LEADER 02979nam 2200649 a 450 001 9910145815603321 005 20170815110054.0 010 $a0-470-48668-6 010 $a1-119-19765-1 010 $a1-282-00903-6 010 $a9786612009037 010 $a0-470-43265-9 035 $a(CKB)1000000000715881 035 $a(EBL)416206 035 $a(OCoLC)320623115 035 $a(SSID)ssj0000268066 035 $a(PQKBManifestationID)11240733 035 $a(PQKBTitleCode)TC0000268066 035 $a(PQKBWorkID)10214516 035 $a(PQKB)11454545 035 $a(MiAaPQ)EBC416206 035 $a(CaSebORM)9780470382059 035 $a(EXLCZ)991000000000715881 100 $a20081120d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 14$aThe visual investor$b[electronic resource] $ehow to spot market trends /$fJohn J. Murphy 205 $a2nd ed. 210 $aHoboken, N.J. $cWiley$dc2009 215 $a1 online resource (339 p.) 225 1 $aWiley trading 300 $aIncludes index. 311 $a0-470-38205-8 327 $aTHE VISUAL INVESTOR: HOW TO SPOT MARKET TRENDS, SECOND EDITION; Contents; Preface; Acknowledgments; Introduction; Chapter 1: What Is Visual Investing?; Chapter 2: The Trend Is Your Friend; Chapter 3: Pictures That Tell a Story; Chapter 4: Your Best Friend in a Trend; Chapter 5: Is It Overbought or Oversold?; Chapter 6: How to Have the Best of Both Worlds; Chapter 7: Market Linkage; Chapter 8: Market Breadth; Chapter 9: Relative Strength and Rotation; Chapter 10: Sectors and Industry Groups; Chapter 11: Mutual Funds; Chapter 12: Exchange-Traded Funds; Conclusion; Appendix A: Getting Started 327 $aAppendix B: Japanese CandlesticksAppendix C: Point-and-Figure Charting; Index 330 $aThe Visual Investor, Second Edition breaks down technical analysis into terms that are accessible to even individual investors. Aimed at the typical investor--such as the average CNBC viewer--this book shows investors how to follow the ups and downs of stock prices by visually comparing the charts, without using formulas or having a necessarily advanced understanding of technical analysis math and jargon. Murphy covers all the fundamentals, from chart types and market indicators to sector analysis and global investing, providing examples and easy-to-read charts so that any reader can become a 410 0$aWiley trading. 606 $aInvestment analysis 606 $aPortfolio management 608 $aElectronic books. 615 0$aInvestment analysis. 615 0$aPortfolio management. 676 $a332.6 676 $a332.63/22 676 $a332.632042 676 $a332.6322 700 $aMurphy$b John J$0109302 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910145815603321 996 $aThe visual investor$91933204 997 $aUNINA LEADER 04464nam 2200649 450 001 9910132269203321 005 20200520144314.0 010 $a1-119-11925-1 010 $a1-119-11618-X 010 $a1-119-11926-X 035 $a(CKB)3710000000366202 035 $a(EBL)1964138 035 $a(SSID)ssj0001548105 035 $a(PQKBManifestationID)16145743 035 $a(PQKBTitleCode)TC0001548105 035 $a(PQKBWorkID)14797686 035 $a(PQKB)11151289 035 $a(Au-PeEL)EBL1964138 035 $a(CaPaEBR)ebr11027516 035 $a(CaONFJC)MIL770191 035 $a(OCoLC)905919672 035 $a(CaSebORM)9781848217553 035 $a(MiAaPQ)EBC1964138 035 $a(PPN)189412003 035 $a(EXLCZ)993710000000366202 100 $a20150312h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAdvances in information systems set$hVolume 1$iFrom big data to smart data /$fFernando Iafrate 205 $a1st edition 210 1$aLondon, England ;$aHoboken, New Jersey :$ciSTE :$cWiley,$d2015. 210 4$dİ2015 215 $a1 online resource (89 p.) 225 1 $aInformation Systems Web and Pervasive Computing Series 300 $aDescription based upon print version of record. 311 $a1-84821-755-2 320 $aIncludes bibliographical references and index. 327 $aCover; Title Page; Copyright; Contents; Preface; List of Figures and Tables; Introduction; I.1. Objectives; I.2. Observation; I.2.1. Before 2000 (largely speaking, before e-commerce); I.2.2. Between 2000 and 2010 (the boom of e-commerce, then the advent of social networks); I.2.3. Since 2010 (mobility and real-time become keywords); I.2.4. And then ... (connected objects...); I.3. In sum; 1: What is Big Data?; 1.1. The four "V"s characterizing Big Data; 1.1.1. V for "Volume"; 1.1.2. V for "Variety"; 1.1.3. V for "Velocity"; 1.1.4. V for "Value", associated with Smart Data 327 $a1.1.4.1. What value can be taken from Big Data?1.2. The technology that supports Big Data; 2: What is Smart Data?; 2.1. How can we define it?; 2.1.1. More formal integration into business processes; 2.1.2. A stronger relationship with transactionsolutions; 2.1.3. The mobility and the temporality of information; 2.1.3.1. The automation of analysis; 2.2. The structural dimension; 2.2.1. The objectives of a BICC; 2.3. The closed loop between Big Data and Smart Data; 3: Zero Latency Organization; 3.1. From Big Data to Smart Data for a zero latency organization; 3.2. Three types of latency 327 $a3.2.1. Latency linked to data3.2.2. Latency linked to analytical processes; 3.2.3. Latency linked to decision-making processes; 3.2.4. Action latency; 4: Summary by Example; 4.1. Example 1: date/product/price recommendation; 4.1.1. Steps "1" and "2"; 4.1.2. Steps "3" and "4": enter the world of "SmartData"; 4.1.3. Step "5": the presentation phase; 4.1.4. Step "6": the "Holy Grail" (the purchase); 4.1.5. Step "7": Smart Data; 4.2. Example 2: yield/revenue management (rate controls); 4.2.1. How it works: an explanation based on the Tetrisprinciple (see Figure 4.4) 327 $a4.3. Example 3: optimization of operational performance4.3.1. General department (top management) ; 4.3.2. Operations departments (middle management); 4.3.3. Operations management (and operationalplayers); Conclusion; Bibliography; Glossary; Index 330 $a A pragmatic approach to Big Data by taking the reader on a journey between Big Data (what it is) and the Smart Data (what it is for). Today's decision making can be reached via information (related to the data), knowledge (related to people and processes), and timing (the capacity to decide, act and react at the right time). The huge increase in volume of data traffic, and its format (unstructured data such as blogs, logs, and video) generated by the "digitalization" of our world modifies radically our relationship to the space (in motion) and time, dimension and by capillarity, the enterpr 410 0$aInformation systems, web and pervasive computing series. 606 $aBig data 615 0$aBig data. 676 $a005.74023 700 $aIafrate$b Fernando$0901013 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910132269203321 996 $aAdvances in information systems set$92013989 997 $aUNINA