A good pair: alternative pairs-trading strategies

This paper studies alternative techniques for identifying stock pairs in a pairs-trading strategy over 1980–2014. We consider two main techniques: the distance approach and the cointegration approach. We also consider a range of parameterizations of the trading system design. Parameterization of the trading system matters for the profitability of pairs trading. We find that the cointegration approach, despite using an optimal in-sample parameterization, yields significant returns only in the 1980s. The distance approach performs better, producing significantly positive risk-adjusted returns in all sub-periods. However, when transaction costs are properly taken into account, the returns largely disappear in recent years.     

Decision trees unearth return sign predictability in the S&P 500

Technical trading rules and linear regression models are often used by practitioners to find trends in asset returns. However, these models typically neglect interaction terms between the lagged daily directional movements. We propose a decision tree forecasting model that has the flexibility to capture arbitrary interaction patterns. To study the importance of interaction terms, we construct a binary Markov process with a deterministic component that cannot be predicted without interaction terms between the lagged directional movements. We show that some tree based strategies achieve trading performance significant at the 99% confidence level on the S&P 500 over the past 20 years, after adjusting for multiple testing. The best strategy breaks even with the buy-and-hold strategy at 21 bps in transaction costs per round trip. A four-factor regression analysis shows significant intercept, and correlation with the market. The directional predictability is strongest during the bursts of the dotcom bubble, financial crisis, and European debt crisis. The return sign predictability during these periods confirms the necessity of interaction terms to model daily returns.     

Profitability of pairs trading strategy in an illiquid market with multiple share classes

We investigate the practical issues of implementing the self-financing pairs portfolio trading strategy presented by Gatev et al. (2006). We also provide new evidence on the profitability of pairs trading under different weighting structures and trade initiation conditions. Using data from the Finnish stock market over the period 1987–2008, we find pairs trading to be profitable even after allowing for a one day delay in the trade initiation after the signal. On average, the annualized return can be as high as 12.5%, though requiring trading on days a pair is traded lowers the return approximately by three percentage points. On the other hand, lowering the threshold for opening a pair is found to increase returns even after accounting for trading costs, indicating that there might be a more optimal trade initiation threshold available than that presented in earlier literature. The profits are not related to market risk. Pairs trading strategy is found to produce positive alpha during the sample period.     

Are Momentum Profits Robust to Trading Costs?

We test whether momentum strategies remain profitable after considering market frictions induced by trading. Intraday data are used to estimate alternative measures of proportional and non-proportional (price impact) trading costs. The price impact models imply that abnormal returns to portfolio strategies decline with portfolio size. We calculate break-even fund sizes that lead to zero abnormal returns. In addition to equal- and value-weighted momentum strategies, we derive a liquidity-weighted strategy designed to reduce the cost of trades. Equal-weighted strategies perform the best before trading costs and the worst after trading costs. Liquidity-weighted and hybrid liquidity/value-weighted strategies have the largest break-even fund sizes: $5 billion or more (relative to December 1999 market capitalization) may be invested in these momentum strategies before the apparent profit opportunities vanish.     

Pairs trading with a mean-reverting jump–diffusion model on high-frequency data

This paper develops a pairs trading framework based on a mean-reverting jump–diffusion model and applies it to minute-by-minute data of the S&P 500 oil companies from 1998 to 2015. The established statistical arbitrage strategy enables us to perform intraday and overnight trading. Essentially, we conduct a three-step calibration procedure to the spreads of all pair combinations in a formation period. Top pairs are selected based on their spreads’ mean-reversion speed and jump behaviour. Afterwards, we trade the top pairs in an out-of-sample trading period with individualized entry and exit thresholds. In the back-testing study, the strategy produces statistically and economically significant returns of 60.61% p.a. and an annualized Sharpe ratio of 5.30, after transaction costs. We benchmark our pairs trading strategy against variants based on traditional distance and time-series approaches and find its performance to be superior relating to risk–return characteristics. The mean-reversion speed is a main driver of successful and fast termination of the pairs trading strategy.     

Pairs trading with partial cointegration

Partial cointegration is a weakening of cointegration that allows for the ‘cointegrating’ residual to contain a random walk and a mean-reverting component. We derive its representation in state space, provide a maximum likelihood-based estimation routine, and a suitable likelihood ratio test. Then, we explore the use of partial cointegration as a means for identifying promising pairs and for generating buy and sell signals. Specifically, we benchmark partial cointegration against several classical pairs trading variants from 1990 until 2015, on a survivor bias free data-set of the S&P 500 constituents. We find annualized returns of more than 12% after transaction costs. These results can only partially be explained by common sources of systematic risk and are well superior to classical distance-based or cointegration-based pairs trading variants on our data-set.     

Currency momentum strategies

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

A flexible regime switching model with pairs trading application to the S&P 500 high-frequency stock returns

This paper develops the regime classification algorithm and applies it within a fully-fledged pairs trading framework on minute-by-minute data of the S&P 500 constituents from 1998 to 2015. Specifically, the highly flexible algorithm automatically determines the number of regimes for any stochastic process and provides a complete set of parameter estimates. We demonstrate its performance in a simulation study—the algorithm achieves promising results for the general class of Lévy-driven Ornstein–Uhlenbeck processes with regime switches. In our empirical back-testing study, we apply our regime classification algorithm to propose a high-frequency pair selection and trading strategy. The results show statistically and economically significant returns with an annualized Sharpe ratio of 3.92 after transaction costs—results remain stable even in recent years. We compare our strategy with existing quantitative trading frameworks and find its results to be superior in terms of risk and return characteristics. The algorithm takes full advantage of its flexibility and identifies various regime patterns over time that are well-documented in the literature.     

The predictability of real estate returns and market timing

Recent evidence suggests that all asset returns are predictable to some extent with excess returns on real estate relatively easier to forecast. This raises the issue of whether we can successfully exploit this level of predictability using various market timing strategies to realize superior performance over a buy-and-hold strategy. We find that the level of predicability associated with real estate leads to moderate success in market timing, although this is not necessarily the case for the other asset classes examined in general. Besides this, real estate stocks typically have higher trading profits and higher mean risk-adjusted excess returns when compared to small stocks as well as large stocks and bonds even though most real estate stocks are small stocks.     

Intraday pairs trading strategies on high frequency data: the case of oil companies

This paper introduces novel ‘doubly mean-reverting’ processes based on conditional modelling of model spreads between pairs of stocks. Intraday trading strategies using high frequency data are proposed based on the model. This model framework and the strategies are designed to capture ‘local’ market inefficiencies that are elusive for traditional pairs trading strategies with daily data. Results from real data back-testing for two periods show remarkable returns, even accounting for transaction costs, with annualized Sharpe ratios of 3.9 and 7.2 over the periods June 2013–April 2015 and 2008, respectively. By choosing the particular sector of oil companies, we also confirm the observation that the commodity price is the main driver of the share prices of commodity-producing companies at times of spikes in the related commodity market.     

Pairs Trading: Performance of a Relative-Value Arbitrage Rule

We test a Wall Street investment strategy, "pairs trading,"with daily data over 1962–2002. Stocks are matched intopairs with minimum distance between normalized historical prices.A simple trading rule yields average annualized excess returnsof up to 11% for self-financing portfolios of pairs. The profitstypically exceed conservative transaction-cost estimates. Bootstrapresults suggest that the "pairs" effect differs from previouslydocumented reversal profits. Robustness of the excess returnsindicates that pairs trading profits from temporary mispricingof close substitutes. We link the profitability to the presenceof a common factor in the returns, different from conventionalrisk measures.     

Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market

We study the impact of algorithmic trading (AT) in the foreign exchange market using a long time series of high‐frequency data that identify computer‐generated trading activity. We find that AT causes an improvement in two measures of price efficiency: the frequency of triangular arbitrage opportunities and the autocorrelation of high‐frequency returns. We show that the reduction in arbitrage opportunities is associated primarily with computers taking liquidity. This result is consistent with the view that AT improves informational efficiency by speeding up price discovery, but that it may also impose higher adverse selection costs on slower traders. In contrast, the reduction in the autocorrelation of returns owes more to the algorithmic provision of liquidity. We also find evidence consistent with the strategies of algorithmic traders being highly correlated. This correlation, however, does not appear to cause a degradation in market quality, at least not on average.     

The side effects of quantitative easing: Evidence from the UK bond market

We examine the returns to UK government bonds before, during and between the phases of quantitative easing to identify the side effects for the market itself. We show that the onset of QE led to a sustained reduction in the costs of trading and removed some return regularities. However, controlling for a wide range of market activity, including issuance and QE announcements, we find evidence that investors could have earned excess returns after costs by trading in response to the purchase auction calendar. Drawing on economic theory, we explore the implications of these findings for both the efficiency of the market and the costs of government debt management in both the short and long run.     

Pairs trading in the UK equity market: risk and return

In this paper, we provide the first comprehensive UK evidence on the profitability of the pairs trading strategy. Evidence suggests that the strategy performs well in crisis periods, so we control for both risk and liquidity to assess performance. To evaluate the effect of market frictions on the strategy, we use several estimates of transaction costs. We also present evidence on the performance of the strategy in different economic and market states. Our results show that pairs trading portfolios typically have little exposure to known equity risk factors such as market, size, value, momentum and reversal. However, a model controlling for risk and liquidity explains a far larger proportion of returns. Incorporating different assumptions about bid-ask spreads leads to reductions in performance estimates. When we allow for time-varying risk exposures, conditioned on the contemporaneous equity market return, risk-adjusted returns are generally not significantly different from zero.     

Profitable predictability in the cross section of stock returns

Haugen and Baker (1996) report that a long-short stock selection strategy based on more than 50 measures of accounting information and past return behavior would have generated excess returns of approximately 3% per month. We find that the Haugen and Baker strategies do not provide attractive returns after transaction costs if an investor already has access to strategy portfolios based on book-to-market and momentum. We also provide an extensive analysis of transaction costs over a long sample and we report results of independent interest to researchers in market microstructure.     

Pairs trading based on statistical variability of the spread process

This research proposes a new non-parametric approach to pairs trading based on renko and kagi constructions which originated from Japanese charting indicators and were introduced to academic studies by Pastukhov. The method exploits statistical information about the variability of the tradable process. The approach does not find a long-run mean of the process and trade towards it like other methods of pairs trading. The only assumption we need is that the statistical properties of the spread process volatility remain reasonably constant. The theoretical profitability of the method has been demonstrated for the Ornstein–Uhlenbeck process. Tests on the daily market data of American and Australian stock exchanges show statistically significant average excess returns ranging from 1.4 to 3.6% per month and annualized Sharpe ratio from 1.5 to 3.4.     

Predictability and Persistence of the Price Movements of the S&P/Case-Shiller House Price Indices

This paper examines persistence in price movements and predictability of the US housing market both on a local level across 20 cities in the US and on a nationwide level. We use a time series approach instead of often applied multivariate approaches to exclude potential biases across local markets and provide trading strategies to compare predictability across markets and to test whether or not the detected persistence can be exploited by investors to earn excess returns. The results from the monthly and quarterly transaction-based S&P/Case-Shiller house price indices from 1987 to 2009 provide empirical evidence on strong persistence. This is confirmed by both parametric and non-parametric tests for nominal and real house prices based on expected inflation. Furthermore, the empirical findings suggest that investors might be able to obtain excess returns from both autocorrelation-based and moving average-based trading strategies compared to a buy-and-hold strategy, although the results depend on the transaction costs individual investors face.     

Do optimal diversification strategies outperform the 1/N strategy in U.K. stock returns?

This paper examines whether optimal diversification strategies outperform the 1/N strategy in U.K. stock returns. The study focuses on the performance of recent strategies developed by Tu and Zhou (2011) and Kirby and Ostdiek (2010). I find that a number of optimal asset allocation strategies can significantly outperform the 1/N strategy even after adjusting for trading costs. The strategies developed by Kirby and Ostdiek outperform the 1/N strategy, even at higher trading costs, due to the low turnover of these strategies. The strategies of Tu and Zhou have mixed performance after adjusting for trading costs due to the high turnover of these strategies. The results of the paper provide support for the use of optimal diversification strategies.     

The intra-day performance of market timing strategies and trading systems based on Japanese candlesticks

Abstract

We develop market timing strategies and trading systems to test the intra-day predictive power of Japanese candlesticks at the 5-minute interval on the 30 constituents of the DJIA index. Around a third of the candlestick rules outperform the buy-and-hold strategy at the conservative Bonferroni level. After adjusting for trading costs, however, just a few rules remain profitable. When we correct for data snooping by applying the SSPA test on double-or-out market timing strategies, no single candlestick rule beats the buy-and-hold strategy after transaction costs. We also design fully automated trading systems by combining the best-performing candlestick rules. No evidence of out-performance is found after transaction costs. Although Japanese candlesticks can somewhat predict intra-day returns on large US caps, we show that such predictive power is too limited for active portfolio management to outperform the buy-and-hold strategy when luck, risk, and trading costs are correctly measured.     

Market Efficiency and the Returns to Simple Technical Trading Rules: New Evidence from U.S. Equity Market and Chinese Equity Markets

Numerous studies in the finance literature have investigated technical analysis to determine its validity as an investment tool. This study is an attempt to explore whether some forms of technical analysis can predict stock price movement and make excess profits based on certain trading rules in markets with different efficiency level. To avoid using arbitrarily selected 26 trading rules as did by Brock, Lakonishok and LeBaron (1992) and later by Bessembinder and Chan (1998), this paper examines predictive power and profitability of simple trading rules by expanding their universe of 26 rules to 412 rules. In order to find out the relationship between market efficiency and excess return by applying trading rules, we examine excess return over periods in U.S. markets and also compare the excess returns between U.S. market and Chinese markets. Our results found that there is no evidence at all supporting technical forecast power by these trading rules in U.S. equity index after 1975. During the 1990s break-even costs turned to be negative, –0.06%, even failing to beat a buy-holding strategyin U.S. equity market. In comparison, our results provide support for the technical strategies even in the presence of trading cost in Chinese stock markets.