This document describes a project that uses neural networks to analyze factors influencing the behavior of Indian markets. It evaluates 20 macroeconomic and microeconomic variables from the Reserve Bank of India and other sources and 20 financial ratios of companies. Factor analysis is used to identify underlying factors from the variables. A neural network model is developed with the factors as inputs to predict market behavior metrics as outputs. The neural network aims to generate a model that can be used for predictive analysis of Indian financial markets.
Kiss Ágnes informatika tanár, Galamb József
Mezőgazdasági Szakközépiskola, Makó
"Európában elismert szakmai bizonyítvány informatikai szakképzésben tanuló diákoknak" konferencia, 2015. szeptember 29.
This document is a curriculum vita for Abd El Rahman Ezzat Abd El Rahim Moustafa. It includes his personal details such as name, address, phone number, email, date of birth, and marital status. It outlines his education including graduating from Alex University's Faculty of Commerce with an accounting degree in 2011. It describes his work experiences including roles as an accountant in Egypt from 2009 to 2013 and as a sales executive in Qatar since 2014. It also lists his languages as Arabic and English, computer skills in Microsoft Office, and soft skills such as working under pressure and problem solving. His objective is stated as seeking an accounting role.
Experiences of EUCIP module exam: OperateITStudy Ltd.
Erika Menyhárt deputy director, BMSZC NJSZKI
"European Level Competitive IT Certification for the Hungarian Vocational Education" Conference
Budapest, 29th of September
Eucip Core Operate - Üzemeltetés modul - tapasztalatokITStudy Ltd.
Menyhárt Erika igazgatóhelyettes, Budapesti
Műszaki Szakképzési Centrum Neumann János
Számítástechnikai Szakközépiskolája, Budapest
"Európában elismert szakmai bizonyítvány informatikai szakképzésben tanuló diákoknak" konferencia, 2015. szeptember 29.
Kiss Ágnes informatika tanár, Galamb József
Mezőgazdasági Szakközépiskola, Makó
"Európában elismert szakmai bizonyítvány informatikai szakképzésben tanuló diákoknak" konferencia, 2015. szeptember 29.
This document is a curriculum vita for Abd El Rahman Ezzat Abd El Rahim Moustafa. It includes his personal details such as name, address, phone number, email, date of birth, and marital status. It outlines his education including graduating from Alex University's Faculty of Commerce with an accounting degree in 2011. It describes his work experiences including roles as an accountant in Egypt from 2009 to 2013 and as a sales executive in Qatar since 2014. It also lists his languages as Arabic and English, computer skills in Microsoft Office, and soft skills such as working under pressure and problem solving. His objective is stated as seeking an accounting role.
Experiences of EUCIP module exam: OperateITStudy Ltd.
Erika Menyhárt deputy director, BMSZC NJSZKI
"European Level Competitive IT Certification for the Hungarian Vocational Education" Conference
Budapest, 29th of September
Eucip Core Operate - Üzemeltetés modul - tapasztalatokITStudy Ltd.
Menyhárt Erika igazgatóhelyettes, Budapesti
Műszaki Szakképzési Centrum Neumann János
Számítástechnikai Szakközépiskolája, Budapest
"Európában elismert szakmai bizonyítvány informatikai szakképzésben tanuló diákoknak" konferencia, 2015. szeptember 29.
MEG, Micro Experimental Growing, is an Internet connected, open source digital greenhouse. Thanks to the control of all vital parameters of a plant and the free sharing of recipes for growth, it has created new horizons in the domestic cultivation anywhere in the world.
Delivered at the Network Society World Congress in Turin, Italy, on October 15, 2015 - http://netsoc.org/congress
Tapasztalatok és ajánlások - EUCIP Core vizsgák MagyarországonITStudy Ltd.
Balassa Ildikó, SZÁMALK-Szalézi Szakközépiskola, Budapest
"Európában elismert szakmai bizonyítvány informatikai szakképzésben tanuló diákoknak" konferencia, 2015. szeptember 29.
Workshop vluchtelingen en asielzoekers - versie sept 2015Tumult vzw
Workshop over vluchtelingen en asielzoekers. Gemaakt door Tumult, op maat van het vierde en vijfde middelbaar. Meer info op http://tumult.be/producten/workshop-vluchtelingen-en-asielzoekers
EUCIP Core vizsga - PLAN - Tervezés modulITStudy Ltd.
Gondán Anita gazdasági és marketing tanár,
Öveges József Szakképző Iskola, Balatonfűzfő
"Európában elismert szakmai bizonyítvány informatikai szakképzésben tanuló diákoknak" konferencia, 2015. szeptember 29.
La pandemia de COVID-19 ha tenido un impacto significativo en la economía mundial. Muchos países experimentaron fuertes caídas en el PIB y aumentos en el desempleo debido a los cierres generalizados y las restricciones a los viajes. Aunque las vacunas ofrecen esperanza de una recuperación económica en 2021, el panorama a corto plazo sigue siendo incierto dado el resurgimiento de casos en algunas partes del mundo.
This document provides requirements for implementing self-organizing network (SON) and operations and maintenance (O&M) use cases as recommended by the NGMN Alliance. It describes requirements for self-configuration use cases like planning and optimizing radio parameters for new network nodes. It also outlines requirements for self-optimization use cases such as neighbor cell list optimization and interference control. Additional sections cover fault management and correction requirements as well as O&M related SON use cases. The document is intended to provide vendors with guidelines for developing SON solutions that are generic, align with 3GPP standards, and address key use cases identified by mobile network operators.
Indian poultry sector has been growing at around 8-10% annually over the last decade with broiler meat volumes growing at more than 10% while table egg at 5-6% driven by increased domestic consumption. Domestic poultry meat production (broiler - carcass weight) is estimated to have increased from less than 1.0 million tons in 2000 to 3.4 million tons in 2012 with per capita consumption increasing from 0.8 kg to 2.8 kg p.a during same period. Table egg production is estimated to have increased from 30 billion eggs in 2000 to 66 billion eggs in 2012 with per capita egg consumption increasing from 28 to 55 eggs during that period. The healthy growth in poultry output over last decade makes India one of the fastest growing major world market in the segment with future growth potential remaining strong on back of wide gap against global per capital consumption norms and favorable socio economic factors. This sector has ever-growing and never ending demand in domestic as well as foreign market. Entrepreneurs interested can well venture in this sector.
Tags
How to Start Broiler Poultry Farming Processing Industry, Broiler Chicken Processing Industry, Most Profitable Broiler Production Business Ideas, Poultry Farming Processing & Broiler Chicken Based Profitable Projects, Poultry & Broiler Farming Processing Projects, Small Scale Broiler Farming Processing Projects, Starting Broiler Production Business, How to Start Broiler Chicken Production Business, Broiler Chicken Based Small Scale Industries Projects, New Small Scale Ideas In Poultry & Broiler Farming Processing Industry, Project Report on Poultry Farming Industries, Detailed Project Report on Poultry & Broiler Farming, Project Report on Chicken Broiler Production, Pre-Investment Feasibility Study on Broiler Chicken Production, Techno-Economic Feasibility Study on Poultry & Broiler Farming, Feasibility Report on Broiler Farming, Free Project Profile on Chicken Broiler Production, Project Profile on Broiler Farming, Download Free Project Profile on Poultry Farming, Industrial Project Report, Project Consultant, Project Consultancy, Npcs, Niir, Process Technology Books, Business Consultancy, Business Consultant, Project Identification and Selection, Preparation of Project Profiles, Startup, Business Guidance, Business Guidance to Clients, Startup Project for Poultry & Broiler Farming, Startup Project, Startup Ideas, Project for Startups, Startup Project Plan, Business Start-Up, Business Plan for Startup Business, Great Opportunity for Startup, Small Start-Up Business Project, Start-Up Business Plan for Broiler Production, Start Up India, Stand Up India, Small Scale Chicken Broiler Production Line, Modern Small and Cottage Scale Industries, Profitable Small and Cottage Scale Industries, Setting Up and Opening Your Poultry Farming Business, How to Start Chicken Farm Business?,
Fisheries and Aquaculture Sector in India: Business Opportunities in Fisherie...Ajjay Kumar Gupta
These days, people are turning to fish and shellfish as high-quality sources of protein. In fact, according to Seafood Business, nearly 10 billion pounds of seafood will be consumed each year. This high demand means lucrative business opportunities are available for entrepreneurs in the fisheries and aquaculture sector—including those who want to start their own processing plant, retail operation or import/export company.
𝐂𝐨𝐧𝐭𝐚𝐜𝐭 𝐮𝐬
NIIR PROJECT CONSULTANCY SERVICES, DELHI
An ISO 9001:2015 Company
ENTREPRENEUR INDIA
106-E, Kamla Nagar, Opp. Mall ST,
New Delhi-110007, India.
Email: npcs.ei@gmail.com
info@entrepreneurindia.co
Tel: +91-11-23843955, 23845654, 23845886
Mobile: +91-9097075054, 8800733955
Website: https://www.entrepreneurindia.co
https://www.niir.org
The generation, transmission, and distribution of power involve electrical facilities, apparatus, and components, to carry the electrical energy from its generating site to where it is utilized. An important part of this power system is the cable system that is used exclusively to carry power from the main substations to secondary substations at load centers.
Tags
How to Start PVC (polyvinyl chloride) Wires and Cables Processing Industry in India, PVC Wires and Cables manufacturing Industry in India, Most Profitable PVC Wires and Cables manufacturing Business Ideas , Small Scale PVC Wires and Cables manufacturing Projects, Starting a PVC Wires and Cables manufacturing Business, How to Start a PVC Wires and Cables Production Business, Detailed Project Report on PVC Wires and Cables manufacturing industry , Pre Feasibility report on PVC Wires and Cables manufacturing industry, Technical, Material Market Report on PVC Wires and Cables manufacturing industry,, PVC Wires and Cables manufacturing Market Industry - Trends, Prices, Manufacturing, Pre-Investment Feasibility Study on PVC Wires and Cables manufacturing industry, Techno-Economic feasibility study on PVC Wires and Cables manufacturing industry, Feasibility report on PVC Wires and Cables manufacturing industry, Free Project Profile on PVC Wires and Cables manufacturing industry, Project consultancy, NPCS, Niir, Process technology books, Business consultant, Startup Project for PVC (polyvinyl chloride) Wires and Cables manufacturing industry , Start-up Business Plan for PVC Wires and Cables manufacturing industry, PVC Wires and Cables Making Small Business Manufacturing, PVC Wires and Cables making machine factory, Modern small and cottage scale industries, How to Start a PVC Wires and Cables manufacturing business?, How to start a successful PVC Wires and Cables manufacturing business, PVC Wires and Cables manufacturing Business, Profitable Small Scale Manufacturing, City Profile & Geotechnical Site Characterization, Polyvinyl Chloride (PVC) Compounds, PVC Insulated Cables, Uses & Application of PVC Wires and Cables, Advantages Of PVC Wires & Cable, Different Types Of Wire & Its Designation, Market Survey on Power Cables, survey on Market Structure, survey report on Strategic Alliances, survey on Product Segmentation and Lead Players, Financials & Comparison Of Major Indian Players/Companies of PVC Wires & Cable, Profits & Appropriations of PVC Wires & Cable, Total Liabilities and Net Cash Flow From Operating Activities for PVC Wires & Cable, Name of Company of PVC Wires & Cable with Contact Details and Name of Director, Plant Capacity, Location of Plant, Name of Raw Material(S) Consumed With Quantity & Cost for PVC Wires & Cable, PVC Wire - Polyvinyl Chloride Wire Suppliers,
The Textile and Apparel Sector in India: A Booming Industry worth Investing In!Ajjay Kumar Gupta
If you’re thinking of starting your own business, you should consider the Indian textile and apparel sector, one of the most promising sectors in India today. As the world’s second-largest producer of cotton, which comprises 70% of the country’s exports. With rising disposable incomes and global demand for Indian products, there are many opportunities in this rapidly expanding sector. If you want to start your own business in this industry, learn more here!
𝐂𝐨𝐧𝐭𝐚𝐜𝐭 𝐮𝐬
NIIR PROJECT CONSULTANCY SERVICES, DELHI
An ISO 9001:2015 Company
ENTREPRENEUR INDIA
106-E, Kamla Nagar, Opp. Mall ST,
New Delhi-110007, India.
Email: npcs.ei@gmail.com
info@entrepreneurindia.co
Tel: +91-11-23843955, 23845654, 23845886
Mobile: +91-9097075054, 8800733955
Website: https://www.entrepreneurindia.co
https://www.niir.org
Lawn Tennis Balls Manufacturing Business | Start Your Own Lawn Tennis Balls B...Ajjay Kumar Gupta
Starting the production of lawn tennis balls is an exciting adventure that culminates in a tangible good. To ensure that the product is manufactured in a safe, effective, and cost-effective way, careful planning, research, and the appropriate resources are needed. We'll be looking at the processes involved in starting the production of lawn tennis balls from conception to completion in this blog post. We'll examine some of the crucial factors that must be taken into account before beginning production and talk about how to make sure the project is successful.
𝐂𝐨𝐧𝐭𝐚𝐜𝐭 𝐮𝐬
NIIR PROJECT CONSULTANCY SERVICES, DELHI
An ISO 9001:2015 Company
ENTREPRENEUR INDIA
106-E, Kamla Nagar, Opp. Mall ST,
New Delhi-110007, India.
Email: npcs.ei@gmail.com
info@entrepreneurindia.co
Tel: +91-11-23843955, 23845654, 23845886
Mobile: +91-9097075054, 8800733955
Website: https://www.entrepreneurindia.co
https://www.niir.org
The document discusses penetration testing methodologies used by EC-Council. It describes several certification programs offered through EC-Council Press that provide training for security analysts, network security administrators, disaster recovery professionals and other IT security roles. The document also outlines EC-Council's mission to address the need for well-educated information security practitioners and describes the organization's global network of subject matter experts who help set cybersecurity standards.
Start Your Own Business of Fiberglass Transparent LPG Cylinder with Our Step-...Ajjay Kumar Gupta
Are you interested in finding out how to make an LPG cylinder made of translucent fiberglass? In that case, you've found the proper site! We'll be talking about how Fiberglass Transparent LPG Cylinders are made in this blog post. We'll go over the supplies, equipment, and methods required to create a dependable, secure, and robust product. So continue reading if you want instructions on how to create your own Fiberglass Transparent LPG Cylinder!
𝐂𝐨𝐧𝐭𝐚𝐜𝐭 𝐮𝐬
NIIR PROJECT CONSULTANCY SERVICES, DELHI
An ISO 9001:2015 Company
ENTREPRENEUR INDIA
106-E, Kamla Nagar, Opp. Mall ST,
New Delhi-110007, India.
Email: npcs.ei@gmail.com
info@entrepreneurindia.co
Tel: +91-11-23843955, 23845654, 23845886
Mobile: +91-9097075054, 8800733955
Website: https://www.entrepreneurindia.co
https://www.niir.org
Ngen mvpn with pim implementation guide 8010027-002-enNgoc Nguyen Dang
This document provides guidance on implementing an NGEN Multicast Virtual Private Network (MVPN) using Protocol Independent Multicast (PIM) on a Juniper Networks network. It describes the network topology, hardware, software, and configuration steps used for validation. The configuration sections cover enabling PIM on core and provider edge routers, configuring inet-mvpn for IBGP sessions, enabling MVPN for individual VPN instances, and associating provider tunnels. Validation is done through checking the control and data plane operation, including PIM states and multicast traffic flows.
Microsoft Word - Quocirca - Managed Hosting in Europe - June 2009webhostingguy
Managed hosting describes the provision of ready-to-use IT infrastructure by providers to host customer applications. While traditionally each customer had dedicated servers, virtualization now allows infrastructure sharing between customers. The report analyzes the European managed hosting market, identifying four main provider types and reviewing major suppliers. It finds that while charging models vary, services typically adhere to standards for security and management and are driven by demand for Windows and Linux infrastructure.
The Agriculture Sector in India: Industry Overview and Business Opportunities...Ajjay Kumar Gupta
If you’re looking to put your money in an investment that will pay off handsomely over the next few years, you might want to consider the Indian agriculture sector. In recent years, there have been sweeping changes in the country’s farming policy as well as a big push towards agricultural exports and organic food production, all of which make this industry quite lucrative. Today, it’s time to take a look at some of the most promising opportunities in the field and the role that investors can play in the process.
𝐂𝐨𝐧𝐭𝐚𝐜𝐭 𝐮𝐬
NIIR PROJECT CONSULTANCY SERVICESAn ISO 9001:2015 Company
ENTREPRENEUR INDIA
106-E, Kamla Nagar, Opp. Mall ST,
New Delhi-110007, India.
Email: npcs.ei@gmail.com
info@entrepreneurindia.co
Tel: +91-11-23843955, 23845654, 23845886
Mobile: +91-9097075054, 8800733955
Website: https://www.entrepreneurindia.co
https://www.niir.org
The Indian Pharmaceuticals Sector: Investment Opportunities in Drugs, Medicin...Ajjay Kumar Gupta
The Indian pharmaceutical industry is one of the fastest growing sectors in India, and one of the main reasons for this booming growth has been the changing lives of Indian citizens who are now much more aware of their health and well-being. Here we’ll take a look at some of the major drivers affecting the growth of pharmaceuticals market in India, as well as what opportunities there are within this rapidly growing industry.
𝐂𝐨𝐧𝐭𝐚𝐜𝐭 𝐮𝐬
NIIR PROJECT CONSULTANCY SERVICES, DELHI
An ISO 9001:2015 Company
ENTREPRENEUR INDIA
106-E, Kamla Nagar, Opp. Mall ST,
New Delhi-110007, India.
Email: npcs.ei@gmail.com
info@entrepreneurindia.co
Tel: +91-11-23843955, 23845654, 23845886
Mobile: +91-9097075054, 8800733955
Website: https://www.entrepreneurindia.co
https://www.niir.org
MEG, Micro Experimental Growing, is an Internet connected, open source digital greenhouse. Thanks to the control of all vital parameters of a plant and the free sharing of recipes for growth, it has created new horizons in the domestic cultivation anywhere in the world.
Delivered at the Network Society World Congress in Turin, Italy, on October 15, 2015 - http://netsoc.org/congress
Tapasztalatok és ajánlások - EUCIP Core vizsgák MagyarországonITStudy Ltd.
Balassa Ildikó, SZÁMALK-Szalézi Szakközépiskola, Budapest
"Európában elismert szakmai bizonyítvány informatikai szakképzésben tanuló diákoknak" konferencia, 2015. szeptember 29.
Workshop vluchtelingen en asielzoekers - versie sept 2015Tumult vzw
Workshop over vluchtelingen en asielzoekers. Gemaakt door Tumult, op maat van het vierde en vijfde middelbaar. Meer info op http://tumult.be/producten/workshop-vluchtelingen-en-asielzoekers
EUCIP Core vizsga - PLAN - Tervezés modulITStudy Ltd.
Gondán Anita gazdasági és marketing tanár,
Öveges József Szakképző Iskola, Balatonfűzfő
"Európában elismert szakmai bizonyítvány informatikai szakképzésben tanuló diákoknak" konferencia, 2015. szeptember 29.
La pandemia de COVID-19 ha tenido un impacto significativo en la economía mundial. Muchos países experimentaron fuertes caídas en el PIB y aumentos en el desempleo debido a los cierres generalizados y las restricciones a los viajes. Aunque las vacunas ofrecen esperanza de una recuperación económica en 2021, el panorama a corto plazo sigue siendo incierto dado el resurgimiento de casos en algunas partes del mundo.
This document provides requirements for implementing self-organizing network (SON) and operations and maintenance (O&M) use cases as recommended by the NGMN Alliance. It describes requirements for self-configuration use cases like planning and optimizing radio parameters for new network nodes. It also outlines requirements for self-optimization use cases such as neighbor cell list optimization and interference control. Additional sections cover fault management and correction requirements as well as O&M related SON use cases. The document is intended to provide vendors with guidelines for developing SON solutions that are generic, align with 3GPP standards, and address key use cases identified by mobile network operators.
Indian poultry sector has been growing at around 8-10% annually over the last decade with broiler meat volumes growing at more than 10% while table egg at 5-6% driven by increased domestic consumption. Domestic poultry meat production (broiler - carcass weight) is estimated to have increased from less than 1.0 million tons in 2000 to 3.4 million tons in 2012 with per capita consumption increasing from 0.8 kg to 2.8 kg p.a during same period. Table egg production is estimated to have increased from 30 billion eggs in 2000 to 66 billion eggs in 2012 with per capita egg consumption increasing from 28 to 55 eggs during that period. The healthy growth in poultry output over last decade makes India one of the fastest growing major world market in the segment with future growth potential remaining strong on back of wide gap against global per capital consumption norms and favorable socio economic factors. This sector has ever-growing and never ending demand in domestic as well as foreign market. Entrepreneurs interested can well venture in this sector.
Tags
How to Start Broiler Poultry Farming Processing Industry, Broiler Chicken Processing Industry, Most Profitable Broiler Production Business Ideas, Poultry Farming Processing & Broiler Chicken Based Profitable Projects, Poultry & Broiler Farming Processing Projects, Small Scale Broiler Farming Processing Projects, Starting Broiler Production Business, How to Start Broiler Chicken Production Business, Broiler Chicken Based Small Scale Industries Projects, New Small Scale Ideas In Poultry & Broiler Farming Processing Industry, Project Report on Poultry Farming Industries, Detailed Project Report on Poultry & Broiler Farming, Project Report on Chicken Broiler Production, Pre-Investment Feasibility Study on Broiler Chicken Production, Techno-Economic Feasibility Study on Poultry & Broiler Farming, Feasibility Report on Broiler Farming, Free Project Profile on Chicken Broiler Production, Project Profile on Broiler Farming, Download Free Project Profile on Poultry Farming, Industrial Project Report, Project Consultant, Project Consultancy, Npcs, Niir, Process Technology Books, Business Consultancy, Business Consultant, Project Identification and Selection, Preparation of Project Profiles, Startup, Business Guidance, Business Guidance to Clients, Startup Project for Poultry & Broiler Farming, Startup Project, Startup Ideas, Project for Startups, Startup Project Plan, Business Start-Up, Business Plan for Startup Business, Great Opportunity for Startup, Small Start-Up Business Project, Start-Up Business Plan for Broiler Production, Start Up India, Stand Up India, Small Scale Chicken Broiler Production Line, Modern Small and Cottage Scale Industries, Profitable Small and Cottage Scale Industries, Setting Up and Opening Your Poultry Farming Business, How to Start Chicken Farm Business?,
Fisheries and Aquaculture Sector in India: Business Opportunities in Fisherie...Ajjay Kumar Gupta
These days, people are turning to fish and shellfish as high-quality sources of protein. In fact, according to Seafood Business, nearly 10 billion pounds of seafood will be consumed each year. This high demand means lucrative business opportunities are available for entrepreneurs in the fisheries and aquaculture sector—including those who want to start their own processing plant, retail operation or import/export company.
𝐂𝐨𝐧𝐭𝐚𝐜𝐭 𝐮𝐬
NIIR PROJECT CONSULTANCY SERVICES, DELHI
An ISO 9001:2015 Company
ENTREPRENEUR INDIA
106-E, Kamla Nagar, Opp. Mall ST,
New Delhi-110007, India.
Email: npcs.ei@gmail.com
info@entrepreneurindia.co
Tel: +91-11-23843955, 23845654, 23845886
Mobile: +91-9097075054, 8800733955
Website: https://www.entrepreneurindia.co
https://www.niir.org
The generation, transmission, and distribution of power involve electrical facilities, apparatus, and components, to carry the electrical energy from its generating site to where it is utilized. An important part of this power system is the cable system that is used exclusively to carry power from the main substations to secondary substations at load centers.
Tags
How to Start PVC (polyvinyl chloride) Wires and Cables Processing Industry in India, PVC Wires and Cables manufacturing Industry in India, Most Profitable PVC Wires and Cables manufacturing Business Ideas , Small Scale PVC Wires and Cables manufacturing Projects, Starting a PVC Wires and Cables manufacturing Business, How to Start a PVC Wires and Cables Production Business, Detailed Project Report on PVC Wires and Cables manufacturing industry , Pre Feasibility report on PVC Wires and Cables manufacturing industry, Technical, Material Market Report on PVC Wires and Cables manufacturing industry,, PVC Wires and Cables manufacturing Market Industry - Trends, Prices, Manufacturing, Pre-Investment Feasibility Study on PVC Wires and Cables manufacturing industry, Techno-Economic feasibility study on PVC Wires and Cables manufacturing industry, Feasibility report on PVC Wires and Cables manufacturing industry, Free Project Profile on PVC Wires and Cables manufacturing industry, Project consultancy, NPCS, Niir, Process technology books, Business consultant, Startup Project for PVC (polyvinyl chloride) Wires and Cables manufacturing industry , Start-up Business Plan for PVC Wires and Cables manufacturing industry, PVC Wires and Cables Making Small Business Manufacturing, PVC Wires and Cables making machine factory, Modern small and cottage scale industries, How to Start a PVC Wires and Cables manufacturing business?, How to start a successful PVC Wires and Cables manufacturing business, PVC Wires and Cables manufacturing Business, Profitable Small Scale Manufacturing, City Profile & Geotechnical Site Characterization, Polyvinyl Chloride (PVC) Compounds, PVC Insulated Cables, Uses & Application of PVC Wires and Cables, Advantages Of PVC Wires & Cable, Different Types Of Wire & Its Designation, Market Survey on Power Cables, survey on Market Structure, survey report on Strategic Alliances, survey on Product Segmentation and Lead Players, Financials & Comparison Of Major Indian Players/Companies of PVC Wires & Cable, Profits & Appropriations of PVC Wires & Cable, Total Liabilities and Net Cash Flow From Operating Activities for PVC Wires & Cable, Name of Company of PVC Wires & Cable with Contact Details and Name of Director, Plant Capacity, Location of Plant, Name of Raw Material(S) Consumed With Quantity & Cost for PVC Wires & Cable, PVC Wire - Polyvinyl Chloride Wire Suppliers,
The Textile and Apparel Sector in India: A Booming Industry worth Investing In!Ajjay Kumar Gupta
If you’re thinking of starting your own business, you should consider the Indian textile and apparel sector, one of the most promising sectors in India today. As the world’s second-largest producer of cotton, which comprises 70% of the country’s exports. With rising disposable incomes and global demand for Indian products, there are many opportunities in this rapidly expanding sector. If you want to start your own business in this industry, learn more here!
𝐂𝐨𝐧𝐭𝐚𝐜𝐭 𝐮𝐬
NIIR PROJECT CONSULTANCY SERVICES, DELHI
An ISO 9001:2015 Company
ENTREPRENEUR INDIA
106-E, Kamla Nagar, Opp. Mall ST,
New Delhi-110007, India.
Email: npcs.ei@gmail.com
info@entrepreneurindia.co
Tel: +91-11-23843955, 23845654, 23845886
Mobile: +91-9097075054, 8800733955
Website: https://www.entrepreneurindia.co
https://www.niir.org
Lawn Tennis Balls Manufacturing Business | Start Your Own Lawn Tennis Balls B...Ajjay Kumar Gupta
Starting the production of lawn tennis balls is an exciting adventure that culminates in a tangible good. To ensure that the product is manufactured in a safe, effective, and cost-effective way, careful planning, research, and the appropriate resources are needed. We'll be looking at the processes involved in starting the production of lawn tennis balls from conception to completion in this blog post. We'll examine some of the crucial factors that must be taken into account before beginning production and talk about how to make sure the project is successful.
𝐂𝐨𝐧𝐭𝐚𝐜𝐭 𝐮𝐬
NIIR PROJECT CONSULTANCY SERVICES, DELHI
An ISO 9001:2015 Company
ENTREPRENEUR INDIA
106-E, Kamla Nagar, Opp. Mall ST,
New Delhi-110007, India.
Email: npcs.ei@gmail.com
info@entrepreneurindia.co
Tel: +91-11-23843955, 23845654, 23845886
Mobile: +91-9097075054, 8800733955
Website: https://www.entrepreneurindia.co
https://www.niir.org
The document discusses penetration testing methodologies used by EC-Council. It describes several certification programs offered through EC-Council Press that provide training for security analysts, network security administrators, disaster recovery professionals and other IT security roles. The document also outlines EC-Council's mission to address the need for well-educated information security practitioners and describes the organization's global network of subject matter experts who help set cybersecurity standards.
Start Your Own Business of Fiberglass Transparent LPG Cylinder with Our Step-...Ajjay Kumar Gupta
Are you interested in finding out how to make an LPG cylinder made of translucent fiberglass? In that case, you've found the proper site! We'll be talking about how Fiberglass Transparent LPG Cylinders are made in this blog post. We'll go over the supplies, equipment, and methods required to create a dependable, secure, and robust product. So continue reading if you want instructions on how to create your own Fiberglass Transparent LPG Cylinder!
𝐂𝐨𝐧𝐭𝐚𝐜𝐭 𝐮𝐬
NIIR PROJECT CONSULTANCY SERVICES, DELHI
An ISO 9001:2015 Company
ENTREPRENEUR INDIA
106-E, Kamla Nagar, Opp. Mall ST,
New Delhi-110007, India.
Email: npcs.ei@gmail.com
info@entrepreneurindia.co
Tel: +91-11-23843955, 23845654, 23845886
Mobile: +91-9097075054, 8800733955
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𝐂𝐨𝐧𝐭𝐚𝐜𝐭 𝐮𝐬
NIIR PROJECT CONSULTANCY SERVICES, DELHI
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NIIR PROJECT CONSULTANCY SERVICES, DELHI
An ISO 9001:2015 Company
ENTREPRENEUR INDIA
106-E, Kamla Nagar, Opp. Mall ST,
New Delhi-110007, India.
Email: npcs.ei@gmail.com
info@entrepreneurindia.co
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vinay-project-report
1. Using Neural Networks to Explain Behavior of Indian Markets 06-October-2005
1
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Towards partial fulfillment of the requirements for the Post Graduate Diploma in Software
Enterprise Management of the Indian Institute of Management Bangalore
2. Using Neural Networks to Explain Behavior of Indian Markets 06-October-2005
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TTAABBLLEE OOFF CCOONNTTEENNTTSS
INTRODUCTION........................................................................................................................................... 3
VARIABLES EVALUATED ............................................................................................................................... 3
STRATEGY .................................................................................................................................................... 3
FACTORS ...................................................................................................................................................... 3
Factor 1 – RBI influence and Core sector................................................................................................. 3
Factor 2 – Foreign Exchange and Crude.................................................................................................. 3
Factor 3 – Agriculture, Total Domestic Product ....................................................................................... 3
Factor 4 – Company Financials ............................................................................................................... 3
Factor 5 – Company Ratios...................................................................................................................... 3
Factor 6 – Agriculture, Community services, debt structure with RBI........................................................ 3
Factor 7 – Company Capital structure, profitability ratios and other indicators........................................ 3
Factor 8 – Banking system residuals ........................................................................................................ 3
Factor 9 – Company Liquidity Ratios....................................................................................................... 3
Factor 10 – Company stock performance.................................................................................................. 3
Factor 11 – RBI balance sheet debt structure and errors .......................................................................... 3
Factor 12 – RBI balance sheet errors....................................................................................................... 3
Factor 13 – Company indicators (residuals)............................................................................................. 3
Factor 14 – Banking system residuals....................................................................................................... 3
Factor 15 – Company financial ratios, Residuals...................................................................................... 3
Factor 16 – Foreign Exchange, Crude and interest rate, Residuals........................................................... 3
Factor 17 – Company Financial Ratios, Residuals.................................................................................... 3
Factor 18 – Company Financial Ratios, Residuals.................................................................................... 3
Factor 19 – USD Forward Spot rate......................................................................................................... 3
Factor 20 – IDBI lending rate and crude prices........................................................................................ 3
COMPANIES .................................................................................................................................................. 3
CHOICE OF NEURAL NETWORK ............................................................................................................. 3
INPUTS AND OUTPUTS ................................................................................................................................... 3
HIDDEN LAYERS ........................................................................................................................................... 3
APPENDIX – FACTOR ANALYSIS.............................................................................................................. 3
TOTAL VARIANCE EXPLAINED ....................................................................................................................... 3
COMPONENT MATRIX.................................................................................................................................... 3
APPENDIX – NEURAL NET GENERATOR................................................................................................ 3
3. Using Neural Networks to Explain Behavior of Indian Markets 06-October-2005
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SOURCE FOR NEURALNETSCREENER ............................................................................................................. 3
SOURCE FOR STOCKMARKETNEURALNETCREATOR ....................................................................................... 3
4. Using Neural Networks to Explain Behavior of Indian Markets 06-October-2005
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IINNTTRROODDUUCCTTIIOONN
This project evaluates the impact of changes in macro and micro economic variables on
Indian financial markets. The data that is evaluated for this effort contains variables directly
controlled by Reserve Bank of India and other variables like price of crude etc.
The project uses a combination of statistical analysis and artificial intelligence techniques to
generate a model that could be used to predict the behavior of the markets.
Variables Evaluated
Following variables are evaluated as part of constructing this model.
• assetsWithBankingSystem – Total assets with the banking system
• bankCredit – Bank credit in India
• cash – Cash in hand
• investmentAtBookValue – Total bank investments at book value
• liabilitiesToBankingSystem – Total liabilities of banks to banking system
• liabilitiesToOthers – Total liability of banks other than banking system
• curcredit – Current account credit in INR
• curdebit – Current account debit in INR
• capcredit – Capital account credit in INR
• capdebit – Capital account debit in INR
• errcredit – Errors credit
• errdebit – Errors debit
• balcredit – Balance credit
• baldebit – Balance debit
• monmovcredit – Monetary movements credit
• monmovdebit – Monetary movements debit
• callMoneyHigh – Call money rate, High
• callMoneyLow – Call money rate, Low
• eps – Earning per share of the company
• ceps – Cash earning per share of the company
5. Using Neural Networks to Explain Behavior of Indian Markets 06-October-2005
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• bookValue – Book value of the company
• div – Dividend paid per share of the company
• opProfitPerShare – Operating profit per share of the company
• netOperatingIncomePerShare – Net operating income per share of the company
• freeReserves – Free reserves with the company
• opm – Operating profit margin of the company
• gpm – Gross profit margin of the company
• npm – Net profit margin of the company
• ronw – Return on net work of the company
• debtToEquity – Debt to equity ratio of the company
• currentRatio – Current ratio of the company
• quickRatio – Quick ratio of the company
• interestCover – Interest cover of the company
• salesByTotalAssets – Sales by total assets of the company
• salesByFixedAssets – Sales by fixed assets of the company
• salesByCurrentAssets – Sales by current assets of the company
• noOfDaysOfWorkingCapital – No of days of working capital with the company
• cpi – Consumer price index
• br – Bank Rate
• idbiRate – IDBI minimum term lending rate
• maxCMR – Maximum Call Money Rate
• maxPLR – Maximum prime lending rate
• minPLR – Minimum Prime lending rate
• price – Crude price
• totalINRdebt – Total debt in Indian Rupees
• concessionalDebtAsPercOfTotal – Concessional debt as a percentage of total
• shortTermDebtAsPercOfTotal – Short term debt as a percentage of total
• affConstant – Agriculture, Forestry and Fishing, GDP factor cost, Constant prices
• affCurrent – Agriculture, Forestry and Fishing, GDP factor cost, Current prices
6. Using Neural Networks to Explain Behavior of Indian Markets 06-October-2005
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• cspsConstant – Community social and personal services, GDP factor cost,
Constant prices
• cspsCurrent – Community social and personal services, GDP factor cost, Current
prices
• consConstant – Construction, GDP factor cost, Constant prices
• consCurrent – Construction, GDP factor cost, Current prices
• egwsConstant – Electricity, Gas and Water Services, GDP factor cost, Constant
prices
• egwsCurrent – Electricity, Gas and Water Services, GDP factor cost, Current
prices
• firebsConstant – Finance, Insurance, Real Estate and Business services, GDP
factor cost, Constant prices
• firebsCurrent – Finance, Insurance, Real Estate and Business services, GDP factor
cost, Current prices
• manuConstant – Manufacturing, GDP factor cost, Constant prices
• manuCurrent – Manufacturing, GDP factor cost, Current prices
• maqConstant – Mining and quarrying, GDP factor cost, Constant prices
• maqCurrent – Mining and quarrying, GDP factor cost, Current prices
• tdpConstant – Total domestic product, GDP factor cost, Constant prices
• tdpCurrent – Total domestic product, GDP factor cost, Current prices
• thrConstant – Trade, Hotel and Restaurant, GDP factor cost, Constant prices
• thrCurrent – Trade, Hotel and Restaurant, GDP factor cost, Current prices
• aff – Agriculture, Forestry and Fishing, GDP factor cost
• csps – Community social and personal services, GDP factor cost
• cons – Construction, GDP factor cost
• egws – Electricity, Gas and Water Services, GDP factor cost
• firb – Finance, Insurance, Real Estate and Business services, GDP factor cost
• manuf – Manufacturing, GDP factor cost
• min – Mining, GDP factor cost
• tdp – Total domestic product, GDP factor cost
7. Using Neural Networks to Explain Behavior of Indian Markets 06-October-2005
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• thr – Trade, Hotel and Restaurant, GDP factor cost
• currencyWithPublic – Total currency with Public
• m3 – Money supply, also referred to as stock of legal currency in the economy
• timeDepositsWithBank – Total time deposits with the bank
• totalIncome – Total income of RBI
• totalExpenditure – Total expenditure of RBI
• netAvailableBalance – Net available balance in RBI
• surplusToCentralGovernment – Surplus payable to central government from RBI
• totalIssuesLiabilities – Total liabilities, Issues
• totalIssuesAssets – Total assets, Issues
• totalBankingLiabilities – Total liabilities, Banking
• totalBankingAssets – Total assets, Banking
• reserveMoneyLiabilities – Reserve Money, Liabilities
• reserveMoneyAssets – Reserve Money, Assets
• forwardCashSpot – Forward Cash Spot, USD forward premia
• forwardCashOneMonth – Forward Cash one month, USD forward premia
• forwardCashThreeMonth – Forward Cash three months, USD forward premia
• forwardCashSixMonth – Forward Cash six months, USD forward premia
• forwardCash12Month – Forward cash twelve months, USD forward premia
• referenceRate – RBI reference rate for USD
• rate – US interest rate
• quantitiy – Quantity of particular stock traded
• turnover – Total turn over of stock traded
We would use above macro and micro economic indicators establish the relationship of these
indicators with following data for each company and sensitive index for a specific day.
• Previous day close
• Day open
• Day high
• Day low
• Day close
8. Using Neural Networks to Explain Behavior of Indian Markets 06-October-2005
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For each company and index, the model would be developed to predict prices four time
periods.
• Next day prices – +1d
• Prices after seven days – + 7d
• Prices after 15 days – +15d
• Prices after six months - +6m
Strategy
Since we are looking at very large number of input variables related to economic indicators
which may have heavy correlation between themselves, we will first use factor analysis to
identify a manageable set of factors that could be used as inputs for the neural network later to
develop the prediction model.
For each company four models would be constructed as follows.
• 1D model, which would predict the prices for next day given the stock price,
turnover and quantity for a day earlier to previous day.
• 7D model, which would predict the prices for next week given the stock price,
turnover and quantity for a week earlier
• 15D model, which would predict the prices 15 days down the line.
• 180D model, which would predict the prices six months down the line given the
stock price for a day.
Factors
After the factor analysis of the data, 96 inputs are reduced to 20 inputs with 95% of the
variance explained. These factors are as follows. As we can see from Appendix – Factor
Analysis, first 5 factors contribute 75% of the variance in the data while rest of the 15 factors
only adds approximately 20% of data.
As we go to later factors, these mostly cover the residual values from initial factors.
Factor 1 – RBI influence and Core sector
First factor signifies RBI’s influence and GDP related to the core sector on the market. It is
very highly correlated to the money supply variables and variables related to the RBI’s
9. Using Neural Networks to Explain Behavior of Indian Markets 06-October-2005
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balance sheet. It is also correlated to GDP in construction, manufacturing, agriculture and
forestry.
Variable Correlation
Total bank investments at book value (investmentAtBookValue) 0.991
Money Supply, stock of legal currency in the economy (m3) 0.984
Total currency with public (currencyWithPublic) 0.983
Construction GDP at factor cost, current prices (consCurrent) 0.983
Bank Credit in India (bankCredit) 0.981
Finance, Insurance, Realestate and Business services, GDP at factor cost, current prices
(firebsCurrent)
0.981
Total time deposits with bank (timeDepositsWithBank) 0.977
Reserve Money Assets (reserveMoneyAssets) 0.977
Finance, Insurance, Realestate and Business services, GDP at factor cost, constant
prices (firebsConstant)
0.972
Current account debit (curdebit) 0.961
Manufacturing, GDP at factor cost, current prices (manuCurrent) 0.961
Construction GDP at factor cost, constant prices (consConstant) 0.955
Total banking liabilities (totalBankingLiabilities) 0.955
Consumer price index (cpi) 0.954
Trade, hotel and Restaurant, Current prices (thrCurrent) 0.946
Total assets (issues) (totalIssuesAssets) 0.945
Electricity, Gas and Water services, GDP at factor cost (Egws) 0.938
Total domestic product (Tdp) 0.937
Maximum prime lending rate (maxPLR) -0.925
Total expenditure of RBI totalExpenditure 0.904
Mining and quarrying, GDP at factor cost, current prices (maqCurrent) 0.878
Surplus payable to central government from RBI(surplusToCentralGovernment) -0.875
Call money rate, Low (callMoneyLow) -0.855
Agriculture, Forestry and Fishing (Aff) 0.846
Monetory movements, debit (Monmovdebit) 0.824
10. Using Neural Networks to Explain Behavior of Indian Markets 06-October-2005
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Total debt in INR (totalINRdebt) 0.819
Cash with RBI (Cash) 0.813
Short term debt as percentage of total debt (shortTermDebtAsPercOfTotal) 0.577
Factor 2 – Foreign Exchange and Crude
The second factor primarily signifies impact of international events on markets. It correlated
highly with crude prices and prices of USD.
Variable Correlation
USD rate 0.542
Crude price 0.555
Tourism, Hotels and Restaurants -0.569
Capital account credit 0.607
Bank rate 0.615
Capital account credit (RBI) 0.691
RBI reference rate -0.811
Factor 3 – Agriculture, Total Domestic Product
Third factor has very high correlation with Agriculture, Forestry and Fishing. It also correlates
well with total domestic product.
Variable Correlation
Agriculture, Forestry and Fishing, GDP at factor cost, Constant prices (affConstant) 0.596
Community social and personal services, GDP at factor cost, constant prices (cspsConstant) 0.595
Total domestic product, constant prices (tdpConstant) 0.577
Agriculture, Forestry and Fishing, GDP at factor cost, Current prices (affCurrent) 0.515
Community social and personal services, GDP at factor cost, current prices (cspsCurrent) 0.502
Concessional debt as a percentage of total debt. (concessionalDebtAsPercOfTotal) -0.498
Factor 4 – Company Financials
Factor 4 seems to have very high correlation with the financials of the company.
Variable Correlation
Cash earnings per share (ceps) 0.903
11. Using Neural Networks to Explain Behavior of Indian Markets 06-October-2005
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Operating profit per share (opProfitPerShare) 0.878
Book value of the company (bookValue) 0.770
Free reserves with the company (freeReserves) 0.762
Net operating income per share (netOperatingIncomePerShare) 0.679
Earnings per share (eps) 0.544
Dividend (div) 0.527
Factor 5 – Company Ratios
Factor 5 correlates well with the financial ratios of the company.
Variable Correlation
Gross profit margin(gpm) 0.859
Operating profit margin (opm) 0.818
Current ratio (currentRatio) 0.712
Number of days of working capital (noOfDaysOfWorkingCapital) 0.649
Quick ratio (quickRatio) 0.552
Factor 6 – Agriculture, Community services, debt structure with RBI
Variables Correlation
Concessional debt as percentage of total (concessionalDebtAsPercOfTotal) 0.616
Agriculture, Forestry and Fishing, GDP factor cost, Current prices (affCurrent) 0.535
Assets with banking system (assetsWithBankingSystem) -0.528
Community social and personal services, GDP factor cost, Constant prices (cspsConstant) -0.452
Factor 7 – Company Capital structure, profitability ratios and other indicators
Variable Correlation
Share capital (shareCapital) 0.802
Total outstanding shares (outstandingShares) 0.786
Sales by total assets (salesByTotalAssets) 0.532
Return on net worth (ronw) -0.505
12. Using Neural Networks to Explain Behavior of Indian Markets 06-October-2005
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Sales by fixed assets (salesByFixedAssets) -0.443
Sales by current assets (salesByCurrentAssets) -0.300
Factor 8 – Banking system residuals
Variables Correlation
Monetary movement of credit (monmovcredit) 0.577
Banks liability to others (liabilitiesToOthers) 0.527
Banks liability to banking system (liabilitiesToBankingSystem) -0.423
Factor 9 – Company Liquidity Ratios
Variable Correlation
Debt to equity (debtToEquity) 0.863
Sales by fixed assets (salesByFixedAssets) 0.772
Quick Ratio (quickRatio) 0.644
Factor 10 – Company stock performance
Variable Correlation
Earning per share (eps) 0.649
Dividend paid (div) 0.444
Interest cover (interestCover) 0.422
Net operating income per share (netOperatingIncomePerShare) -0.373
Book value of the company (bookValue) -0.339
Factor 11 – RBI balance sheet debt structure and errors
Variables Correlation
Errors in credit (errcredit) -0.584
Error in debit (errdebit) 0.539
Concessional debt as percentage of total debt (concessionalDebtAsPercOfTotal) -0.448
13. Using Neural Networks to Explain Behavior of Indian Markets 06-October-2005
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IDBI lending rate (idbiRate) 0.395
Short term debt as percentage of total debt (shortTermDebtAsPercOfTotal) 0.276
Factor 12 – RBI balance sheet errors
Variables Correlation
Error in debit (errdebit) -0.566
Error in credit (errcredit) 0.410
Banks liabilities to banking system (liabilitiesToBankingSystem) -0.292
Short term debt as percentage of total debt (shortTermDebtAsPercOfTotal) 0.289
Factor 13 – Company indicators (residuals)
Variable Correlation
Net profit margin (npm) -0.614
Company identification (companyId) -0.447
Number of days of working capital (noOfDaysOfWorkingCapital) 0.429
Current ratio of company (currentRatio) 0.402
Factor 14 – Banking system residuals
Variable Correlation
Forward Cash Spot (forwardCashSpot) 0.416
Assets with banking system (assetsWithBankingSystem) 0.401
Liabilities with banking system (liabilitiesToBankingSystem) 0.329
14. Using Neural Networks to Explain Behavior of Indian Markets 06-October-2005
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Factor 15 – Company financial ratios, Residuals
Variables Correlation
Sales by current assets (salesByCurrentAssets) 0.540
Return on net worth (ronw) 0.445
Sales by total assets (salesByTotalAssets) 0.249
Factor 16 – Foreign Exchange, Crude and interest rate, Residuals
Variables Correlation
Interest cover (interestCover) 0.300
IDBI lending rate (idbiRate) 0.257
Reference rate of RBI (referenceRate) 0.241
Price of crude (price) 0.235
Forward cash spot (forwardCashSpot) -0.227
Factor 17 – Company Financial Ratios, Residuals
Variables Correlation
Company identifier (companyId) 0.695
Net profit margin (npm) -0.369
Dividend paid (div) 0.236
Sales by current assets (salesByCurrentAssets) 0.201
Sales by total assets (salesByTotalAssets) -0.186
Debt to equity (debtToEquity) -0.133
15. Using Neural Networks to Explain Behavior of Indian Markets 06-October-2005
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Factor 18 – Company Financial Ratios, Residuals
Variables Correlation
interestCover 0.491
salesByTotalAssets 0.299
opProfitPerShare -0.240
eps -0.226
npm 0.199
Factor 19 – USD Forward Spot rate
Variables Correlation
Forward cash spot (forwardCashSpot) 0.489
Banks liabilities to others (liabilitiesToOthers) -0.465
Factor 20 – IDBI lending rate and crude prices
Variables Correlation
Banks liability to others (liabilitiesToOthers) -0.281
Electricity, Gas and Water services, GDP at factor cost, current prices (egwsCurrent) -0.275
IDBI lending rate (idbiRate) 0.252
Crude oil price (price) 0.218
Companies
We would construct the models for majority of the companies in NSE-50 index. These
companies are listed below.
Reliance Industries TISCO SAIL
16. Using Neural Networks to Explain Behavior of Indian Markets 06-October-2005
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IPCL State Bank of India ITC
Tata Motors Maruti Udyog Satyam
VSNL Infosys TCS
ONGC Bharati L & T
Zee HDFC Ranbaxy
M & M BPCL ICICI Bank
Hero Honda Glaxo Colgate
National Aluminum Dabur SCI
Tata Power Sun Pharma Tata Tea
BHEL ABB Grasim
Gujrat Ambuja Cement HCL Tech Tata Chemicals
MTNL Oriental Bank Reliance Energy
GAIL Wipro Punjab National Bank
Bajaj Auto CIPLA Dr. Reddy
ACC HDFC Bank Hindustan Petro
Two companies have been omitted which are part of NSE-50 because enough data is not
available for them.
17. Using Neural Networks to Explain Behavior of Indian Markets 06-October-2005
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CCHHOOIICCEE OOFF NNEEUURRAALL NNEETTWWOORRKK
Inputs and Outputs
As shown in the Appendix – Factor Analysis, the economic indicators for model related to the
company have been factored into 20 factors that explain most of these numbers. Additional 3
inputs are company specific and are related to the past stock price data with respect to that
company.
• Previous Close
• Previous Turn Over
• Previous Quantity
These make up for the 23 variables that are used as inputs for neural network. Three different
neural networks are used for following three output variables
• High
• Low
• Close
Hidden Layers
It is assumed given the richness of the data that atleast 2 hidden layers would be required to
for a meaningful neural network. The neural network will have 23 inputs and will have 1
output.
Different neural networks would be created and a training run would be performed for a 1500
cycles of data set. At the end of sample run the best network would be chosen for further
training.
Neural networks that were evaluated are with
• 1 input layer with 23 inputs
• first hidden layer with nodes 31 to 351
• second hidden layer with nodes 8 to 31
• 1 output layer
18. Using Neural Networks to Explain Behavior of Indian Markets 06-October-2005
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Figure I Training and validation errors for different NN architectures
As see in Figure I, the neural network with hidden layer 1 of 130 nodes and hidden layer 2 of
17 node comes with best error values to be further used. This NN architecture was used to
further train the network with following different data sets.
• Daily prices
o High
o Low
o Close
• Weekly prices
o High
o Low
o Close
• Fortnightly prices
o High
o Low
o Close
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• Six Monthly prices
o High
o Low
o Close
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AAPPPPEENNDDIIXX –– NNEEUURRAALL NNEETT GGEENNEERRAATTOORR
Source for NeuralNetScreener
/*
* NeuralNetScreener.java
*
* Created on October 12, 2005, 8:46 PM
*
*/
package com.avasthi.stockneuralnetgenerator;
import java.text.SimpleDateFormat;
import java.util.*;
import java.io.*;
import javax.print.attribute.standard.Finishings;
import org.joone.engine.*;
import org.joone.engine.learning.*;
import org.joone.net.*;
import org.joone.util.DynamicAnnealing;
import org.joone.io.*;
import org.joone.util.*;
import org.joone.engine.weights.*;
/**
*
* @author binny
*/
public class NeuralNetScreener implements org.joone.engine.NeuralNetListener,
org.joone.net.NeuralValidationListener{
String baseFolder_;
int trainingDataPoints_;
int validationDataPoints_;
String trainingFile_;
String validationFile_;
int trainingStart_;
int validationStart_;
int testingStart_;
String inputColumnSelector_;
String outputColumnSelector_;
org.joone.net.NeuralNet net_;
int inputSize_;
int hidden1Size_;
int hidden2Size_;
int outputSize_;
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String filename_;
PrintStream pStrm_;
ObjectOutputStream nnStrm_;
int totCicles_;
/** Creates a new instance of NeuralNetScreener */
public NeuralNetScreener(String baseFolder,
String trainingFile,
String validationFile,
int trainingDataPoints,
int validationDataPoints,
int inputSize,
int hidden1Size,
int hidden2Size,
int outputSize,
int totCicles) {
totCicles_ = totCicles;
baseFolder_ = baseFolder;
trainingFile_ = trainingFile;
validationFile_ = validationFile;
trainingDataPoints_ = trainingDataPoints;
validationDataPoints_ = validationDataPoints;
inputColumnSelector_ = new String("1-23");
outputColumnSelector_ = new String("24-30");
net_ = new org.joone.net.NeuralNet();
inputSize_ = inputSize;
hidden1Size_ = hidden1Size;
hidden2Size_ = hidden2Size;
outputSize_ = outputSize;
String filename = baseFolder + "/stockPrediction." + inputSize_ + "." + hidden1Size_ + "."
+ hidden2Size_ + "." +outputSize_;
System.out.println("File name :" + filename);
try {
filename_ = filename;
FileOutputStream fos = new FileOutputStream(filename + ".txt");
pStrm_ = new PrintStream(fos);
FileOutputStream stream = new FileOutputStream(filename + ".ser");
nnStrm_ = new ObjectOutputStream(stream);
}
catch (Exception ex) {
System.out.println("File count not be opened " + filename);
pStrm_ = System.out;
}
}
/**
* used to build a network
*/
public void buildNetwork(boolean singleOutput, String outputColumn) {
// build the input, hidden and output layer
LinearLayer input = new LinearLayer();
SigmoidLayer hidden1 = new SigmoidLayer();
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SigmoidLayer hidden2 = new SigmoidLayer();
SigmoidLayer output = new SigmoidLayer();
input.setLayerName("input");
hidden1.setLayerName("hidden1");
hidden2.setLayerName("hidden2");
output.setLayerName("output");
input.setRows(inputSize_);
hidden1.setRows(hidden1Size_);
hidden2.setRows(hidden2Size_);
if (singleOutput) {
output.setRows(1);
}
else {
output.setRows(outputSize_);
}
// add the layers to the net.
net_.addLayer(input, NeuralNet.INPUT_LAYER);
net_.addLayer(hidden1, NeuralNet.HIDDEN_LAYER);
net_.addLayer(hidden2, NeuralNet.HIDDEN_LAYER);
net_.addLayer(output, NeuralNet.OUTPUT_LAYER);
// creating the synapses to link the layers
FullSynapse inputHiddenSynapse = new FullSynapse(); // In -> Hid
FullSynapse hiddenHiddenSynapse = new FullSynapse(); // Hid -> Hid
FullSynapse hiddenOutputSynapse = new FullSynapse(); // Hid -> Out
inputHiddenSynapse.setName("InputHidden");
hiddenHiddenSynapse.setName("HiddenHidden");
hiddenOutputSynapse.setName("HiddenOutput");
// wire them
input.addOutputSynapse(inputHiddenSynapse);
hidden1.addInputSynapse(inputHiddenSynapse);
hidden1.addOutputSynapse(hiddenHiddenSynapse);
hidden2.addInputSynapse(hiddenHiddenSynapse);
hidden2.addOutputSynapse(hiddenOutputSynapse);
output.addInputSynapse(hiddenOutputSynapse);
// add the monitor.
Monitor monitor = new Monitor();
monitor.getLearners().add(0, new String("org.joone.engine.BasicLearner"));
monitor.setLearningMode(0);
monitor.setUseRMSE(true);
// adding the monitor to the layers.
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input.setMonitor(monitor);
hidden1.setMonitor(monitor);
hidden2.setMonitor(monitor);
output.setMonitor(monitor);
net_.setMonitor(monitor);
FileInputSynapse trainingInputData = new FileInputSynapse();
trainingInputData.setFileName(trainingFile_);
/* The output values are on the third column of the file */
trainingInputData.setAdvancedColumnSelector(inputColumnSelector_);
trainingInputData.setFirstRow(1);
FileInputSynapse validationInputData = new FileInputSynapse();
validationInputData.setFileName(validationFile_);
/* The output values are on the third column of the file */
validationInputData.setAdvancedColumnSelector(inputColumnSelector_);
validationInputData.setFirstRow(1);
LearningSwitch ils = new LearningSwitch();
ils.addTrainingSet(trainingInputData);
ils.addValidationSet(validationInputData);
input.addInputSynapse(ils);
monitor.setLearningRate(0.8);
monitor.setMomentum(0.3);
monitor.addNeuralNetListener(this);
/* Setting of the file containing the desired responses, provided by a FileInputSynapse */
FileInputSynapse trainingOutputData = new FileInputSynapse();
trainingOutputData.setFileName(trainingFile_);
/* The output values are on the third column of the file */
if (singleOutput) {
trainingOutputData.setAdvancedColumnSelector(outputColumn);
}
else {
trainingOutputData.setAdvancedColumnSelector(outputColumnSelector_);
}
trainingOutputData.setFirstRow(1);
FileInputSynapse validationOutputData = new FileInputSynapse();
validationOutputData.setFileName(validationFile_);
/* The output values are on the third column of the file */
if (singleOutput) {
validationOutputData.setAdvancedColumnSelector(outputColumn);
}
else {
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validationOutputData.setAdvancedColumnSelector(outputColumnSelector_);
}
validationOutputData.setFirstRow(1);
LearningSwitch ols = new LearningSwitch();
ols.addTrainingSet(trainingOutputData);
ols.addValidationSet(validationOutputData);
TeachingSynapse trainer = new TeachingSynapse();
/* We give it the monitor's reference */
trainer.setDesired(ols);
trainer.setMonitor(monitor);
output.addOutputSynapse(trainer);
monitor.setTrainingPatterns(trainingDataPoints_); /* # of rows contained in the input file
*/
monitor.setTotCicles(totCicles_); /* How many times the net must be trained on the input
patterns */
monitor.setLearning(true); /* The net must be trained */
input.start();
hidden1.start();
hidden2.start();
output.start();
monitor.Go(); /* The net starts the training job */
net_.join();
}
public void saveNetwork(int kount) {
try {
FileOutputStream stream = new FileOutputStream(filename_ + "-"+ kount + ".ser");
ObjectOutputStream nnStrm = new ObjectOutputStream(stream);
nnStrm.writeObject(net_);
}
catch (Exception ex) {
System.out.println("Count not save neural network ");
}
}
public static NeuralNet restoreNeuralNet(String fileName) {
NeuralNet nnet = null;
try {
FileInputStream stream = new FileInputStream(fileName);
ObjectInputStream inp = new ObjectInputStream(stream);
nnet = (NeuralNet)inp.readObject();
}
catch (Exception excp) {
excp.printStackTrace();
}
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return nnet;
}
public static void loadAndTestData(String nnFile, String inputFile, int inputDataSize,
String outputFile) {
try {
NeuralNet net = restoreNeuralNet(nnFile);
Layer input = net.getInputLayer();
input.removeAllInputs();
org.joone.io.FileInputSynapse inp = new org.joone.io.FileInputSynapse();
inp.setFileName(inputFile);
inp.setAdvancedColumnSelector("1-23");
inp.setFirstRow(1);
input.addInputSynapse(inp);
Layer output = net.getOutputLayer();
output.removeAllOutputs();
org.joone.io.FileOutputSynapse out = new org.joone.io.FileOutputSynapse();
out.setFileName(outputFile);
output.addOutputSynapse(out);
net.getMonitor().setTotCicles(1);
net.getMonitor().setTrainingPatterns(inputDataSize);
net.getMonitor().setLearning(false);
net.start();
net.getMonitor().Go();
}
catch (Exception ex) {
System.out.println("Count not load neural network ");
}
}
public void netStarted(NeuralNetEvent ev) {
System.out.println("Net Started " +ev.toString());
}
public void netStopped(NeuralNetEvent ev) {
System.out.println("Net Stopped "+ev.toString());
}
public void netStoppedError(NeuralNetEvent ev, String str) {
System.out.println("Net Stopped Error "+ev.toString() + str);
}
public void errorChanged(NeuralNetEvent ev) {
}
/* Validation Event */
public void netValidated(NeuralValidationEvent event) {
// Shows the RMSE at the end of the cycle
NeuralNet NN = (NeuralNet)event.getSource();
pStrm_.println(",Validation Error,"+NN.getMonitor().getGlobalError());
}
public void cicleTerminated(NeuralNetEvent ev) {
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Monitor mon = (Monitor)ev.getSource();
// Prints out the current epoch and the training error
int cycle = mon.getCurrentCicle()+1;
if (cycle % 100 == 0) { // We validate the net every 100 cycles
String msg = new String("Epoch #"+(mon.getTotCicles() - cycle));
msg += ",Training Error,"+mon.getGlobalError();
// Creates a copy of the neural network
net_.getMonitor().setExporting(true);
NeuralNet newNet = net_.cloneNet();
net_.getMonitor().setExporting(false);
// Cleans the old listeners
// This is a fundamental action to avoid that the validating net
// calls the cicleTerminated method of this class
newNet.removeAllListeners();
// Set all the parameters for the validation
NeuralNetValidator nnv = new NeuralNetValidator(newNet);
nnv.addValidationListener(this);
newNet.getMonitor().setValidation(true);
newNet.getMonitor().setValidationPatterns(validationDataPoints_);
nnv.start(); // Validates the
Monitor m = (Monitor) (ev.getSource());
pStrm_.print("Cicle Terminated " + msg);
if (cycle % 1000 == 0) {
saveNetwork(cycle);
}
}
}
}
Source for StockMarketNeuralNetCreator
/*
* StockMarketPredictorGenerator.java
*
* Created on October 12, 2005, 8:44 PM
*/
package com.avasthi.stockneuralnetgenerator;
/**
*
* @author binny
*/
public class StockMarketNeuralNetCreator {
/**
* @param args the command line arguments
*/
public static void main(String[] args) {
int inputs = 23;
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int outputs = 7;
int trainingDataPoints = Integer.parseInt(args[3]);
int validationDataPoints = Integer.parseInt(args[4]);
int layer1 = Integer.parseInt(args[5]);
int layer2 = Integer.parseInt(args[6]);
int totCicles = 1500;
if (args.length >= 9) {
totCicles = Integer.parseInt(args[8]);
}
try {
System.out.println("Trying neural network with hidden layers "+layer1
+","+layer2);
NeuralNetScreener nns = new NeuralNetScreener(args[0],
args[1],
args[2],
trainingDataPoints,
validationDataPoints,
inputs,
layer1,
layer2,
outputs,
totCicles);
System.out.println("Base Directory" +
args[0] +
" Data File " +
args[1] +
" Number of Items " +
args[2]);
nns.buildNetwork(true, args[7]);
} catch(Exception ex) {
System.out.println(ex.toString());
}
}
}