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This paper aims to demonstrate a dynamic cointegration-based pairs trading strategy, including an optimal look-back window framework in the cryptocurrency market and evaluate its return and risk by applying three different scenarios.

This study uses the Engle-Granger methodology, the Kapetanios-Snell-Shin test and the Johansen test as cointegration tests in different scenarios. This study calibrates the mean-reversion speed of the Ornstein-Uhlenbeck process to obtain the half-life used for the asset selection phase and look-back window estimation.

By considering the main limitations in the market microstructure, the strategy of this paper exceeds the naive buy-and-hold approach in the Bitmex exchange. Another significant finding is that this study implements a numerous collection of cryptocurrency coins to formulate the model’s spread, which improves the risk-adjusted profitability of the pairs trading strategy. Besides, the strategy’s maximum drawdown level is reasonably low, which makes it useful to be deployed. The results also indicate that a class of coins has better potential arbitrage opportunities than others.

This research has some noticeable advantages, making it stand out from similar studies in the cryptocurrency market. First is the accuracy of data in which minute-binned data create the signals in the formation period. Besides, to backtest the strategy during the trading period, this study simulates the trading signals using best bid/ask quotes and market trades. This study exclusively takes the order execution into account when the asset size is already available at its quoted price (with one or more period gaps after signal generation). This action makes the backtesting much more realistic.

The purpose of this paper is to present an alternative approach to equity trading that is based on cointegration. If there are long-run equilibria among financial assets, a cointegration-based trading strategy can exploit profitable opportunities by capturing mean-reverting short-run deviations.

First, the author introduces an equity indexing technique to form cointegration tracking portfolios that are able to replicate an index effectively. The author later enhances this tracking methodology in order to construct more complex portfolio-trading strategies that can be approximately market neutral. The author monitors the performance of a wide range of trading strategies under different specifications, and conducts an in-depth sensitivity analysis of the factors that affect the optimal portfolio construction. Several statistical-arbitrage tests are also carried out in order to examine whether the profitability of the cointegration-based trading strategies could indicate a market inefficiency.

The author shows that under certain parameter specifications, an efficient tracking portfolio is able to produce similar patterns in terms of returns and volatility with the market. The author also finds that a successful long-short strategy of two cointegration portfolios can yield an annualized return of more than 8 percent, outperforming the benchmark and also demonstrating insignificant correlation with the market. Even though some cointegration-based pairs-trading strategies can consistently generate significant cumulative profits, yet they do not seem to converge to risk-less arbitrages, and thus the hypothesis of market efficiency cannot be rejected.

The primary contribution of the research lies within the detailed analysis of the factors that affect the tracking-portfolio performance, thus revealing the optimal conditions that can lead to enhanced returns. Results indicate that cointegration can provide the means to successfully reproducing the risk-return profile of a benchmark and to implementing market-neutral strategies with consistent profitability. By testing for statistical arbitrage, the author also provides new evidence regarding the connection between the profit accumulation of cointegration-based pairs-trading strategies and market efficiency.

The purpose of this paper is to compare the profitability of different pairs selection and spread trading methods using the complete data set of commodity futures from Dalian Commodity Exchange, Shanghai Futures Exchange and Zhengzhou Commodity Exchange.

Paris trading methods that are proposed in the literature are compared in terms of the risk-adjusted returns visa in-sample and out-of-sample backtesting and bootstrapping for robustness.

The empirical results show that pairs trading in the Chinese commodity futures market offers high returns, whereas, the profitability of these strategies primarily depends on the identification of suitable pairs. The observed high returns are a compensation for the spread divergence risk during the potentially longer holding periods, which implies that the maximum drawdown is more crucial compared to other risk-adjusted return measures such as the Sharpe ratio.

Complementary to the existing literature, for the Chinese commodity futures market, it is shown that if shorter maximum holding periods are introduced for the spread positions, then the pairs trading profits decreases. Therefore, the returns do not necessarily imply market inefficiency when the higher maximum drawdown associated with the holding period of the spread position is taken into account.

Motivated by the debate on the patterns and sources of commodity futures returns, this paper investigates the performance of three investment trading strategies, namely, the momentum strategy of Jegadeesh and Titman (1993), the 52-week high momentum strategy of George and Hwang (2004) and the pairs trading strategy of Gatev et al. (2006) in the commodity futures market.

The three strategies are those given by Jegadeesh and Titman (1993), George and Hwang (2004) and Gatev et al. (2006), respectively.

The authors find that there is no significant reversal profit across 189 formation-holding windows for all the three strategies. However, there are statistical and economically significant momentum profits, and the profitability increases with the rising of formation-holding periods. Momentum returns are quite sensitive to market conditions but the crash of momentum returns is partly predictable. Return seasonality, risk and herding also provide partial explanation of the momentum profits.

The authors are the first to compare the performances of the pairs trading strategy of Gatev et al. (2006), the conventional momentum of Jegadeesh and Titman (1993), and the 52-week high momentum of George and Hwang (2004) under 189 formation-holding windows. Also, the authors are the first to investigate the association between herding behaviour and momentum returns in the commodity futures market.

The KOSPI200 mini option introduced in July 2015 is the derivative of which trading multiplier is reduced to one-fifth of the regular options. This study explored the pairs trading opportunities arising from the price spread between the KOSPI200 regular options and the mini options during the sample period from August 2015 to March 2016 and measured the profits of pairs trading. The main results are summarized as follows. First, the most frequency of pairs trading with high profit was observed for in-the-money options. On the other hands, the frequency of pairs trading opportunities is low and the profit is relatively small for out-of (at)-the money options. Second, for in-the-money options, arbitrage opportunities were captured every three minutes on an average, but the elapsed time between arbitrage opportunity opportunities on out-of-the money options exceeded 10 minutes on average. Third, pairs trading opportunities occur uniformly throughout the day, but profit tends to increase in the afternoon than in the morning. This indicates that price efficiency in options market deteriorates and profit of arbitrage trading with price disparity is higher in the afternoon than that of the morning trading. In addition, the profitability of pairs trading with low liquidity was cross-sectionally higher than those with high liquidity.

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.

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.

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.

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.

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).

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.

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.

This study aims to validate the “expectancy theory” of asset pricing and explores the price discovery process in metals futures markets.

This paper adopts the Johansen cointegration and vector error correction model approach to investigate the potentials of Pairs trading in the metals market during the period 2008–2019.

The results find the price movements in metal markets are not random walk and the current “futures” prices are the reasonable estimate of the “spot” metal prices in future. This study does not notice any significant differences in the price efficiency across metals markets, which signal the effects of limited idiosyncratic forces in price transmission.

The research suggests the covert use of metal futures to make gains from arbitrage trading.

The study emphasizes the potential of “pair trading” in commodity market context that is seldom discussed in academic papers.

This paper aims to present a methodology for constructing cointegrated portfolios consisting of different cryptocurrencies and examines the performance of a number of trading strategies for the cryptocurrency portfolios.

The authors apply a series of statistical methods, including the Johansen test and Engle–Granger test, to derive a linear combination of cryptocurrencies that form a mean-reverting portfolio. Trading systems are designed and different trading strategies with stop-loss constraints are tested and compared according to a set of performance metrics.

The paper finds cointegrated portfolios involving four cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Bitcoin Cash (BCH) and Litecoin (LTC), and the corresponding trading strategies are shown to be profitable under different configurations.

The main contributions of the study are the use of multiple altcoins in addition to bitcoin to construct a cointegrated portfolio, and the detailed comparison of the performance of different trading strategies with and without stop-loss constraints.

The present research aims to examine a range of momentum trading strategies for the tourism and hospitality sector.

The paper followed the methodology of Jegadeesh and Titman (1993) to construct the portfolios. In this methodology, all portfolios were formed and evaluated by their cumulative stock returns over the past J periods and holding the position for the next K periods. In total, nine formation and holding periods were used, represented by 3, 6 and 12. For example, strategy 3–3 (that is, strategy with J = 3 and K = 3) refers to the strategy that stocks are ranked based on their previous three months and then held for the next three months.

The findings demonstrated that none of these momentum investing strategies was profitable. Most of the results, however, show positive, but insignificant momentum returns. This finding can be interpreted as price reversal over a horizon of three to twelve months in the US hospitality and tourism sector. These results are robust to size, different formation and holding combinations, beta and turnover.

Regarding the research limitations, this paper only considers the US tourism and hospitality sector. Therefore, the extension of results to other developed and developing markets should be taken carefully. Also, this paper relies only on the methodology of Jegadeesh and Titman (1993). Other methodologies could be suitable avenues for future research.

Investors and portfolio managers who seek for earning abnormal returns by investing in the US HT stocks can attain their hopes by constructing portfolios based on existing guidelines in the literature and adopting a short-term reversal trading strategy or by buying past losers and selling past winners of the US tourism and hospitality stocks.

This research contributes to the hospitality finance literature by offering the investors who are interested in the US hospitality and tourism sector an uncomplicated trading rule that uses real return data and is expected to generate actual returns. Moreover, the momentum strategy of Jegadeesh and Titman (1993) is never used in the hospitality finance literature.

本研究旨在探討各種可應用於旅遊及酒店業的動量交易策略。

本文按照 Jegadeesh 和 Titman（1993）的研究方法來建造投資組合。使用這研究方法時，所有投資組合均以它們在過去J 時期的累積股票收益和在未來K 時期的持倉來建立及評價的。九個組成方式及持有期被使用，以3、6、12來表示。例如，策略3-3（那就是說，該策略以J = 3和 K = 3）指的策略是以有關的股票基於過去三個月而被分等級，繼而在未來三個月被持有。

研究結果顯示，這些投資策略全沒帶來利潤；唯大部分結果顯示正動能策略報酬，雖報酬是微不足道的。這研究結果或許可理解為在美國酒店及旅遊業為期三至十二個月的價格逆轉。這些結果就規模、不同組成方式和持有組合、beta 和成交量而言是強而有力的。

就研究的局限而言，本文只是考慮美國的酒店及旅遊業；因此，如把研究結果伸延至其它已開發或發展中的市場，則需小心處理。另外，本文只依賴 Jegadeesh 和 Titman（1993）的研究方法，就此，使用其它研究方法會是日後相關研究的適當途徑。

欲透過投資於美國酒店及旅遊股票而尋求賺取異常收益的投資者和投資組合經理可如願以償，方法是基於文獻內現存的準則建造投資組合，以及採用短期的逆轉交易策略，或買入美國酒店及旅遊業過去輸家及賣出過去贏家。

本研究為酒店金融文獻作出貢獻，因研究為對美國酒店及旅遊業有興趣的投資者提供了使用實際收益數據及預期可創造實際回報的簡單交易規則；而且， Jegadeesh 和 Titman（1993）的動量策略從未在酒店金融文獻內被使用過。