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Asynchronous ADRs: overnight vs intraday returns and trading strategies

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  • Jamie Kang
  • Tim Leung

Abstract

Purpose - The purpose of this study is to analyze the overnight and intraday returns of the most traded American Depositary Receipts (ADRs) of Asian companies, understand the different levels of volatilities realized in these asynchronous markets and develop trading strategies based on empirical findings. Design/methodology/approach - This study presents an empirical analysis on the overnight and intraday returns of Asian ADRs. The authors propose a measure to quantify the relative contributions of the intraday and overnight returns to the ADR's total volatility. Furthermore, the return difference between S&P500 index and each ADR is fitted to an Ornstein–Uhlenbeck model via maximum-likelihood estimation. Findings - This study finds that ADRs' overnight returns are more volatile, whereas the intraday returns are significantly more strongly correlated with the US market returns. The return spreads between the S&P500 and ADRs are found to be a mean-reverting time series and motivate a pairs trading strategy. Research limitations/implications - The methodology used in this study is not limited to Asian ADRs and can be adapted to analyze the overnight and intraday returns of other non-Asian ADRs and stocks. Practical implications - Investors should be aware of the overnight price fluctuations while intraday traders may consider strategies that capture the mean-reverting return spread between an ADR (or an Exchange-Traded Funds [ETF] of Asian stocks) and the S&P500 index ETF (SPY). Social implications - ADRs are among the most popular securities for investing in foreign (non-US) companies. The total global investments in ADRs are estimated to be close to US$1tn. Understanding the risks of ADRs is important to not only individual/institutional investors but also regulators. Originality/value - This study provides a new measure to quantify and compare the relative contributions of volatility by overnight and intraday returns. Optimized pairs trading strategies involving ADRs and ETFs are developed and backtested.

Suggested Citation

  • Jamie Kang & Tim Leung, 2017. "Asynchronous ADRs: overnight vs intraday returns and trading strategies," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 34(4), pages 580-596, October.
    • Handle: RePEc:eme:sefpps:sef-10-2016-0254
    DOI: 10.1108/SEF-10-2016-0254
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    References listed on IDEAS

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    1. Tian Yuan & Rakesh Gupta & Eduardo Roca, 2015. "Home Country Macroeconomic Fundamentals and the ADR Market: The Case of the BRICs," Discussion Papers in Finance finance:201502, Griffith University, Department of Accounting, Finance and Economics.
    2. Athanasios Koulakiotis & Katerina Lyroudi & Nikos Thomaidis & Nicholas Papasyriopoulos, 2010. "The impact of cross‐listings on the UK and the German stock markets," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 27(1), pages 4-18, March.
    3. Gupta, Rakesh & Yuan, Tian & Roca, Eduardo, 2016. "Linkages between the ADR market and home country macroeconomic fundamentals: Evidence in the context of the BRICs," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 230-239.
    4. Owen Williams, 2016. "Foreign currency exposure within country exchange traded funds," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 33(2), pages 222-243, June.
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    6. Riedel, Christoph & Wagner, Niklas, 2015. "Is risk higher during non-trading periods? The risk trade-off for intraday versus overnight market returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 39(C), pages 53-64.
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    10. Javier Rodríguez & Wilfredo Toledo, 2015. "Chinese single-listed ADRs: returns and volatility," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 11(4), pages 480-502, September.
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    More about this item

    Keywords

    ADR; Intraday return; Mean-reverting; Ornstein–Uhlenbeck model; Overnight return; Pairs trading; C10; G11; G15;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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