A comparative assessment of frequentist forecasting models: Evidence from the S&P 500 pharmaceuticals index

This paper compares three forecasting methods, the autoregressive integrated moving average (ARIMA), generalized autoregressive conditional heteroscedasticity (GARCH), and neural network autoregression (NNAR) methods, using the S&P 500 Pharmaceuticals Index. The objective is to identify the most accurate model based on the mean average forecasting error (MAFE). The results consistently show the NNAR model to outperform ARIMA and GARCH and to exhibit a significantly lower MAFE. The existing literature presents conflicting findings on forecasting model accuracy for stock indexes. While studies have explored various models, no universally applicable model exists. Therefore, a comparative analysis is crucial. The methodology includes data collection and cleaning, exploratory analysis, and model building. The daily closing prices of pharmaceutical stocks from the S&P 500 serve as the dataset. The exploratory analysis reveals an upward trend and increasing heteroscedasticity in the pharmaceuticals index, with the unit root tests confirming non-stationarity. To address this, the dataset has been transformed into stationary returns using logarithmic and differencing techniques. Model building involves splitting the dataset into training and test sets. The training set determines the best-fit models for each method. The models are then compared using MAFE on the test set, with the model possessing the lowest MAFE being considered the best. The findings provide insights into model accuracy for pharmaceutical industry indexes, aiding investor predictions, with the comparative analysis emphasizing tailored forecasting models for specific indexes and datasets

Is the effect of a health crisis symmetric for physical and digital financial assets? An assessment of gold and bitcoin during the pandemic

The emergence of the covid-19 health crisis, in this advanced technological era where connections between markets, nations, and economies have grown stronger than ever before, the shock of the COVID-19 pandemic quickly had an impact on both physical and digital financial assets. The Chinese financial market experienced the first consequences of the covid-19 pandemic, then spilled over to other financial markets, including those for cryptocurrencies and the precious metals. This study examines the impact of the covid-19 pandemic on the volatilities of the dynamics of bitcoin and gold. Both assets share some characteristics, such as online trading platforms, however, gold is a tangible financial asset unlike bitcoin, which is digitally generated without any physical form. This study argues that the similarities and differences between bitcoin and gold play major roles in how the covid19 crisis affected their respective dynamics. Using daily data ranging from 9/22/2014 to 1/ 31/2023 and employing ARMA as the mean equation for GARCH model, the impact of the health crisis (covid-19) is examined on the volatilities of the prices and volumes of bitcoin and gold. Empirical evidence points out that, the pandemic has a symmetric impact on the volatilities of bitcoin and gold price returns, causing them to be more volatile. The impact of the covid-19 observed on the volume returns of the assets, however, is asymmetrical. The empirical results give evidence to the role that the vital differences existing between these assets played during the covid-19 pandemic

Most stringent test of null of cointegration: A Monte Carlo comparison

To test for the existence of long run relationship, a variety of null of cointegration tests have been developed in literature. This study is aimed at comparing these tests on basis of size and power using stringency criterion: a robust technique for comparison of tests as it provides with a single number representing the maximum difference between a test’s power and maximum possible power in the entire parameter space. It is found that in general, asymptotic critical values tends to produce size distortion and size of test is controlled when simulated critical values are used. The simple LM test based on KPSS statistic is the most stringent test at all sample sizes for all three specifications of deterministic component, as it has the maximum difference approaching to zero and lesser than 20% for the entire parameter space

The impact of US sanctions on the Consumer Price Index (CPI) of Turkey

This paper addresses the assessment of effect of the sanctions imposed on Turkey by the United States of America in the year 2018 on the Consumer Price Index (CPI) of Turkey. The study used a cross sectional data from the 81 provinces in Turkey for the periods of 2016 to 2018 from Turkish Statistical Institute (TUIK). Dummy variable with Ordinary Least Squares (OLS) estimation method is used to determine that how the sanctions affected the CPI over that period by looking at the years before 2018, the year the sanctions were imposed

A W-Shaped Convolutional Network for Robust Crop and Weed Classification in Agriculture

Agricultural image and vision computing are significantly different from other object classification-based methods because two base classes in agriculture, crops and weeds, have many common traits. Efficient crop, weeds, and soil classification are required to perform autonomous (spraying, harvesting, etc.) activities in agricultural fields. In a three-class (crop-weed-background) agricultural classification scenario, it is usually easier to accurately classify the background class than the crop and weed classes because the background class appears significantly different feature-wise than the crop and weed classes. However, robustly distinguishing between the crop and weed classes is challenging because their appearance features generally look very similar. To address this problem, we propose a framework based on a convolutional W-shaped network with two encoder-decoder structures of different sizes. The first encoder-decoder structure differentiates between background and vegetation (crop and weed), and the second encoder-decoder structure learns discriminating features to classify crop and weed classes efficiently. The proposed W network is generalizable for different crop types. The effectiveness of the proposed network is demonstrated on two crop datasets – a tobacco dataset and a sesame dataset, both collected in this study and made available publicly online for use by the community – by evaluating and comparing the performance with existing related methods. The proposed method consistently outperforms existing related methods on both datasets

Film mulching counteracts the adverse effects of mild moisture deficiency, and improves the quality and yield of Cyperus esculentus. L grass and tuber in the oasis area of Tarim Basin

IntroductionPlastic film mulching (PFM) and deficit irrigation (DI) are vital water-saving approaches in arid agriculture. Cyperus esculentus is a significant crop in dry zones. However, scant data exists on the impacts of these water-saving methods on C. esculentus yield and quality.MethodUsing randomized block experiment design. Three irrigation strategies were tested: CK (standard irrigation), RW20 (20% water reduction), and RW40 (40% water reduction). Mulchin treatments included film mulching (FM) and no film mulching (NFM).ResultsResults revealed substantial effects of film mulching and drip irrigation on soil nutrients and physical properties, with minor influence on grass, root, and tuber stoichiometry. PF treatment, DI treatments, and their interaction significantly affected C. esculentus forage and tuber yields. Initially, grass and tuber yields increased and then decreased with reduced irrigation. The highest yields were under RW20 (3716.31 and 4758.19 kg/ha). FM increased grass and tuber yield by 17.99% and 8.46%, respectively, over NFM. The water reduction augmented the biomass distribuiton of the leaf and root, while reducing the tuber biomass in NFM. FM significantely impacted grass ether extract content, while reduced water influenced grass and tuber crude protein and tuber ether extract content. Mild water stress increased ether extract, crude protein, and soluble matter in grass and tubers, while excessive RW decreased them.ConclusionIntegrating soil traits, nutrients, yield, and quality, findings indicate C. esculentus yield and quality primarily hinge on soil water content, pond hydrogenase, and electrical conductivity. Based on this results, the recommended strategy is to reduce irrigation by 20% for cultivating C. esculentus in this area

N-Benzyl-N,4-dimethyl­benzene­sulfonamide

The mol­ecule of the title compound, C15H17NO2S, has a C—S—N—C torsion angle of 71.4 (2)°, and the dihedral angle between the benzene rings is 82.83 (16)°. In the crystal, mol­ecules are linked into chains along the b axis via C—H⋯O hydrogen bonds. A C—H⋯π inter­action is also present in the crystal structure