ∆𝜆 = 0.

To detect if there is any difference between 𝜆1 and 𝜆0 in (4), we perform a statistical test for the

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null hypothesis 𝐻0 in (6). If there is evidence to reject 𝐻0 , it suggests switches of timing behaviour of

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hedge funds. We use 𝑡(∆𝜆) to denote the t-value for the above null hypothesis7. Next, we consider the transition matrix of the Markov process in (5). To reflect the dynamics

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of the switches between states, the TVTP matrix in (5) is assumed to be related to some exogenous

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variables. This allows us to better capture the underlying driving forces that lead to the switches of hedge funds timing behaviour. Similar modelling approaches are also considered in the literature. For

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instance, Diebold and Weinbach (1994) and Filardo (1994) allow time-varying transition probabilities

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to evolve as a logistic function of leading economic fundamentals. Durland and McCurdy (1994), Filardo and Gordon (1998) and Lam (2004) consider durations of the state.

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Specifically, we assume that the transition probabilities in (5) are affected by a vector of financial variables, denoted as 𝐙𝑡 = [𝑧1,𝑡 , … , 𝑧𝐾,𝑡 ]𝑇 , and we rewrite the transition probabilities such that they explicitly depend on 𝐙𝑡 :

Pr{𝑆𝑡 = 0| 𝑆𝑡−1 = 0, 𝐙𝑡 } = 𝑝00,𝑡 = 𝑝(𝐙𝑡 ),

(7)

Pr{𝑆𝑡 = 1| 𝑆𝑡−1 = 1, 𝐙𝑡 } = 𝑞11,𝑡 = 𝑞(𝐙𝑡 ). Note that 𝑝01,𝑡 = 1 − 𝑝(𝐙𝑡 ) and 𝑞10,𝑡 = 1 − 𝑞(𝐙𝑡 ). In this case, the transition probability matrix in (5) is denoted as 𝐏𝑡 (𝐙𝑡 ).

7 Statistically, let 𝛌̂ = [𝜆̂1 , 𝜆̂0 ]T be a (2 × 1) vector of the maximum likelihood (ML) estimates of the timing coefficients, and ̂ Cov(𝛌) the covariance matrix of 𝛌̂. In addition, we denote var(∆𝛌̂) to be the variance of ∆𝜆̂ and 𝑔(. ) to be the transformation of 𝛌 into ∆𝜆. Then the ML estimate of ∆𝜆 and its variance are obtained as ∆𝛌̂ = 𝜆̂1 − 𝜆̂0 and 𝜕𝑔(𝛌) 𝜕𝑔(𝛌) 𝑇 1 ̂ ̂ ̂ var(∆𝛌) = ( ) Cov(𝛌) ( ) |𝛌= 𝛌̂ = [1 −1]Cov(𝛌)[ ]. Therefore, we can calculate the corresponding t-statistic for testing 𝐻0 in 𝜕𝛌 𝜕𝛌 −1 (6): 𝑡(∆𝛌̂) = ∆𝛌̂/√var(∆𝛌̂).

Journal Pre-proof We follow Diebold, Lee and Weinbach (1994) and Filardo (1994), and use the logit model for the Markov transition probabilities: 𝑝(𝐙𝑡 ) =

exp(𝜃𝑝0 +∑𝐾 𝑘=1 𝜃𝑝𝑘 𝑧𝑘,𝑡 ) 𝐾1 1+exp(𝜃𝑝0 +∑𝑘=1 𝜃𝑝𝑘 𝑧𝑘,𝑡 )

and

𝑞(𝐙𝑡 ) =

exp(𝜃𝑞0 +∑𝐾 𝑘=1 𝜃𝑞𝑘 𝑧𝑘,𝑡 ) 𝐾

,

2 𝜃 𝑧 ) 1+exp(𝜃𝑞0 +∑𝑘=1 𝑞𝑘 𝑘,𝑡

(8)

where 𝜃𝑝𝑘 and 𝜃𝑞𝑘 (for 𝑘 = 0, … , 𝐾) are parameters to be estimated. If 𝜃𝑝𝑘 = 𝜃𝑞𝑘 = 0 for all 𝑘 > 0, the TVTP model collapses to the FTP model. This can be statistically tested during statistical inference. The parameters 𝜃𝑝𝑘 and 𝜃𝑞𝑘 (for 𝑘 = 1, … , 𝐾) in equations (7) and (8) are important for the

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understanding of regime switching. They reflect the effects of the financial variables 𝑧1,𝑡 , … , 𝑧𝐾,𝑡 on

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the likelihood of switching to a different regime. Specifically, with a positive (or negative) 𝜃𝑞𝑘 , a

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higher value of the financial variable 𝑧𝑘,𝑡 leads to a larger (or smaller) transition probability 𝑞(𝐙𝑡 ) =

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Pr{𝑆𝑡 = 1| 𝑆𝑡−1 = 1, 𝒁𝑡 }. This means that when the regime at time period 𝑡 − 1 is the “timing” state (i.e. 𝑆𝑡−1 = 1), it increases (or decreases) the likelihood that the regime will continue to stay at the

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“timing” state (𝑆𝑡 = 1) in the next time period, and hence reduces (or enhances) the likelihood that the

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regime will switch to the “non-timing” state. The interpretation for 𝜃𝑝𝑘 is similar. Collectively, equations (3-5) and (7-8) are termed as the state-dependent FX liquidity timing

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(SDLT) model in this paper. The regression coefficients in the liquidity timing model (3) and the Markov-switching parameters in equations (4) and (8) are jointly estimated using the maximum likelihood method for statistical inference.

4.

Empirical Analysis on Timing Behaviour In this section, we apply the SDLT model developed in the previous section to analyse hedge

funds’ liquidity timing behaviour in the FX market.

4.1.

Main results We hypothesise that the market liquidity in the FX market drives hedge funds’ motives for

liquidity timing: hedge funds may behave differently when the overall market liquidity level is high or

Journal Pre-proof low. We therefore define a binary variable representing the market liquidity level by dichotomising the overall market liquidity based on whether its value is above/below its mean8: 𝐷𝑡𝐹𝑋 = {

1 0

if 𝐿𝐹𝑋,𝑡 > 𝐿̅𝐹𝑋 . otherwise

The following analysis focuses on the effect of the overall market liquidity level on the regime switching and investigates how different market liquidity levels affect the changes in the liquidity timing behaviour. Technically, this means that we specify 𝐙𝑡 to be the binary market liquidity level 𝐷𝑡𝐹𝑋 . Based on equation (8), we can statistically test if the market liquidity level is associated with the

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regime switches of hedge funds’ timing behaviour.

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The SDLT model developed in the previous section is applied to the Global Derivatives

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category, and each of its four sub-categories. The estimation results for the SDTS model are reported

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in Table 3.

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(Table 3 is here)

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First, we consider the results for the timing equation (3). Table 3 shows a significant, positive beta coefficient, 𝛽𝑖𝐹𝑋 , for the FX market factor 𝐹𝑋𝐹𝑡+1 for all categories, except for the Volatility

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category, indicating significant exposure to risk arising from the FX market for most of the hedge funds in these categories.

Next, we focus on the null hypothesis 𝐻0 in (6). Table 3 shows that, for the Global Derivatives category, the t-value for testing 𝐻0 in (6) is equal to 𝑡(∆𝜆) =4.17. Hence, we have sufficient evidence to reject the hypothesis 𝐻0 at the 1% significance level. This indicates that statistically there are two distinct timing regimes, with two significantly different timing coefficients 𝜆1 and 𝜆0 . Therefore, the Global Derivatives fund managers do switch their timing behaviour from time to time. The estimated liquidity timing coefficients for the “timing” state 𝑆𝑡 = 1 and the “nontiming” state 𝑆𝑡 = 0 are 2.25 and -8.46 respectively; both are statistically significant at the 5% level. 8 Due to the potential asymmetric nature of the FX market liquidity, we have also explored the analysis where the mean 𝐿̅𝐹𝑋 is replaced with the median of the market liquidity when defining the dummy 𝐷𝑡𝐹𝑋 , as a robustness check. It turns out the obtained results are similar and consistent with those displayed in Table 3 and Table 4. .

Journal Pre-proof For state 𝑆𝑡 = 1, this implies the intention of the Global Derivative hedge fund managers to reduce (increase) their funds’ FX market exposure prior to the deteriorations (recovery) of liquidity. Next, we examine the breakdown analysis for the sub-categories. We note that the t-value for testing 𝐻0 in (6) is high for all the four sub-categories in Table 3. Hence, we have sufficient evidence to reject the hypothesis 𝐻0 at the 1% significance level for all sub-categories, showing there are two distinct timing regimes for all four sub-categories. In addition, 𝜆1 is positive and except for Global Macro, it is statistically significant at the 5% level for all the other sub-categories, indicating that the

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fund managers in these sub-categories do time the market liquidity at the “timing” state. To explain the potential financial force that drives the regime switches of hedge funds’ timing

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behaviour, we include the market liquidity level 𝐷𝑡𝐹𝑋 into the Markov transition probabilities in the

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analysis. From Table 3, we can see that the estimated coefficient 𝜃𝑞1 of the market liquidity level

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𝐷𝑡𝐹𝑋 is negative (-2.52) and significant at the 10% significance level for the Global Derivatives funds,

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as well as for two sub-categories, i.e. Currency and Systematic Futures, with the estimated coefficient 𝜃𝑞1 of 𝐷𝑡𝐹𝑋 being -2.33 and -2.40 respectively. This indicates that the lower FX liquidity level (i.e.

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𝐷𝑡𝐹𝑋 = 0) is associated with a higher probability that the funds will continue to stay in the “timing” phase, and hence a lower probability that they will switch to the “non-timing” state. These findings

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are consistent with Luo, Tee, and Li (2017) who argue that Currency and Systematic Futures usually pay more attention to the FX liquidity than the other two sub-categories. We now focus on the Global Derivatives fund category. For the scenario where the current market liquidity level is low (i.e. 𝐷𝑡𝐹𝑋 = 0), by substituting the parameters in Table 3 into equations (7) and (8), we can obtain the estimated Markov transition probability matrix: 𝑝00,𝑡 𝐏𝑡 (𝐷𝑡𝐹𝑋 = 0) = [𝑝 01,𝑡

𝑞10,𝑡 0.40 0.16 𝑞11,𝑡 ] = [0.60 0.84].

Similarly, when the current market liquidity level is high (hence 𝐷𝑡𝐹𝑋 = 1), we can obtain the estimated Markov transition probability matrix: 𝑝00,𝑡 𝐏𝑡 (𝐷𝑡𝐹𝑋 = 1) = [𝑝 01,𝑡

𝑞10,𝑡 0.54 0.70 𝑞11,𝑡 ] = [0.45 0.30].

Journal Pre-proof This shows that when the FX market is relatively illiquid, the Global Derivatives funds tend to stay in the ‘timing’ regime, with a probability of 0.84. On the other hand, when the FX market is relatively liquid, they tend to switch from the ‘timing’ regime to ‘non-timing’ regime, with a probability of 0.70. This can be explained by the fact that when sudden shocks of FX liquidity crisis occur, FX liquidity starts to become an important consideration for the managers, who then observe and time the market liquidity level as closely as possible to reduce the FX exposure if possible.

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(Figure 1 is here)

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Now, we investigate the time path of the regime switching. We incorporate a commonly used rule and classify any time period 𝑡 to be regime 1 (“timing” state) if the corresponding probability

if 𝑃𝑟{𝑆𝑡 = 1} ≥ 0.5 . otherwise

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1 𝑆̂𝑡 = { 0

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𝑃𝑟{𝑆𝑡 = 1} ≥ 0.5 at time t, and regime 0 (‘non-timing’ state) if 𝑃𝑟{𝑆𝑡 = 1} < 0.5:

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Figure 1 depicts the time path of the regime indicator 𝑆̂𝑡 of the smoothed probabilities of the “timing” state. It reveals more details about the switches of the FX liquidity-timing behaviour by the

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Global Derivatives funds. From Figure 1, the ‘timing’ regime on average is found to be more persistent than the ‘non-timing’ regime. It shows that the Global Derivatives managers on average stayed in the “timing” state for most of the study period. This is not surprising: there were several liquidity crises that occurred during the study period. The most notable was the Quant liquidity crisis of summer 20079, which marked the beginning of a volatile period in market liquidity, and reached a peak in September 2008 with a negative liquidity shock. These liquidity crises corresponded with the beginning of the financial crisis in summer 2007, with Bear Stearns’ eminent demise in March 2008 and the peak of the financial crisis, i.e. Lehman Brothers’ bankruptcy in September 2008 and the consequent freefall of the global economy (Melvin and Taylor, 2009; Sadka, 2010; Cao, Chen, Liang and Lo, 2013). The financial market was still very much affected by the credit crisis with Greece's Bailout taking place around the period after 2009. It was also around the same period that various 9

We define the period after July 2007 as the period "after the financial crisis", following Ben-David, Franzoni and Moussawi (2012).

Journal Pre-proof episodes of Quantitative Easing (QE) took place. This included the UK QE, US QE1, QE2 and QE3 until around the third quarter of 2013. The U.S. QE3 launched in 2012 subsequently led to some market speculations in 2013 about further plans to purchase securities later the year as announced in June 2013. This occurred as the market started to speculate the timing of the “tapering¨ of the asset purchased to be made by the U.S. Fed10. This eventually resulted in the FX market undergoing period of liquidity shock towards the end of 2013. Figure 1 shows that the “timing” state now switched to the “non-timing” state in the beginning of 2014, implying the possibility of a reduction in the liquidity

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shock leading to a weaker motivation to time FX liquidity. The findings also shed some light on the existing findings on the hedge fund managers’

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timing abilities. Luo, Tee, and Li (2017) in their two-period analysis show that the Global Derivatives

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hedge funds had stronger timing ability after the recent financial crisis than before the crisis. This is

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consistent with Figure 1 which shows that these hedge funds stayed much longer in the “timing” state after the recent financial crisis period.

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The time path of timing behaviour, as indicated in Figure 1, also show clearly the potential issues in the previous studies on the timing effect: when the “timing” and “non-timing” states are

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pooled together for averaging purposes (via, e.g., regression analysis), the state-dependent timing

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skills exhibited in different time periods may be averaged out, and hence it could affect the statistical significance of the results in the analysis. Next, we turn our attention to the time path of the regime indicator 𝑆̂𝑡 for each of the four subcategories, shown in Figure 2. We see that the QE implementations influence both the Currency and Systematic Futures hedge funds, with the Currency hedge funds experience a short period of switch back to the “non-timing” state towards the end of 2010. We also find that the Currency, Global Macro and Systematic Futures hedge funds experience a switch to “non-timing” state towards the end of 2013 following the reduction in the FX liquidity shock effect as explained earlier. For the Volatility

10

Market sources such as those reported in the FT (See Strauss, D. (2014) “Forex trading shrinks sharply in dismal end to 2013”. Financial Times. 7 January 2014, to reference this properly later) revealed that the wrong interpretation of the timing had led to suffering of losses arising from wrongly betting the direction of the FX market. Both the systematic currency and discretionary hedge funds were affected and had led to the collapse of “FX concept” in the last quarter of 2013. Furthermore, the trading volume in the FX market was also affected by the continued probe into the suspected cases of manipulation of the benchmark rates in the U.K. It was also noted that the average daily trading volumes at ICAP, the world’s biggest interdealer broker, fell to $71bn in December 2013, a 23% drop from the same month in 2012 and the lowest level since ICAP bought its currency trading business EBS in 2006.

Journal Pre-proof sub-category, however, we find that the volatility hedge funds stayed in the “non-timing” state mostly; this is to be expected as volatility strategy hedge funds rely on trading volatility for profiteering as their strategies and priority is not on market liquidity.

(Figure 2 is here)

4.2.

Controlling for market return and volatility timing

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This sub-section further evaluates the regime switching of liquidity timing behaviour of

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Global Derivatives hedge funds. Chen and Liang (2007) find that hedge fund managers have the

skills to time market return, market volatility and even jointly time them, especially in the

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bear and volatile market conditions. In general, high expected volatility makes the manager

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behave conservatively when adjusting portfolio holdings.

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Given that market liquidity is positively correlated with market returns and negatively correlated with market volatility, it is possible that the evidence on liquidity timing in the previous

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sub-section can be partially attributed to the fund managers’ activities of timing market return or market volatility. It is therefore necessary to control for both of them when investigating liquidity-

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timing behaviour of hedge fund managers. Following this, we adjust the SDLT model in Section 3 for market return and market volatility timings. Specifically, motivated by the joint timing model of Chen and Liang (2007), we add two 𝐹𝑋 𝐹𝑋 additional terms for return-timing and volatility-timing skills, i.e. 𝛾𝑅𝑖 (𝑅𝑚,𝑡+1 − 𝑅̅𝑚 ) ∗ 𝐹𝑋𝐹𝑡+1 𝐹𝑋 𝐹𝑋 and 𝛾𝜎𝑖 (𝜎𝑚,𝑡+1 − 𝜎̅𝑚 ) ∗ 𝐹𝑋𝐹𝑡+1 , into equation (3) as follows:

𝑅 𝐹𝑋 ̅ ̅ 𝐹𝑋 𝑟𝑖,𝑡+1 = 𝛼𝑖 + 𝛽𝑖 𝐹𝑋𝐹𝑡+1 + 𝜆𝐹𝑋 𝑖,𝑆𝑡 (𝐿𝐹𝑋,𝑡+1 − 𝐿𝐹𝑋 ) ∗ 𝐹𝑋𝐹𝑡+1 + 𝛾𝑖 (𝑅𝑚,𝑡+1 − 𝑅𝑚 ) ∗ 𝐹𝑋𝐹𝑡+1 𝐹𝑋 𝐹𝑋 +𝛾𝜎𝑖 (𝜎𝑚,𝑡+1 − 𝜎̅𝑚 ) ∗ 𝐹𝑋𝐹𝑡+1 + ∑7𝑗=1 𝛾𝑖𝑗 𝑓𝑗,𝑡+1 + 𝜀𝑖,𝑡+1

(9)

𝐹𝑋 𝐹𝑋 𝐹𝑋 where 𝑅𝑚,𝑡+1 is the FX market return at the time 𝑡 + 1 and 𝑅̅𝑚 is the average FX return. 𝜎𝑚,𝑡+1

is FX volatility (calculated as monthly standard deviation of daily FX return) at the time 𝑡 + 1 and

Journal Pre-proof 𝐹𝑋 𝜎̅𝑚 is the average FX market volatility. 𝛾𝑅𝑖 and 𝛾𝜎𝑖 measure FX return-timing and FX volatility-

timing abilities, respectively. We apply the timing model (9) with the Markov switching model (4) and (8) to analyse the Global Derivatives funds and the corresponding sub-categories. The results for the analysis are reported in Table 4. Overall, the results in Table 4 are consistent with those in Table 3, indicating the results in Table 3 still hold after controlling for the return timing and volatility timing. This implies that the needs to time market return and volatility do not interfere with those of timing the FX

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liquidity condition. This is because hedge funds in the Global Derivative category often transact in

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different financial markets with exposures to different risk types, and hence likely to have a set of

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multiple timing strategies.

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(Table 4 is here)

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Figure 3 displays the timing regime indicator over the study period for the Global Derivatives

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funds after controlling for return timing and volatility timing. Comparing with its counterpart in

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Figure 1, we can see that the captured switches of liquidity timing behaviour are similar.

(Figure 3 is here)

In addition, the same conclusion can also be drawn for the four sub-categories. Figure 4 depicts the timing regime indicator for each of the four sub-categories of the Global Derivatives after controlling for return timing and volatility timing. Clearly, the time path of the regime switching in Figure 4 is similar to those in Figure 2: on average, the funds in the Currency, Global Macro, and Systematic Futures sub-categories stayed longer time in the “timing” state, whereas the funds in the Volatility sub-category stayed longer in the “non-timing” state. Hence, the results show that both market return and volatility timing do not affect the regime-switching behaviour of liquidity timings among the Global Derivatives hedge funds.

Journal Pre-proof

(Figure 4 is here)

5.

Timing Behaviour at the Individual Fund Level The previous section investigated the liquidity timing behaviour of the Global Derivatives

hedge funds at the category level. In this section, we investigate the regime switching of liquidity timing behaviour at the individual hedge funds’ level.

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We apply the model (3-5) to analyse each of the individual funds. The empirical results show

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substantial variation of the timing switching behaviour of hedge funds with some staying in the timing

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state longer than others. Based on the obtained results, we classify hedge funds into two broad groups, i.e. “active” timers and “inactive” timers, depending on their durations in the timing state: Active timers: over 50% of time in the “timing” state (𝑆𝑡 = 1);



Inactive timers: over 50% of time in the “non-timing” state (𝑆𝑡 = 0).

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

As active (or inactive) timers represent hedge funds that spent longer (or shorter) duration in

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the “timing” state than the “non-timing” state, it is important to know if this would impact on funds’ performances. We ask the following questions: (a) why hedge funds time the market liquidity

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differently? (b) do the active timers achieve higher and get more rewards than the inactive timers? To answer these questions, we follow Chen and Liang (2007), and report the fund’s alpha calculated from equation (3) as a measure of abnormal performance for the individual hedge funds, as reported in Table 5.

(Table 5 is here)

Table 5 shows the results of various performance measures at the individual fund level for all hedge funds’ strategies underlying the Global Derivatives category. First, we can see that, although all the funds in the Global Derivatives category invest in the global markets and the FX market plays an important role in their investments, there are substantial

Journal Pre-proof differences in the proportion of the active timers within each sub-category: 18.8% (13 out of 69) active timers in the Currency, 14.3% (62 out of 433) in the Global Macro, 11.9% (74 out of 622) in the Systematic Futures, and only 6.0% (4 out of 67) in the Volatility sub-category. As we discussed earlier, whether hedge funds are active timers is likely to be the consequences of the hedge funds’ investment strategies. The ways that hedge funds perceive the FX market and view if currencies are asset classes would also affect how they would profit from currencies and manage their exposures. It is therefore not surprising that there are more hedge funds in the Currency category to be active timers

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while there are lesser in the Volatility category. Next, we compare the abnormal returns characterised as the corresponding funds’ alphas

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averaged across the different strategies. We see from Table 5 that for each sub-category (Currency,

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Global Marco, Systematic Futures, and Volatility), the active timers have higher average abnormal

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performances than the inactive timers, although the gain for the Volatility category is only marginal (1.09 versus 1.07). Hence, in general, active timers tend to outperform non-active timers.

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When we consider only those hedge funds with positive funds’ alphas, it shows that, after removing those with negative alphas, the average abnormal performances become higher for both

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active timers and inactive timers. Finally, we focus on the top liquidity-timing funds with t-values of

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alphas greater than 1.96 (note that, by definition, the t-value of the alpha represents the ratio of the abnormal return to its standard deviation). We find that the top active timers achieve very high abnormal returns.

In summary, when comparing the active timers with the inactive timers, we can see that the active timers tend to have a higher abnormal return. This reinforces that, for the Global Derivatives hedge funds, liquidity timing in the FX market adds value to their investments.

6.

Conclusions

Existing literature on the timing of market liquidity condition of the hedge funds often investigates from the asset pricing perspective to evaluate if such timing ability explains hedge funds’

Journal Pre-proof returns. This approach measures the average timing ability of hedge funds that may potentially average out the state-dependent timing effect, and impact upon the significance of the findings. To provide more accurate findings, this paper has developed a new approach to test the timing effect with an embedded Markov regime switching process to describe the regime switches of funds’ timing behaviour over time, termed as the SDLT model. We argue that among the Global Derivatives hedge funds, their currencies exposures are managed differently based on the extent they treat currency as an asset class. We hypothesize that

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these hedge funds strategically choose to be an active/inactive timer in different time periods,

changes of liquidity timing behaviour of hedge funds.

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depending on the market condition at the time. We have applied the SDLT model to investigate the

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This paper found empirical evidence of changes in the timing behaviours among the hedge

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funds over time. We first conduct the analysis by using the aggregate strategies categories. Among all the reported strategies, we found the Volatility strategy has the longest duration in the “non-timing”

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state. This is expected as the Volatility strategy aims at profiting from the trend in the implied volatility in derivatives referencing other asset classes and so FX liquidity timing may not be the

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priority in its strategy planning. Regarding the other strategies, our analysis reveals evidence of

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staying in the “timing state” mostly during the credit period from 2007 to after 2009. Among them, significant findings also suggest a driving force originated from the FX market liquidity condition for the Currency and Systematic Futures strategies. Our findings are also independent of fund managers’ abilities to time market return or market volatility. This finding supports the hypothesis that hedge funds in the Global Derivative category, that are known to be transacting in different financial markets and with exposures to different risk types, are likely to have a set of different timing strategies. We also extend our analysis to evaluate liquidity timing behaviours at the individual funds’ level. The empirical evidence reveals hedge funds that actively time liquidity in the FX market have better abnormal performances than those who time the liquidity less frequently. This, therefore, shows that for hedge funds that attempt FX liquidity timing actively, it adds value to their investments. This has important implications on investment decisions for hedge funds managers when managing the exposure based on the FX market liquidity condition.

Journal Pre-proof References Admati, A.R., Bhattacharya, S., Pfleiderer, P., Ross, S.A., 1986. On timing and selectivity. The Journal of Finance 41(3), 715-730. Aggarwal, R.K., Jorion, P., 2010. The performance of emerging hedge funds and managers. Journal of Financial Economics 96(2), 238-256. Bank for International Settlements, 2016. Foreign Exchange Turnover in April 2016. Triennial Central Bank Survey.

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Ben-David I., Franzoni F., Moussawi R., 2012. Hedge fund stock trading in the financial crisis of

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2007-2009. Review of Financial Studies 25, 1-54.

Journal of Finance 65(5), 1789-1816.

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Boyson, N.M., Stahel, C.W., Stulz, R.M., 2010. Hedge fund contagion and liquidity shocks. The

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Busse, J., 1999. Volatility timing in mutual funds: evidence from daily returns. Review of Financial Studies12, 1009-1041.

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Cao, C., Chen, Y., Liang, B., Lo, A.W., 2013. Can hedge funds time market liquidity? Journal of

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Financial Economics 109(2), 493-516.

Cao, C., Simin, T.T., Wang, Y., 2013. Do mutual fund managers time market liquidity? Journal of

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Financial Markets 16(2), 279-307.

Chelley-Steeley, P, Lambertides, N, Savva, C.S., 2013, Illiquidity shocks and the comovement between stocks: New evidence using smooth transition. Journal of Empirical Finance 23, 1-15. Chen, Y., Liang, B., 2007. Do market timing hedge funds time the market? Journal of Financial and Quantitative Analysis 42(04), 827-856. Diebold, F.X., Lee, J.H., Weinbach, G.C., 1994. Regime switching with time-varying transition probabilities. Business Cycles: Durations, Dynamics, and Forecasting, 144-165. Durland, J.M., McCurdy, T.H., 1994. Duration-dependent transitions in a Markov model of US GNP growth. Journal of Business & Economic Statistics 12(3), 279-288. Eling, M., Faust, R., 2010. The performance of hedge funds and mutual funds in emerging markets. Journal of Banking & Finance 34(8), 1993-2009

Journal Pre-proof El Kalak, I., Azevedo, A., Hudson, R., 2016. Reviewing the hedge funds literature I: Hedge funds and hedge funds' managerial characteristics. International Review of Financial Analysis 48, 85-97. Ferson, W., Schadt, R., 1996. Measuring fund strategy and performance in changing economic conditions. Journal of Finance 51, 425–460. Filardo, A.J., 1994. Business-cycle phases and their transitional dynamics. Journal of Business & Economic Statistics 12(3), 299-308. Filardo, A.J., Gordon, S.F., 1998. Business cycle durations. Journal of Econometrics 85(1), 99-123.

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French, C.W., Ko, D.B., 2007. How hedge funds beat the market. Journal of Investment Management 5(2), 112-125.

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Fung, W., Hsieh, D.A., 2004. Hedge fund benchmarks: A risk-based approach. Financial Analysts

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Journal 60(5), 65-80.

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Guidolin, M, Hyde, S., 2012, Can VAR models capture regime shifts in asset returns? A long-horizon strategic asset allocation perspective. Journal of Banking and Finance 36, 695-716.

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Hamilton, J.D., 1989. A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 357-384.

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Hamilton, J.D., 1990. Analysis of time series subject to changes in regime. Journal of Econometrics

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45(1), 39-70.

Harris, R.D., Mazibas, M., 2010. Dynamic hedge fund portfolio construction. International Review of Financial Analysis 19(5), 351-357.

Hawawini, G.A., 1983. The theory of risk aversion and liquidity preference: A geometric exposition. The American Economist 27(2), 42-49. Karnaukh, N., Ranaldo, A., Söderlind, P., 2015. Understanding FX liquidity. Review of Financial Studies 28(11), 3073-3108. Kazemi, H., Li, Y., 2009. Market timing of CTAs: An examination of systematic CTAs vs. discretionary CTAs. Journal of Futures Markets 29(11), 1067–1099. Kessler, S., Scherer, B., 2011. Hedge fund return sensitivity to global liquidity. Journal of Financial Markets 14(2), 301-322.

Journal Pre-proof Kim, C.J., 1994. Dynamic linear models with Markov-switching. Journal of Econometrics 60(1-2), 122. Jiang, G.J., Yao, T., Yu, T., 2007. Do mutual funds time the market? Evidence from portfolio holdings. Journal of Financial Economics 86(3), 724-758. Lam, P.S., 2004. A Markov‐switching model of GNP growth with duration dependence. International Economic Review 45(1), 175-204. Li, B., Luo, J., Tee, K.H., 2017. The Market Liquidity Timing Skills of Debt‐oriented Hedge Funds.

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European Financial Management 23(1), 32-54. Luo, J., Tee, K.H., Li, B., 2017. Timing liquidity in the foreign exchange market: Did hedge funds do

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it? Journal of Multinational Financial Management 40, 47-62.

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Mancini, L., Ranaldo, A., Wrampelmeyer, J., 2013. Liquidity in the foreign exchange market:

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Measurement, commonality, and risk premiums. The Journal of Finance 68(5), 1805-1841. Melvin, M., Taylor, M.P., 2009. The crisis in the foreign exchange market. Journal of International

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Money and Finance 28(8), 1317-1330.

Nasypbek, S., Rehnam, S, S., 2011. Explaining the returns of active currency managers, Bank for

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international Settlement, Working Paper No. 58.

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Nucera, F., Valente, G., 2013. Carry trades and the performance of currency hedge funds. Journal of International Money and Finance 33, 407-425. Sadka, R., 2010. Liquidity risk and the cross-section of hedge-fund returns. Journal of Financial Economics 98(1), 54-71.

Siegmann, A., Stefanova, D., 2017. The evolving beta-liquidity relationship of hedge funds. Journal of Empirical Finance 44, 286-303. Shanken, J., 1990. Intertemporal asset pricing: An empirical investigation. Journal of Econometrics 45(1-2), 99-120. Tobin, J., 1958. Liquidity preference as behavior towards risk. The Review of Economic Studies 25(2), 65-86. Treynor, J., Mazuy, K., 1966. Can mutual funds outguess the market? Harvard Business Review 44, 131-136.

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Table 1: Descriptive statistics This table reports a statistical summary of hedge fund returns, risk factors, and the FX illiquidity.

Maximu Variables

Minimum

Std. Mean

m

Deviation

Panel A: Summary of the hedge funds returns Global Derivatives

-2.31

Systematics Futures

-6.14

Volatility

1.87

5.76

0.45

1.40

4.54

0.51

1.16

9.59

0.74

2.80

7.57

0.83

1.92

-p

-8.69

0.65

ro

-3.09

Global Macro

7.34

of

-3.74

Currency

Panel B: Descriptive statistics of the hedge fund risk factors

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EMF Size Spread ∆Credit

COM-LBS

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FX market factor

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B-LBS FX-LBS

4.72

2.35

0.92

1.39

6.96

3.45

1.46

-1.08

0.95

-0.02

0.26

-0.79

1.53

0.00

0.23

-26.63

50.50

-2.89

14.63

-31.81

69.22

-0.87

19.05

-24.65

42.87

-0.65

14.47

-10.65

4.41

0.01

1.68

4.36

0.58

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∆Term

1.16

Panel C: Descriptive statistics of FX illiquidity

Proportionally spread (bps)

2.13

6.57

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Table 2: Currency distribution of OTC FX turnover This table summarizes the net-basis percentage shares of average daily turnover. 2 R

S

R

US rency

hare 8

ank 1

hare 8

DEU R JP

9.9 3 7.9 2

2

YGB PAU

3.5 1 3 4

4

DCA DCH F SE KSG

2

S

R

ank 1

hare 8

8 3 7.4 2

2

4

7

0.8 1 6.5 6

.3 4 .5 6

6

4

7

5

5

2

8

.2 6 2

.5 1

1

.2 0

1

001

004

3

Note:

the D Sources.1 come 2 from .9

(https://www.bis.org/publ/rpfx13fx.pdf)

and

3

6

8

Triennial

4

Triennial

S

R

ank 1

hare 8

5.6 3 7 1

2

7.2 1 4.9 6

4

.6 4 .3 6

7

.8 2 .7 1

9

007

3

6

5

1

Central Bank .2 3 Central

Bank

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2

S

R

ank 1

hare 8

4.9 3 9 1

2

9 1 2.9 7

4

.6 5 .3 6

7

010

.3 2 .2 1

3

5

Survey

.4

Survey

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(http://www.bis.org/publ/rpfx16fx.pdf).

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2

S

R

ank 1

hare 8

ank 1

7 3 3.4 2

2

7.6 3 1.4 2

2

3 1 1.8 8

4

1.6 1 2.8 6

4

.6 4 .6 5

7

.9 5 .1 4

6

.2 1 .8 1

1

.8 2 .2 1

9

013

3

5

of

S

2

-p

r Cur

2

6 9

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Yea

1

6

1 1

2Foreign .4exchange 5 Foreign

exchange

016

3

5

7

1

turnover

.8

in

2 April

2013

turnover

in

April

2016

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Table 3 Coefficient estimates of the SDLT model This table reports the coefficient estimates and the corresponding t-statistics in parentheses for the SDLT model of funds underlying Global Derivatives category and its sub-categories: Currency, Global Macro, Systematic Futures and Volatility. The coefficients 𝜆1 and 𝜆0 measure the FX liquidity-timing effect in the “timing” and “non-timing” states respectively. 𝛼 measures excess return. 𝑡(∆𝜆) is the t-statistic for testing the null hypothesis in (6). Currency

Global Macro

Volatility

Futures

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Derivatives

Systematic

of

Global

Parameters in the Markov model (4) and (8)

𝜃𝑝1

𝜃𝑞0 𝜃𝑞1

(6.77) (2.41 ) (0.26 ) (0.30 ) (1.82 ) (1.75)

3.29 1 .35 2.30 3 .26 1.20 0 .20

(3.51) (1. 57) (1.52) (1. 55) (0.96) (0. 09)

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𝜃𝑝0

7.86 2 .15 0 .25 0 .38 1 .51 2.33

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𝜆1

(3.66) (2. 01) (0.28) (0. 27) (1. 48) (1.68)

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8.46 2.2 5 0.41 0.5 9 1.6 7 2.52

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𝜆0

13.59 4. 12 0.11 0.07 1. 81 2.40

(4.44) (2.6 3) (0.09) (0.04) (1.7 7) (1.84)

3.10 10 .63 0. 17 1. 54 2.51 2. 28

(3.53) (5. 53) (0. 21) (1. 14) (1.58) (0. 82)

0.

(2. 61) (2. 04) (2.22) (1.13) (1.35) (0. 48) (1.36) (0.26) (0. 76)

Regression coefficients in the timing model (3)

EMF Size ∆Term Spread ∆Credit B-LBS FXLBSCOMLBSFXF

8

0.

49

0.30 1.57 1.09 0.0 1 0.0 3 0.0 2 0.3 1

(1. 19) (0. 73) (0.71) (3.06) (1.67) (1. 10) (3. 93) (2. 69) (3. 71)

0

.08

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0.3

𝛼

0

.53

0.32 1.10 1.19 0.01 0 .03 0 .00 0 .25

(0.34 ) (1.10 ) (1.06) (2.99) (2.52) (2.36) (5.78 ) (0.77 ) (3.68 )

0 .64

0

.46

0.38 0.52 1.51 0 .00 0 .02 0 .01 0 .20

(3. 30) (1. 14) (1.50) (1.54) (3.70) (0.15) (3. 75) (1. 31) (4. 09)

0. 35

0.

45

0.25 2.28 0.76 0. 02 0. 04 0. 03 0. 41

(0.7 4) (0.4 6) (0.40) (3.02) (0.79) (1.4 8) (3.7 0) (3.0 2) (3.2 5)

78

1.

29

0.87 0.58 0.84 0. 00 0.01 0. 00 0. 05

t-value for testing 𝐻0 in (6) 𝑡(∆𝜆)

4.1 7

6 .94

3 .79

4. 98

6. 84

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Journal Pre-proof Table 4 Coefficients estimates of the market-return and volatility-timing adjusted SDLT model This table reports the coefficient estimates and the corresponding t-statistics in parentheses for the SDLT model of funds underlying Global Derivatives category and its sub-categories: the Currency, Global Macro, Systematic Futures and Volatility after controlling for return timing and volatility timing. The coefficients 𝜆1 and 𝜆0 measure the FX liquidity-timing effect in the “timing” and “non-timing” states respectively. 𝛼 measures excess return. 𝑡(∆𝜆) is the t-statistic for testing the null hypothesis in (6). Currency

Global

Derivatives

Macro

Systematic

Volatility

Futures

of

Global

𝜃𝑝0 𝜃𝑝1

𝜃𝑞0 𝜃𝑞1

7.10 3 .11 0 .79 0 .73 1 .55 1.97

(6.83) (2. 83) (1. 06) (0. 67) (1. 86) (1.35)

3.41 1 .42 2.37 3 .24 1.15 0 .10

(3.34) (1. 65) (1.56) (1. 55) (0.90) (0. 05)

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𝜆1

(3.57) (2. 40) (0.95) (1. 21) (1. 71) (1.73)

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6.96 2 .72 1.10 2 .14 1 .60 2.89

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𝜆0

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Parameters in the Markov model (4) and (8) 10.50 4.7 5 1.05 2.0 2 1.6 6 2.60

(3.14 (2.8 4) (1.00) (1.1 7) (1.9 1) (1.79)

3.22 9 .99 0 .35 1 .66 2.36 2 .90

(3.41) (3. 85) (0. 40) (0. 74) (1.47) (0. 72)

0

(2. 47) (1. 98) (2.20) (1.03) (1.07) (0. 28) (1.29) (0.23) (0. 37) (0.59) (0. 92)

0 EMF Size

∆Term Spread ∆Credit B-LBS FXLBSCOMLBSFXF 𝛾𝑅 𝛾𝜎

.37 .58

0

(1. 13) (0. 89) (0.84) (3.47) (2.26) (1. 42) (3. 85) (2. 61) (4. 29) (1. 14) (2.19)

0

.04

0

.39

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𝛼

na

Regression coefficients in the timing model (9)

0.34 1.84 1.55 0 .01 0 .03 0 .02 0 .36 0 .60 0.68

0.22 1.02 1.28 0.01 0 .03 0 .01 0 .33 0.27 0.10

(0. 19) (0. 80) (0.72) (2.64) (2.67) (1.65) (5. 48) (0. 92) (4. 48) (0.79) (0.48)

0 .68

0

.45

0.38 0.54 1.52 0.00 0 .02 0 .01 0 .19 0 .24 0.06

(3. 34) (1. 12) (1.50) (1.56) (3.55) (0.15) (3. 70) (1. 33) (3. 57) (0. 73) (0.34)

0.3 4

0.6

5

0.35 2.73 1.49 0.0 2 0.0 4 0.0 3 0.4 7 1.0 4 1.15

(0.7 1) (0.6 8) (0.58) (3.45) (1.46) (1.7 7) (3.7 3) (2.9 1) (3.7 5) (1.3 2) (2.45)

.79

1

.24

0.85 0.54 0.69 0 .00 0.01 0 .00 0 .03 0.33 0 .31

t-value for testing 𝐻0 in (6) 𝑡(∆𝜆)

4 .33

6 .80

3 .78

4.2 3

5 .34

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Journal Pre-proof Table 5 Liquidity timing behaviour and abnormal returns at the individual hedge fund level This table summarizes the average abnormal performances averaged over individual hedge fund categories for the active timers and inactive timers, respectively, for the four sub-categories within the Global Derivatives: Currency, Global macro, Systematic Futures and Volatility. “Active” (or “Inactive”) represents hedge funds that have longer (shorter) duration in the “timing” state than the “non-timing” state.

𝛼̅ (𝛼 > 0)

(No. of funds)

Active

1.86 (13)

Inactive

0.95 (56)

Global Macro

1.08 (433)

1.61 (55)

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1.12 (69)

2.31 (11)

𝛼̅ (𝑡𝛼 > 1.96)

(No. of funds) 2.49 （17） 2.97 （6） 2.23 （11）

2.84 (298)

4.06 (69)

1.31 (62)

2.87 (43)

4.66 (15)

1.04 (371)

2.84 (255)

3.90 (54)

1.04 (622)

2.74 (418)

5.78 (55)

1.73 (74)

3.07 (54)

9.10 (5)

0.95 (548)

2.69 (364)

5.45 (50)

Volatility

1.07 (67)

2.58 (44)

4.94 (16)

Active

1.09 (4)

3.17 (2)

6.17 (1)

Inactive

1.07 (63)

2.56 (42)

4.85 (15)

Inactive

Systematic Futures Active Inactive

re

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Active

-p

1.43 (44)

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Currency

of

Overall 𝛼̅

(No. of funds)

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Figure 1. Regime indicator for the Global Derivatives with ‘1’ representing the “timing” state and ‘0’ representing the “non-timing” state.

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Figure 2. Regime indicator for the four sub-categories of Global Derivatives with ‘1’ representing the “timing” state and ‘0’ representing the “non-timing” state.

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Figure 3. Regime indicator for the Global Derivatives with ‘1’ representing the “timing” state and ‘0’ representing the “non-timing” state after controlling for return timing and volatility timing.

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Figure 4. Regime indicator for the four sub-categories of Global Derivatives with ‘1’ representing the “timing” state and ‘0’ representing the “non-timing” state after controlling for return timing and volatility timing.

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Are hedge funds active market liquidity timers? Highlights

We test if actively timing foreign exchange liquidity adds value to hedge funds’ investments;

We develop a liquidity timing model embedded with Markov regime switching;

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We reveal changes in timing behaviour are driven by the liquidity condition.