Time series regression-based pairs trading in the Korean equities market

Kim, Saejoon; Heo, Jun

doi:10.1080/0952813x.2016.1259265

Cited by 13 publications

(5 citation statements)

References 15 publications

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“…First, it is employed in speech recognition to compensate non-linear time shifts between two speech patterns as a consequence of different pronunciation [53][54][55]. Most recently, dynamic time warping is mainly used in the field of chemistry [56,57], gesture recognition [58,59], finance [34,60,61], and medicine [62,63].…”

Section: Dynamic Time Warpingmentioning

confidence: 99%

Epidemiology of Coronavirus COVID-19: Forecasting the Future Incidence in Different Countries

Stübinger

Schneider

2020

Healthcare

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This paper forecasts the future spread of COVID-19 by exploiting the identified lead-lag effects between different countries. Specifically, we first determine the past relation among nations with the aid of dynamic time warping. This procedure allows an elastic adjustment of the time axis to find similar but phase-shifted sequences. Afterwards, the established framework utilizes information about the leading country to predict the Coronavirus spread of the following nation. The presented methodology is applied to confirmed Coronavirus cases from 1 January 2020 to 28 March 2020. Our results show that China leads all other countries in the range of 29 days for South Korea and 44 days for the United States. Finally, we predict a future collapse of the healthcare systems of the United Kingdom and Switzerland in case of our explosion scenario.

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Section: Dynamic Time Warpingmentioning

confidence: 99%

Epidemiology of Coronavirus COVID-19: Forecasting the Future Incidence in Different Countries

Stübinger

Schneider

2020

Healthcare

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“…Notably, the proposed directional trading represents no statistical arbitrage strategy in the sense of Avellaneda and Lee (2010). Kim and Heo (2017) use dynamic time warping for detecting similar patterns on daily prices of the KOSPI 100 index stocks from January 2005 to June 2015.…”

Section: Introductionmentioning

confidence: 99%

Statistical arbitrage with optimal causal paths on high-frequency data of the S&P 500

Stübinger

2018

Quantitative Finance

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This paper develops the optimal causal path algorithm and applies it within a fully-fledged statistical arbitrage framework to minute-by-minute data of the S&P 500 constituents from 1998 to 2015. Specifically, the algorithm efficiently determines the optimal non-linear mapping and the corresponding lead-lag structure between two time series. Afterwards, this study explores the use of optimal causal paths as a means for identifying promising stock pairs and for generating buy and sell signals. For this purpose, the established trading strategy exploits information about the leading stock to predict future returns of the following stock. The value-add of the proposed framework is assessed by benchmarking it with variants relying on classic similarity measures and a buy-and-hold investment in the S&P 500 index. In the empirical back-testing study, the trading algorithm generates statistically and economically significant returns of 54.98 percent p.a. and an annualized Sharpe ratio of 3.57 after transaction costs. Returns are well superior to the benchmark approaches and do not load on any common sources of systematic risk. The strategy outperforms in the context of cryptocurrencies even in recent times due to the fact that stock returns contain substantial information about the future bitcoin returns.

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“…ARIMA has been developed in 1970 by Box and Jenkin. ARIMA is one of the best fit models for short-term forecasts [56] and is an active predictor that relates past and current with future estimates [57]. Many separated micro and macrodynamics have been tested using the ARIMA model.…”

Section: Arima Model and Open Innovationmentioning

confidence: 99%

Can International Market Indices Estimate TASI’s Movements? The ARIMA Model

Assous

Al-Rousan

AL-Najjar

et al. 2020

Journal of Open Innovation: Technology, Market, and Complexity

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This study investigates the effectiveness of six of the key international indices in estimating Saudi financial market (TADAWUL) index (TASI) movement. To investigate the relationship between TASI and other variables, six equations were built using two independent variables of time and international index, while TASI was the dependent variable. Linear, logarithmic, quadratic, cubic, power, and exponential equations were separately used to achieve the targeted results. The results reveal that power equation is the best equation for forecasting the TASI index with a low error rate and high determination coefficient. Additionally, findings of the AutoRegressive Integrated Moving Average (ARIMA) model represent the most important variables to use in order to build a prediction model that can estimate the TASI index. The ARIMA model (with Expert Modeler) coefficients are described as ARIMA (0,1,14). The results show that the SP500, NIKKEI, CAC40, and HSI indices are the most suitable variables for estimating TASI with an R2 and RMSE equal to 0.993 and 113, respectively. This relationship can be used on the previous day to estimate the opening price of TASI based on the closing prices of international indices.

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Time series regression-based pairs trading in the Korean equities market

Cited by 13 publications

References 15 publications

Epidemiology of Coronavirus COVID-19: Forecasting the Future Incidence in Different Countries

Epidemiology of Coronavirus COVID-19: Forecasting the Future Incidence in Different Countries

Statistical arbitrage with optimal causal paths on high-frequency data of the S&P 500

Can International Market Indices Estimate TASI’s Movements? The ARIMA Model

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