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Open Access
Article
Publication date: 15 August 2022

Zhuo (June) Cheng and Jing (Bob) Fang

This study examines the effect of stock liquidity on the magnitude of the accrual anomaly.

Abstract

Purpose

This study examines the effect of stock liquidity on the magnitude of the accrual anomaly.

Design/methodology/approach

This paper examines the relation—both time-series and cross-sectional—between stock liquidity and the magnitude of the accrual anomaly and use the 2001 minimum tick size decimalization as a quasi-experiment to establish causality.

Findings

There is both cross-sectional and time-series evidence that stock liquidity is negatively related to the magnitude of the accrual anomaly. Moreover, the extent to which investors overestimate the persistence of accruals decreases with stock liquidity. Results from a difference-in-differences analysis conducted using the 2001 minimum tick size decimalization as a quasi-experiment suggest that the effect of stock liquidity on the accrual anomaly is causal. The findings of this study are consistent with the enhancing effect of stock liquidity on pricing efficiency.

Originality/value

The study's findings are well aligned with the mispricing-based explanation for the accrual anomaly, suggesting that the improvement in market-wide stock liquidity drives the contemporaneous decline in the magnitude of the accrual anomaly, at least to a great extent.

Details

China Accounting and Finance Review, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1029-807X

Keywords

Article
Publication date: 31 May 2022

Maqsood Ahmad, Qiang Wu and Yasar Abbass

This study aims to explore and clarify the mechanism by which recognition-based heuristic biases influence the investment decision-making and performance of individual…

Abstract

Purpose

This study aims to explore and clarify the mechanism by which recognition-based heuristic biases influence the investment decision-making and performance of individual investors, with the mediating role of fundamental and technical anomalies.

Design/methodology/approach

The deductive approach was used, as the research is based on behavioral finance's theoretical framework. A questionnaire and cross-sectional design were employed for data collection from the sample of 323 individual investors trading on the Pakistan Stock Exchange (PSX). Hypotheses were tested through the structural equation modeling (SEM) technique.

Findings

The article provides further insights into the relationship between recognition-based heuristic-driven biases and investment management activities. The results suggest that recognition-based heuristic-driven biases have a markedly positive influence on investment decision-making and negatively influence the investment performance of individual investors. The results also suggest that fundamental and technical anomalies mediate the relationships between the recognition-based heuristic-driven biases on the one hand and investment management activities on the other.

Practical implications

The results of the study suggested that investment management activities that rely on recognition-based heuristics would not result in better returns to investors. The article encourages investors to base decisions on investors' financial capability and experience levels and to avoid relying on recognition-based heuristics when making decisions related to investment management activities. The results provides awareness and understanding of recognition-based heuristic-driven biases in investment management activities, which could be very useful for decision-makers and professionals in financial institutions, such as portfolio managers and traders in commercial banks, investment banks and mutual funds. This paper helps investors to select better investment tools and avoid repeating the expensive errors that occur due to recognition-based heuristic-driven biases.

Originality/value

The current study is the first to focus on links recognition-based heuristic-driven biases, fundamental and technical anomalies, investment decision-making and performance of individual investors. This article enhanced the understanding of the role that recognition-based heuristic-driven biases plays in investment management. More importantly, the study went some way toward enhancing understanding of behavioral aspects and the aspects' influence on investment decision-making and performance in an emerging market.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

Abstract

Details

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

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).

Details

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

Keywords

Article
Publication date: 4 November 2021

Md. Bokhtiar Hasan, M. Kabir Hassan, Md. Mamunur Rashid, Md. Sumon Ali and Md. Naiem Hossain

In this study, the authors evaluate seven calendar anomalies’–the day of the week, weekend, the month of the year, January, the turn of the month (TOM), Ramadan and Eid…

Abstract

Purpose

In this study, the authors evaluate seven calendar anomalies’–the day of the week, weekend, the month of the year, January, the turn of the month (TOM), Ramadan and Eid festivals–effects in both the conventional and Islamic stock indices of Bangladesh. Also, the authors examine whether these anomalies differ between the two indices.

Design/methodology/approach

The authors select the Dhaka Stock Exchange (DSE) Broad Index (DSEX) and the DSEX Shariah Index (DSES) of the DSE as representatives of the conventional and Islamic stock indices respectively. To carry out the investigation, the authors employ the generalized autoregressive conditional heteroskedasticity (GARCH) typed models from January 25, 2011, to March 25, 2020.

Findings

The study’s results indicate the presence of all these calendar anomalies in either conventional or Islamic indices or both, except for the Ramadan effect. Some significant differences in the anomalies between the two indices (excluding the Ramadan effect) are detected in both return and volatility, with the differences being somewhat more pronounced in volatility. The existence of these calendar anomalies argues against the efficient market hypothesis of the stock markets of Bangladesh.

Practical implications

The study’s results can benefit investors and portfolio managers to comprehend different market anomalies and make investment strategies to beat the market for abnormal gains. Foreign investors can also be benefited from cross-border diversifications with DSE.

Originality/value

To the authors’ knowledge, first the calendar anomalies in the context of both conventional and Islamic stock indices for comparison purposes are evaluated, which is the novel contribution of this study. Unlike previous studies, the authors have explored seven calendar anomalies in the Bangladesh stock market's context with different indices and data sets. Importantly, no study in Bangladesh has analyzed calendar anomalies as comprehensively as the authors’.

Details

Managerial Finance, vol. 48 no. 2
Type: Research Article
ISSN: 0307-4358

Keywords

Article
Publication date: 24 December 2021

Neetika Jain and Sangeeta Mittal

A cost-effective way to achieve fuel economy is to reinforce positive driving behaviour. Driving behaviour can be controlled if drivers can be alerted for behaviour that…

Abstract

Purpose

A cost-effective way to achieve fuel economy is to reinforce positive driving behaviour. Driving behaviour can be controlled if drivers can be alerted for behaviour that results in poor fuel economy. Fuel consumption must be tracked and monitored instantaneously rather than tracking average fuel economy for the entire trip duration. A single-step application of machine learning (ML) is not sufficient to model prediction of instantaneous fuel consumption and detection of anomalous fuel economy. The study designs an ML pipeline to track and monitor instantaneous fuel economy and detect anomalies.

Design/methodology/approach

This research iteratively applies different variations of a two-step ML pipeline to the driving dataset for hatchback cars. The first step addresses the problem of accurate measurement and prediction of fuel economy using time series driving data, and the second step detects abnormal fuel economy in relation to contextual information. Long short-term memory autoencoder method learns and uses the most salient features of time series data to build a regression model. The contextual anomaly is detected by following two approaches, kernel quantile estimator and one-class support vector machine. The kernel quantile estimator sets dynamic threshold for detecting anomalous behaviour. Any error beyond a threshold is classified as an anomaly. The one-class support vector machine learns training error pattern and applies the model to test data for anomaly detection. The two-step ML pipeline is further modified by replacing long short term memory autoencoder with gated recurrent network autoencoder, and the performance of both models is compared. The speed recommendations and feedback are issued to the driver based on detected anomalies for controlling aggressive behaviour.

Findings

A composite long short-term memory autoencoder was compared with gated recurrent unit autoencoder. Both models achieve prediction accuracy within a range of 98%–100% for prediction as a first step. Recall and accuracy metrics for anomaly detection using kernel quantile estimator remains within 98%–100%, whereas the one-class support vector machine approach performs within the range of 99.3%–100%.

Research limitations/implications

The proposed approach does not consider socio-demographics or physiological information of drivers due to privacy concerns. However, it can be extended to correlate driver's physiological state such as fatigue, sleep and stress to correlate with driving behaviour and fuel economy. The anomaly detection approach here is limited to providing feedback to driver, it can be extended to give contextual feedback to the steering controller or throttle controller. In the future, a controller-based system can be associated with an anomaly detection approach to control the acceleration and braking action of the driver.

Practical implications

The suggested approach is helpful in monitoring and reinforcing fuel-economical driving behaviour among fleet drivers as per different environmental contexts. It can also be used as a training tool for improving driving efficiency for new drivers. It keeps drivers engaged positively by issuing a relevant warning for significant contextual anomalies and avoids issuing a warning for minor operational errors.

Originality/value

This paper contributes to the existing literature by providing an ML pipeline approach to track and monitor instantaneous fuel economy rather than relying on average fuel economy values. The approach is further extended to detect contextual driving behaviour anomalies and optimises fuel economy. The main contributions for this approach are as follows: (1) a prediction model is applied to fine-grained time series driving data to predict instantaneous fuel consumption. (2) Anomalous fuel economy is detected by comparing prediction error against a threshold and analysing error patterns based on contextual information.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 15 no. 4
Type: Research Article
ISSN: 1756-378X

Keywords

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.

Details

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

Keywords

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…

1526

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.

Details

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

Keywords

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

Keywords

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

Keywords

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