Krill-Herd Support Vector Regression and heterogeneous autoregressive leverage: evidence from forecasting and trading commodities

Stasinakis, Charalampos; Psaradellis, Ioannis; Verousis, Thanos

doi:10.1080/14697688.2016.1211800

Cited by 14 publications

(19 citation statements)

References 51 publications

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“…Yuan (2012) claims that the combination of of GA and SVR provides better forecasts for sales volume than traditional SVR and NN models. Recently, Stasinakis et al (2016) and Sermpinis et al (2017) combine KH with SVR and LSVR, respectively, in a forecasting and trading application of exchange traded funds. Their results show that KH SVR optimization is superior to the traditional SVRs and GA-SVR models, while the advantages of LSVR are also validated.…”

Section: Brief Literature Reviewmentioning

confidence: 99%

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Forecasting government bond spreads with heuristic models: evidence from the Eurozone periphery

2018

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This study investigates the predictability of European long-term government bond spreads through the application of heuristic and metaheuristic Support Vector Regression (SVR) hybrid structures. Genetic, krill herd and sine-cosine algorithms are applied to the parameterization process of the SVR and Locally weighted SVR (LSVR) methods. The inputs of the SVR models are selected from a large pool of linear and non-linear individual predictors. The statistical performance of the main models is evaluated against a Random Walk (RW), an Autoregressive Moving Average (ARMA), the best individual prediction model and the traditional SVR and LSVR structures. All models are applied to forecast daily and weekly government bond spreads of Greece, Ireland, Italy, Portugal and Spain over the sample period 2000-2017. The results show that the sine-cosine LSVR is outperforming its counterparts in terms of statistical accuracy, while metaheuristic approaches seem to benefit the parameterization process more than the heuristic ones.

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Section: Brief Literature Reviewmentioning

confidence: 99%

“…The MLP structure is three-layered and follows the training principles of backpropagation of errors and 'early stopping' explained by Shapiro (2000). For more information on MLPs refer to Stasinakis et al (2016). The second NN is the RNN as proposed by Elman (1990).…”

Section: Appendixmentioning

confidence: 99%

Forecasting government bond spreads with heuristic models: evidence from the Eurozone periphery

2018

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“…Furthermore, SVRs have demonstrated superior performance in time series prediction relative to both conventional modeling approaches and alternative machine learning techniques such as neural networks; see Cao and Tay (2001) and Stasinakis et al (2016) for comprehensive overviews of SVR financial modeling applications.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, we go one step beyond many SVR forecasting studies of financial market time series that, similar to technical analysis traders, tend to rely on predictive content in the historical data of the target variable alone for constructing input variables. Examples are Law and Shawe-Taylor (2017) who forecast the U.K. and U.S. based stock market indices, commodity futures, government bond yields and corporate CDS, Stasinakis et al (2016) and Sermpinis et al (2017b) who focus on predicting U.S. based commodity exchange traded funds (ETF) and European stock market ETFs, respectively. In contrast, our predictive models are motivated by economic theory and as such are aligned with investment strategies of more sophisticated fundamental traders, employing as input variables an expanded array of predictors containing both global and domestic fundamentals.…”

Section: Introductionmentioning

confidence: 99%

“…During the first stage, the modeler selects specific SVR kernels and must set a small number of tuning parameters that determine how well the model produced by the subsequent SVR optimization stage will characterize the data (Cao and Tay, 2001). While the methodology for the second, optimization stage is theoretically sound and relatively straight forward to implement, Stasinakis et al (2016), Law and Shawe-Taylor (2017), Sermpinis et al (2017a) and Sermpinis et al (2017b) among others emphasize that SVR forecasting performance is highly sensitive to the kernel, or tuning, parameters selected at the first stage, pointing out that there is little formal guidance in the literature on how to set these parameters.…”

Section: Introductionmentioning

confidence: 99%

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A Coherent Framework for Predicting Emerging Market Credit Spreads with Support Vector Regression

Anderson¹,

Audzeyeva²

2019

FEDS

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We propose a coherent framework using support vector regression (SVR) for generating and ranking a set of high quality models for predicting emerging market sovereign credit spreads. Our framework adapts a global optimization algorithm employing an hv-block cross-validation metric, pertinent for models with serially correlated economic variables, to produce robust sets of tuning parameters for SVR kernel functions. In contrast to previous approaches identifying a single "best" tuning parameter setting, a task that is pragmatically improbable to achieve in many applications, we proceed with a collection of tuning parameter candidates, employing the Model Confidence Set test to select the most accurate models from the collection of promising candidates. Using bond credit spread data for three large emerging market economies and an array of input variables motivated by economic theory, we apply our framework to identify relatively small sets of SVR models with superior out-of-sample forecasting performance. Benchmarking our SVR forecasts against random walk and conventional linear model forecasts provides evidence for the notably superior forecasting accuracy of SVR-based models. In contrast to routinely used linear model benchmarks, the SVR-based models can generate accurate forecasts using only a small set of input variables limited to the country-specific credit-spread-curve factors, lending some support to the rational expectation theory of the term structure in the context of emerging market credit spreads. Consequently, our evidence indicates a better ability of highly flexible SVR to capture investor expectations about future spreads reflected in today's credit spread curve.

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Evaluation of current research on stock return predictability

Reschenhofer

Mangat

Zwatz

et al. 2019

Journal of Forecasting

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The results of recent replication studies suggest that false positive findings are a big problem in empirical finance. We contribute to this debate by reviewing a sample of articles dealing with the short-term directional forecasting of the prices of stocks, commodities, and currencies. Screening all relevant articles published in 2016 by one of the 96 journals covered by the Social Sciences Citation Index in the category "Business, Finance," we select only those studies that use easily accessible data of daily or higher frequency. We examine each study in detail, from the selection of the dataset to the interpretation of the results. We also include empirical analyses to illustrate the shortcomings of certain approaches. There are three main findings from our review. First, the number of selected papers is very low, which is surprising even when the strict selection criteria are taken into account. Second, there are hardly any relevant studies that use high-frequency data-despite the hype about financial big data and machine learning. Third, the economic significance of the findings-for example, their usefulness for trading purposes-is questionable. In general, apparently good forecasting performance does not translate into profitability once realistic transaction costs and the effect of data snooping are taken into account. Other typical problems include unsuitable benchmarks, short evaluation periods, and nonoperational trading strategies.

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Krill-Herd Support Vector Regression and heterogeneous autoregressive leverage: evidence from forecasting and trading commodities

Cited by 14 publications

References 51 publications

Forecasting government bond spreads with heuristic models: evidence from the Eurozone periphery

Forecasting government bond spreads with heuristic models: evidence from the Eurozone periphery

A Coherent Framework for Predicting Emerging Market Credit Spreads with Support Vector Regression

Evaluation of current research on stock return predictability

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