A flexible regime switching model with pairs trading application to the S&P 500 high-frequency stock returns - Endres, Sylvia; Stübinger, Johannes (Quantitative Finance, 2019)
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 Levy-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.
CRIS-Team WoS Importer:2019-04-02

Dispersion trading based on the explanatory power of S&P 500 stock returns - Schneider, Luisa; Stübinger, Johannes (Mathematics, 2020)
This paper develops a dispersion trading strategy based on a statistical index subsetting procedure and applies it to the S&P 500 constituents from January 2000 to December 2017. In particular, our selection process determines appropriate subset weights by exploiting a principal component analysis to specify the individual index explanatory power of each stock. In the following out-of-sample trading period, we trade the most suitable stocks using a hedged and unhedged approach. Within the large-scale back-testing study, the trading frameworks achieve statistically and economically significant returns of 14.52 and 26.51 percent p.a. after transaction costs, as well as a Sharpe ratio of 0.40 and 0.34, respectively. Furthermore, the trading performance is robust across varying market conditions. By benchmarking our strategies against a naive subsetting scheme and a buy-and-hold approach, we find that our statistical trading systems possess superior risk-return characteristics. Finally, a deep dive analysis shows synchronous developments between the chosen number of principal components and the S&P 500 index.
CRIS-Team Scopus Importer:2020-10-02
Keywords: Computational finance; Correlation risk premium; Dispersion trading; Econometrics; Option arbitrage; Volatility trading

Epidemiology of Coronavirus COVID-19: Forecasting the Future Incidence in Different Countries - Stübinger, Johannes; Schneider, Luisa (Healthcare, 2020)
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.
CRIS-Team WoS Importer:2020-07-24

Exploiting social media with higher-order Factorization Machines: statistical arbitrage on high-frequency data of the S&P 500 - Knoll, Julian; Stübinger, Johannes; Grottke, Michael (Quantitative Finance, 2019)
Over the past 15 years, there have been a number of studies using text mining for predicting stock market data. Two recent publications employed support vector machines and second-order Factorization Machines, respectively, to this end. However, these approaches either completely neglect interactions between the features extracted from the text, or they only account for second-order interactions. In this paper, we apply higher-order Factorization Machines, for which efficient training algorithms have only been available since 2016. As Factorization Machines require hyperparameters to be specified, we also introduce a novel adaptive-order algorithm for automatically determining them. Our study is the first one to make use of social media data for predicting minute-by-minute stock returns, namely the ones of the S&P 500 stock constituents. We show that, unlike a trading strategy employing support vector machines, Factorization-Machine-based strategies attain positive returns after transactions costs for the years 2014 and 2015. Especially the approach applying the adaptive-order algorithm outperforms classical approaches with respect to a multitude of criteria, and it features very favorable characteristics.
CRIS-Team WoS Importer:2019-04-12

How to identify varying lead-lag effects in time series data: Implementation, validation, and application of the generalized causality algorithm - Stübinger, Johannes; Adler, Katharina (Algorithms, 2020)
This paper develops the generalized causality algorithm and applies it to a multitude of data from the fields of economics and finance. Specifically, our parameter-free algorithm efficiently determines the optimal non-linear mapping and identifies varying lead-lag effects between two given time series. This procedure allows an elastic adjustment of the time axis to find similar but phase-shifted sequences-structural breaks in their relationship are also captured. A large-scale simulation study validates the outperformance in the vast majority of parameter constellations in terms of efficiency, robustness, and feasibility. Finally, the presented methodology is applied to real data from the areas of macroeconomics, finance, and metal. Highest similarity show the pairs of gross domestic product and consumer price index (macroeconomics), S&P 500 index and Deutscher Aktienindex (finance), as well as gold and silver (metal). In addition, the algorithm takes full use of its flexibility and identifies both various structural breaks and regime patterns over time, which are (partly) well documented in the literature.
CRIS-Team Scopus Importer:2020-05-22
Keywords: Finance; Generalized causality algorithm; Lead-lag effect; Optimal causal path; Quantitative economics; Simulation study; Structural break

Machine-Learning-Based Statistical Arbitrage Football Betting - Knoll, Julian; Stübinger, Johannes (Künstliche Intelligenz, 2020)
Across countries and continents, football (soccer) has drawn increasingly more attention over the last decades and developed into a huge commercial complex. Consequently, the market of bookmakers providing the possibility to bet on the result of football matches grew rapidly, especially with the appearance of the internet. With a high number of games every week in multiple countries, football league matches hold enormous potential for generating profits over time with the use of advanced betting strategies. In this paper, we use machine learning for predicting the outcome of football league matches by exploiting data about match characteristics. Based on insights from the field of statistical arbitrage stock market trading, we show that one could generate meaningful profits over time by betting accordingly. A simulation study analyzing the matches of the five top European football leagues from season 2013/14 to 2017/18 presented economically and statistically significant returns achieved by exploiting large data sets with modern machine learning algorithms. In contrast to these modern algorithms, the break-even point could not be reached with an ordinary linear regression approach or simple betting strategies, e.g. always betting on the home team.
CRIS-Team Scopus Importer:2020-07-24
Keywords: Betting strategy; Football; Machine learning; Sports forecasting; Statistical arbitrage

Machine learning in football betting: Prediction of match results based on player characteristics - Stübinger, Johannes; Mangold, Benedikt; Knoll, Julian (Applied Sciences, 2020)
In recent times, football (soccer) has aroused an increasing amount of attention across continents and entered unexpected dimensions. In this course, the number of bookmakers, who offer the opportunity to bet on the outcome of football games, expanded enormously, which was further strengthened by the development of the world wide web. In this context, one could generate positive returns over time by betting based on a strategy which successfully identifies overvalued a betting odds. Due to the large number of matches around the globe, football matches in particular have great potential for such a betting strategy. This paper utilizes machine learning to forecast the outcome of football games based on match and player attributes. A simulation study which includes all matches of the five greatest European football leagues and the corresponding second leagues between 2006 and 2018 revealed that an ensemble strategy achieves statistically and economically significant returns of 1.58% per match. Furthermore, the combination of different machine learning algorithms could neither be outperformed by the individual machine learning approaches nor by a linear regression model or naive betting strategies, such as always betting on the victory of the home team.
CRIS-Team Scopus Importer:2020-02-18
Keywords: Football betting; Machine learning; Profitable investment; Quantitative finance application; Sports forecasting; Statistical arbitrage; Tme-series prediction; Trading system

Nonlinear dependence modeling with bivariate copulas: Statistical arbitrage pairs trading on the S&P 100. - Krauss, Christopher; Stübinger, Johannes (Applied economics, 2017)

Non-linear dependence modelling with bivariate copulas: statistical arbitrage pairs trading on the S&P 100 - Krauss, Christopher; Stübinger, Johannes (Applied economics, 2017)
Keywords: copula; quantitative strategies; pairs trading; Statistical arbitrage

Optimal trading strategies for Levy-driven Ornstein-Uhlenbeck processes - Endres, Sylvia; Stübinger, Johannes (Applied economics, 2019)
This study derives an optimal pairs trading strategy based on a Levy-driven Ornstein-Uhlenbeck process and applies it to high-frequency data of the S&P 500 constituents from 1998 to 2015. Our model provides optimal entry and exit signals by maximizing the expected return expressed in terms of the first-passage time of the spread process. An explicit representation of the strategy's objective function allows for direct optimization without Monte Carlo methods. Categorizing the data sample into 10 economic sectors, we depict both the performance of each sector and the efficiency of the strategy in general. Results from empirical back-testing show strong support for the profitability of the model with returns after transaction costs ranging from 31.90% p.a. for the sector 'Consumer Staples' to 278.61% p.a. for the sector 'Financials'. We find that the remarkable returns across all economic sectors are strongly driven by model parameters and sector size. Jump intensity decreases over time with strong outliers in times of high market turmoil. The value-add of our Levy-based model is demonstrated by benchmarking it with quantitative strategies based on Brownian motion-driven processes.
CRIS-Team WoS Importer:2019-04-12

Pairs trading with a mean-reverting jump-diffusion model on high-frequency data. - Stübinger, Johannes; Endres, Sylvia (Quantitative Finance, 2018)

Statistical arbitrage pairs trading with high-frequency data. - Stübinger, Johannes; Bredthauer, Jens (International Journal of Economics and Financial Issues, 2017)

Statistical Arbitrage with Mean-Reverting Overnight Price Gaps on High-Frequency Data of the S&P 500 - Stübinger, Johannes; Schneider, Luisa (Journal of Risk and Financial Management, 2019)
This paper develops a fully-fledged statistical arbitrage strategy based on a mean-reverting jump-diffusion model and applies it to high-frequency data of the S&P 500 constituents from January 1998-December 2015. In particular, the established stock selection and trading framework identifies overnight price gaps based on an advanced jump test procedure and exploits temporary market anomalies during the first minutes of a trading day. The existence of the assumed mean-reverting property is confirmed by a preliminary analysis of the S&P 500 index; this characteristic is particularly significant 120 min after market opening. In the empirical back-testing study, the strategy delivers statistically- and economically-significant returns of 51.47 percent p.a.and an annualized Sharpe ratio of 2.38 after transaction costs. We benchmarked our trading algorithm against existing quantitative strategies from the same research area and found its performance superior in a multitude of risk-return characteristics. Finally, a deep dive analysis shows that our results are consistently profitable and robust against drawdowns, even in recent years.
CRIS-Team WoS Importer:2019-07-30

Statistical arbitrage with optimal causal paths on high-frequency data of the S&P 500 - Stübinger, Johannes (Quantitative Finance, 2019)
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% 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.
CRIS-Team Scopus Importer:2019-05-28
Keywords: Cryptocurrency; Finance; High-frequency trading; Lead–lag structure; Optimal causal path; Statistical arbitrage

Statistical arbitrage with vine copulas - Stübinger, Johannes; Mangold, Benedikt; Krauss, Christopher (Quantitative Finance, 2018)

Understanding smart city—a data-driven literature review - Stübinger, Johannes; Schneider, Luisa (Sustainability, 2020)
This paper systematically reviews the top 200 Google Scholar publications in the area of smart city with the aid of data-driven methods from the fields natural language processing and time series forecasting. Specifically, our algorithm crawls the textual information of the considered articles and uses the created ad-hoc database to identify the most relevant streams “smart infrastructure”, “smart economy & policy”, “smart technology”, “smart sustainability”, and “smart health”. Next, we automatically assign each manuscript into these subject areas by dint of several interdisciplinary scientific methods. Each stream is evaluated in a deep-dive analysis by (i) creating a word cloud to find the most important keywords, (ii) examining the main contributions, and (iii) applying time series methodologies to determine the past and future relevance. Due to our large-scaled literature, an in-depth evaluation of each stream is possible, which ultimately reveals strengths and weaknesses. We hereby acknowledge that smart sustainability will come to the fore in the next years—this fact confirms the current trend, as minimizing the required input of energy, water, food, waste, heat output and air pollution is becoming increasingly important.
CRIS-Team Scopus Importer:2020-10-30
Keywords: Data-driven review; Google Scholar; Natural language processing; Smart city; Smart economy & policy; Smart health; Smart infrastructure; Smart sustainability; Smart technology; Time series analysis

Using Multi-Dimensional Dynamic Time Warping to Identify Time-Varying Lead-Lag Relationships - Stübinger, Johannes; Walter, Dominik (Sensors, 2022)
This paper develops a multi-dimensional Dynamic Time Warping (DTW) algorithm to identify varying lead-lag relationships between two different time series. Specifically, this manuscript contributes to the literature by improving upon the use towards lead-lag estimation. Our two-step procedure computes the multi-dimensional DTW alignment with the aid of shapeDTW and then utilises the output to extract the estimated time-varying lead-lag relationship between the original time series. Next, our extensive simulation study analyses the performance of the algorithm compared to the state-of-the-art methods Thermal Optimal Path (TOP), Symmetric Thermal Optimal Path (TOPS), Rolling Cross-Correlation (RCC), Dynamic Time Warping (DTW), and Derivative Dynamic Time Warping (DDTW). We observe a strong outperformance of the algorithm regarding efficiency, robustness, and feasibility.
CRIS-Team Scopus Importer:2022-10-07
Keywords: big data processing; data science; dynamic time warping; econometric modeling; multi-dimensional; simulation study; thermal optimal path; time-varying lead–lag effect