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Rutgers Studies in Accounting Analytics: Audit Analytics in the Financial Industry
Type: Book
ISBN: 978-1-78743-086-0

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Book part
Publication date: 21 October 2019

Jordan French

This chapter used empirical data from five developed markets and five emerging markets to perform an examination of anomalies using common financial economic approaches…

Abstract

This chapter used empirical data from five developed markets and five emerging markets to perform an examination of anomalies using common financial economic approaches along with more innovative econometric models. Of the methodologies used to test for anomalies, the data-driven panel and quantile regressions were empirically found to be better suited over the traditionally common approaches to describe the non-linear, switching behavior of the anomalies. In the developed markets, the statistically significant small firms (size) had the highest average returns. In the developing markets, the lower price-to-earnings (P/E) ratios (value) had the highest average returns. In addition, the research found (1) a small country effect, (2) sales had a negative relationship with returns, and (3) a lower (higher) book-to-market (B/M) was associated with higher returns in the developed (developing) markets, indicating investors received a higher premium for growth (value) equities. The semi-strong form of the efficient market hypothesis was also found to be violated. The anomalies’ behavior varied between sorted portfolios, industries, and developed to emerging markets; though it was found to be consistent through time (not disrupted by bear or bull markets).

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Disruptive Innovation in Business and Finance in the Digital World
Type: Book
ISBN: 978-1-78973-381-5

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Article
Publication date: 7 January 2021

Wang Jianhong and Wang Yanxiang

The purpose of this paper is to deal with the anomaly detection problem in multi-unmanned aerial vehicles (UAVs) formation that can be transformed to identify some unknown…

Abstract

Purpose

The purpose of this paper is to deal with the anomaly detection problem in multi-unmanned aerial vehicles (UAVs) formation that can be transformed to identify some unknown parameters; a more general nonlinear dynamical model for each UAV is considered to include two terms. Due to an unknown parameter corresponding to the normal or abnormal state for each UAV, the bias-compensated approach is proposed to obtain the unbiased parameter estimation. Meanwhile, the biased error and accuracy analysis are also given in case of strict statistical description of the uncertainty or white noise. To relax this strict statistical description on external noise, an analytic center approach is proposed to identify the unknown parameters in presence of bounded noise, such that two inner and outer ellipsoidal approximations are constructed to include the membership set. To be precise, this paper is regarded as one extension and summary for the author’s previous research on the anomaly detection in multi-UAV formation. Finally, one simulation example is given to confirm the theoretical results.

Design/methodology/approach

Firstly, one extended nonlinear relation is constructed to embody the mutual relationship of UAVs. Secondly, to obtain the unbiased parameter estimations, the bias-compensated approach is applied to achieve it under the condition of white noise. Thirdly, in case of unknown but bounded noise, an analytic center approach is proposed to deal with this special case. Without loss of generality, the author thinks this paper can be used as one extension and summary for research on multi-UAVs formation anomaly detection.

Findings

An anomaly detection problem in multi-UAVs formation can be transformed into a problem of nonlinear system identification, and in modeling the nonlinear dynamical model for each UAV, two terms are considered simultaneously to embody the mutual relationships with other nearest UAV.

Originality/value

To the best knowledge of the authors, this problem of the anomaly detection problem in multi-UAVs formation is proposed by the authors’ previous work, and the problem of multi-UAVs formation anomaly detection can be transferred into one problem of parameter identification. In case of unknown but bounded noise, an analytic center approach is proposed to identify the unknown parameters, which correspond to achieve the goal of the anomaly detection.

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Aircraft Engineering and Aerospace Technology, vol. 93 no. 1
Type: Research Article
ISSN: 1748-8842

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Article
Publication date: 7 July 2020

Xiang Xie, Qiuchen Lu, David Rodenas-Herraiz, Ajith Kumar Parlikad and Jennifer Mary Schooling

Visual inspection and human judgement form the cornerstone of daily operations and maintenance (O&M) services activities carried out by facility managers nowadays. Recent…

Abstract

Purpose

Visual inspection and human judgement form the cornerstone of daily operations and maintenance (O&M) services activities carried out by facility managers nowadays. Recent advances in technologies such as building information modelling (BIM), distributed sensor networks, augmented reality (AR) technologies and digital twins present an immense opportunity to radically improve the way daily O&M is conducted. This paper aims to describe the development of an AR-supported automated environmental anomaly detection and fault isolation method to assist facility managers in addressing problems that affect building occupants’ thermal comfort.

Design/methodology/approach

The developed system focusses on the detection of environmental anomalies related to the thermal comfort of occupants within a building. The performance of three anomaly detection algorithms in terms of their ability to detect indoor temperature anomalies is compared. Based on the fault tree analysis (FTA), a decision-making tree is developed to assist facility management (FM) professionals in identifying corresponding failed assets according to the detected anomalous symptoms. The AR system facilitates easy maintenance by highlighting the failed assets hidden behind walls/ceilings on site to the maintenance personnel. The system can thus provide enhanced support to facility managers in their daily O&M activities such as inspection, recording, communication and verification.

Findings

Taking the indoor temperature inspection as an example, the case study demonstrates that the O&M management process can be improved using the proposed AR-enhanced inspection system. Comparative analysis of different anomaly detection algorithms reveals that the binary segmentation-based change point detection is effective and efficient in identifying temperature anomalies. The decision-making tree supported by FTA helps formalise the linkage between temperature issues and the corresponding failed assets. Finally, the AR-based model enhanced the maintenance process by visualising and highlighting the hidden failed assets to the maintenance personnel on site.

Originality/value

The originality lies in bringing together the advances in augmented reality, digital twins and data-driven decision-making to support the daily O&M management activities. In particular, the paper presents a novel binary segmentation-based change point detection for identifying temperature anomalous symptoms, a decision-making tree for matching the symptoms to the failed assets, and an AR system for visualising those assets with related information.

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Engineering, Construction and Architectural Management, vol. 27 no. 8
Type: Research Article
ISSN: 0969-9988

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Article
Publication date: 17 January 2020

Dinesh Jaisinghani, Muskan Kaur and Mohd Merajuddin Inamdar

The purpose of this paper is to analyze different seasonal anomalies for the Israeli securities markets for the pre- and post-global financial crisis periods.

Abstract

Purpose

The purpose of this paper is to analyze different seasonal anomalies for the Israeli securities markets for the pre- and post-global financial crisis periods.

Design/methodology/approach

The closing values of six indices of the Tel Aviv Stock Exchange (TASE) of Israel have been considered. The time frame ranges from 2000 to 2018. Further, the overall time frame has been segregated into pre- and post-financial crisis periods. The study employs dummy variable regression technique for assessing different calendar anomalies.

Findings

The results show evidence pertaining to different seasonal anomalies for the Israeli markets. The results specifically show that the anomalies change considerably across the pre- and post-financial crisis periods. The results are more apparent for three anomalies including the day of the week effect, the month of the year effect and the holiday effect. However, anomalies including the Halloween effect and the trading month effect are found to be insignificant across both pre- and post-financial crisis periods.

Originality/value

The study is first of its kind that analyzes different seasonal anomalies across pre- and post-financial crisis periods for the Israeli markets. The study provides newer insights about the overall return patterns observed in different indices of the TASE.

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Managerial Finance, vol. 46 no. 3
Type: Research Article
ISSN: 0307-4358

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Content available
Article
Publication date: 30 July 2020

Minyeon Han, Dong-Hyun Lee and Hyoung-Goo Kang

This paper aims to replicate 148 anomalies and to examine whether the performance of the Korean market anomalies is statistically and economically significant. First, the…

Abstract

This paper aims to replicate 148 anomalies and to examine whether the performance of the Korean market anomalies is statistically and economically significant. First, the authors observe that only 37.8% anomalies in the universe of the KOSPI and the KOSDAQ and value-weighted portfolios have t-statistics that exceed 1.96. When the authors impose a higher threshold (an absolute value of t-statistics of 2.78), only 27.7% of the 148 anomalies survive. Second, microcaps have large impacts. The results vary significantly depending on whether the sample included stocks in the KOSDAQ and whether value-weighted or equal-weighted portfolios are used. The results suggest that data mining explains large portion of abnormal returns. Any tactical asset allocation strategies based on market anomalies should be applied very cautiously.

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Journal of Derivatives and Quantitative Studies: 선물연구, vol. 28 no. 2
Type: Research Article
ISSN: 2713-6647

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Article
Publication date: 3 August 2015

Saumya Ranjan Dash and Jitendra Mahakud

This paper aims to investigate whether the use of conditional and unconditional Fama and French (1993) three-factor and Carhart (1997) four-factor asset pricing models…

Abstract

Purpose

This paper aims to investigate whether the use of conditional and unconditional Fama and French (1993) three-factor and Carhart (1997) four-factor asset pricing models (APMs) captures the role of asset pricing anomalies in the context of emerging stock market like India.

Design/methodology/approach

The first step time series regression approach has been used to drive the risk-adjusted returns of individual securities. For examining the predictability of firm characteristics or asset pricing anomalies on the risk-adjusted returns of individual securities, the panel data estimation technique has been used.

Findings

Fama and French (1993) three-factor and Carhart (1997) four-factor model in their unconditional specifications capture the impact of book-to-market price and liquidity effects completely. When alternative APMs in their conditional specifications are tested, the importance of medium- and long-term momentum effects has been captured to a greater extent. The size, market leverage and short-term momentum effects still persist even in the case of alternative unconditional and conditional APMs.

Research limitations/implications

The empirical analysis does not extend for different market scenarios like high and low volatile market or good and bad macroeconomic environment. Because of the constraint of data availability, the authors could not include certain important anomalies like net operating assets, change in gross profit margin, external equity and debt financing and idiosyncratic risk.

Practical implications

Although the active investment approach in stock market shares a common ground of semi-strong form of market efficiency hypothesis which also supports the presence of asset pricing anomalies, less empirical evidence has been explored in this regard to support or repute such belief of practitioners. Our empirical findings make an attempt in this regard to suggest certain anomaly-based trading strategy that can be followed for active portfolio management.

Originality/value

From an emerging market perspective, this paper provides out-of-sample empirical evidence toward the use of conditional Fama and French three-factor and Carhart four-factor APMs for the complete explanation of market anomalies. This approach retains its importance with respect to the comprehensiveness of analysis considering alternative APMs for capturing unique effects of market anomalies.

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Journal of Asia Business Studies, vol. 9 no. 3
Type: Research Article
ISSN: 1558-7894

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Article
Publication date: 1 May 2018

Meher Shiva Tadepalli and Ravi Kumar Jain

Market efficiency suggests that price of the security must reflect its intrinsic value by impounding all the available and accessible information. Asset pricing in capital…

Abstract

Purpose

Market efficiency suggests that price of the security must reflect its intrinsic value by impounding all the available and accessible information. Asset pricing in capital markets has been an exceptionally dynamic area of scholarly research and is considered as a barometer for assessing market efficiency. This phenomenon was very well explained by several market pricing models and theories over the last few decades. However, several anomalies, which cannot be explained by the traditional asset pricing models due to seasonal and psychological factors, were observed historically. The same has been studied by several researchers over the years and is well captured in the literature pertaining to market asset pricing. The purpose of this paper is to revisit the research studies related to a few asset pricing anomalies, collectively referred to as “calendar anomalies”, such as – day-of-the-week, turn-of-the-month, turn-of-the-year and the holiday effects. In this pursuit, a thorough survey of literature in this area, published over the last 80 years (from 1934 to 2016) across 24 prominent journals, has been made and presented in a comprehensive, structured and chronologically arranged major findings and learnings. This literature survey reveals that the existing literature do provide a great depth of understanding around these calendar anomalies often with reference to specific markets, the size of the firm and investor type. The paper also highlights a few aspects where the existing literature is silent or provides little support leaving a gap that needs to be addressed with further research in this area.

Design/methodology/approach

The goal of the study requires a comprehensive review of the past literature related to calendar anomalies. As a consequence, to identify papers which sufficiently represent the area of study, the authors examined the full text of articles within EBSCOHost, Elsevier-Science direct, Emerald insight and JSTOR databases with calendar anomalies related keywords for articles published since inception. Further, each article was classified based on the anomaly discussed and the factors used to sub-categorize the anomaly. Once all the identified fields were populated, we passed through another article by constantly updating the master list till all the 99 articles were populated.

Findings

It is also important to understand at this juncture that most of the papers surveyed discuss the persistence of the asset pricing anomalies with reference to the developed markets with a very few offering evidences from emerging markets. Thus leaving a huge scope for further research to study the persistence of asset pricing anomalies, the degree and direction of the effect on asset pricing among emerging markets such as India, Russia, Brazil vis-a-vis the developed markets. Further, regardless of the markets with reference to which the study is conducted, the research so far appears to have laid focus only on the overall market returns derived from aggregate market indices to explain the asset pricing anomalies. Thus leaving enough scope for further research to study and understand the persistence of these anomalies with reference to various strategic, thematic and sectoral indices in various markets (developed, emerging and underdeveloped countries) across different time periods. It will be also interesting to understand how, some or all of, these established asset pricing anomalies behave over a certain time period when markets move across the efficiency maturity model (from weak form to semi-strong to strong form of efficiency).

Originality/value

The main purpose of the study entails a detailed review of all the past literature pertinent to the calendar anomalies. In order to explore the prior literature that sufficiently captures the research area, various renowned databases were examined with keywords related to the calendar anomalies under scope of current study. Furthermore, based on the finalized articles, a comprehensive summary table was populated and provided in the Appendix which gives a snapshot of all the articles under the current assessment. This helps the readers of the article to directly relate the findings of each article with its background information.

Details

American Journal of Business, vol. 33 no. 1/2
Type: Research Article
ISSN: 1935-5181

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Article
Publication date: 5 March 2018

Xu Kang and Dechang Pi

The purpose of this paper is to detect the occurrence of anomaly and fault in a spacecraft, investigate various tendencies of telemetry parameters and evaluate the…

Abstract

Purpose

The purpose of this paper is to detect the occurrence of anomaly and fault in a spacecraft, investigate various tendencies of telemetry parameters and evaluate the operation state of the spacecraft to monitor the health of the spacecraft.

Design/methodology/approach

This paper proposes a data-driven method (empirical mode decomposition-sample entropy-principal component analysis [EMD-SE-PCA]) for monitoring the health of the spacecraft, where EMD is used to decompose telemetry data and obtain the trend items, SE is utilised to calculate the sample entropies of trend items and extract the characteristic data and squared prediction error and statistic contribution rate are analysed using PCA to monitor the health of the spacecraft.

Findings

Experimental results indicate that the EMD-SE-PCA method could detect characteristic parameters that appear abnormally before the anomaly or fault occurring, could provide an abnormal early warning time before anomaly or fault appearing and summarise the contribution of each parameter more accurately than other fault detection methods.

Practical implications

The proposed EMD-SE-PCA method has high level of accuracy and efficiency. It can be used in monitoring the health of a spacecraft, detecting the anomaly and fault, avoiding them timely and efficiently. Also, the EMD-SE-PCA method could be further applied for monitoring the health of other equipment (e.g. attitude control and orbit control system) in spacecraft and satellites.

Originality/value

The paper provides a data-driven method EMD-SE-PCA to be applied in the field of practical health monitoring, which could discover the occurrence of anomaly or fault timely and efficiently and is very useful for spacecraft health diagnosis.

Details

Aircraft Engineering and Aerospace Technology, vol. 90 no. 2
Type: Research Article
ISSN: 1748-8842

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Article
Publication date: 1 April 2006

Chein‐I Chang and Mingkai Hsueh

The paper aims to characterize anomaly detection in hyperspectral imagery.

Abstract

Purpose

The paper aims to characterize anomaly detection in hyperspectral imagery.

Design/methodology/approach

This paper develops an adaptive causal anomaly detector (ACAD) to investigate several issues encountered in hyperspectral image analysis which have not been addressed in the past. It also designs extensive synthetic image‐based computer simulations and real image experiments to substantiate the work proposed in this paper.

Findings

This paper developed an ACAD and custom‐designed computer simulations and real image experiments to successfully address several issues in characterizing anomalies for detection, which are – first, how large size for a target to be considered as an anomaly? Second, how an anomaly responds to its proximity? Third, how sensitive for an anomaly to noise? Finally, how different anomalies to be detected? Additionally, it also demonstrated that the proposed ACAD can be implemented in real time processing and implementation.

Originality/value

This paper is the first work on investigation of several issues related to anomaly detection in hyperspectral imagery via extensive synthetic image‐based computer simulations and real image experiments. In addition, it also develops a new developed an ACAD to address these issues and substantiate its performance.

Details

Sensor Review, vol. 26 no. 2
Type: Research Article
ISSN: 0260-2288

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