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Abstract This paper emphasizes the joint time‐frequency interpretation of eigensubspace representation for network statistics as features for identification and tracking traffic flows based on the link level activity. Eigencoefficients (frequency domain feature) and eigenvector components (time domain feature) are jointly utilized to quantify their combined significance on the representation of each link data (each component of the link traffic vector) in the eigensubspace. The joint time‐frequency method is employed to analyze the traffic data obtained from the Internet2 network. It is shown that the analysis with link‐level resolution brings advantages for network traffic engineering applications. A machine learning method is investigated to identify network paths using eigenanalysis of link statistics as the feature set. The merit of the method is validated by experimental studies of the network scenarios considered in the paper. Eigenvectors and eigenflows in the subspace are jointly used as factors (features) for linear regression to forecast the network link traffic. It is demonstrated that the eigensubspace based auto‐regressive order two, AR (2), predictor is superior to the time‐domain based predictor to forecast the link level traffic of a network.

Purpose – Transaction cost becomes significant when one holds many securities in a large portfolio where capital allocations are frequently rebalanced due to variations in non-stationary statistical characteristics of the asset returns. The purpose of this paper is to employ a sparsing method to sparse the eigenportfolios, so that the transaction cost can be reduced and without any loss of its performance. Design/methodology/approach – In this paper, the authors have designed pdf-optimized mid-tread Lloyd-Max quantizers based on the distribution of each eigenportfolio, and then employed them to sparse the eigenportfolios, so those small size orders may usually be ignored (sparsed), as the result, the trading costs have been reduced. Findings – The authors find that the sparsing technique addressed in this paper is methodic, easy to implement for large size portfolios and it offers significant reduction in transaction cost without any loss of performance. Originality/value – In this paper, the authors investigated the performance the sparsed eigenportfolios of stock returns in S&P500 Index. It is shown that the sparsing method is simple to implement and it provides high levels of sparsity without causing PNL loss. Therefore, transaction cost of managing a large size portfolio is reduced by employing such an efficient sparsity method.

Purpose - The purpose of this paper is to present an overview of the flash crash, and explain why and how it happened. Design/methodology/approach - The author summarizes several studies suggesting various perspectives on the flash crash and its causes. Furthermore, the author highlights recently proposed and introduced improvements and regulations to reduce the risk of having similar market collapses in the future. Findings - It is an overview paper that highlights the state of the art on the subject. Research limitations/implications - Paper does not report any research findings of the author. Practical implications - High-frequency trading (HFT) along with its pros and cons is the new normal for most of the current electronic trading activity in the markets. It is well recognized by the experts that HFT may have its important shortcomings whenever the rules and regulations are not up to date to match the technological progress offering faster computational and execution capabilities. Social implications - HFT has created a societal discussion about its benefits and potential deficiencies as the common practice for trading due to potentially unequal access to market data by various categories of participants. Such arguments help the regulators to develop improvements to reduce the market risk and nurture more robust and fair markets for all. Originality/value - The paper has a tutorial value and summarizes the current state of HFT. The readers of more interest are guided to the most relevant literature for further reading.

Human facial expressions are regarded as a vital indicator of one’s emotion and intention, and even reveal the state of health and wellbeing. Emotional states have been associated with information processing within and between subcortical and cortical areas of the brain, including the amygdala and prefrontal cortex. In this study, we evaluated the relationship between spontaneous human facial affective expressions and multi-modal brain activity measured via non-invasive and wearable sensors: functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) signals. The affective states of twelve male participants detected via fNIRS, EEG, and spontaneous facial expressions were investigated in response to both image-content stimuli and video-content stimuli. We propose a method to jointly evaluate fNIRS and EEG signals for affective state detection (emotional valence as positive or negative). Experimental results reveal a strong correlation between spontaneous facial affective expressions and the perceived emotional valence. Moreover, the affective states were estimated by the fNIRS, EEG, and fNIRS + EEG brain activity measurements. We show that the proposed EEG + fNIRS hybrid method outperforms fNIRS-only and EEG-only approaches. Our findings indicate that the dynamic (video-content based) stimuli triggers a larger affective response than the static (image-content based) stimuli. These findings also suggest joint utilization of facial expression and wearable neuroimaging, fNIRS, and EEG, for improved emotional analysis and affective brain−computer interface applications.