The job of forecasting the stock market returns in the emerging markets is challeng ing due to some peculiar characteristics of these markets. For years, conventional forecasting methods have been developed, but they have succeeded partially or have failed entirely to deal with the nonlinear and complex nature of stock re turns. Artificial Neural Networks approach is a relatively new and promising field of the prediction of stock returns. Neural networks approach is a mathematical model, flexible enough to accommo date both linear and non-linear aspect of stock returns and act like human brains to simulate the behavior of the stock prices. The literature review reveals that there are a large number of studies trying to forecast the stock market returns using conventional statistical techniques. However, there is a dearth of literature on the use of machine learning techniques in the area of asset pricing. The study is an attempt to fill this gap by addressing the major issue of using the asset pric ing models for prediction of portfolio returns in the presence of Artificial Neural Networks. We investigate the forecasting ability of single factor CAPM, Fama and French three factor and five factor model by using Artificial Neural Networks. This study employs the monthly returns of all the companies listed on Pakistan Stock Ex change for the period 2000-2015. Data on market capitalization, book-to-market ratio, total assets and operating profit is used to construct factors used in multi factor models. The factors of Size, value, investment, and profitability are con structed by following the industry standards. Thirty Portfolios are constructed by beta; resulting into high, medium and low beta portfolios based on monthly re turns. These factors are used as inputs and outputs in the neural network system. We construct an artificial neural networks system to predict portfolio returns in two stages; in stage one, the study identifies the best-fit combination of training, testing, and validation along with the number of neurons for the three asset pricing models for a full sample from 2000 to 2015. In stage two, the study uses this best combination to forecast the model under 48-month rolling window analysis and x evaluate its ability to forecast the stock returns in an emerging market. In-sample and out-sample comparisons, regression and goodness of fit test and actual and predicted values of the stock returns of the ANN model are conducted. A comprehensive methodology of the neural networks is applied to achieve the primary purpose of forecasting. The methodology includes the initial architecture consists of three layers, i.e., an input layer, hidden layer, and an output layer. The hidden layer utilizes 1-50 neurons for processing. The study uses varying param eters for an effective Artificial Neural Networks system. The study also employs rolling windows to calculate and compare forecasting error among competing as set pricing models by using 16 data combinations. The Artificial Neural Networks take the values of monthly returns of the first 48 months as a training set and predict the 49th value for the monthly returns. Mean Squared Error measures the performance of the Artificial Neural Networks. The significant findings of the study are: firstly, CAPM-based networks models have predicted 48%, while the Fama and French three factors and five factors models based networks returned 94% and 98% respectively of the time periods ac curately. Secondly, the number of the optimum number of neurons does not follow some mathematical rule instead it is based on the presentiment of the researcher to apply an exhaustive search for the number of optimum neurons. Thirdly the performance of the CAPM-based networks is the best at the 75-10-15 dataset and 16 neurons. The Fama and French three factors model generate the best results at 60-20-20 dataset and 27 neurons and the Fama and French five factors model return the best results at 28 neurons and 75-20-05 dataset. The magnification of the performance with the increase in the number of neurons is a useful heuristic for the future researchers. The fourth significant finding is that the difference of errors between the testing and training data set is minimum and the networks are not suffering from the over-fitting phenomenon. The fifth finding is that the predicted value of high beta portfolios is better than the low beta and mid beta portfolios. This finding reinforces the investment principle that the market compensates the high-risk portfolios more than other classes. The xi Fama and French five factors model show more promising results as compared to the other two models. The best results are converging at 75-20-05 Dataset at 28 neurons, and the success rate of accurate prediction is 98%. This implies that the addition of the investment and profitability factors demonstrate good predictive power in this market. Our findings reinforce the investment principle that the markets compensate the high-risk portfolios more than the other classes. The proposed prediction methodology will significantly improve the return on investment against the buy and holds strategy.The proposed model achieves a significant improvement in the return on investment, and the investors can magnify their profitability. Our methodology using ANN models,although, have accurately predicted the re turns, it remains open to more experimentation. At this point, given the ‘black box’ nature of the ANN, it is difficult to offer any explanation beyond the well known ability of the ANN to capture ‘hidden’ relationships between inputs and outputs. Future researchers should focus on clustering, classification, hybridiza tion of other nonlinear techniques with a neural network system. The portfolio selection can also be optimized using particle swarm optimization and other ar tificial intelligence techniques. We hope that future research in the fields of both asset pricing and artificial intelligence would be able to offer an opportunity for in terdisciplinary research and present more challenges to the established investment theories.
Psychological morbidity is emerging as animportant issue for medical students after entering clinical training due to extensiveworking hours. Students find themselves unable to balance competing demands on their time and cannot allocate appropriate time to family, spouses and even to themselves; this leads to complaints about exhaustion and lack of efficiency in their profession. Objective: To assess Psychological morbidity in interns, medical officers and residents Methods: a cross sectional study was conducted. A google form was designed with questions adopting from the three scales of the Maslach Burnout Inventory-General Survey (MBI-GS). Question from all three categories of MBI-GS such as emotional exhaustion, depersonalization and personal accomplishment were considered. The form was distributed via email and Whatsapp to 87 house officers, medical officers and residents of six public healthcare facilities in Lahore. Responses were captured from 73 participants who completed the General Health questionnaire. Seven of these forms were filled by nurses, so in the present study only the response ofthose 66 participants who conformed to the study cross-section were considered. The results were analyzedusing SPSS version 22 for descriptive statistics and chi-square association. Results: A significant association was found between long working hours and emotional exhaustion, depersonalization and personal accomplishment questions in particular. The findings suggests long working hours ruin the mental health of medical professionals, which ultimately produce psychological health impacts. Conclusions: It was concluded that these factors should be considered to reduce psychological morbidity in health professionals.
The Chemistry and biological studies of heterocyclic compounds has been important field for a long time in Medicinal Chemistry. It is a fundamental need for the development of new drugs having potent activities. The discovery of new drug candidates has been the burning issue of all the times owing to new emerging diseases. Synthetic and natural heterocyclic compounds are the subject of R & D units of many pharmacological, agrochemical and industrial laboratories. Around 90% of new medications contain heterocyclic moieties. The presented work is a contribution in the field of pharmaceutical industry regarding the discovery of new drug candidates. The amalgamation of two heterocyclic moieties i.e. 1,3-thiazole and 1,3,4-oxadiazole, were carried out in the designed molecules to impart them possible therapeutic properties. The new compounds have been synthesized by encompassing different bioactive moieties including 1,3-thiazole, 1,3,4-oxadiazole, alkyl halide, Acetamide and propanamide. The synthesized molecules have been subjected to evaluation of their antibacterial, enzyme inhibitionand hemolytic potential. Furthermore, enzyme inhibition potential results have been supported by computational docking in order to find the types of interactions with the active site of involved enzymes. Six (06) schemes have been used to demonstrate the synthesis of ninety (90) compounds. In Scheme-1, 1,3-thiazole-2-amine (1) wasstirred with 2-bromoacetyl bromide (2) in basic medium to yieldN-(1,3-thiazol-2-yl)-2-bromoacetamide (3) as an electrophiles. In a parallel reaction different 5-substituted-1,3,4-oxadiazol (7a-o) were synthesized from corresponding aryl carboxylic acids (4a-o) through esterification and hydrazide formation. The final compounds, 8a-o, were synthesized by stirring 7a-o and 3 in an aprotic polar solvent. In Scheme-2, the synthesis was initiated by the reaction of 4-methyl-1,3-thiazol-2-amine (9) with bromoacetyl bromide (2) in aqueous basic medium to obtain an electrophile,2-bromo-N-(4-methyl-1,3-thiazol-2-yl)acetamide (10). In parallel reactions, a series of carboxylic acids, 4a-o, was converted, through a sequence of three steps, into respective 1,3,4-oxadiazole heterocyclic cores, 7a-o, to utilize as nucleophiles. Finally, a series of compound, 11a-o, was synthesized by coupling 7a-o, individually, with 10 in an aprotic polar solvent. In Scheme-3, firstly, an electrophile, 2-bromo-N- XII (5-methyl-1,3-thiazol-2-yl)acetamide (13), was synthesized by the reaction of 5-methyl-1,3-thiazol-2-amine (12) and bromoacetyl bromide (2) in an aqueous medium. Then, the electrophile 13 was coupled with the aforementioned1,3,4-oxadiazoles (7a-o)to obtain the targeted bi-heterocycles (14a-o). In Scheme-4, the synthesis was initiated by the conversion of ethyl 2-(2-amino-1,3-thiazol-4-yl)acetate (15) to corresponding 2-(2-amino-1,3-thiazol-4-yl)acetohydrazide (16) by the reaction with hydrazine hydrate in methanol. The treatment of acid hydrazide, 16, with carbon disulfide gave a bi-heterocyclic 5-[(2-amino-1,3-thiazol-4-yl)methyl]-1,3,4-oxadiazole-2-thiol (17). The target compounds, 19a-o, were synthesized by stirring the parent 17 with different electrophiles, 18a-o, in DMF using LiH as weak base and activator. In Scheme-5, the synthesis of a new series of S-substituted derivatives, 23a-o, of 5-[(2-amino-1,3-thiazol-4-yl)methyl]-1,3,4-oxadiazol-2-thiol (17) were synthesized and evaluated for enzyme inhibition study along with cytotoxic behavior. Different electrophiles, 22a-o, was synthesized by the reaction of aniline (20a-o) and 2-bromoacetyl bromide (21) in an aqueous medium. The target compounds were synthesized by stirring 17 with different electrophiles, 22a-o, in DMF using LiH as weak base and activator.In Scheme-6, the synthesis of a novel series of bi-heterocycles, 26a-o, was accomplished by S-substitution of 5-(2-amino-1,3-thiazol-4-yl)methyl)-1,3,4-oxadiazol-2-thiol (17). A series of electrophiles, 25a-o, were synthesized by stirring primary amines (20a-o) with 3-bromopropanoyl chloride (24) in a basic aqueous medium. The target compounds, 26a-o, were synthesized by stirring 17 with synthesized electrophiles, 25a-o, in DMF using LiH as a weak base and activator. The synthesized compounds were initially confirmed through thin layer chromatography (TLC) and then finally corroborated through spectral data of IR (Infra Red), 1H-NMR (Proton Nuclear Magnetic Resonance), 13C-NMR (Carbon-13 Nuclear Magnetic Resonance) and EIMS (Electron Impact Mass Spectrometry). Some spectra are also given for structural elucidation in the discussion section of chapter 4. The physical datalike color, state, yield, melting point (not for sticky solids), molecular formula and molecular mass of all the synthesized compounds also have been provided. Four enzymes, namely, acetylcholinesterase (AChE), butyrylcholinesterase (BChE), α-Glucosidase and urease were used to establish the structure-activity relationship of all these synthesized bi-heterocyclic compounds. The antibacterial potential against different bacterial strains was conducted through thedisc diffusion method. Activity through diffusion method was compared with XIII Ampicillin. Doxorubicin was used as astandard to find out cytotoxicity of these synthesized compounds by killing brime shrimps at different concentration. All synthesized derivatives were computationally docked against AChE, BChE α-glucosidase, and urease to explore the binding modes of the ligands. Among the synthesized ninety (90) compounds, various compounds have shown pharmacological activity potential. The structure-activity relationship (SAR) of these synthesized compounds has been elaborated in chapter 4 under the discussion section. The most potent antibacterial agents and enzyme inhibitors with less toxicity might be subjected to in vivo study for further analysis as drug candidates. These compounds might be considered for the pharmacological industries as new drug candidates for a drug discovery program.