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This research aims to examine the time-varying behavior of the Weekend, Turn-of-the-Month, January, and Halloween effects in eight foreign exchange rates against the U.S. dollar from the Adaptive Market Hypothesis (AMH) perspective. It also explores whether these anomalies can generate excess returns compared to a buy-and-hold strategy.

Using daily return data from January 2004 to December 2023 in a rolling-window framework, the study employs the Concordance Coefficient test and AR-GARCH models to assess the time-varying behavior of four calendar anomalies. It also assesses the statistical significance of the trading strategies implied by these anomalies using t-tests and applies F-tests for subperiod analysis.

The results reveal a generalized time-varying presence of calendar anomalies in emerging currencies and, to a lesser extent, developed currencies. However, the trading strategies implied by these anomalies generally did not show statistical significance, except for the Turn-of-the-Month effect, which exhibited statistically significant unprofitability.

The study pioneers an analysis of five calendar anomalies across various currencies from the standpoint of the AMH and proposes case-specific explanations for their occurrence. It also examines the potential for the anomalies’ implied trading strategies to generate excess returns compared to a straightforward buy-and-hold strategy. Additionally, the study introduces the recently developed Concordance Coefficient test as a valuable alternative to other non-parametric methods.

The anomaly detection task for oil and gas pipelines based on acoustic signals faces issues such as background noise coverage, lack of effective features, and small sample sizes, resulting in low fault identification accuracy and slow efficiency. The purpose of this paper is to study an accurate and efficient method of pipeline anomaly detection.

First, to address the impact of background noise on the accuracy of anomaly signals, the adaptive multi-threshold center frequency variational mode decomposition method(AMTCF-VMD) method is used to eliminate strong noise in pipeline signals. Secondly, to address the strong data dependency and loss of local features in the Swin Transformer network, a Hybrid Pyramid ConvNet network with an Agent Attention mechanism is proposed. This compensates for the limitations of CNN’s receptive field and enhances the Swin Transformer’s global contextual feature representation capabilities. Thirdly, to address the sparsity and imbalance of anomaly samples, the SpecAugment and Scaper methods are integrated to enhance the model’s generalization ability.

In the pipeline anomaly audio and environmental datasets such as ESC-50, the AMTCF-VMD method shows more significant denoising effects compared to wavelet packet decomposition and EMD methods. Additionally, the model achieved 98.7% accuracy on the preprocessed anomaly audio dataset and 99.0% on the ESC-50 dataset.

This paper innovatively proposes and combines the AMTCF-VMD preprocessing method with the Agent-SwinPyramidNet model, addressing noise interference and low accuracy issues in pipeline anomaly detection, and providing strong support for oil and gas pipeline anomaly recognition tasks in high-noise environments.

Powder bed additive manufacturing has recently seen substantial growth, yet consistently producing high-quality parts remains challenging. Recoating streaking is a common anomaly that impairs print quality. Several data-driven models for automatically detecting this anomaly have been proposed, each with varying effectiveness. However, comprehensive comparisons among them are lacking. Additionally, these models are often tailored to specific data sets. This research addresses this gap by implementing and comparing these anomaly detection models for recoating streaking in a reproducible way. This study aims to offer a clearer, more objective evaluation of their performance, strengths and weaknesses. Furthermore, this study proposes an improvement to the Line Profiles detection model to broaden its applicability, and a novel preprocessing step was introduced to enhance the models’ performances.

All found anomaly detection models have been implemented along with several preprocessing steps. Additionally, a new universal benchmarking data set has been constructed. Finally, all implemented models have been evaluated on this benchmarking data set and the effect of the different preprocessing steps was studied.

This comparison shows that the improved Line Profiles model established it as the most efficient detection approach in this study’s benchmark data set. Furthermore, while most state-of-the-art neural networks perform very well off the shelf, this comparison shows that specialised detection models outperform all others with the correct preprocessing.

This comparison gives new insights into different recoater streaking (RCS) detection models, showcasing each one with its strengths and weaknesses. Furthermore, the improved Line Profiles model delivers compelling performance in detecting RCS.

Investigation of the anomalies associated with crashes and jackpots in the Chinese stock market.

We propose a logit model to predict the events of crashes and jackpots in the Chinese stock market. The model introduces a new variable of the price-to-sales ratio and takes into account the market states, Up and Down.

The anomalies associated with crashes and jackpots are not related to variations in economic conditions, but are associated with limits to arbitrage. High-liquidity stocks have strong mispricing effects. The institutions’ speculative trading will push liquid stock prices further away from their fundamentals but avoid buying illiquid stocks with a higher probability of price crashes and jackpots.

We propose a logit model to predict the extreme events of both crash and jackpot in the Chinese stock market. Our model effectively disentangles from CRASHP and JACKP. Compared with the traditional model, it substantially enhances in-sample and out-sample predictions. Based on the predictions of the extreme events, we find two strong and robust pricing effects associated with ex ante CRASHP and JACKP in the Chinese stock market.

We aim to examine the impact of COVID-19 on the efficiency of Gold and Bitcoin returns. In particular, our efficiency tests are based on the popular calendar anomaly, the turn-of-the-month (TOM) effect in these markets.

We define the TOM days as the final trading day of a month and initial three trading days of the immediate next month. To understand the TOM effect, we estimate the typical Ordinary Least Squares (OLS) regression model using the Heteroskedasticity and Autocorrelation Consistent (HAC) standard errors and covariances.

Though in the full sample, a positive and significant TOM effect is observed only for Bitcoin, during COVID period, the TOM effect appears in Gold returns and becomes stronger for Bitcoin, implying that the considered securities become inefficient during COVID period.

Based on these results, we create a trading strategy which is found to surpass the buy-and-hold strategy for both the full sample as well as the COVID period for Bitcoin while only during the COVID period for Gold. Our results provide useful implications for investors and policymakers as the Gold and Bitcoin markets can be timed by taking positions especially based on the behavior of the TOM effect.

We examine the TOM effect in the two important securities – Gold and Bitcoin. Though, a few studies have examined this anomaly in currency, equity and cryptocurrency markets, however, they have not considered the Gold market. Additionally, no study has examined the impact of COVID-19 on the TOM effect in these markets, and hence, market efficiency. We believe that our study is the first to examine the TOM effect in these markets simultaneously.

The authors evaluate the Sell in May effect in the Canadian context to comprehensively explore the Sell in May effect as well as its interactions with the size effect and risk and with multiple indices.

The authors use ordinary least squares (OLS) regressions to examine the Sell in May effect and Huber M-estimation to handle potential outliers. They also use the generalized autoregressive conditional heteroskedasticity (GARCH) models to explore the role of risk in the Sell in May effect.

The results demonstrate that the Sell in May effect is present in all three main Canadian stock market indices. More telling, the anomaly is strongest in small cap indices and in indices that give equal weighting to small and large cap stocks. They do not find that the effect is driven by risk.

While several papers have explored the Sell in May phenomenon in several countries, little scholarly attention has been paid to this effect in Canada and to its interaction with the size effect. The authors contribute to the literature by examining of the interactions between Sell in May and the size effect in Canada. They examine the Sell in May effect using CFMRC value-weighted and equally weighted indices of all Canadian companies. They also incorporate in their analysis the role of risk.

The purpose of this paper is to analyse five biases in the valuation of financial investments using a mental time travel framework involving thought investments – with no objective time passing.

An investment’s initial value, together with any periodic funding cash-flows, are mentally projected forward (at an expected rate of return) to give the value at the investment horizon; and this projected value is mentally discounted back to the present. If there is a difference between the initial and present values, then this can imply a bias in valuation.

The study identifies (and gives examples of) five real-world valuation biases: biased funding cash-flow estimates (e.g., mega infrastructure projects); biased rate of return projections (e.g., market crises, tech stock carve-outs); biased discount rate estimates (e.g., dual-listed shares, dual-class shares, short-termism, time-risk misperception, and long-termism); time-duration misestimation or perception bias when projecting (e.g., time-contracted projections which lead to short-termism); and time-duration misestimation or perception bias when discounting (e.g., time-extended discounting which also leads to short-termism). More than one bias can be operating at the same time and we give an example of low levels of retirement savings being the result of the biased discounting of biased projections. Finally, we consider the effects of the different biases of different agents operating simultaneously.

The paper examines key systematic misestimation and psychological biases underlying financial investment valuation pricing anomalies.

This study aims to investigate the role of freight forwarding companies in detecting and reporting trade-based money laundering. The proximity of freight forwarding companies to shipping-related trade-based money laundering red flags places them in an ideal position to detect suspicious transactions.

The study used semi-structured interviews with expert participants in freight forwarding shipping and compliance aspects around freight forwarding. This study focuses on the South African context.

Freight forwarding companies are well-positioned to detect, investigate and report on trade-based money laundering schemes. However, the companies are not always aware of the guidelines designed to assist in identifying trade-based money laundering schemes. Thus, freight forwarding companies develop internal processes to identify trade anomalies but are often unable to link trade anomalies to illegal financial flows and trade-based money laundering schemes.

The current regulations on money laundering can be extended to freight forwarding companies by the respective regulators for enhanced anti-money laundering protection. This study is limited to freight forwarding companies in a South African context.

Increased awareness among staff in freight forwarding companies can assist them in identifying trade-based money laundering red flags to detect and prevent trade-based money laundering schemes.

This paper assists other role players and policymakers in the trade process to create a better awareness of trade-based money laundering. The limited obligations on freight forwarding companies to comply with anti-money laundering regulations lead to a more volunteer-like compliance practice.

To the best of the authors’ knowledge, this is the first paper that offers insight into the role of freight forwarding companies in detecting trade-based money laundering in South Africa.

The world of investing has changed drastically. Investors are willing to invest the companies that give high priority to environmental, social and governance issues (ESG). This study delves into the performance of the BSE CARBONEX index in comparison to the BSE 100, BSE Sensex, BSE Energy and BSE Oil & Gas. It seeks to examine the impact of calendar anomalies, particularly focusing on the day-of-the-week effect, on these indices. To accomplish this, daily closing prices of the BSE CARBONEX, BSE 100, BSE Sensex, BSE Energy and BSE Oil & Gas were gathered from the BSE official website. The study period was divided into three segments: the full period, period I (2017–2020) and period II (2020–2022). The study's findings reveal that throughout the full period, period I and period II, BSE Energy exhibited the highest mean daily return compared to the other selected indices. There appears to be a discernible Tuesday effect on the daily average mean returns of BSE CARBONEX, BSE 100, BSE Sensex, BSE Energy and BSE Oil & Gas in both the full sample period and period II. Results from ordinary least squares (OLS) analysis by day indicate a notably high positive and statistically significant daily return on Tuesdays, particularly during the full sample period and period II. Furthermore, the GARCH (1,1) model suggests a significant Tuesday effect on the BSE Energy and BSE Oil & Gas indices.

This paper analyzes alternative performance evaluation models applied to equity mutual funds under conditional and unconditional approaches in the Brazilian market.

The analysis is conducted using CAPM's single factor, Fama–French three and five factors, under their conditional and unconditional versions in a sample of 896 equity mutual funds from 2008 to 2019.

The results suggest that the use of three- or five-factor models is especially relevant to reduce the effect of market anomalies in performance assessment. Additionally, results show that conditional approaches, adding time-varying alphas and betas with macroeconomic variables, provide higher explanatory power than their unconditional peers.

The results are relevant in the unique economic environment characterized by historically high interest rate and high market volatility.