SPA Test Research Articles

Comparative Study of Various Diagnostic Methods for Detection of Mycoplasma gallisepticum and Mycoplasma synoviae in Egyptian Chicken Flocks

The significance of Mycoplasma gallisepticum (MG) and Mycoplasma synoviae (MS) in the poultry industry underscores the critical importance of diagnosing avian mycoplasmosis within the field of veterinary medicine. The present study aimed to compare various diagnostic methods for detecting MG and MS in Egyptian Chicken Flocks. A total of 360 samples were collected from breeder, layer, and broiler chickens from four governorates in Egypt. Conventional isolation methods and polymerase chain reaction (PCR) were used for the direct detection of MG and MS, while serum plate agglutination test (SPA) and Enzyme-linked immunosorbent assay (ELISA) were used for detecting antibodies against MG and MS. The highest detection rate of MG was found in commercial layers, followed by breeders, and broilers. Regarding MS, the highest detection rate was found in breeders, followed by commercial layers, and broilers. By comparing the used diagnostic methods, MG and MS were determined by the SPA test (40% and 31.1% respectively), ELISA test (31.7% and 23.6%), PCR (16.7% and 11.7%), and by the conventional culture method (10.8% and 3.9%). It could be concluded that the serological methods and PCR gave better sensitivity than culture methods and can be used in the diagnosis of avian mycoplasmosis.

Forced distance learning at a Russian technical university: A comparative analysis of key challenges assessment

Introduction. On the example of a technical university, the article presents a comparative analysis of assessments of key difficulties identified among the faculty during the transition to distance learning in the spring of 2020 and 2022 throughout the COVID-19 pandemic. The authors identified trigger challenges, which do not lose relevance in the context of the recurrence of a pandemic (and not only) and a person’s ability to adapt to the situation. Generalised recommendations are proposed to compensate for the influence of socio-psychological and communicative barriers to improve the quality of teaching activities of the faculty mediated by information and communication technologies.Aim. The presents paper aims at conducting a comparative analysis to assess key challenges, which are determined by the human factor and affect the implementation of the educational process by the lecturers during distant work with students.Methodology and research methods. The analysis was carried out during spring semesters 2020 and 2022 amid the realisation of forced distance learning. The authors used the methods of scientific and theoretical analysis, synthesis; empirical collection of a dataset of 2 cross-sectional studies using a questionnaire, the C. Rogers & R. Diamond SPA test, and the social stratification level test. Two respondent pools included 307 lecturers from Tomsk Polytechnic University. The results were processed by comparative analysis and using the SPSS 26.0 statistical package program for social sciences.Results. The comparative analysis of the results in two pools showed that the key challenges were: 1) socio-psychological challenges associated with frustration, the challenge for adapting to the remote implementation of the educational process due to the certain disorientation and unprepared teachers; 2) communicative barriers related to the complexity of keeping students’ attention and reducing the quality of materials, the necessity for lecturers to have special communicative skills that meet the conditions of remote work (linguistic, subject, discursive, pragmatic, socio-cultural, technical), and supraprofessional competencies necessary when working in conditions different from traditional methods of teaching. It is concluded that the prerequisites for successful distance learning are determined by the presence of digital educational platforms and hardware resources for their implementation, and by the socio-psychological training of the faculty. In this regard, the authors made the recommendations on the organisation of consistent systematic work by the university personnel service in terms of organising teacher training in the specifics to work in a remote format from the point of view of not only technological (acquisition of skills to develop electronic courses, introduce types of tasks focused on interactive, group collaboration, gamification elements, fractional materials and their visualisation, etc.), but also the psychological acceptance of the situation, the development of emotional competence, adaptability, socio-psychological stability, as well as the improvement of communicative competence that meets today’s challenges.Scientific novelty and practical significance. The comparative analysis in dynamics revealed that the socio-psychological and communication barriers do not lose significance and influence on the lecturers, despite the repeatability of situations associated with a sharp transition in the implementation of the educational process from the traditional to the remote format. There is, therefore, a need for comprehensive work of the university HR service to compensate for the identified difficulties and build a trajectory for the consistent preparation of the faculty for the likely repetition of the situation with the forced distance learning, since the risk of its repetition due to a pandemic, a change in geopolitical or other conditions, is high enough. The implementation of the proposed recommendations for building a vector of readiness for remote work will reduce the negative socio-psychological impact of stressors and improve the quality of the educational process.

IRRATIONAL BELIEFS AS A FACTOR OF PROFESSIONAL DISADAPTATION OF MEDICAL WORKERS

Relevance: The WHO Bulletin (2000) indicated that between one and five people in the working population have some form of mental health problem at any given time (1). This figure is on the rise and has been proven to affect their employability, work efficiency and quality of life. These problems are also common among healthcare workers, with more than 20% of healthcare workers worldwide experiencing mental health problems (2-4). Irrational and rational cognitions/beliefs are evaluative cognitive attitudes (structures) consistently associated with distress and psychopathology; at the same time, rational thinking is a determining factor for emotional stability. Goals of the study: 1. To conduct a comparative analysis of irrational attitudes in medical residents as a factor of professional maladaptation at the stage of clinical residency. 2. To formulate recommendations for its timely correction. Research Methodology: The methodology for diagnosing irrational attitudes (Survey of Personal Beliefs, SPB) was developed on the basis of the theory of rational-emotive therapy (RET) by Albert Ellis. The following authors’ methods were used: 1) Diagnosis of irrational attitudes - the Survey of Personal Beliefs (SPB) by H. Kassinove; 2) Questionnaire of socio-psychological adaptation, SPA (Test of Personal Adjustment) by K. Rogers, R. Diamond. Results: It was revealed that the majority of medical residents had a high value of irrational attitudes on the scales "Obligation towards oneself" and "Catastrophization". The results of the empirical study revealed significant differences between the two groups of residents using the Statistics 21.0 application package and the Mann-Whitney univariate statistical test. Conclusions and Recommendations: Residents at the stage of training in clinical residency use two irrational (erroneous) attitudes as Obligation to oneself and Catastrophization, which interfere with adequate cognitive processing, and also serve as an additional source of stress for medical workers. The schemes of psychological support of the residents were developed in order to identify irrational beliefs and timely correct them using psychological techniques (cognitive-behavioral therapy techniques, rational-emotive therapy) at the stage of training in clinical residency.

Relationship between neuropsychological test scores and hippocampal atrophy in non-demented Japanese older adults

BackgroundIt is essential to detect morphological changes in the brain prior to the onset of cognitive decline. Although hippocampal volumes have been reported to be correlated with scores on neuropsychological tests, this relationship is still unclear among Japanese non-demented older adults. Thus, this exploratory retrospective study aimed to identify neuropsychological tests that correlate with the degree of hippocampal atrophy in Japanese non-demented older adults. MethodsThirteen non-demented Japanese older adults aged 71.2 ± 5.2 years were included. Hippocampal atrophy was evaluated by assessing the participants’ MRI scans using the voxel-based specific regional analysis system for Alzheimer’s disease (VSRAD) software. Spearman’s partial correlation analyses were conducted to reveal relationships between VSRAD scores and neuropsychological test scores, including Raven’s Colored Progressive Matrices test (RCPM), the Standard Verbal Paired Associate Learning test (SP-A), and the Trail Making Test (TMT) scores by controlling for age, sex, and years of education. ResultsThe Spearman’s partial correlation analysis results revealed that there were significant correlations between the degree of hippocampal atrophy and RCPM (rho = -0.691, p = 0.027), TMT part B (rho = 0.823, p = 0.003), and the related SP-A test scores (rho = -0.663, p = 0.027). ConclusionsOur results indicate that neuropsychological tests, particularly the TMT part B, might be used in assessing the degree of hippocampal atrophy in non-demented Japanese elderly individuals and should be studied further in the future to develop screening methods to detect brain atrophy.

An innovative ensemble learning air pollution early-warning system for China based on incremental extreme learning machine

Air pollution has lots of adverse effects on industrial production and public life. Thus, it is an urgent task to construct an efficient air quality early-warning system to guide public life and production. This paper proposes an innovative air pollution early-warning system, including four main modules: clustering, preprocessing, forecasting and evaluation. In the clustering module, with the aim of building an efficient air pollution warning system, the air pollution situation of 31 provincial capitals is clustered and the study areas of the current study are selected based on the clustering result. A new data preprocessing algorithm is conducted to excavate the potential characteristics of the raw time series in the first place in the preprocessing module. Then, the length-changeable incremental extreme learning machine is used to forecast each component. In the evaluation module, the air quality is qualitatively analyzed by the fuzzy evaluation method. Moreover, the DM test and the SPA test are employed to test the accuracy of the forecasting model. The experimental results of eighteen data sets from three cities show that the hybrid air quality early-warning system establish in the study not only has higher accuracy and generalization ability than other benchmark models, but can provide sufficient air quality information, which is essential to control air pollution.

Forecasting price of financial market crash via a new nonlinear potential GARCH model

Financial market crash is one of the most extreme manifestations of financial market instability. It is of great significance to study its underlying structure and its forecasting methods. From the perspective of Econophysics, in addition to the influence of factors such as fluctuation on the financial price crash, we consider that the financial price crash originates from the change of the underlying structure described by the nonlinear potential function in the financial system. So we first propose a new nonlinear potential GARCH model by introducing nonlinear potential function and combining with GARCH model. In order to only analyze the effects of potential functions and eliminate the effects of fluctuations on prices, we combine the realized volatility and propose the likelihood function estimations of the proposed model and the benchmark GARCH and ARCH models. Then, based on the 5-minute high-frequency data of CSI300 and WTI crude oil, the out of sample forecasting with rolling time window and the SPA test method with Bootstrap characteristics are calculated. Finally, Akaike information criterion and Bayesian information criterion are employed to test the Goodness of fit of three models for in-sample and out-of-sample. The dynamic forecasting performance of the proposed new nonlinear potential GARCH model is compared with that of the benchmark GARCH and ARCH models. The main empirical results show that the proposed new nonlinear potential GARCH model is better than the benchmark GARCH and ARCH models for forecasting the returns and prices in the financial price crash for the given data. In addition, the potential well of the underlying nonlinear potential function changes obviously in the financial price crash.

Investing in the S&P 500 index: Can anything beat the buy‐and‐hold strategy?

AbstractDetermining whether investment strategies exist that provide higher (risk‐adjusted) returns than buying and holding the S&P 500 stock market index is not only highly relevant for finance theory, but also for the asset management industry. This study conducts a comprehensive test using realistic investment strategies based on monthly seasonalities, technical indicators, and fundamental factors (over 4,100 strategies in total). To assess statistical significance, we use Hansen's data‐snooping‐resistant SPA test. The results show that only investment strategies trying to exploit underreaction and overreaction effects with technical indicators dominate the buy‐and‐hold strategy in some simulation setups. These investment strategies are clearly superior to the strategies based on seasonalities and fundamental factors. Given that underreaction and overreaction effects are mainly justified with cognitive biases, our results support the economic relevance of behavioral finance insights.

Forecasting Realized Volatility in Financial Markets Based on a Time-Varying Non-Parametric Model

This paper proposes a new non-parametric adaptive combination model for the prediction of realized volatility on the basis of applying and extending the time-varying probability density function theory. We initially construct an adaptive time-varying weight mechanism for a combination forecast. To compare the predictive power of the models, we take the SPA test, which uses bootstrap as the evaluation criterion and employs the rolling window strategy for out-of-sample forecasting. The empirical study shows that the non-parametric TVF model forecasts more accurately than the HAR-RV model. In addition, the average combination forecast model does not have a significant advantage over any single model while our adaptive combination model does.

Investing In The S&P 500 Index: Can Anything Beat The Buy-And-Hold Strategy?

Determining whether investment strategies exist that provide higher (risk-adjusted) returns than buying and holding the S&P 500 stock market index is not only highly relevant for finance theory, but also for the asset management industry. This study conducts a comprehensive test using realistic investment strategies based on monthly seasonalities, technical indicators, and fundamental factors (over 4,100 strategies in total). To assess statistical significance, we use Hansen’s (2005) data-snooping-resistant SPA test. The results show that only investment strategies trying to exploit underreaction and overreaction effects with technical indicators dominate the buy-and-hold strategy in some simulation setups. These investment strategies are clearly superior to the strategies based on seasonalities and fundamental factors. Given that underreaction and overreaction effects are mainly justified with cognitive biases, our results support the economic relevance of behavioral finance insights.

Profitability of simple stationary technical trading rules with high-frequency data of Chinese Index Futures

Technical trading rules have been widely used by practitioners in financial markets for a long time. The profitability remains controversial and few consider the stationarity of technical indicators used in trading rules. We convert MA, KDJ and Bollinger bands into stationary processes and investigate the profitability of these trading rules by using 3 high-frequency data(15s,30s and 60s) of CSI300 Stock Index Futures from January 4th 2012 to December 31st 2016. Several performance and risk measures are adopted to assess the practical value of all trading rules directly while ADF-test is used to verify the stationarity and SPA test to check whether trading rules perform well due to intrinsic superiority or pure luck. The results show that there are several significant combinations of parameters for each indicator when transaction costs are not taken into consideration. Once transaction costs are included, trading profits will be eliminated completely. We also propose a method to reduce the risk of technical trading rules.

Binomial Markov-Switching Multifractal model with Skewed t innovations and applications to Chinese SSEC Index

Abstract This paper presents the Binomial Markov-switching Multifractal (BMSM) model of asset returns with Skewed t innovations (BMSM-Skewed t for short), which considers the fat tails, skewness and multifractality in asset returns simultaneously. The parameters of BMSM-Skewed t model can be estimated by Maximum Likelihood (ML) methods, and volatility forecasting can be accomplished via Bayesian updating. In order to evaluate the performance of BMSM-Skewed t model, BMSM model with Normal innovations (BMSM-N), BMSM model with Student-t innovations (BMSM-t) and GARCH(1,1) models (GARCH-N, GARCH-t and GARCH-Skewed t) are chosen for comparison. Through empirical studies on Shanghai Stock Exchange Composite Index (SSEC), we find that for sample estimation, BMSM models outperform the GARCH(1,1) models through BIC and AIC rules, and BMSM-Skewed t performs the best among all the models due to its fat tails, skewness and multifractality. In addition, BMSM-Skewed t model dominates other models at most forecasting horizons for out-of-sample volatility forecasts in terms of MSE, MAE and SPA test.

Trading strategy based on dynamic mode decomposition: Tested in Chinese stock market

Abstract Dynamic mode decomposition (DMD) is an effective method to capture the intrinsic dynamical modes of complex system. In this work, we adopt DMD method to discover the evolutionary patterns in stock market and apply it to Chinese A-share stock market. We design two strategies based on DMD algorithm. The strategy which considers only timing problem can make reliable profits in a choppy market with no prominent trend while fails to beat the benchmark moving-average strategy in bull market. After considering the spatial information from spatial–temporal coherent structure of DMD modes, we improved the trading strategy remarkably. Then the DMD strategies profitability is quantitatively evaluated by performing SPA test to correct the data-snooping effect. The results further prove that DMD algorithm can model the market patterns well in sideways market.

Evaluation of glycerin as preserving agent of chicken serum for plate agglutination test

Serum is widely used for the purpose of monitoring and diagnosis support for most of poultry diseases. In the case of the serum plate agglutination test (SPA), commonly used to detect antibodies for Salmonella Pullorum (SP), Mycoplasma gallisepticum (MG) and Mycoplasma synoviae (MS), serum cannot be frozen because it may result in false positive. Without freezing, serum can last only for a few days. In this experiment, glycerin was evaluated as a serum preservering agent. About 50 samples for each disease and analyzed by SPA test previously were separated. Glycerin was added to serum from commercial chickens, with and without antibodies for SP, MG and MS, in the proportion of 1:1 (serum:glycerin) and kept at refrigerated conditions (2 to 8 ºC). For four years they were tested by the SPA, initially weekly, afterward monthly and then annually. The results show that serum with glycerin give consistent and valid results according to the kind of antibodies present for the period tested. Sera that glycerin was not added to, the results were valid only for the first week. From the second week on, microbial growth affected the test results of the sera without glycerin. Our investigation shows that glycerin can be used to preserve chicken serum for SPA under refrigerated conditions. It is an easy, simple and cheap procedure that can extend serum shelf life, useful mainly for control sera.

Which is the better forecasting model? A comparison between HAR-RV and multifractality volatility

In this paper, by taking the 5-min high frequency data of the Shanghai Composite Index as example, we compare the forecasting performance of HAR-RV and Multifractal volatility, Realized volatility, Realized Bipower Variation and their corresponding short memory model with rolling windows forecasting method and the Model Confidence Set which is proved superior to SPA test. The empirical results show that, for six loss functions, HAR-RV outperforms other models. Moreover, to make the conclusions more precise and robust, we use the MCS test to compare the performance of their logarithms form models, and find that the HAR-log(RV) has a better performance in predicting future volatility. Furthermore, by comparing the two models of HAR-RV and HAR-log(RV), we conclude that, in terms of performance forecasting, the HAR-log(RV) model is the best model among models we have discussed in this paper.

Forecasting Return Volatility of the CSI 300 Index Using the Stochastic Volatility Model with Continuous Volatility and Jumps

The logarithmic realized volatility is divided into the logarithmic continuous sample path variation and the logarithmic discontinuous jump variation on the basis of the SV-RV model in this paper, which constructs the stochastic volatility model with continuous volatility (SV-CJ model). Then, we use high-frequency transaction data for five minutes of the CSI 300 stock index as the study sample, which, respectively, make parameter estimation on the SV, SV-RV, and SV-CJ model. We also comparatively analyze these three models' prediction accuracy by using the loss functions and SPA test. The results indicate that the prior logarithmic realized volatility and the logarithmic continuous sample path variation can be used to predict the future return volatility in China's stock market, while the logarithmic discontinuous jump variation is poor at its prediction accuracy. Besides, the SV-CJ model has an obvious advantage over the SV and SV-RV model as to the prediction accuracy of the return volatility, and it is more suitable for the research concerning the problems of financial practice such as the financial risk management.

INFLATIONARY EXPECTATIVES: PREDICTING, PERSISTENCE, AND DISAGREEMENT

Because the expectations of inflation are considered a key-variable in the specification of macroeconomic models and due to the current discussion in literature on the disagreement concerning expectations, the present work aimed to study the divergence problem, starting from the statistical verification of the superior predicting ability of institutions that take part in the research (Focus- Market Report) of the Gerin/Bacen. The application of the SPA test, in short and long term analyses, suggested strength in the disagreement in the expectations of inflation, in which some models/institutions exert superior predicting ability. It is also possible to observe the presence of some degree of inertia in the dynamics of the expectations, in which the measures of persistence suggest sharp shock effects that diminish as time passes, in most of the cases analyzed.

Minimum variance hedging with bivariate regime-switching model for WTI crude oil

This paper proposes a four-regime bivariate Markov regime-switching model to estimate the daily time-varying minimum variance hedge ratios for West Texas Intermediate (WTI) crude oil, and evaluates its in- and out-of-sample hedging performances with two-regime model, CC-GARCH, TVC-GARCH, and OLS models. Empirical results reveal that the four-regime Markov switching model outperforms the other models for both in- and out-of-sample hedging performance. Based on Hansen’s SPA test (2005), the four-regime model significantly outperforms the other models for only in-sample hedging.

Data Snooping and Market-Timing Rule Performance

We reassess the performance of market-timing rules when controlling for data-snooping biases. For the first time, a comprehensive set of simple and complex market-timing rules is examined and tested for statistical significance, using the White (2000) “Reality Check,” the Hansen (2005) SPA test, as well as their stepwise extensions by Romano and Wolf (2005) and Hsu, Hsu, and Kuan (2010). Even though individual market-timing rules significantly outperform a buy-and-hold strategy at both daily and monthly frequencies when considered in isolation, their outperformance, generally, does not remain significant after correcting for data snooping. Relative to the alternative of investing in the risk-free rate, however, we find significant outperformance of the best rules, even after data-snooping adjustment, when testing at a monthly timing frequency. (JEL: G11, G14)

Profitability of technical analysis in financial and commodity futures markets — A reality check

Based on the SPA test (test for superior predictive ability), Sortino and reversed Sortino ratios, we examined the profitability of a universe of 8061 technical trading rules in ten futures markets including five financial and five commodity underlying assets. We tested whether the best performing rule really beats its buy-and-hold benchmark strategy in bullish and bearish markets, respectively, during the in-sample testing period. The best rules' performance relative to the benchmark is also tested during the one-year out-of-sample period for all ten sets of data. A novel set of multi-indicator rules, MFI–RSI, and four popular categories of single-indicator rules, filter rules, moving averages, on-balance volume averages and momentum strategy in volume, were employed to form our universe of trading rules. The results on the SPA test suggest market efficiency in nine of the ten futures markets, while the results on the Sortino and reversed Sortino ratios reveal persistent outperformance of the best ‘downside’ and ‘upside’ rules relative to the buy-and-hold benchmark across time in four and three futures markets, respectively.

Testing the predictive ability of technical analysis using a new stepwise test without data snooping bias

Abstract In the finance literature, statistical inferences for large-scale testing problems usually suffer from data snooping bias. In this paper we extend the “superior predictive ability” (SPA) test of Hansen (2005, JBES ) to a stepwise SPA test that can identify predictive models without potential data snooping bias. It is shown analytically and by simulations that the stepwise SPA test is more powerful than the stepwise Reality Check test of Romano and Wolf (2005, Econometrica ). We then apply the proposed test to examine the predictive ability of technical trading rules based on the data of growth and emerging market indices and their exchange traded funds (ETFs). It is found that technical trading rules have significant predictive power for these markets, yet such evidence weakens after the ETFs are introduced.