...Using Neural Networks to Forecast Stock Market Prices Abstract This paper is a survey on the application of neural networks in forecasting stock market prices. With their ability to discover patterns in nonlinear and chaotic systems, neural networks offer the ability to predict market directions more accurately than current techniques. Common market analysis techniques such as technical analysis, fundamental analysis, and regression are discussed and compared with neural network performance. Also, the Efficient Market Hypothesis (EMH) is presented and contrasted with chaos theory and neural networks. This paper refutes the EMH based on previous neural network work. Finally, future directions for applying neural networks to the financial markets are discussed. 1 Introduction From the beginning of time it has been man’s common goal to make his life easier. The prevailing notion in society is that wealth brings comfort and luxury, so it is not surprising that there has been so much work done on ways to predict the markets. Various technical, fundamental, and statistical indicators have been proposed and used with varying results. However, no one technique or combination of techniques has been successful enough to consistently "beat the market". With the development of neural networks, researchers and investors are hoping that the market mysteries can be unraveled. This paper is a survey of current market forecasting techniques with an emphasis on why they are insufficient...
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... Slide Background Mix 3 4 5 6 7 8 9 10 2 Modeling Predictive Modeling Genius Forecasting Inventory Forecasting Simulations Modeling Decision Tree Conclusion Many companies and businesses use forecasting. Whether its to predict sales growth, consumer demand, profit or plan production, management wants to know how to proceed in making an informed decision about the future. This presentation will examine some of today’s most popular forecasting models by highlighting how leading companies are putting them to use. 3 Mix modeling – Marketing strategy Mix modeling can help with marketing strategies by measuring the potential value of all market input and marketing investments. The goal is a long-term revenue growth. Mix modeling’s multiple-regression technique is conducted based on the number of inputs and how these inputs relate to an outcome. The data that go into creating a marketing mix model includes: • Economic data • Industry data • Category data • Advertising data (including copy testing) Promotional data Competitive data Service data • Product data- Pricing data, Features & performance • Market outcome data- sales, revenue, profits Reference: http://www.decisionanalyst.com/Services/MarketingMixModeling.dai 4 Predictive Modeling for Consumer Demand forecast Predictive modeling is an effective forecasting model for consumer demand. The technique is based on accumulated data regarding consumer behavior. Within...
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...Institute Ljubljana, Slovenia Stavanger, May 8th 2012 Introduction Techniques Tools Applications Literature ◦ What is Big data? ◦ Why Big-Data? ◦ When Big-Data is really a problem? ‘Big-data’ is similar to ‘Small-data’, but bigger …but having data bigger consequently requires different approaches: …to solve: ◦ techniques, tools & architectures ◦ New problems… ◦ …and old problems in a better way. From “Understanding Big Data” by IBM Big-Data Key enablers for the growth of “Big Data” are: ◦ Increase of storage capacities ◦ Increase of processing power ◦ Availability of data NoSQL MapReduce Storage Servers ◦ DatabasesMongoDB, CouchDB, Cassandra, Redis, BigTable, Hbase, Hypertable, Voldemort, Riak, ZooKeeper ◦ Hadoop, Hive, Pig, Cascading, Cascalog, mrjob, Caffeine, S4, MapR, Acunu, Flume, Kafka, Azkaban, Oozie, Greenplum ◦ S3, Hadoop Distributed File System ◦ EC2, Google App Engine, Elastic, Beanstalk, Heroku ◦ R, Yahoo! Pipes, Mechanical Turk, Solr/Lucene, ElasticSearch, Datameer, BigSheets, Tinkerpop Processing …when the operations on data are complex: ◦ …e.g. simple counting is not a complex problem ◦ Modeling and reasoning with data of different kinds can get extremely complex Good news about big-data: ◦ Often, because of vast amount of data, modeling techniques can get simpler (e.g. smart counting can replace complex model based analytics)… ◦ …as...
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...BUSINESS INTELLIGENCE DATA MINING Business intelligence is a computerized technique used is searching, storing and analyzing useful business information (http://en.wikipedia.org/wiki/Business_intelligence). Business intelligence is an increasing strategy employed by many modern ventures, in the attempt to providing quick access to information and helps the business in making appropriate decisions. Holistic information on ones business environment is an important tool, since it does not only shows your past trend, but also prepares the firm for the future improvements. This sets a challenge in establishing the methods to source for the information, and how to use this information to improve a business position . Data mining is the sourcing of any hidden and predictive business information from a relevant database. It involves a thorough analysis of data gained from various sources, manipulating it into useful tool - a tool that leads to raising business revenue, saving on the running costs or both (http://en.wikipedia.org/wiki/Data_mining). Data mining tool incorporates analytical tools that helps build a useful predictive relationship. Data mining tools helps get answers as it scrutinizes data from different perspective to a precision, than any expert could do. Interplay of data mining process with software and hardware utilities is a big step in data analysis. The integration of artificial intelligence and databases heightens the data-mining goal as the information is translated...
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...http://www.slideshare.net/ajaysuman/artificial-intelligence-in-business ARTIFICIAL INTELLIGENCE IN BUSINESS Introduction Business applications utilize the specific technologies mentioned earlier to try and make better sense of potentially enormous variability (for example, unknown patterns/relationships in sales data, customer buying habits, and so on). However, within the corporate world, AI is widely used for complex problem-solving and decision-support techniques in real-time business applications. The business applicability of AI techniques is spread across functions ranging from finance management to forecasting and production. In the fiercely competitive and dynamic market scenario, decision-making has become fairly complex and latency is inherent in many processes. In addition, the amount of data to be analyzed has increased substantially. AI technologies help enterprises reduce latency in making business decisions, minimize fraud and enhance revenue opportunities. Definition of AI AI is a broad discipline that promises to simulate numerous innate human skills such as automatic programming, case-based reasoning, neural networks, decision-making, expert systems, natural language processing, pattern recognition and speech recognition etc. AI technologies bring more complex data-analysis features to existing applications. There are many definitions that attempt to explain what Artificial Intelligence (AI) is. I like to think of AI as a science that investigates...
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...discipline specific modeling techniques. Additionally, the author will introduce an example problem for each type of abstract model presented. Deterministic Modeling Deterministic modeling are “precisely determined through known relationships among states and events, without any room for random variation” (Business Dictionary, 2014, para 4). In deterministic modeling, a certain given input will always generate the same output. An example would be a chemical reaction. When two hydrogen atoms attach to an oxygen atom, the known result produced is water (H2O). A deterministic model can be representated mathematically with the formula y = f (x). One can see real-life determinitic modeling in a retail scenario where y = amount paid and x = almount of the good. Physical and engineering industries are much more difficult to use detiministic modeling for risk mititagtion. For example, the amount of water (capacity) a dam can hold may seems like it could be solve through detministic modeling. Unfortunately, it is impossible to accurately calculate the physical and structural make up of the concrete used down to the atomic level (Texas Tech University, 2010). Therefore, it is impossible to accurately determine the maximinun stress livel that the dam can bear. Determinisitc modeling is even more difficult to utilize in a social sciences arena. For example, there is no way one could accurately predict the gains or losses of the stock market. Probability Modeling ...
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...working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications. © 2012 by Torben G. Andersen, Tim Bollerslev, Peter F. Christoffersen, and Francis X. Diebold. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source. Financial Risk Measurement for Financial Risk Management Torben G. Andersen, Tim Bollerslev, Peter F. Christoffersen, and Francis X. Diebold NBER Working Paper No. 18084 May 2012 JEL No. C1,G1 ABSTRACT Current practice largely follows restrictive approaches to market risk measurement, such as historical simulation or RiskMetrics. In contrast, we propose flexible methods that exploit recent developments in financial econometrics and are likely to produce more accurate risk assessments, treating both portfolio-level...
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...5 Supply Chain Management Based on Modeling & Simulation: State of the Art and Application Examples in Inventory and Warehouse Management Francesco Longo Modeling & Simulation Center – Laboratory of Enterprise Solutions (MSC-LES) Mechanical Department, University of Calabria Via P. Bucci, Cubo 44C, third floor, 87036 Rende (CS) Italy 1. Introduction The business globalization has transformed the modern companies from independent entities to extended enterprises that strongly cooperate with all supply chain actors. Nowadays supply chains involve multiple actors, multiple flows of items, information and finances. Each supply chain node has its own customers, suppliers and inventory management strategies, demand arrival process and demand forecast methods, items mixture and dedicated internal resources. In this context, each supply chain manager aims to reach the key objective of an efficient supply chain: ‘the right quantity at the right time and in the right place’. To this end, each supply chain node (suppliers, manufacturers, distribution centers, warehouses, stores, etc.) carries out various processes and activities for guarantying goods and services to final customers. The competitiveness of each supply chain actor depends by its capability to activate and manage change processes, in correspondence of optimistic and pessimistic scenarios, to quickly capitalize the chances given by market. Such capability is a critical issue for improving the performance of the ‘extended...
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...Introduction to Supply Chain Management Supply chain management (SCM) is the management of an interconnected or interlinked between network, channel and node businesses involved in the provision of product and service packages required by the end customers in a supply chain. Supply chain management spans the movement and storage of raw materials, work-in-process inventory, and finished goods from point of origin to point of consumption. It is also defined as the "design, planning, execution, control, and monitoring of supply chain activities with the objective of creating net value, building a competitive infrastructure, leveraging worldwide logistics, synchronizing supply with demand and measuring performance globally." SCM draws heavily from the areas of operations management, logistics, procurement, and information technology, and strives for an integrated approach. Supply Chain Model Supply Chain Management addresses the following areas: * Distribution network configuration * Distribution strategy * Trade-offs in logistical activities * Information * Inventory management * Cash flow Supply chain execution means managing and coordinating the movement of materials, information and funds across the supply chain. The flow is bi-directional. SCM applications provide real-time analytical systems that manage the flow of products and information throughout the supply chain network. Supply Chain and Bullwhip Effect The bullwhip effect (or whiplash...
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...derived. Northwestern Memorial prides itself on its physicians and hospital staff so of course they pursue the best of the best to be employed by the organization. In order to recruit those types of candidates, the organization has to provide a very competitive compensation strategy. We’ve learned that compensation packages are made up of base pay, employee benefits, individual incentives, and group incentives. When it comes to base pay NMH needs to set the standard in order to draw in the talent they desire. They can’t offer what’s below market or the equivalent of the market. They have to set the bar. They have to show potential candidates that they are willing to pay more than other organization for a person with the right set of skills. Plus, due to the high level expertise, the candidate is going to expect more pay to be offered. So I would check with BLS and research comparable salaries for the job position in other markets similar to Chicago and devise the salary to be paid accordingly. In addition to salary, an organization’s benefit package can really set it apart from other organizations. In particular I am referring to discretionary benefits. In regards to...
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...courses can be provided either at our training facilities or on-site at clients’ location. Our training programs are lively, interactive, and include role-playing and demonstrations of real-life workplace issues and solutions. bdjobstraining.com Page |2 bdjobstraining.com Page |3 TRAINING TRACKS Marketing/ Sales Track • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 1111 Selling Techniques for Excellence 13 Lessons to turn every Company into Fantastic Brands Advance Selling Techniques Art of Pharmaceutical Sales Brand Management–Walking the Talk Branding for Bangladeshi Business Constructive & Modern Leadership Approach in Selling through Team Building Corporate Sales Management for Excellence Creating New Prospects and Managing Sales Pipeline Creative & Successful Selling Techniques for Excellence Customer Relationship Management (CRM) Digital / On-line Marketing - New Era of Brand Management Effective Print Advertising: 13 Tips to Create Powerful Advertising Effective Selling Techniques Essential Territory Management &...
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...Kochanski This version: 2012/12/23 _________________________________________________________________________________________ Abstract The intention of this paper is to review research on lapse in life insurance and to outline potential new areas of research in this field. We consider theoretical lapse rate models as well as empirical research on life insurance lapse and provide a classification of these two streams of research. More than 50 theoretical and empirical papers from this important field of research are reviewed. Challenges for lapse rate modeling, lapse risk mitigation techniques, and possible trends in future lapse behavior are discussed. The risks arising from lapse are of high economic importance. As such, lapsation is of interest not only to academics, but is also highly relevant for the industry, regulators, and policymakers. JEL classification: G22; G28 Keywords: Lapse; Surrender; Lapse Modeling; Life Insurance _________________________________________________________________________________________ 1. Introduction Today’s insurance policies allow policyholders to choose among a large number of options that can significantly influence the extent of the insurer’s liabilities (see Bauer et al., 2006; Gatzert, 2009; Kling et al., 2011). For example, policyholders can surrender their policy and receive a surrender value (the so-called surrender option) or they can opt to discontinue premium payments (the so-called paid-up option). Originally, the...
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...In Britain references began to appear in the literature from about 1000AD, and in 1192 the monk Richard of Devizes referred to the number of soap makers in Bristol and the unpleasant smells which their activities produced A century later soap making was reported in Coventry. Other early centers of production included York and Hull. In London a 15th century "sopehouse" was reported in Bishopsgate, with other sites at Cheapside, where there existed Soper's Lane (later renamed Queen Street), and by the Thames at Blackfriars Andrew pears. In 1789, he commenced production of a transparent soap at a factory in Wells Street, off Oxford Street and became hugely successful. OBJECTIVE: The main objectives of the study are: 1) To study market demand and supply of DOVE with respect to other soaps. 2) To forecast demand for the soap...
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...(Steiger, Strong, & Wilson, 2009). In the world of finance, the most difficult objective to achieve for individuals, corporations, and small businesses is the balance between financial risk and reward. This paper will look at both historical tools and avenues modern technology has adopted to create a balance for financial investors in the market today and how these tools are implemented in today’s businesses. Identifying opportunities and recognizing the risk associated with them is crucial to financial growth and success. Entities are continuing to find ways of leveraging risk by using different modeling tools to understand the source of risk, measure risk and transfer risk (Schwartz, 1996). Due to the expansion and growth of companies into new markets, risk has become an increasing concern for many businesses. It is clear through the recent market crash that more robust risk management tools must evolve with the changing investment practices that are taking place in today’s society. “The world’s financial markets have exploded with new products and new techniques such as derivatives and securitizations giving rise to huge new markets” (Epetimehin, 2012). If implemented strategically and used correctly, risk management tools can aid businesses in their journey of financial success and help them develop finely tuned investment planning and business strategies (Anonymous, 1983). It is critical for businesses to understand the risk associated with each transaction and investment...
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...revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. CRM: In today’s competitive scenario in corporate world, “Customer Retention” strategy in Customer Relationship Management (CRM) is an increasingly pressed issue. Data mining techniques play a vital role in better CRM. This paper attempts to bring a new perspective by focusing the issue of data mining Applications, opportunities and challenges in CRM. It covers the topic such as customer retention, customer services, risk assessment, fraud detection and some of the data mining tools which are widely used in CRM. Supply Chain Management (SCM) environments are often dynamic markets providing a plethora of Information, either complete or incomplete. It is, therefore, evident that such environments demand intelligent solutions, which can perceive variations and act in order to achieve maximum revenue. To do so, they must also provide some sophisticated mechanism for exploiting the full potential of the environments they inhabit. Advancing on the way autonomous solutions usually deal with the SCM process, we have built a robust and highly-adaptable mechanism for efficiently dealing with all...
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