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  • Broschiertes Buch

The task of personal and family wealth management seems like it requires so many interrelated complex decisions with multiple risk dimensions that most people feel overwhelmed. However, we are all our own wealth managers, regardless of who you pay for advice and trade execution. Given the potential conflicts of interest, managerial risk and high fees, it is not difficult to do better for yourself than what most professionals can do for you. Even if you choose to pay a professional, it is still your responsibility to monitor them concerning suitable strategies and performance net of fees. In…mehr

Produktbeschreibung
The task of personal and family wealth management seems like it requires so many interrelated complex decisions with multiple risk dimensions that most people feel overwhelmed. However, we are all our own wealth managers, regardless of who you pay for advice and trade execution. Given the potential conflicts of interest, managerial risk and high fees, it is not difficult to do better for yourself than what most professionals can do for you. Even if you choose to pay a professional, it is still your responsibility to monitor them concerning suitable strategies and performance net of fees. In this book, an author (Stanley J. Kon, PhD) with long careers as a professor of finance and in the practice of institutional investment management, will take you through a disciplined "Do-It-Yourself" investment process for wealth portfolio construction, monitoring and revision involving many accounts and many investments with overlapping risk dimensions. Back Cover: "This book makes a compelling case that a disciplined investment process is a valuable life skill. It also contains substantive insights for asset allocation strategies and risk management that is relevant for the current low interest rate environment. Investment decisions are illuminated with the innovative Ripsaw Wealth Tools software." Frank J Fabozzi Editor, The Journal of Portfolio Management Editor, The Journal of Financial Data Science