10 Disruptive Trends in Wealth Management
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Global Wealth Inequality
EC11CH05_Zucman ARjats.cls August 7, 2019 12:27 Annual Review of Economics Global Wealth Inequality Gabriel Zucman1,2 1Department of Economics, University of California, Berkeley, California 94720, USA; email: [email protected] 2National Bureau of Economic Research, Cambridge, MA 02138, USA Annu. Rev. Econ. 2019. 11:109–38 Keywords First published as a Review in Advance on inequality, wealth, tax havens May 13, 2019 The Annual Review of Economics is online at Abstract economics.annualreviews.org This article reviews the recent literature on the dynamics of global wealth https://doi.org/10.1146/annurev-economics- Annu. Rev. Econ. 2019.11:109-138. Downloaded from www.annualreviews.org inequality. I first reconcile available estimates of wealth inequality inthe 080218-025852 United States. Both surveys and tax data show that wealth inequality has in- Access provided by University of California - Berkeley on 08/26/19. For personal use only. Copyright © 2019 by Annual Reviews. creased dramatically since the 1980s, with a top 1% wealth share of approx- All rights reserved imately 40% in 2016 versus 25–30% in the 1980s. Second, I discuss the fast- JEL codes: D31, E21, H26 growing literature on wealth inequality across the world. Evidence points toward a rise in global wealth concentration: For China, Europe, and the United States combined, the top 1% wealth share has increased from 28% in 1980 to 33% today, while the bottom 75% share hovered around 10%. Recent studies, however, may underestimate the level and rise of inequal- ity, as financial globalization makes it increasingly hard to measure wealth at the top. -
Four Strategies for Becoming an Elite Financial Advisor
INVESTMENT PROFESSIONAL USE ONLY. NOT FOR PUBLIC DISTRIBUTION. MANAGING YOUR PRACTICE: A DIMENSIONAL PODCAST SERIES Four Strategies for Becoming an Elite Financial Advisor Catherine Williams: Hi, everyone, and thank you for joining us today. I'm Catherine Williams, Head of Practice Management for Dimensional Fund Advisors. Today, I want to talk about what is probably the number one question me and my team receive as we engage with advisors around the world. How do I grow? And probably then shortly followed by the question of, what are faster growing advisors are doing? We see in our annual advisor benchmark study, which includes nearly a thousand advisors this year, that there are in fact some unique behaviors and characteristics these top advisors demonstrate both in their business and how they live their lives and certainly how they engage with clients. So that's what we're going to talk about today. And joining me to share his unique and expert perspective on this topic is John Bowen, founder and CEO of CEG Worldwide. Hi, John. Thanks for joining us today. John Bowen: Well, Catherine, thank you for having me. Catherine Williams: I want to give a little bit of a background for the few folks out there that may not know your name or be familiar with CEG, you founded CEG nearly 20 years ago and your organization, I will be so bold as to say, is absolutely one of the world's leading coaching firms when it comes to financial advisors. You focus on coaching elite advisors thinking about their business, their personal lives, their legacies, and you and your team have delivered hundreds of workshops and presentations to thousands of advisors around the world. -
Bit Nove Signal Interface Processor
www.audison.eu bit Nove Signal Interface Processor POWER SUPPLY CONNECTION Voltage 10.8 ÷ 15 VDC From / To Personal Computer 1 x Micro USB Operating power supply voltage 7.5 ÷ 14.4 VDC To Audison DRC AB / DRC MP 1 x AC Link Idling current 0.53 A Optical 2 sel Optical In 2 wire control +12 V enable Switched off without DRC 1 mA Mem D sel Memory D wire control GND enable Switched off with DRC 4.5 mA CROSSOVER Remote IN voltage 4 ÷ 15 VDC (1mA) Filter type Full / Hi pass / Low Pass / Band Pass Remote OUT voltage 10 ÷ 15 VDC (130 mA) Linkwitz @ 12/24 dB - Butterworth @ Filter mode and slope ART (Automatic Remote Turn ON) 2 ÷ 7 VDC 6/12/18/24 dB Crossover Frequency 68 steps @ 20 ÷ 20k Hz SIGNAL STAGE Phase control 0° / 180° Distortion - THD @ 1 kHz, 1 VRMS Output 0.005% EQUALIZER (20 ÷ 20K Hz) Bandwidth @ -3 dB 10 Hz ÷ 22 kHz S/N ratio @ A weighted Analog Input Equalizer Automatic De-Equalization N.9 Parametrics Equalizers: ±12 dB;10 pole; Master Input 102 dBA Output Equalizer 20 ÷ 20k Hz AUX Input 101.5 dBA OPTICAL IN1 / IN2 Inputs 110 dBA TIME ALIGNMENT Channel Separation @ 1 kHz 85 dBA Distance 0 ÷ 510 cm / 0 ÷ 200.8 inches Input sensitivity Pre Master 1.2 ÷ 8 VRMS Delay 0 ÷ 15 ms Input sensitivity Speaker Master 3 ÷ 20 VRMS Step 0,08 ms; 2,8 cm / 1.1 inch Input sensitivity AUX Master 0.3 ÷ 5 VRMS Fine SET 0,02 ms; 0,7 cm / 0.27 inch Input impedance Pre In / Speaker In / AUX 15 kΩ / 12 Ω / 15 kΩ GENERAL REQUIREMENTS Max Output Level (RMS) @ 0.1% THD 4 V PC connections USB 1.1 / 2.0 / 3.0 Compatible Microsoft Windows (32/64 bit): Vista, INPUT STAGE Software/PC requirements Windows 7, Windows 8, Windows 10 Low level (Pre) Ch1 ÷ Ch6; AUX L/R Video Resolution with screen resize min. -
Leaving a Legacy: a Lasting Gift to Loved Ones Table of Contents
life stage series: end of life/legacy. Leaving a legacy: A lasting gift to loved ones Table of contents . 1. Background 3. Introduction 4 . What legacy means 10. Anticipating the end of life 14. Getting affairs in order 21. Personal action plan 22 Summary Leaving a legacy: A lasting gift to loved ones Background About Merrill Lynch Wealth Management About Age Wave Merrill Lynch Wealth Management is a leading Age Wave is the nation’s foremost thought leader provider of comprehensive wealth management and on population aging and its profound business, investment services for individuals and businesses social, financial, healthcare, workforce and cultural globally. With 14,690 financial advisors and $2.4 implications. Under the leadership of Founder/CEO trillion in client balances as of June 30, 2019, it is Ken Dychtwald, Ph.D., Age Wave has developed a among the largest businesses of its kind in the world. unique understanding of new generations of maturing Bank of America Corporation, through its subsidiaries, consumers and workers and their expectations, specializes in goals-based wealth management, attitudes, hopes and fears regarding their including planning for retirement, education, legacy, longer lives. Since its inception in 1986, the firm and other life goals through investment, cash and has provided breakthrough research, compelling credit management. Within this business, Merrill presentations, award-winning communications, Private Wealth Management focuses on the unique education and training systems, and results-driven and personalized needs of wealthy individuals, families marketing and consulting initiatives to over half and their businesses. These clients are served by the Fortune 500. For more information, please approximately 200 highly specialized private wealth visit www.agewave.com. -
Signedness-Agnostic Program Analysis: Precise Integer Bounds for Low-Level Code
Signedness-Agnostic Program Analysis: Precise Integer Bounds for Low-Level Code Jorge A. Navas, Peter Schachte, Harald Søndergaard, and Peter J. Stuckey Department of Computing and Information Systems, The University of Melbourne, Victoria 3010, Australia Abstract. Many compilers target common back-ends, thereby avoid- ing the need to implement the same analyses for many different source languages. This has led to interest in static analysis of LLVM code. In LLVM (and similar languages) most signedness information associated with variables has been compiled away. Current analyses of LLVM code tend to assume that either all values are signed or all are unsigned (except where the code specifies the signedness). We show how program analysis can simultaneously consider each bit-string to be both signed and un- signed, thus improving precision, and we implement the idea for the spe- cific case of integer bounds analysis. Experimental evaluation shows that this provides higher precision at little extra cost. Our approach turns out to be beneficial even when all signedness information is available, such as when analysing C or Java code. 1 Introduction The “Low Level Virtual Machine” LLVM is rapidly gaining popularity as a target for compilers for a range of programming languages. As a result, the literature on static analysis of LLVM code is growing (for example, see [2, 7, 9, 11, 12]). LLVM IR (Intermediate Representation) carefully specifies the bit- width of all integer values, but in most cases does not specify whether values are signed or unsigned. This is because, for most operations, two’s complement arithmetic (treating the inputs as signed numbers) produces the same bit-vectors as unsigned arithmetic. -
Metaclasses: Generative C++
Metaclasses: Generative C++ Document Number: P0707 R3 Date: 2018-02-11 Reply-to: Herb Sutter ([email protected]) Audience: SG7, EWG Contents 1 Overview .............................................................................................................................................................2 2 Language: Metaclasses .......................................................................................................................................7 3 Library: Example metaclasses .......................................................................................................................... 18 4 Applying metaclasses: Qt moc and C++/WinRT .............................................................................................. 35 5 Alternatives for sourcedefinition transform syntax .................................................................................... 41 6 Alternatives for applying the transform .......................................................................................................... 43 7 FAQs ................................................................................................................................................................. 46 8 Revision history ............................................................................................................................................... 51 Major changes in R3: Switched to function-style declaration syntax per SG7 direction in Albuquerque (old: $class M new: constexpr void M(meta::type target, -
Meta-Class Features for Large-Scale Object Categorization on a Budget
Meta-Class Features for Large-Scale Object Categorization on a Budget Alessandro Bergamo Lorenzo Torresani Dartmouth College Hanover, NH, U.S.A. faleb, [email protected] Abstract cation accuracy over a predefined set of classes, and without consideration of the computational costs of the recognition. In this paper we introduce a novel image descriptor en- We believe that these two assumptions do not meet the abling accurate object categorization even with linear mod- requirements of modern applications of large-scale object els. Akin to the popular attribute descriptors, our feature categorization. For example, test-recognition efficiency is a vector comprises the outputs of a set of classifiers evaluated fundamental requirement to be able to scale object classi- on the image. However, unlike traditional attributes which fication to Web photo repositories, such as Flickr, which represent hand-selected object classes and predefined vi- are growing at rates of several millions new photos per sual properties, our features are learned automatically and day. Furthermore, while a fixed set of object classifiers can correspond to “abstract” categories, which we name meta- be used to annotate pictures with a set of predefined tags, classes. Each meta-class is a super-category obtained by the interactive nature of searching and browsing large im- grouping a set of object classes such that, collectively, they age collections calls for the ability to allow users to define are easy to distinguish from other sets of categories. By us- their own personal query categories to be recognized and ing “learnability” of the meta-classes as criterion for fea- retrieved from the database, ideally in real-time. -
Wmcp™ Wealth Management Certified Professional® the Modern Way to Manage Wealth
WMCP™ WEALTH MANAGEMENT CERTIFIED PROFESSIONAL® THE MODERN WAY TO MANAGE WEALTH. The WMCP™ delivers an advanced specialization in personal wealth and investment management unlike any other professional credential available today. Moving beyond simple investment management, the WMCP™ helps advisors transform modern investment theory into applied knowledge that brings a new level of value to client relationships. WMCP™ is designed for advisors serving a global marketplace where mass-affluent and high-net-worth individuals seek professionals with a true understanding of their unique needs and goals. DEEP KNOWLEDGE. GOAL-BASED. WEALTH MANAGEMENT REDEFINED. The WMCP™ program takes learners beyond selecting investments by taking a deep dive into the specialized knowledge needed to build a long-term wealth management plan through advanced strategies that effectively meet client financial goals. Advisors with WMCP™ have extensive knowledge of personal wealth and investment management, including portfolio theory, mastery of investment tools, and advanced wealth management strategies. Delivered through state-of-the-art digital coursework, the WMCP™ program curriculum leverages transformative educational design to cement learning for the long-term benefit of advisors. The education uses a competency-based approach to focus learning in areas where the student needs to build expertise, including tax rules, financial products, behavioral finance, household portfolio theory, and asset allocation. TM Who Should Enroll in WMCP ? The WMCP™ designation is intended for financial planning professionals who need to cultivate a deep knowledge of goal-based personal investment management. Mass-affluent and high-net-worth clients are likely to find the most value in a WMCP™ designee’s understanding of modern, goal-based investment theory. -
Loss and Inheritance Taking Charge of Both
Loss and inheritance Taking charge of both When a loved one dies, you may experience a flood of mixed emotions— combined with the uncertainty of inheriting an estate. This article will help you understand and take charge of this complex transition so that you can move forward with more confidence—and help ensure that you and your family are protected. The first piece of advice for those who have inherited significant wealth is “take your time.” Before making big decisions, you may need time to grieve for the loved one you have lost, sort through what has been left and assess your own perspectives on investments, spending and even gifting to others. In the meantime, you may consider parking any liquid assets in lower risk, The first piece of advice for accessible vehicles like a bank account, money market fund or short-term those who have inherited certificates of deposit. If you inherit stocks, bonds or mutual funds, a Financial significant wealth is “take Advisor can help you consider whether to simply maintain the existing investment plan until you’re ready to implement a plan of your own or your time.” determine if there are certain assets that warrant more immediate attention based on your risk tolerance. And, if you receive real estate or other physical assets, make sure that the home or other property is maintained and kept secure until you decide what to do with it. Understand what your additional wealth can mean for your life Depending on the amount you inherit, your new wealth may be life-changing. -
Wealth Management and Private Banking Connecting with Clients and Reinventing the Value Proposition 2015 Contents
Wealth Management and Private Banking Connecting with clients and reinventing the value proposition 2015 Contents This document provides a perspective on the evolution of client value propositions in Wealth Management & Private Banking 3 Foreword 4 Scope and reach 6 Snapshot of key messages 8 Executive Summary 16 Strategic priorities 20 Products and services 26 Channels 36 Pricing 40 Our International Wealth Management & Private Banking practice at a glance 41 Our services 42 Key contributors 43 Contacts 2 Foreword Dear Readers, Deloitte and Efma are pleased to present you the results of our recent survey, providing a perspective on the evolution of client value propositions in Wealth Management and Private Banking. We invite you to consider the challenges facing the industry and how players are adapting their value proposition and connecting with clients in the new landscape. In the past few years, the Wealth Management and Private Banking industries have changed significantly. The financial crisis has increased investors’ sensitivity to risk, and the current low yield environment has made it more challenging to meet investors’ expectations of returns while limiting risk. In addition, the pressure for global tax transparency from governments around the world to crack down on tax evasion and tax fraud, has caused a significant shift from offshore to onshore wealth. The frontiers of demand are also being pushed beyond traditional borders, with emerging market players entering developed markets to follow their clients, and developed market players seeking growth outside of their home markets. This has resulted in volume losses (e.g. wealth repatriation) and/or decreased revenue margins as fiscal arbitrages have become obsolete and competition for onshore assets has increased. -
Bit, Byte, and Binary
Bit, Byte, and Binary Number of Number of values 2 raised to the power Number of bytes Unit bits 1 2 1 Bit 0 / 1 2 4 2 3 8 3 4 16 4 Nibble Hexadecimal unit 5 32 5 6 64 6 7 128 7 8 256 8 1 Byte One character 9 512 9 10 1024 10 16 65,536 16 2 Number of bytes 2 raised to the power Unit 1 Byte One character 1024 10 KiloByte (Kb) Small text 1,048,576 20 MegaByte (Mb) A book 1,073,741,824 30 GigaByte (Gb) An large encyclopedia 1,099,511,627,776 40 TeraByte bit: Short for binary digit, the smallest unit of information on a machine. John Tukey, a leading statistician and adviser to five presidents first used the term in 1946. A single bit can hold only one of two values: 0 or 1. More meaningful information is obtained by combining consecutive bits into larger units. For example, a byte is composed of 8 consecutive bits. Computers are sometimes classified by the number of bits they can process at one time or by the number of bits they use to represent addresses. These two values are not always the same, which leads to confusion. For example, classifying a computer as a 32-bit machine might mean that its data registers are 32 bits wide or that it uses 32 bits to identify each address in memory. Whereas larger registers make a computer faster, using more bits for addresses enables a machine to support larger programs. -
Floating Point Arithmetic
Systems Architecture Lecture 14: Floating Point Arithmetic Jeremy R. Johnson Anatole D. Ruslanov William M. Mongan Some or all figures from Computer Organization and Design: The Hardware/Software Approach, Third Edition, by David Patterson and John Hennessy, are copyrighted material (COPYRIGHT 2004 MORGAN KAUFMANN PUBLISHERS, INC. ALL RIGHTS RESERVED). Lec 14 Systems Architecture 1 Introduction • Objective: To provide hardware support for floating point arithmetic. To understand how to represent floating point numbers in the computer and how to perform arithmetic with them. Also to learn how to use floating point arithmetic in MIPS. • Approximate arithmetic – Finite Range – Limited Precision • Topics – IEEE format for single and double precision floating point numbers – Floating point addition and multiplication – Support for floating point computation in MIPS Lec 14 Systems Architecture 2 Distribution of Floating Point Numbers e = -1 e = 0 e = 1 • 3 bit mantissa 1.00 X 2^(-1) = 1/2 1.00 X 2^0 = 1 1.00 X 2^1 = 2 1.01 X 2^(-1) = 5/8 1.01 X 2^0 = 5/4 1.01 X 2^1 = 5/2 • exponent {-1,0,1} 1.10 X 2^(-1) = 3/4 1.10 X 2^0 = 3/2 1.10 X 2^1= 3 1.11 X 2^(-1) = 7/8 1.11 X 2^0 = 7/4 1.11 X 2^1 = 7/2 0 1 2 3 Lec 14 Systems Architecture 3 Floating Point • An IEEE floating point representation consists of – A Sign Bit (no surprise) – An Exponent (“times 2 to the what?”) – Mantissa (“Significand”), which is assumed to be 1.xxxxx (thus, one bit of the mantissa is implied as 1) – This is called a normalized representation • So a mantissa = 0 really is interpreted to be 1.0, and a mantissa of all 1111 is interpreted to be 1.1111 • Special cases are used to represent denormalized mantissas (true mantissa = 0), NaN, etc., as will be discussed.