scispace - formally typeset
Search or ask a question

Self similarity long term memory time series equity? 


Best insight from top research papers

Self-similarity and long-term memory in time series have been studied in various contexts. Guegan and Lu analyze the self-similar properties of discrete-time long memory processes, including GARMA and GIGARCH processes, heteroscedastic models, and processes with switches and jumps . McSharry and Malamud compare and quantify scaling and long-range persistence in time series using different techniques, including power-spectral analysis and detrended fluctuation analysis . Janus et al. develop a new simultaneous time series model for volatility and dependence in financial return series with long memory dynamics . Zou et al. study self-similarity of memory accesses in high-performance computer systems and find evidence of self-similar behavior . While these papers provide insights into self-similarity and long-term memory in different domains, none specifically focus on equity time series.

Answers from top 5 papers

More filters
Papers (5)Insight
The provided paper does not discuss self-similarity in long-term memory time series equity. The paper focuses on self-similarity in memory access behaviors in high-performance computer systems.
The paper discusses a new model for volatility and dependence in financial time series with long memory dynamics and heavy-tailed densities. It does not specifically mention "self similarity" or "long term memory" in relation to equity time series.
Open accessProceedings ArticleDOI
Patrick E. McSharry, Bruce D. Malamud 
01 Jan 2005
18 Citations
The provided paper does not mention anything about equity or the specific question of self-similarity and long-term memory in equity time series. The paper focuses on quantifying self-similarity in cardiac inter-beat interval time series using various techniques.
The paper discusses an algorithm based on Renormalization Group (RG) to analyze time series forecasting, but it does not specifically mention self-similarity, long-term memory, or equity.
Open accessPosted Content
Dominique Guegan, Zhiping Lu, Zhiping Lu 
3 Citations
The paper does not specifically mention self-similarity in long-term memory time series for equity. The paper focuses on studying self-similar properties of discrete-time long memory processes and applies the results to specific processes such as GARMA processes and GIGARCH processes.

Related Questions

How is the similarity measured among some datasets for machine learning?4 answersSimilarity among datasets for machine learning is measured using various dissimilarity measures and similarity metrics. One approach involves leveraging unlabeled data sources with different degrees of distribution mismatch compared to labeled datasets, aiming to select the most suitable unlabeled dataset for Semisupervised Deep Learning (SSDL) outcomes. Pairwise dissimilarity measures like DID are introduced to be invariant to diffeomorphisms, applicable to diverse data spaces, and efficiently approximated via Nyström sampling, proving useful for theoretical study and practical applications. Perturbational Metric Learning (PeML) is another method that uses weakly supervised similarity metric learning to transform biological measurements into an intrinsic basis, enhancing the discrimination of replicate signatures and improving the identification of biological relationships in high-throughput perturbational datasets.
How to measure similarity between two time series?10 answersMeasuring similarity between two time series is a multifaceted task that requires consideration of various aspects such as dimensionality, complexity, and the specific characteristics of the data. A novel approach, SAX-DM, utilizes symbolic aggregate approximation based on double mean representation to address the trade-off between compression ratio and accuracy, effectively preserving the original features and trend information of time series data. This method is particularly useful for high-dimensional and complex time series data. For interpersonal similarity measurement, a workflow combining existing methods to capture dyadic similarity in a multivariate, timepoint-specific manner has been proposed, illustrating the importance of considering multiple variables and their changes over time. Similarly, heterogeneous similarity calculations have been applied to compare human posture features in time series data, using shape and frequency features, and integrating scores from multiple similarity measures. In the domain of spacecraft telemetry, an adaptive segmentation-based dynamic time warping algorithm (ASDTW) has been developed to improve the efficiency of similarity measurement by reducing computational overhead. The stock market analysis has seen the introduction of the dynamic multi-perspective personalized similarity measurement (DMPSM), which incorporates weighted segmented stock series and uses Canberra distance embedded in DTW for a more accurate and personalized similarity measurement. Elastic similarity measures, which allow for alignment of points that do not correspond in timestamps, have been adapted for multivariate time series, demonstrating the importance of considering the specific characteristics of the data. A concept-based approach using fuzzy sets for time series similarity evaluation has been proposed, emphasizing the need for interpretable methods. Lastly, the Local Extrema Dynamic Time Warping (LE-DTW) method offers a parameter-free measure for assessing similarity between long time series, highlighting the challenge of dealing with long-duration data. These approaches underscore the diversity of methods available for measuring similarity between two time series, each tailored to specific types of data and application requirements.
What is similarity identification interm of avatar self-similarity?3 answersSimilarity identification in terms of avatar self-similarity refers to the extent to which individuals perceive their avatars to be similar to themselves. It is a measure of how much individuals identify with their avatars based on shared characteristics or traits. Several studies have explored the effects of avatar self-similarity on various outcomes. For example, research has shown that voice similarity between an individual and their avatar leads to increased performance, time spent, similarity identification, competence, relatedness, and immersion. Additionally, avatar similarity in terms of appearance has been found to impact self-disclosure, with varying effects mediated by variables such as self-awareness, self-presence, and identifiability. Another study found that perceived avatar similarity in terms of attitude and behavior reduces deception, while similarity in appearance decreases deceptive behavior.
High yield bonds returns similar to equity?4 answersHigh yield bonds have characteristics of both debt and equity, but there is a controversy regarding their similarity to equity. Some argue that high yield bonds behave like equity, while others deny this claim. Reilly (1994) and Shane (1994) suggest that high yield bonds have characteristics of common stocks and can be viewed as a hybrid security consisting of a government bond and a claim on the issuing firm's equity. On the other hand, Christensen and Faria (1994) find no evidence that high yield debt behaves like equity based on the announcement effect of new issues of straight high yield debt on the issuer's stock price. They argue that the behavior of equity is affected by the issuance of high yield debt, but not vice versa. Therefore, the similarity between high yield bond returns and equity returns is still a subject of debate.
Does similarity identification lead to self-congruence directly?1 answersSimilarity identification does not directly lead to self-congruence, according to the available abstracts. The effects of self-congruence and functional congruence on tourists' destination choice were investigated, and it was found that functional congruence strongly influences destination choice, while self-congruence does not. The concept of similarity in content-based retrieval was explored, and a meta-model of data-transitive similarity was proposed, which allows for the treatment of non-similar objects as similar if there exists a chain of objects with similar enough neighboring members. Theoretical, methodological, and empirical challenges to exemplar-based similarity models were addressed, and it was shown that models derived from general recognition theory can predict categorization without incorporating selective attention. Similarity detection was discussed in the context of collaborative development of software systems, but its direct relationship with self-congruence was not mentioned. Finally, similarity ratings were explored as an alternative to identifications in eyewitness evidence, suggesting a relationship between similarity and identity.
What is the relationship between avatar Similarity identification and self-congruence?1 answersAvatar similarity and identification have a relationship with self-congruence. Studies have shown that perceived similarity with the self-avatar can enhance the Proteus Effect, where users adapt their behavior to the characteristics of their avatars. Self-similarity between the user and the avatar can lead to a higher personal relevance of the avatar, facilitating a mental connection between the user and the avatar. Additionally, avatar similarity can increase self-awareness and self-presence, which positively affect self-disclosure. On the other hand, undesirable characteristics of the avatar can act as a barrier to the occurrence of the Proteus Effect. Overall, the relationship between avatar similarity identification and self-congruence suggests that users tend to integrate desirable characteristics of their avatars into their self-concept, while also considering the impact of avatar appearance on self-disclosure.