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Stock market prediction using DQN with DQNReg loss function
There have been many developments in predicting stock market prices usingreinforcement learning. Recently, Google released a paper that designed a new loss function,specifically for meta-learning reinforcement learning. In this paper, implementation is doneusing this loss function to the reinforcement learning model, whose objective is to predict thestock price based on certain parameters. The reinforcement learning used is an encoderdecoderframework that is useful for extracting features from long sequence prices. TheDQNReg loss function is implemented in the encoder-decoder model as it could providestrong adaptation performance in a variety of settings. The model can buy and sell the index, and the reward is the portfolio return after the days trading has concluded. To maximizeyield the model must optimize reward function. The DQNReg loss implemented DQN network and the Huber loss DQN network is compared with the Sharpe ratio considered for return. 2024 The Author(s). -
Stock market prediction using DQN with DQNReg loss function
There have been many developments in predicting stock market prices using reinforcement learning. Recently, Google released a paper that designed a new loss function, specifically for meta-learning reinforcement learning. In this paper, implementation is done using this loss function to the reinforcement learning model, whose objective is to predict the stock price based on certain parameters. The reinforcement learning used is an encoderdecoder framework that is useful for extracting features from long sequence prices. The DQNReg loss function is implemented in the encoder-decoder model as it could provide strong adaptation performance in a variety of settings. The model can buy and sell the index, and the reward is the portfolio return after the days trading has concluded. To maximize yield the model must optimize reward function. The DQNReg loss implemented DQN network and the Huber loss DQN network is compared with the Sharpe ratio considered for return. 2024 selection and editorial matter, Arvind Dagur, Karan Singh, Pawan Singh Mehra & Dhirendra Kumar Shukla; individual chapters, the contributors. -
Stock market prediction using artificial neural networks in python /
Patent Number: 202231052415, Applicant: Dr. Rashel Sarkar.
When the issue of forecasting time series is mentioned, the reader, listener, or observer instantly considers forecasting stock prices. This should help individuals determine when to sell and when to purchase more. On occasion, we encounter resources that explain how this is possible. Throughout Deep Learning with Python, Chollet cautions against using time series prediction algorithms to estimate market values. You should not attempt to predict how the stock market will behave in the future based on past performance. Due to the design of the martingale system, the present price of a share of stock is the most accurate indicator of its future price (in terms of the error associated with estimation). -
Stock market prediction using artificial neural networks in python /
Patent Number: 202231052415, Applicant: Dr. Rashel Sarkar.
When the issue of forecasting time series is mentioned, the reader, listener, or observer instantly considers forecasting stock prices. This should help individuals determine when to sell and when to purchase more. On occasion, we encounter resources that explain how this is possible. Throughout Deep Learning with Python, Chollet cautions against using time series prediction algorithms to estimate market values. You should not attempt to predict how the stock market will behave in the future based on past performance. Due to the design of the martingale system, the present price of a share of stock is the most accurate indicator of its future price (in terms of the error associated with estimation). -
Stock market prediction using artificial neural networks in python /
Patent Number: 202231052415, Applicant: Dr. Rashel Sarkar.
When the issue of forecasting time series is mentioned, the reader, listener, or observer instantly considers forecasting stock prices. This should help individuals determine when to sell and when to purchase more. On occasion, we encounter resources that explain how this is possible. Throughout Deep Learning with Python, Chollet cautions against using time series prediction algorithms to estimate market values. You should not attempt to predict how the stock market will behave in the future based on past performance. Due to the design of the martingale system, the present price of a share of stock is the most accurate indicator of its future price (in terms of the error associated with estimation). -
Stock Market Prediction Techniques Using Artificial Intelligence: A Systematic Review
This paper systematically reviews the literature related to stock price prediction systems. The reviewers collected 6222 research works from 12 databases. The reviewers reviewed the full-text of 10 studies in preliminary search and 70 studies selected based on PRISMA. This paper uses the PRISMA-based Python framework systematic-reviewpy to conduct this systematic review and browser-automationpy to automate downloading of full-texts. The programming code with comprehensive documentation, citation data, input variables, and reviews spreadsheets is provided, making this review replicable, open-source, and free from human errors in selecting studies. The reviewed literature is categorized based on type of prediction systems to demonstrate the evolution of techniques and research gaps. The reviewed literature is 7 % statistical, 9% machine learning, 23% deep learning, 20% hybrid, 25% combination of machine learning and deep learning, and 14% studies explore multiple categories of techniques. This review provides detailed information on prediction techniques, competitor techniques, performance metrics, input variables, data timing, and research gap to enable researchers to create prediction systems per technique category. The review showed that stock trading data is most used and collected from Yahoo! Finance. Studies showed that sentiment data improved stock prediction, and most papers used tweets from Twitter. Most of the reviewed studies showed significant improvements in predictions to previous systems. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Stock market prediction employing ensemble methods: the Nifty50 index
Accurately forecasting stock fluctuations can yield high investment returns while minimizing risk. However, market volatility makes these projections unlikely. As a result, stock market data analysis is significant for research. Analysts and researchers have developed various stock price prediction systems to help investors make informed judgments. Extensive studies show that machine learning can anticipate markets by examining stock data. This article proposed and evaluated different ensemble learning techniques such as max voting, bagging, boosting, and stacking to forecast the Nifty50 index efficiently. In addition, an embedded feature selection is performed to choose an optimal set of fundamental indicators as input to the model, and extensive hyperparameter tuning is applied using grid search to each base regressor to enhance performance. Our findings suggest the bagging and stacking ensemble models with random forest (RF) feature selection offer lower error rates. The bagging and stacking regressor model 2 outperformed all other models with the lowest root mean square error (RMSE) of 0.0084 and 0.0085, respectively, showing a better fit of ensemble regressors. Finally, the findings show that machine learning algorithms can help fundamental analyses make stock investment decisions. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
Stock market linkages in emerging Asia-Pacific markets
This study examines the stock market integration among major stock markets of emerging Asia-Pacific economies, viz. India, Malaysia, Hong Kong, Singapore, South Korea, Taiwan, Japan, China, and Indonesia. The Johansen and Juselius multivariate cointegration test, Granger causality/Block exogeneity Wald test based on the vector error correction model (VECM) approach, and variance decomposition analysis were used to investigate the dynamic linkages between markets. Cointegration test confirmed a well-defined long-run equilibrium relationship among the major stock markets, implying that there exists a common force, such as arbitrage activity, which brings these stock markets together in the long run. The results of Granger causality/Block exogeneity Wald test based on VECM and variance decomposition analysis revealed the stock market interdependencies and dynamic interactions among the selected emerging Asia-Pacific economies. This result implies that investors can gain feasible benefits from international portfolio diversification in the short run. On the whole, the study results suggest that although long-term diversification benefits from exposure to these markets might be limited, short-run benefits might exist due to substantial transitory fluctuations. The Author(s) 2013. -
Stock Market Linkages in Emerging Asia-Pacific Markets
Stock Market Linkages in Emerging Asia-Pacific Markets -
Stock Market Efficiency and COVID-19 with Multiple Structural Breaks: Evidence from India
The objective of the study is to investigate the influence of the coronavirus pandemic (endogenous crisis) on the stock market efficiency of India during the multiple break periods. The empirical analysis is performed using conditional heteroscedasticity and a small sample robust wild bootstrap automatic variance ratio test and automatic portmanteau test on a daily stock return data of two benchmark indices, that is, NIFTY and SENSEX. The empirical results demonstrate that the stock return of two indices deviates from market efficiency during some periods of the analysis, notably during the nationwide lockdown and peak periods of coronavirus cases in India. These findings indicate that changing stock market behaviour becomes more speculative and earns abnormal profits. To the best of the authors knowledge, this study provides the first evidence of investigating the variations in the stock market efficiency of India in response to this endogenous crisis. 2022 International Management Institute, New Delhi. -
Stochastic frontier analysis to measure technical efficiency: Evidence from skilled and unskilled agricultural labour in india
This paper comprises the stochastic frontier model which has been applied to measure the technical efficiency of skilled and unskilled labour. By considering the certain input variables listed in the cost of cultivation suggested by the Commission of Agricultural Costs and Prices (CACP) for Indian states during the main cropping season. Result of the study shows that the evaluated average technical efficiency estimates have been found between 71 to 84 % for both type of labour. Factors i.e. use of seeds (77 % efficient), fertilizers (29 % inefficient), manure (3 % efficient), land, human (9 % efficient), attached (10 % efficient) and casual (103 % efficient) labor, animal labor (is between 1 to 4 % efficient), hired machine (33 % inefficient), owned machine (7 % efficient), insecticides (20 % efficient), irrigational cost (31 % efficient), fixed cost (36 % inefficient) and operational cost (197 % inefficient) have a significant at 1, 5 and 10 % level of significance1. 2020 DAV College. All rights reserved. -
STOCHASTIC BEHAVIOUR OF AN ELECTRONIC SYSTEM SUBJECT TO MACHINE AND OPERATOR FAILURE
A stochastic model is developed by assuming the human (operator) redundancy in cold standby. For constructing this model, one unit is taken as electronic system which consists of hardware and software components and another unit is operator (human being). The system can be failed due to hardware failure, software failure and human failure. The failed hardware component goes under repair immediately and software goes for upgradation. The operator is subjected to failure during the manual operation. There are two separate service facilities in which one repairs/upgrades the hardware/software component of the electronic system and other gives the treatment to operator. The failure rates of components and operator are considered as constant. The repair rates of hardware/software components and human treatment rate follow arbitrary distributions with different pdfs. The state transition diagram and transition probabilities of the model are constructed by using the concepts of semi-Markov process (SMP) and regenerative point technique (RPT). These same concepts have been used for deriving the expressions (in steady state) for reliability measures or indices. The behavior of some important measures has been shown graphically by taking the particular values of the parameters. 2024, Gnedenko Forum. All rights reserved. -
Stirling number of the fourth kind and Lucky partitions of a finite set
The concept of Lucky k-polynomials and in particular Lucky ?polynomials was recently introduced. This paper introduces Stirling number of the fourth kind and Lucky partitions of a finite set in order to determine either the Lucky k- or Lucky ?-polynomial of a graph. The integer partitions influence Stirling partitions of the second kind. 2021 Azarbaijan Shahid Madani University. -
Stir Speed and Reinforcement Effects on Tensile Strength in Al-Based Composites
This study focuses on the preparation of Al-based hybrid composites using AA7475 as the main alloy reinforced with two materials, ZrO2 and SiC. The combination of stir-squeeze processing techniques was employed to create various specimens by varying four parameters: Stir-speed, Stir-time, reinforcements, and squeeze pressure. Taguchi design was utilized to generate specimens for analyzing their mechanical properties, specifically tensile strength, hardness, and porosity.The results indicated that the highest porosity (4.44%) was observed in the L16 test, with a combination of 700rpm stir speed, 25 mins stir time, 2wt% reinforcements, and 80MPa squeeze pressure. On the other hand, the lowest porosity (2.61%) was found in the L7 test, with 800rpm stir speed, 30 mins stir time, 2wt% reinforcements, and 100 MPa squeeze pressure.Regarding tensile strength (UTS), the maximum value (285.23MPa) was achieved in the L13 experiment, while the minimum value (187.58 MPa) was observed in the L1 experiment. This variation in UTS can be attributed to the applied load, the strengthening effect of the reinforcements, and the grain size of SiC. 2024 E3S Web of Conferences -
Still Waters Run Deep: Groundwater Contamination and Education Outcomes in India
We investigate the impact of groundwater contamination on educational outcomes in India. Our study leverages variations in the geographical coverage and timing of construction of safe government piped water schemes to identify the effects of exposure to contaminants. Using self-collected survey data from public schools in Assam, one of the most groundwater-contaminated regions in India, we find that prolonged exposure to unsafe groundwater is associated with increased school absenteeism, grade retention, and decreased test scores and Cumulative Grade Point Average (CGPA). To complement our findings and to study the effect of one such contaminant, arsenic, we use a large nationally representative household survey. Using variations in soil textures across districts as an instrument for arsenic concentration levels we find that exposure to arsenic beyond safe threshold levels is negatively associated with school attendance. 2024 Elsevier Ltd -
Stilbenes, a Versatile Class of Natural Metabolites for InflammationAn Overview
Stilbenes are polyphenolic allelochemicals synthesized by plants, especially grapes, peanuts, rhubarb, berries, etc., to defend themselves under stressful conditions. They are now exploited in medicine for their antioxidant, anti-proliferative and anti-inflammatory properties. Inflammation is the immune systems response to invading bacteria, toxic chemicals or even nutrient-deprived conditions. It is characterized by the release of cytokines which can wreak havoc on healthy tissues, worsening the disease condition. Stilbenes modulate NF-?B, MAPK and JAK/STAT pathways, and reduce the transcription of inflammatory factors which result in maintenance of homeostatic conditions. Resveratrol, the most studied stilbene, lowers the Michaelis constant of SIRT1, and occupies the substrate binding pocket. Gigantol interferes with the complement system. Besides these, oxyresveratrol, pterostilbene, polydatin, viniferins, etc., are front runners as drug candidates due to their diverse effects from different functional groups that affect bioavailability and molecular interactions. However, they each have different thresholds for toxicity to various cells of the human body, and thus a careful review of their properties must be conducted. In animal models of autoinflammatory diseases, the mode of application of stilbenes is important to their absorption and curative effects, as seen with topical and microemulsion gel methods. This review covers the diversity seen among stilbenes in the plant kingdom and their mechanism of action on the different inflammatory pathways. In detail, macrophages contribution to inflamed conditions in the liver, the cardiac, connective and neural tissues, in the nephrons, intestine, lungs and in myriad other body cells is explored, along with detailed explanation on how stilbenes alleviate the symptoms specific to body site. A section on the bioavailability of stilbenes is included for understanding the limitations of the natural compounds as directly used drugs due to their rapid metabolism. Current delivery mechanisms include sulphonamides, or using specially designed synthetic drugs. It is hoped that further research may be fueled by this comprehensive work that makes a compelling argument for the exploitation of these compounds in medicine. 2023 by the authors. -
Stigma among COVID-19 patients in South India-A cross-sectional study
Background: COVID-19 has a significant biopsychosocial impact on the lives of people who are infected, with the stigma associated with the illness being one of the major issues. However, the level of stigma based on demographics, gender differences, hospital-based or home-based care is yet to be explored. Hence, this study aimed to infer the level of stigma between these groups in the urban district of south India. Materials and Methods: This cross-sectional study recruited 50 participants who were recently infected with COVID-19 and were receiving either hospital or home-based care. The stigma was assessed using a standardized questionnaire which has four domains. MannWhitney U test was conducted to analyze the data. Results: Median age is 54 years and the majority of the participants are male (74%). The mean score of enacted stigma subscale was 4.48, disclosure fear was 2.34, internalized stigma was 2.82, perceived externalized stigma was 7.32 and the total stigma mean score was 17. The perceived externalized stigma subscale was higher in males (7.57 5.96) when compared to females 6.62 5.53. Total stigma scores were higher for males 17.2 10.1 when compared to females 16.2 10.5. The total stigma score was more (17 10.3) among home isolated COVID patients as compared to hospitalized patients (16.9 10.2). Conclusion: Increased levels of stigma among COVID-19 patients have various important psychosocial implications. This study highlights the need for larger prospective cohort studies to further understand stigma in the context of COVID-19. 2022 by the Author(s). -
Stiffness of a single layered cable assembly over a sheave with internal friction
The stiffness response of a single layered helical strand with a straight core surrounded by a layer of six helical wires has been made with improved relations of wire curvatures & twist and with internal friction considerations. The stranded cable undergoes a constant curvature bending over a sheave/pulley under static loading conditions and experiences the combinations of tension, torsion and bending loadings. A new analytical model has been developed for the cable in contact with the pulley/sheave using thin rod theory under linear elastic conditions. The stiffness coefficients of the cable are evaluated in free bending and constrained bending modes. The resulting wire strains are evaluated and compared with the experimental results. IAEME Publication -
Sterlite Technologies Ltd.: To Buy or Not to Buy is the Question
[No abstract available] -
Stereotype threat and psychological wellbeing in children of prisoners
Stereotypes are ideas that one holds regarding individuals because of their membership to a specific group. The current research was undertaken to study stereotype perception and stereotype threat on the psychological wellbeing of children of prisoners. Eight children of prisoners in the age range of 17 to 25 (females = 4), whose fathers were in prison for more than a year, participated in the study. Semi-structured interview method was used to collect data from the participants and data were analyzed using thematic network analysis method. Results revealed that children of prisoners strongly perceive stereotypes against them and they conform to stereotypes. Further to this, it was found that stereotype threat had positive effects in terms of increased goal-directed behavior and negative effects in terms of reduced happiness and increased feelings of anger and sadness. These Undings are discussed in light of previous literature on the effects of stereotypes on behavior. 2019 International Journal of Criminal Justice Sciences (IJCJS).