Intraday pair 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 across mainland China and Hong Kong stock markets

Motivated by the rationale that market inefficiency arises from a combination of less than fully rational demand and limits to arbitrage, this paper investigates the profitability of pairs trading across Mainland China and Hong Kong on highly liquid large‐cap and midcap stocks from January 1996 to July 2017. We have three main findings. First, we find that pairs trading constrained within each market generates no significant abnormal returns. However, if investors can trade across Mainland China and Hong Kong, pairs trading is profitable after adjusting for risk and transaction costs, where the annualized abnormal return is 9% over the full sample. Second, by using a rolling‐window regression, we find that the profitability of the strategy is time‐varying. The bootstrap simulations suggest that the decline in profitability of the strategy since 2012 is due to random chance rather than poor ability of identifying mispriced stocks. However, the vast majority of profitable periods reflect the strategy's ability to choose profitable stocks rather than random chance. Third, the profitability of the strategy is somewhat sensitive to market conditions, most notably, the strategy is more profitable during longer term market turbulence. Overall, our empirical findings are consistent with the Adaptive Market Hypothesis in that the integration of financial markets and market conditions determine the level of market efficiency

Tracing the temporal evolution of clusters in a financial stock market

We propose a methodology for clustering financial time series of stocks' returns, and a graphical set-up to quantify and visualise the evolution of these clusters through time. The proposed graphical representation allows for the application of well known algorithms for solving classical combinatorial graph problems, which can be interpreted as problems relevant to portfolio design and investment strategies. We illustrate this graph representation of the evolution of clusters in time and its use on real data from the Madrid Stock Exchange market.Comment: 22 pages, 3 figures (submitted for publication

Adaptive pairs trading strategy performance in Turkish derivatives exchange with the companies listed on Istanbul stock exchange

Due to copyright restrictions, the access to the full text of this article is only available via subscription.We implemented model-driven statistical arbitrage strategies in Turkish equities market. Trading signals are generated by optimized parameters of distance method. When the trade in signal is triggered by the model, market-neutral portfolio is created by long in the synthetic ETF, which is based on constrained least squares regression of selected Istanbul Stock Exchange stocks and short in Turkish Derivatives Exchange (Turkdex) index futures contract. We performed pairs trading strategy based on a comparative mean reversion of asset prices with daily data over the period February 2005 through July 2011 in Istanbul Stock Exchange (ISE) and Turkdex. We constructed a hypothetical ISE30 ETF Index on a daily basis in order to originate pairs trading strategy with Turkdex. Because of the leverage rule of (1–10) index futures contracts, we had to evaluate spot stock pairs formation with futures contracts pairs strategy. The results indicate that applied pairs strategy produced overall returns of 901 per cent during the investment period, whereas naive strategy (buy and hold ISE-30 index) return for the same period was 111 per cent. Similar outperformance was observed in the Sharpe and Sortino ratios

FinTech revolution: the impact of management information systems upon relative firm value and risk

The FinTech or ‘financial technology’ revolution has been gaining increasing interest as technologies are fundamentally changing the business of financial services. Consequently, financial technology is playing an increasingly important role in providing relative performance growth to firms. It is also well known that such relative performance can be observed through pairs trading investment. Therefore pairs trading have implications for understanding financial technology performance, yet the relationships between relative firm value and financial technology are not well understood. In this paper we investigate the impact of financial technology upon relative firm value in the banking sector. Firstly, using pairs trade data we show that financial technologies reveal differences in relative operational performance of firms, providing insight on the value of financial technologies. Secondly, we find that contribution of relative firm value growth from financial technologies is dependent on the specific business characteristics of the technology, such as the business application and activity type. Finally, we show that financial technologies impact the operational risk of firms and so firms need to take into account both the value and risk benefits in implementing new technological innovations. This paper will be of interest to academics and industry professionals