Stock price forecasting is a popular and important topic in financial and academic
studies. Share market is an volatile place for predicting since there are no significant
rules to estimate or predict the price of a share in the share market. Many methods
like technical analysis, fundamental analysis, time series analysis and statistical
analysis etc. are used to predict the price in tie share market but none of these
methods are proved as a consistently acceptable prediction tool. In this paper, we
implemented a Random Forest approach to predict stock market prices. Random
Forests are very effectively implemented in forecasting stock prices, returns, and stock
modeling. We outline the design of the Random Forest with its salient features and
customizable parameters. We focus on a certain group of parameters with a relatively
significant impact on the share price of a company. With the help of sentiment
analysis, we found the polarity score of the new article and that helped in forecasting
accurate result. Although share market can never be predicted with hundred per-cent
accuracy due to its vague domain, this paper aims at proving the efficiency of Random
forest for forecasting the stock prices
Forecasted stock prices of Google using historical stock price data and sentiment scores using Sentiment Analyzer in Python from New York Times headlines, implemented different Time Series Models – ARIMA, Exponential Smoothing, Holtwinters, also used Sentiment Score regression models, Fb Prophet, also implemented Deep Learning Models
Forecasted stock prices of Google using historical stock price data and sentiment scores using Sentiment Analyzer in Python from New York Times headlines, implemented different Time Series Models – ARIMA, Exponential Smoothing, Holtwinters, also used Sentiment Score regression models, Fb Prophet, also implemented Deep Learning Models
Stock Price Trend Forecasting using Supervised LearningSharvil Katariya
The aim of the project is to examine a number of different forecasting techniques to predict future stock returns based on past returns and numerical user-generated content to construct a portfolio of multiple stocks in order to diversify the risk. We do this by applying supervised learning methods for stock price forecasting by interpreting the seemingly chaotic market data.
In Stock Market Prediction, the aim is to predict the future value of the financial stocks of a company. The recent trend in stock market prediction technologies is the use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic.
Stock Market Prediction using Machine Learningijtsrd
Stock market prediction is a typical task to forecast the upcoming stock values. It is very difficult to forecast because of unbalanced nature of stocks. In this work, an attempt is made for prediction of stock market trend. This research aims to combine multiple existing techniques into a much more robust prediction model which can handle various scenarios in which investment can be beneficial. By combing both techniques, this prediction model can provide more accurate and flexible recommendations. However instead of using those traditional methods, we approached the problems using machine learning techniques. We tried to revolutionize the way people address data processing problems in stock market by predicting the behavior of the stocks. In fact, if we can predict how the stock will behave in the short term future we can queue up our transactions earlier and be faster than everyone else. In theory, this allows us to maximize our profit without having the need to be physically located close to the data sources. We examined three main models. Firstly we used a complete prediction using a moving average. Secondly we used a LSTM model and finally a model called ARIMA model. The only motive is to increase the accuracy of predictive the stock market price. Each of those models was applied on real stock market data and checked whether it could return profit. Subham Kumar Gupta | Dr. Bhuvana J | Dr. M N Nachappa "Stock Market Prediction using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-3 , April 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49868.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/49868/stock-market-prediction-using-machine-learning/subham-kumar-gupta
Presentation given on TechnicalAnalyst.com event "Machine learning techniques in finance" on 17th November 2016.
- What is machine learning and how it can help predict finnacial markets
- Technical stock analysis vs. behavioural news and social media analysis
- How machine learning can be applied to technical analysis in the stock market
- How machine learning can be applied to new/social media analysis
Stock Market Prediction using Machine LearningAravind Balaji
REPO : https://github.com/rvndbalaji/StockMarketPrediction
Stock Market Prediction using Machine
This is a presentation on Stock Market Prediction application built using R.
This is a part of final year engineering project
This project aims to provide accurate and reliable predictions for stock prices using the power of LSTM (Long Short-Term Memory) and ARIMA (AutoRegressive Integrated Moving Average) models. By analyzing historical stock data and leveraging the capabilities of these advanced forecasting models, we help investors and traders make informed decisions and optimize their investment strategies.
The project workflow begins with gathering comprehensive historical stock price data, including open, high, low, and closing prices, as well as trading volumes and other relevant features. This data is then preprocessed to handle missing values, outliers, and any other inconsistencies that may impact the accuracy of the predictions.
For time series analysis and forecasting, we employ the LSTM model, a variant of recurrent neural networks (RNNs) known for their ability to capture long-term dependencies in sequential data. LSTM models have proven to be highly effective in capturing the complex patterns and trends present in stock price data. By training the LSTM model on historical stock data, we can predict future stock prices with a high degree of accuracy.
In addition to LSTM, we utilize the ARIMA model, a widely used statistical method for time series forecasting. ARIMA models capture the autoregressive, moving average, and integrated components of a time series, allowing us to capture both short-term and long-term trends in stock prices. By incorporating the ARIMA model into our prediction pipeline, we further enhance the accuracy and reliability of our forecasts.
To evaluate the performance of our models, we use appropriate evaluation metrics such as mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). These metrics provide insights into the effectiveness of our models and help us fine-tune the parameters for optimal performance.
The Stock Price Prediction project using LSTM and ARIMA models represents our commitment to leveraging advanced machine learning and statistical techniques to provide valuable insights in the financial domain. By accurately forecasting stock prices, we empower investors and traders to make data-driven decisions, mitigate risks, and optimize their investment strategies. This project showcases our expertise in time series analysis, deep learning, and statistical modeling, and our dedication to delivering solutions that drive tangible business outcomes in the financial sector.
The aim of the project is to determine the forecasting techniques to determine future stock prices of IT stocks using time series analysis & determining the maximum risk involved using Monte Carlo techniques
Nowadays during increasingly developed technology of the World Wide Web and Internet, the data is becoming extremely rich. With the application of data recognition process, the information extracted from data has become the most important part in some areas of society, management field, finance and markets, etc. It is necessary to develop the valid method to understand the knowledge of the data. Whether you are looking for good investments or are into stock trading, stock prediction or forecast plays the most crucial role in determining where to put in the money or which stock to be acquired or sold.
Stock Price Trend Forecasting using Supervised LearningSharvil Katariya
The aim of the project is to examine a number of different forecasting techniques to predict future stock returns based on past returns and numerical user-generated content to construct a portfolio of multiple stocks in order to diversify the risk. We do this by applying supervised learning methods for stock price forecasting by interpreting the seemingly chaotic market data.
In Stock Market Prediction, the aim is to predict the future value of the financial stocks of a company. The recent trend in stock market prediction technologies is the use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic.
Stock Market Prediction using Machine Learningijtsrd
Stock market prediction is a typical task to forecast the upcoming stock values. It is very difficult to forecast because of unbalanced nature of stocks. In this work, an attempt is made for prediction of stock market trend. This research aims to combine multiple existing techniques into a much more robust prediction model which can handle various scenarios in which investment can be beneficial. By combing both techniques, this prediction model can provide more accurate and flexible recommendations. However instead of using those traditional methods, we approached the problems using machine learning techniques. We tried to revolutionize the way people address data processing problems in stock market by predicting the behavior of the stocks. In fact, if we can predict how the stock will behave in the short term future we can queue up our transactions earlier and be faster than everyone else. In theory, this allows us to maximize our profit without having the need to be physically located close to the data sources. We examined three main models. Firstly we used a complete prediction using a moving average. Secondly we used a LSTM model and finally a model called ARIMA model. The only motive is to increase the accuracy of predictive the stock market price. Each of those models was applied on real stock market data and checked whether it could return profit. Subham Kumar Gupta | Dr. Bhuvana J | Dr. M N Nachappa "Stock Market Prediction using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-3 , April 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49868.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/49868/stock-market-prediction-using-machine-learning/subham-kumar-gupta
Presentation given on TechnicalAnalyst.com event "Machine learning techniques in finance" on 17th November 2016.
- What is machine learning and how it can help predict finnacial markets
- Technical stock analysis vs. behavioural news and social media analysis
- How machine learning can be applied to technical analysis in the stock market
- How machine learning can be applied to new/social media analysis
Stock Market Prediction using Machine LearningAravind Balaji
REPO : https://github.com/rvndbalaji/StockMarketPrediction
Stock Market Prediction using Machine
This is a presentation on Stock Market Prediction application built using R.
This is a part of final year engineering project
This project aims to provide accurate and reliable predictions for stock prices using the power of LSTM (Long Short-Term Memory) and ARIMA (AutoRegressive Integrated Moving Average) models. By analyzing historical stock data and leveraging the capabilities of these advanced forecasting models, we help investors and traders make informed decisions and optimize their investment strategies.
The project workflow begins with gathering comprehensive historical stock price data, including open, high, low, and closing prices, as well as trading volumes and other relevant features. This data is then preprocessed to handle missing values, outliers, and any other inconsistencies that may impact the accuracy of the predictions.
For time series analysis and forecasting, we employ the LSTM model, a variant of recurrent neural networks (RNNs) known for their ability to capture long-term dependencies in sequential data. LSTM models have proven to be highly effective in capturing the complex patterns and trends present in stock price data. By training the LSTM model on historical stock data, we can predict future stock prices with a high degree of accuracy.
In addition to LSTM, we utilize the ARIMA model, a widely used statistical method for time series forecasting. ARIMA models capture the autoregressive, moving average, and integrated components of a time series, allowing us to capture both short-term and long-term trends in stock prices. By incorporating the ARIMA model into our prediction pipeline, we further enhance the accuracy and reliability of our forecasts.
To evaluate the performance of our models, we use appropriate evaluation metrics such as mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). These metrics provide insights into the effectiveness of our models and help us fine-tune the parameters for optimal performance.
The Stock Price Prediction project using LSTM and ARIMA models represents our commitment to leveraging advanced machine learning and statistical techniques to provide valuable insights in the financial domain. By accurately forecasting stock prices, we empower investors and traders to make data-driven decisions, mitigate risks, and optimize their investment strategies. This project showcases our expertise in time series analysis, deep learning, and statistical modeling, and our dedication to delivering solutions that drive tangible business outcomes in the financial sector.
The aim of the project is to determine the forecasting techniques to determine future stock prices of IT stocks using time series analysis & determining the maximum risk involved using Monte Carlo techniques
Nowadays during increasingly developed technology of the World Wide Web and Internet, the data is becoming extremely rich. With the application of data recognition process, the information extracted from data has become the most important part in some areas of society, management field, finance and markets, etc. It is necessary to develop the valid method to understand the knowledge of the data. Whether you are looking for good investments or are into stock trading, stock prediction or forecast plays the most crucial role in determining where to put in the money or which stock to be acquired or sold.
OPENING RANGE BREAKOUT STOCK TRADING ALGORITHMIC MODELIJCI JOURNAL
Stock Trading Algorithmic Model is an important research problem that is dealt with knowledge in
fundamental and technical analysis, combined with the knowledge expertise in programming and computer
science. There have been numerous attempts in predicting stock trends, we aim to predict it with least
amount of computation and to decrease the space complexity. The goal of this paper is to create a hybrid
recommendation system that will inform the trader about the future of a stock trend in order to improve the
profitability of a short term investment. We make use of technical analysis tools to incorporate this
recommendation into our system. In order to understand the results, we implemented a prototype in R
programming language.
The International Journal of Engineering and Science (IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Submission Deadline: 30th September 2022
Acceptance Notification: Within Three Days’ time period
Online Publication: Within 24 Hrs. time Period
Expected Date of Dispatch of Printed Journal: 5th October 2022
MODELING AND ANALYSIS OF SURFACE ROUGHNESS AND WHITE LATER THICKNESS IN WIRE-...IAEME Publication
White layer thickness (WLT) formed and surface roughness in wire electric discharge turning (WEDT) of tungsten carbide composite has been made to model through response surface methodology (RSM). A Taguchi’s standard Design of experiments involving five input variables with three levels has been employed to establish a mathematical model between input parameters and responses. Percentage of cobalt content, spindle speed, Pulse on-time, wire feed and pulse off-time were changed during the experimental tests based on the Taguchi’s orthogonal array L27 (3^13). Analysis of variance (ANOVA) revealed that the mathematical models obtained can adequately describe performance within the parameters of the factors considered. There was a good agreement between the experimental and predicted values in this study.
A STUDY ON THE REASONS FOR TRANSGENDER TO BECOME ENTREPRENEURSIAEME Publication
The study explores the reasons for a transgender to become entrepreneurs. In this study transgender entrepreneur was taken as independent variable and reasons to become as dependent variable. Data were collected through a structured questionnaire containing a five point Likert Scale. The study examined the data of 30 transgender entrepreneurs in Salem Municipal Corporation of Tamil Nadu State, India. Simple Random sampling technique was used. Garrett Ranking Technique (Percentile Position, Mean Scores) was used as the analysis for the present study to identify the top 13 stimulus factors for establishment of trans entrepreneurial venture. Economic advancement of a nation is governed upon the upshot of a resolute entrepreneurial doings. The conception of entrepreneurship has stretched and materialized to the socially deflated uncharted sections of transgender community. Presently transgenders have smashed their stereotypes and are making recent headlines of achievements in various fields of our Indian society. The trans-community is gradually being observed in a new light and has been trying to achieve prospective growth in entrepreneurship. The findings of the research revealed that the optimistic changes are taking place to change affirmative societal outlook of the transgender for entrepreneurial ventureship. It also laid emphasis on other transgenders to renovate their traditional living. The paper also highlights that legislators, supervisory body should endorse an impartial canons and reforms in Tamil Nadu Transgender Welfare Board Association.
BROAD UNEXPOSED SKILLS OF TRANSGENDER ENTREPRENEURSIAEME Publication
Since ages gender difference is always a debatable theme whether caused by nature, evolution or environment. The birth of a transgender is dreadful not only for the child but also for their parents. The pain of living in the wrong physique and treated as second class victimized citizen is outrageous and fully harboured with vicious baseless negative scruples. For so long, social exclusion had perpetuated inequality and deprivation experiencing ingrained malign stigma and besieged victims of crime or violence across their life spans. They are pushed into the murky way of life with a source of eternal disgust, bereft sexual potency and perennial fear. Although they are highly visible but very little is known about them. The common public needs to comprehend the ravaged arrogance on these insensitive souls and assist in integrating them into the mainstream by offering equal opportunity, treat with humanity and respect their dignity. Entrepreneurship in the current age is endorsing the gender fairness movement. Unstable careers and economic inadequacy had inclined one of the gender variant people called Transgender to become entrepreneurs. These tiny budding entrepreneurs resulted in economic transition by means of employment, free from the clutches of stereotype jobs, raised standard of living and handful of financial empowerment. Besides all these inhibitions, they were able to witness a platform for skill set development that ignited them to enter into entrepreneurial domain. This paper epitomizes skill sets involved in trans-entrepreneurs of Thoothukudi Municipal Corporation of Tamil Nadu State and is a groundbreaking determination to sightsee various skills incorporated and the impact on entrepreneurship.
DETERMINANTS AFFECTING THE USER'S INTENTION TO USE MOBILE BANKING APPLICATIONSIAEME Publication
The banking and financial services industries are experiencing increased technology penetration. Among them, the banking industry has made technological advancements to better serve the general populace. The economy focused on transforming the banking sector's system into a cashless, paperless, and faceless one. The researcher wants to evaluate the user's intention for utilising a mobile banking application. The study also examines the variables affecting the user's behaviour intention when selecting specific applications for financial transactions. The researcher employed a well-structured questionnaire and a descriptive study methodology to gather the respondents' primary data utilising the snowball sampling technique. The study includes variables like performance expectations, effort expectations, social impact, enabling circumstances, and perceived risk. Each of the aforementioned variables has a major impact on how users utilise mobile banking applications. The outcome will assist the service provider in comprehending the user's history with mobile banking applications.
ANALYSE THE USER PREDILECTION ON GPAY AND PHONEPE FOR DIGITAL TRANSACTIONSIAEME Publication
Technology upgradation in banking sector took the economy to view that payment mode towards online transactions using mobile applications. This system enabled connectivity between banks, Merchant and user in a convenient mode. there are various applications used for online transactions such as Google pay, Paytm, freecharge, mobikiwi, oxygen, phonepe and so on and it also includes mobile banking applications. The study aimed at evaluating the predilection of the user in adopting digital transaction. The study is descriptive in nature. The researcher used random sample techniques to collect the data. The findings reveal that mobile applications differ with the quality of service rendered by Gpay and Phonepe. The researcher suggest the Phonepe application should focus on implementing the application should be user friendly interface and Gpay on motivating the users to feel the importance of request for money and modes of payments in the application.
VOICE BASED ATM FOR VISUALLY IMPAIRED USING ARDUINOIAEME Publication
The prototype of a voice-based ATM for visually impaired using Arduino is to help people who are blind. This uses RFID cards which contain users fingerprint encrypted on it and interacts with the users through voice commands. ATM operates when sensor detects the presence of one person in the cabin. After scanning the RFID card, it will ask to select the mode like –normal or blind. User can select the respective mode through voice input, if blind mode is selected the balance check or cash withdraw can be done through voice input. Normal mode procedure is same as the existing ATM.
IMPACT OF EMOTIONAL INTELLIGENCE ON HUMAN RESOURCE MANAGEMENT PRACTICES AMONG...IAEME Publication
There is increasing acceptability of emotional intelligence as a major factor in personality assessment and effective human resource management. Emotional intelligence as the ability to build capacity, empathize, co-operate, motivate and develop others cannot be divorced from both effective performance and human resource management systems. The human person is crucial in defining organizational leadership and fortunes in terms of challenges and opportunities and walking across both multinational and bilateral relationships. The growing complexity of the business world requires a great deal of self-confidence, integrity, communication, conflict and diversity management to keep the global enterprise within the paths of productivity and sustainability. Using the exploratory research design and 255 participants the result of this original study indicates strong positive correlation between emotional intelligence and effective human resource management. The paper offers suggestions on further studies between emotional intelligence and human capital development and recommends for conflict management as an integral part of effective human resource management.
VISUALISING AGING PARENTS & THEIR CLOSE CARERS LIFE JOURNEY IN AGING ECONOMYIAEME Publication
Our life journey, in general, is closely defined by the way we understand the meaning of why we coexist and deal with its challenges. As we develop the "inspiration economy", we could say that nearly all of the challenges we have faced are opportunities that help us to discover the rest of our journey. In this note paper, we explore how being faced with the opportunity of being a close carer for an aging parent with dementia brought intangible discoveries that changed our insight of the meaning of the rest of our life journey.
A STUDY ON THE IMPACT OF ORGANIZATIONAL CULTURE ON THE EFFECTIVENESS OF PERFO...IAEME Publication
The main objective of this study is to analyze the impact of aspects of Organizational Culture on the Effectiveness of the Performance Management System (PMS) in the Health Care Organization at Thanjavur. Organizational Culture and PMS play a crucial role in present-day organizations in achieving their objectives. PMS needs employees’ cooperation to achieve its intended objectives. Employees' cooperation depends upon the organization’s culture. The present study uses exploratory research to examine the relationship between the Organization's culture and the Effectiveness of the Performance Management System. The study uses a Structured Questionnaire to collect the primary data. For this study, Thirty-six non-clinical employees were selected from twelve randomly selected Health Care organizations at Thanjavur. Thirty-two fully completed questionnaires were received.
Living in 21st century in itself reminds all of us the necessity of police and its administration. As more and more we are entering into the modern society and culture, the more we require the services of the so called ‘Khaki Worthy’ men i.e., the police personnel. Whether we talk of Indian police or the other nation’s police, they all have the same recognition as they have in India. But as already mentioned, their services and requirements are different after the like 26th November, 2008 incidents, where they without saving their own lives has sacrificed themselves without any hitch and without caring about their respective family members and wards. In other words, they are like our heroes and mentors who can guide us from the darkness of fear, militancy, corruption and other dark sides of life and so on. Now the question arises, if Gandhi would have been alive today, what would have been his reaction/opinion to the police and its functioning? Would he have some thing different in his mind now what he had been in his mind before the partition or would he be going to start some Satyagraha in the form of some improvement in the functioning of the police administration? Really these questions or rather night mares can come to any one’s mind, when there is too much confusion is prevailing in our minds, when there is too much corruption in the society and when the polices working is also in the questioning because of one or the other case throughout the India. It is matter of great concern that we have to thing over our administration and our practical approach because the police personals are also like us, they are part and parcel of our society and among one of us, so why we all are pin pointing towards them.
A STUDY ON TALENT MANAGEMENT AND ITS IMPACT ON EMPLOYEE RETENTION IN SELECTED...IAEME Publication
The goal of this study was to see how talent management affected employee retention in the selected IT organizations in Chennai. The fundamental issue was the difficulty to attract, hire, and retain talented personnel who perform well and the gap between supply and demand of talent acquisition and retaining them within the firms. The study's main goals were to determine the impact of talent management on employee retention in IT companies in Chennai, investigate talent management strategies that IT companies could use to improve talent acquisition, performance management, career planning and formulate retention strategies that the IT firms could use. The respondents were given a structured close-ended questionnaire with the 5 Point Likert Scale as part of the study's quantitative research design. The target population consisted of 289 IT professionals. The questionnaires were distributed and collected by the researcher directly. The Statistical Package for Social Sciences (SPSS) was used to collect and analyse the questionnaire responses. Hypotheses that were formulated for the various areas of the study were tested using a variety of statistical tests. The key findings of the study suggested that talent management had an impact on employee retention. The studies also found that there is a clear link between the implementation of talent management and retention measures. Management should provide enough training and development for employees, clarify job responsibilities, provide adequate remuneration packages, and recognise employees for exceptional performance.
ATTRITION IN THE IT INDUSTRY DURING COVID-19 PANDEMIC: LINKING EMOTIONAL INTE...IAEME Publication
Globally, Millions of dollars were spent by the organizations for employing skilled Information Technology (IT) professionals. It is costly to replace unskilled employees with IT professionals possessing technical skills and competencies that aid in interconnecting the business processes. The organization’s employment tactics were forced to alter by globalization along with technological innovations as they consistently diminish to remain lean, outsource to concentrate on core competencies along with restructuring/reallocate personnel to gather efficiency. As other jobs, organizations or professions have become reasonably more appropriate in a shifting employment landscape, the above alterations trigger both involuntary as well as voluntary turnover. The employee view on jobs is also afflicted by the COVID-19 pandemic along with the employee-driven labour market. So, having effective strategies is necessary to tackle the withdrawal rate of employees. By associating Emotional Intelligence (EI) along with Talent Management (TM) in the IT industry, the rise in attrition rate was analyzed in this study. Only 303 respondents were collected out of 350 participants to whom questionnaires were distributed. From the employees of IT organizations located in Bangalore (India), the data were congregated. A simple random sampling methodology was employed to congregate data as of the respondents. Generating the hypothesis along with testing is eventuated. The effect of EI and TM along with regression analysis between TM and EI was analyzed. The outcomes indicated that employee and Organizational Performance (OP) were elevated by effective EI along with TM.
INFLUENCE OF TALENT MANAGEMENT PRACTICES ON ORGANIZATIONAL PERFORMANCE A STUD...IAEME Publication
By implementing talent management strategy, organizations would have the option to retain their skilled professionals while additionally working on their overall performance. It is the course of appropriately utilizing the ideal individuals, setting them up for future top positions, exploring and dealing with their performance, and holding them back from leaving the organization. It is employee performance that determines the success of every organization. The firm quickly obtains an upper hand over its rivals in the event that its employees having particular skills that cannot be duplicated by the competitors. Thus, firms are centred on creating successful talent management practices and processes to deal with the unique human resources. Firms are additionally endeavouring to keep their top/key staff since on the off chance that they leave; the whole store of information leaves the firm's hands. The study's objective was to determine the impact of talent management on organizational performance among the selected IT organizations in Chennai. The study recommends that talent management limitedly affects performance. On the off chance that this talent is appropriately management and implemented properly, organizations might benefit as much as possible from their maintained assets to support development and productivity, both monetarily and non-monetarily.
A STUDY OF VARIOUS TYPES OF LOANS OF SELECTED PUBLIC AND PRIVATE SECTOR BANKS...IAEME Publication
Banking regulations act of India, 1949 defines banking as “acceptance of deposits for the purpose of lending or investment from the public, repayment on demand or otherwise and withdrawable through cheques, drafts order or otherwise”, the major participants of the Indian financial system are commercial banks, the financial institution encompassing term lending institutions. Investments institutions, specialized financial institution and the state level development banks, non banking financial companies (NBFC) and other market intermediaries such has the stock brokers and money lenders are among the oldest of the certain variants of NBFC and the oldest market participants. The asset quality of banks is one of the most important indicators of their financial health. The Indian banking sector has been facing severe problems of increasing Non- Performing Assets (NPAs). The NPAs growth directly and indirectly affects the quality of assets and profitability of banks. It also shows the efficiency of banks credit risk management and the recovery effectiveness. NPA do not generate any income, whereas, the bank is required to make provisions for such as assets that why is a double edge weapon. This paper outlines the concept of quality of bank loans of different types like Housing, Agriculture and MSME loans in state Haryana of selected public and private sector banks. This study is highlighting problems associated with the role of commercial bank in financing Small and Medium Scale Enterprises (SME). The overall objective of the research was to assess the effect of the financing provisions existing for the setting up and operations of MSMEs in the country and to generate recommendations for more robust financing mechanisms for successful operation of the MSMEs, in turn understanding the impact of MSME loans on financial institutions due to NPA. There are many research conducted on the topic of Non- Performing Assets (NPA) Management, concerning particular bank, comparative study of public and private banks etc. In this paper the researcher is considering the aggregate data of selected public sector and private sector banks and attempts to compare the NPA of Housing, Agriculture and MSME loans in state Haryana of public and private sector banks. The tools used in the study are average and Anova test and variance. The findings reveal that NPA is common problem for both public and private sector banks and is associated with all types of loans either that is housing loans, agriculture loans and loans to SMES. NPAs of both public and private sector banks show the increasing trend. In 2010-11 GNPA of public and private sector were at same level it was 2% but after 2010-11 it increased in many fold and at present there is GNPA in some more than 15%. It shows the dark area of Indian banking sector.
EXPERIMENTAL STUDY OF MECHANICAL AND TRIBOLOGICAL RELATION OF NYLON/BaSO4 POL...IAEME Publication
An experiment conducted in this study found that BaSO4 changed Nylon 6's mechanical properties. By changing the weight ratios, BaSO4 was used to make Nylon 6. This Researcher looked into how hard Nylon-6/BaSO4 composites are and how well they wear. Experiments were done based on Taguchi design L9. Nylon-6/BaSO4 composites can be tested for their hardness number using a Rockwell hardness testing apparatus. On Nylon/BaSO4, the wear behavior was measured by a wear monitor, pinon-disc friction by varying reinforcement, sliding speed, and sliding distance, and the microstructure of the crack surfaces was observed by SEM. This study provides significant contributions to ultimate strength by increasing BaSO4 content up to 16% in the composites, and sliding speed contributes 72.45% to the wear rate
ROLE OF SOCIAL ENTREPRENEURSHIP IN RURAL DEVELOPMENT OF INDIA - PROBLEMS AND ...IAEME Publication
The majority of the population in India lives in villages. The village is the back bone of the country. Village or rural industries play an important role in the national economy, particularly in the rural development. Developing the rural economy is one of the key indicators towards a country’s success. Whether it be the need to look after the welfare of the farmers or invest in rural infrastructure, Governments have to ensure that rural development isn’t compromised. The economic development of our country largely depends on the progress of rural areas and the standard of living of rural masses. Village or rural industries play an important role in the national economy, particularly in the rural development. Rural entrepreneurship is based on stimulating local entrepreneurial talent and the subsequent growth of indigenous enterprises. It recognizes opportunity in the rural areas and accelerates a unique blend of resources either inside or outside of agriculture. Rural entrepreneurship brings an economic value to the rural sector by creating new methods of production, new markets, new products and generate employment opportunities thereby ensuring continuous rural development. Social Entrepreneurship has the direct and primary objective of serving the society along with the earning profits. So, social entrepreneurship is different from the economic entrepreneurship as its basic objective is not to earn profits but for providing innovative solutions to meet the society needs which are not taken care by majority of the entrepreneurs as they are in the business for profit making as a sole objective. So, the Social Entrepreneurs have the huge growth potential particularly in the developing countries like India where we have huge societal disparities in terms of the financial positions of the population. Still 22 percent of the Indian population is below the poverty line and also there is disparity among the rural & urban population in terms of families living under BPL. 25.7 percent of the rural population & 13.7 percent of the urban population is under BPL which clearly shows the disparity of the poor people in the rural and urban areas. The need to develop social entrepreneurship in agriculture is dictated by a large number of social problems. Such problems include low living standards, unemployment, and social tension. The reasons that led to the emergence of the practice of social entrepreneurship are the above factors. The research problem lays upon disclosing the importance of role of social entrepreneurship in rural development of India. The paper the tendencies of social entrepreneurship in India, to present successful examples of such business for providing recommendations how to improve situation in rural areas in terms of social entrepreneurship development. Indian government has made some steps towards development of social enterprises, social entrepreneurship, and social in- novation, but a lot remains to be improved.
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...IAEME Publication
Distribution system is a critical link between the electric power distributor and the consumers. Most of the distribution networks commonly used by the electric utility is the radial distribution network. However in this type of network, it has technical issues such as enormous power losses which affect the quality of the supply. Nowadays, the introduction of Distributed Generation (DG) units in the system help improve and support the voltage profile of the network as well as the performance of the system components through power loss mitigation. In this study network reconfiguration was done using two meta-heuristic algorithms Particle Swarm Optimization and Gravitational Search Algorithm (PSO-GSA) to enhance power quality and voltage profile in the system when simultaneously applied with the DG units. Backward/Forward Sweep Method was used in the load flow analysis and simulated using the MATLAB program. Five cases were considered in the Reconfiguration based on the contribution of DG units. The proposed method was tested using IEEE 33 bus system. Based on the results, there was a voltage profile improvement in the system from 0.9038 p.u. to 0.9594 p.u.. The integration of DG in the network also reduced power losses from 210.98 kW to 69.3963 kW. Simulated results are drawn to show the performance of each case.
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Manufacturing industries have witnessed an outburst in productivity. For productivity improvement manufacturing industries are taking various initiatives by using lean tools and techniques. However, in different manufacturing industries, frugal approach is applied in product design and services as a tool for improvement. Frugal approach contributed to prove less is more and seems indirectly contributing to improve productivity. Hence, there is need to understand status of frugal approach application in manufacturing industries. All manufacturing industries are trying hard and putting continuous efforts for competitive existence. For productivity improvements, manufacturing industries are coming up with different effective and efficient solutions in manufacturing processes and operations. To overcome current challenges, manufacturing industries have started using frugal approach in product design and services. For this study, methodology adopted with both primary and secondary sources of data. For primary source interview and observation technique is used and for secondary source review has done based on available literatures in website, printed magazines, manual etc. An attempt has made for understanding application of frugal approach with the study of manufacturing industry project. Manufacturing industry selected for this project study is Mahindra and Mahindra Ltd. This paper will help researcher to find the connections between the two concepts productivity improvement and frugal approach. This paper will help to understand significance of frugal approach for productivity improvement in manufacturing industry. This will also help to understand current scenario of frugal approach in manufacturing industry. In manufacturing industries various process are involved to deliver the final product. In the process of converting input in to output through manufacturing process productivity plays very critical role. Hence this study will help to evolve status of frugal approach in productivity improvement programme. The notion of frugal can be viewed as an approach towards productivity improvement in manufacturing industries.
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In this paper, we investigated a queuing model of fuzzy environment-based a multiple channel queuing model (M/M/C) ( /FCFS) and study its performance under realistic conditions. It applies a nonagonal fuzzy number to analyse the relevant performance of a multiple channel queuing model (M/M/C) ( /FCFS). Based on the sub interval average ranking method for nonagonal fuzzy number, we convert fuzzy number to crisp one. Numerical results reveal that the efficiency of this method. Intuitively, the fuzzy environment adapts well to a multiple channel queuing models (M/M/C) ( /FCFS) are very well.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
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hypothesis suggests that stock prices reflect all currently available information and any price
changes that are not based on newly revealed information thus are inherently unpredictable.
Others disagree and those with this view point possess several methods and technologies
which supposedly allow them to gain future price information.
Predicting how the stock market will perform is one of the most difficult things to do.
Intrinsic volatility in the stock market across the globe makes the task of prediction
challenging. There are so many factors involved in the prediction – physical factors vs.
technical, rational and irrational behavior, etc. All these aspects combine to make share prices
volatile and very difficult to predict with a high degree of accuracy. Using features like the
latest announcements about an organization, their quarterly revenue results, etc., machine
learning techniques have the potential to unearth patterns and insights we didn‟t see
before, and these can be used to make unerringly accurate predictions.
In this paper, we considered historical data about the stock prices of a publicly listed
company to implement machine learning algorithms in predicting the future stock price of
a company, starting with simple algorithms like averaging and linear regression.
Forecasting and diffusion modeling, although effective can't be the panacea to the diverse
range of problems encountered in prediction, short-term or otherwise. Market risk, strongly
correlated with forecasting errors, needs to be minimized to ensure minimal risk in
investment. Stock Market prices can be predicted based on two ways: Current prices of the
stocks and both the prices and the news headings.
Current prices of the stocks – Generally prices vary from day to day on a fixed amount or at
a constant rate. These are the type of general mutual funds where amount if invested, will be
compounded manually. This is not of specific interest as there is nothing use of a machine to
guess the future price. Just a calculator is enough.
Both the prices and the news headings – The prices subjected to these will change from
time to time as based on their actions. Suppose a company launched a product which hit the
market and got very connected to people. Obviously, sales will be increasing for that
company and the investor who invested in that particular company will be profitable. For
these type of calculations, we need some tool to be effective. These predictions are performed
using several traditional methods such as Traditional Time Series, Technical Analysis
Methods, Machine Learning Methods, and Fundamental Analysis Methods. The selection of
the above methods is based on the kind of tool used and the data upon which the tool is
implemented.
Technical Analysis Methods: Method of guessing the correct time to purchase stock pricing.
The reason behind technical analysis is that share prices move in developments uttered by the
repetitively altering qualities of investors in answer to different forces. The technical data
such as price, volume, peak and bottom prices per trade-off period is used for graphic
representation to forecast future stock activities.
Fundamental Analysis Techniques: This practice uses the theory of the firm foundation for
preferred-stock selection. Data of fundamental analysis can be used by forecasters for using
this tech of prediction for having a fully clear idea about the market or for investment. The
growth, the bonus payout, the IR, the risk of investing so on are the standards that will be
used to get the real value for an asset in which they could finance in the market. The main
target of this process is to determine the inherent value of strength.
Traditional Time Series Prediction: Past data is used here and it uses this data to find
coming values for the time series as a linear grouping. Use of Regression depictions has been
used for forecasting stock market time series. Two rudimentary types of time series are simple
and multivariate regressions.
3. Subhadra Kompella and Kalyana Chakravarthy Chilukuri
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Machine Learning Methods: These types of methods use samples of data that is needed for
creating hope for the underlying function that had produced all of the other data. Taking out a
deduction from different samples which are given to the model is the main aim for this. The
Nearest Neighbor and the Neural Networks Practices have been used for forecasting of the
market. Random forest type of models is being used as this model is involved in fields where
we deal with risk.
Sentiment Analysis: Sentiment Analysis is the process of „computationally‟ determining
whether a piece of writing is positive, negative or neutral. It is also known as opinion mining,
deriving the opinion or attitude of a speaker. Some domains in which sentiment analysis is
used are-
Business: In marketing field, companies use it to develop their strategies, to understand
customers‟ feelings towards products or brand, how people respond to their campaigns or
product launches and why consumers don‟t buy some products.
Politics: In the political field, it is used to keep track of political view, to detect consistency
and inconsistency between statements and actions at the government level. It can be used to
predict election results as well!
Public Actions: Sentiment analysis also is used to monitor and analyze social phenomena, for
the spotting of potentially dangerous situations and determining the general mood of the
blogosphere.
Sometimes known as opinion mining, sentiment analysis is the process of contextually
mining text to identify and categorize the subjective opinions expressed by the writers.
Normally it is used to determine whether the writer‟s attitude towards a particular topic or
product, etc. is positive, negative, or neutral. It is also often used to help them understand the
social sentiment of their brand, product or services while monitoring online conversations. In
the context of a Twitter sentiment analysis, at its simplest, sentiment analysis quantifies the
mood of a tweet or comment by counting the number of positive and negative words. By
subtracting the negative from the positive, the sentiment score is generated. The process of
reducing an opinion to a number is bound to have a level of error. For example, sentiment
analysis struggles with sarcasm. But when the alternative is trawling through thousands of
comments, the trade-off becomes easy to make. A little sentiment analysis can get you a long
way when you‟re looking to gauge overall Twitter sentiment on a topic. This is especially true
when you compare the sentiment scores with other data that accompanies the text.
Regression: Regression is a way of describing numerical relationship between a variable to
predictor variables that is the outcome. The dependent variable is also referred to as Y which
is plotted on the vertical axis (ordinate) of a graph. The predictor variable(s) is (are) also
referred as independent prognostic or explanatory variable denoted by X. The horizontal axis
(abscissa) of a graph is used for plotting X.
Regression analysis is a form of predictive modeling technique which investigates the
relationship between a dependent (target) and independent variable (predictor). This technique
is used for forecasting, time series modeling and finding the causal effect relationship
between the variables. For example, the relationship between rash driving and the number of
road accidents by a driver is best studied through regression.
Regression analysis is an important tool for modeling and analyzing data. Here, we fit a
curve/ line to the data points, in such a manner that the differences between the distances of
data points from the curve or line are minimized.
If we want to estimate growth in sales of a company based on current economic conditions
having the recent company data which indicates that the growth in sales is around two and a
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half times the growth in the economy, regression analysis is extremely useful. Using this
insight, we can predict future sales of the company based on current & past information.
There are multiple benefits of using regression analysis. They are as follows:
It indicates the significant relationships between the dependent variable and independent
variable.
It indicates the strength of the impact of multiple independent variables on a dependent
variable.
There are various kinds of regression techniques available to make predictions. These
techniques are mostly driven by three metrics (number of independent variables, type of
dependent variables and shape of the regression line). Logistic regression is used to find the
probability of event=Success and event=Failure. We should use logistic regression when the
dependent variable is binary (0/ 1, True/ False, Yes/ No) in nature. Here the value of Y ranges
from 0 to 1 and it can represent by the following equation.
where p is the probability of the presence of the characteristic of interest.
Figure 1 Logistic Regression Example
2. REGRESSION METRICS
Variance score: Variance is a measure of difference between the observed values to that of
the average of predicted values, i.e., their difference from the predicted value means.
Mean absolute error (MAE): The MAE measures the average magnitude of the errors in a
set of forecasts, without considering their direction. It measures accuracy for continuous
variables. The equation is given in the library references. Expressed in words, the MAE is the
average over the verification sample of the absolute values of the differences between forecast
and the corresponding observation. The MAE is a linear score which means that all the
individual differences are weighted equally in the average.
odds= p/ (1-p) = probability of event occurrence / probability of not event occurrence
ln(odds) = ln(p/(1-p))
logit(p) = ln(p/(1-p)) = b0+b1X1+b2X2+b3X3. ...................... +bkXk
5. Subhadra Kompella and Kalyana Chakravarthy Chilukuri
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Root mean squared error (RMSE): The RMSE is a quadratic scoring rule which measures
the average magnitude of the error. The equation for the RMSE is given in both of the
references. Expressing the formula in words, the difference between forecast and
corresponding observed values are each squared and then averaged over the sample. Finally,
the square root of the average is taken. Since the errors are squared before they are averaged,
the RMSE gives a relatively high weight to large errors. This means the RMSE is most useful
when large errors are particularly undesirable.
The MAE and the RMSE can be used together to diagnose the variation in the errors in a
set of forecasts. The RMSE will always be larger or equal to the MAE; the greater the
difference between them, the greater the variance in the individual errors in the sample. If the
RMSE=MAE, then all the errors are of the same magnitude
Both the MAE and RMSE can range from 0 to ∞. They are negatively-oriented scores:
Lower values are better.
Mean square error (MSE): It is the average of the square of the errors. The larger value of
mean implies larger error. The error, in this case, means the difference between the observed
values y1, y2, y3, … and the predicted one's pred(y1), pred(y2), pred(y3), … We square each
difference (pred(Yn) – Yn)) ** 2, so that negative and positive values do not cancel each other
out.
3. RELATED WORK
[1] proposes an approach towards the prediction of stock market trends using machine
learning models like the Random Forest model and Support Vector Machine. The Random
Forest model is an ensemble learning method that has been an exceedingly successful model
for classification and regression. Support vector machine is a machine learning model for
classification.
[2] studies stock market prediction on the basis of sentiments of Twitter feeds which was
experimented on the S&P 100 index. A continuous Dirichlet Process Mixture model was used
to learn the daily topic set. Stock index and Twitter sentiment time-series were then
regressed to make a prediction.
[3] analyzes data retrieved from Twitter issued to predict the public mood. A Self Organizing
Fuzzy Neural Network is used on predicted mood from the Twitter feeds and Dow Jones
Industrial Average values from the previous day to predict the movement of the stock market.
[4] uses the data from financial news articles to predict short-term movement of stock
price. The movement of the stock price is classified three different classes representing
three different directions ,namely “up ”,“ down” , and “unchanged”. A naive Bayesian text
classifier is used to predict the direction of the movement of the stock price by deriving
a set of indicators from the textual data retrieved from various financial news articles.
[5] presented an overview of artificial neural networks modeling process in predicting stock
market price. This paper also discussed the problems encountered in implementing neural
networks for prediction the future trends of stock market.
[6] developed a framework for power system short term load forecasting using feature
selection model. Along with this the authors also used SVM to forecast load for simple and
nonlinear loads.
[7] This paper evaluates the effectiveness of neural network models which are known to be dynamic and
effective in stock-market predictions. The models analyzed are artificial neural network (ANN) trained with
gradient descent (GD) technique, ANN trained with genetic algorithm (GA) and functional link neural network
(FLANN) trained with GA. Experimental results and analysis has been presented to show the performance of
different models.
6. Stock Market Prediction Using Machine Learning Methods
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[8] The authors in this paper presented a four layered model involving fuzzy multiagent system architecture to
develop an artificial intelligent model to perform tasks like data preprocessing and stock market prediction.
[9] This paper developed a two stage neural network by combining Support vector machines and Empirical
Mode Decomposition for predicting stock market. Experimental results proved that the combined model shows
better prediction when compared to simple SVM.
4. METHODOLOGY
The implementation of this paper begins with preprocessing the data collected from stock
market pickled data set. This preprocessed data is classified using popular machine learning
algorithm to calculate the polarity score. In order to prepare the data ready to apply Random
forest algorithm, noise in the data is removed by smoothing. The working of random forest
algorithm is presented below:
i. Randomly select “k” features from total “m” features, where k << m
ii. Among the “k” features, calculate the node “d” using the best split point.
iii. Split the node into daughter nodes using the best split.
iv. Repeat 1 to 3 steps until the “l” number of nodes has been reached.
v. Build forest by repeating steps 1 to 4 for “n” number times to create “n” number of trees.
Random forest algorithm starts by randomly selecting k features from m available
features. Over the k selected features a point d has to be selected in order to split the features.
This process would be executed iteratively to obtain the tree structure with a root node and
leaf nodes as the target features to be processed further. This results in n number of trees in
the generated forest. The algorithm is now tested for its efficiency by measuring the accuracy
of predicting the stock price and also by calculating the variance score generated by the
algorithm, finally ending up the process by comparing random forest with logistic regression.
The experimental results obtained prove that Random Forest algorithm is efficient in
predicting the stock price through achieving better score of the regression metrics over
logistic regression. The results obtained are plotted in the form of a graph as presented below:
All the calculations are done based upon the four regression values variance score, mean
absolute error, mean squared error, mean squared log error.
Sample Python code for variance score:
def
score(y_tru
e, y_pred):
import
numpy as
np
y_diff_avg = np.average(np.array([y_true]) - np.array([y_pred]))
numerator = np.average((np.array([y_true]) - np.array([y_pred]) - y_diff_avg) ** 2)
y_true_avg = np.average(y_true, axis=0)
denominator = np.average((y_true - y_true_avg) ** 2)
nonzero_numerator =
numerator != 0
nonzero_denominator =
denominator != 0
7. Subhadra Kompella and Kalyana Chakravarthy Chilukuri
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valid_score = nonzero_numerator &
nonzero_denominator output_scores =
np.ones(y_true)
output_scores[valid_score] = 1 - (numerator[valid_score] /
denominator[valid_score]) output_scores[nonzero_numerator &
~nonzero_denominator] = 0
return np.average(output_scores)
Sample Python Code for SMP using RandomForest
def _set_oob_score(self, X, y):
"""Compute out-of-bag score"""
X = check_array(X, dtype=DTYPE, accept_sparse='csr')
n_classes_ =
self.n_classes_
n_samples = y.shape[0]
oob_decision_function
= [] oob_score = 0.0
predictions = []
for k in range(self.n_outputs_):
predictions.append(np.zeros((n_samples,
n_classes_[k])))
for estimator in self.estimators_:
unsampled_indices =
_generate_unsampled_indices(
estimator.random_state, n_samples)
p_estimator = estimator.predict_proba(X[unsampled_indices, :],
check_input=False)
if self.n_outputs_ == 1:
p_estimator = [p_estimator]
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for k in range(self.n_outputs_):
predictions[k][unsampled_indices, :] +=
p_estimator[k]
for k in range(self.n_outputs_):
if (predictions[k].sum(axis=1) == 0).any():
warn("Some inputs do not have OOB scores.
")
decision = (predictions[k] /
predictions[k].sum(axis=1)[:,
np.newaxis])
oob_decision_function.append(decision)
oob_score += np.mean(y[:, k] ==
np.argmax(predictions[k], axis=1), axis=0)
if self.n_outputs_ == 1:
self.oob_decision_function_ = oob_decision_function[0]
else:
self.oob_decision_function_ =
oob_decision_function self.oob_score_ =
oob_score / self.n_outputs_
Regression metrics recorded for logistic regression:
EVS : -3.2762106267273907
Mean absolute error :
1595.0595446425434 Mean squared
error : 3823664.4016104033 Mean
squared log error:
0.013768419455969015
Regression metrics recorded For Random Forest:
EVS : -0.40059426107559504
Mean absolute error :
1139.3280327868883 Mean
squared error :
1679163.3107885325
9. Subhadra Kompella and Kalyana Chakravarthy Chilukuri
http://www.iaeme.com/IJCET/index.asp 28 editor@iaeme.com
Mean squared log error : 0.004777722468450043
Figure 1: Random Forest without Smoothing
Figure 2: Random Forest without Smooting Labelled
Figure 3: Random Forest after Aligning.
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Figure 4 Random Forest with Aligning/Smoothing
Figure 5 Prices Comparison of Original and Predicted Prices without Applying Algorithm
It is very much evident from the above graphs that for all regression metrics Random
Forest algorithm produced better results when compared to Logistic Regression method.
5. CONCLUSION
In this paper, we predicted stock market which takes input as the price of stock and news
heading. We used sentiment analysis to calculate the polarity score and then use it further in
detecting the type of article has a positive or negative impact towards the stock and those can
be used further in the analysis. The obtained scores are used to calculate the prices of stock
and to complete those inputs as a set we used the exponential moving average method and
saved as the impact of stock is correctly determined. The data after calculating is updated and
displayed to the user as a graph. Finally we applied random forest algorithm and compared
with logistic regression for efficiency. Variance score of Random forest is better than that of
logistic regression. Mean absolute score of Random forest is better than that of logistic
regression. Mean squared score of Random forest is better than that of logistic regression.
Mean squared log error score of Random forest is better than that of logistic regression. In all,
it can be concluded that the random forest algorithm is much efficient compared to logistic
regression for the stock market prediction based on sentiment analysis.
11. Subhadra Kompella and Kalyana Chakravarthy Chilukuri
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REFERENCES
[1] Sahaj Singh Maini,Govinda.K, " Stock Market Prediction using Data Mining Techniques,"
IEEE International Conference on Intelligent Sustainable Systems, 2017.
[2] Si, J., Mukherjee, A., Liu, B., Li, Q., Li, H., &Deng, X, Stock Market Prediction On The
Basis Of Sentiments Of TwitterFeeds , 2013.
[3] Mittal, A., & Goel , A., Sentiment Analysis On Twitter Feeds to Discover The
Interrelationship Among “Public Sentiment “And The “Market Sentiment, 2012
[4] Gidofalvi, G., & Elkan, C, Predict Short-term Movement Of Stock Price Using Financial
News Articles, 2003.
[5] Olivier C., Blaise Pascal University: “Neural network modeling for stock movement
prediction, state of art”. 2007
[6] Leng, X. and Miller, H.-G. : “Input dimension reduction for load forecasting based on
support vector machines”, IEEE International Conference on Electric Utility Deregulation,
Restructuring and Power Technologies (DRPT2004), 2004.
[7] Nayak, S.C. :“Index prediction with neuro-genetic hybrid network: A comparative
analysis of performance”, IEEE International Conference on Computing, Communication,
and Applications (ICCCA), pp. 1-6, 2012.
[8] Fazel Z.,Esmaeil H., Turksen B.: “A hybrid fuzzy intelligent agent-based system for stock
price prediction”, International Journal of Intelligent Systems, 2012.
[9] Honghai Y., Haifei L.: “Improved Stock Market Prediction by Combining Support Vector
Machine and Empirical Mode Decomposition”,Fifth International Symposium on
Computational Intelligence and Design (ISCID), 2012