Article HistoryKeywords Finance Social media data Factorization Machine Overnight information Statistical arbitrage High-frequency trading. JEL ClassificationC2, C5, C6, G1, G12. This paper develops a statistical arbitrage strategy based on overnight social media data and applies it to high-frequency data of the S&P 500 constituents from January 2014 to December 2015. The established trading framework predicts future financial markets using Factorization Machines, which represent a state-of-the-art algorithm coping with high-dimensional data in very sparse settings. Essentially, we implement and analyze the effectiveness of support vector machines (SVM), second-order Factorization Machines (SFM), third-order Factorization Machines (TFM), and adaptive-order Factorization Machines (AFM). In the back-testing study, we prove the efficiency of Factorization Machines in general and show that increasing complexity of Factorization Machines provokes higher profitability -annualized returns after transaction costs vary between 5.96 percent for SVM and 13.52 percent for AFM, compared to 5.63 percent for a naive buy-and-hold strategy of the S&P 500 index. The corresponding Sharpe ratios range between 1.00 for SVM and 2.15 for AFM. Varying profitability during the opening minutes can be explained by the effects of market efficiency and trading turmoils. Additionally, the AFM approach achieves the highest accuracy rate and generates statistically and economically remarkable returns after transaction costs without loading on any systematic risk exposure.Contribution/Originality: This study contributes in the existing literature by predicting financial markets based on overnight social media data. For this purpose, we observe tweets about the S&P 500 companies during the time span in which stock markets are closed and forecast the future price changes based on the collected information. stock market by applying support vector machines. Jin et al. (2013) made forecasts by deploying a linear regression model based on news articles, historical stock indices, and currency exchange values. Chatrath et al. (2014) examined the impact of macro news on currency jumps by a stepwise multivariate regression in a Probit model. All of theses studies are not in a position to consider the effect of overnight textual data on future price changes -an obvious deficit since information in social media, news, blogs, forums, and announcements are published 24 hours a day, 7 days a week.
Tightly Coupled Processor Arrays (TCPAs), a class of massively parallel loop accelerators, allow applications to offload computationally expensive loops for improved performance and energy efficiency. To achieve these two goals, executing a loop on a TCPA requires an efficient generation of specific programs as well as other configuration data for each distinct combination of loop bounds and number of available processing elements (PEs). Since both these parameters are generally unknown at compile time—the number of available PEs due to dynamic resource management, and the loop bounds, because they depend on the problem size—both the programs and configuration data must be generated at runtime. However, pure just-in-time compilation is impractical, because mapping a loop program onto a TCPA entails solving multiple NP-complete problems. As a solution, this article proposes a unique mixed static/dynamic approach called symbolic loop compilation. It is shown that at compile time, the NP-complete problems (modulo scheduling, register allocation, and routing) can still be solved to optimality in a symbolic way resulting in a so-called symbolic configuration , a space-efficient intermediate representation parameterized in the loop bounds and number of PEs. This phase is called symbolic mapping . At runtime, for each requested accelerated execution of a loop program with given loop bounds and known number of available PEs, a concrete configuration , including PE programs and configuration data for all other components, is generated from the symbolic configuration according to these parameter values. This phase is called instantiation . We describe both phases in detail and show that instantiation runs in polynomial time with its most complex step, program instantiation, not directly depending on the number of PEs and thus scaling to arbitrary sizes of TCPAs. To validate the efficiency of this mixed static/dynamic compilation approach, we apply symbolic loop compilation to a set of real-world loop programs from several domains, measuring both compilation time and space requirements. Our experiments confirm that a symbolic configuration is a space-efficient representation suited for systems with little memory—in many cases, a symbolic configuration is smaller than even a single concrete configuration instantiated from it—and that the times for the runtime phase of program instantiation and configuration loading are negligible and moreover independent of the size of the available processor array. To give an example, instantiating a configuration for a matrix-matrix multiplication benchmark takes equally long for 4× 4 and 32× 32 PEs.
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