Adaptive Asset Allocation

Adaptive Asset Allocation
Author: Adam Butler
Publisher: John Wiley & Sons
Total Pages: 244
Release: 2016-02-23
Genre: Business & Economics
ISBN: 1119220351

Build an agile, responsive portfolio with a new approach to global asset allocation Adaptive Asset Allocation is a no-nonsense how-to guide for dynamic portfolio management. Written by the team behind Gestaltu.com, this book walks you through a uniquely objective and unbiased investment philosophy and provides clear guidelines for execution. From foundational concepts and timing to forecasting and portfolio optimization, this book shares insightful perspective on portfolio adaptation that can improve any investment strategy. Accessible explanations of both classical and contemporary research support the methodologies presented, bolstered by the authors' own capstone case study showing the direct impact of this approach on the individual investor. Financial advisors are competing in an increasingly commoditized environment, with the added burden of two substantial bear markets in the last 15 years. This book presents a framework that addresses the major challenges both advisors and investors face, emphasizing the importance of an agile, globally-diversified portfolio. Drill down to the most important concepts in wealth management Optimize portfolio performance with careful timing of savings and withdrawals Forecast returns 80% more accurately than assuming long-term averages Adopt an investment framework for stability, growth, and maximum income An optimized portfolio must be structured in a way that allows quick response to changes in asset class risks and relationships, and the flexibility to continually adapt to market changes. To execute such an ambitious strategy, it is essential to have a strong grasp of foundational wealth management concepts, a reliable system of forecasting, and a clear understanding of the merits of individual investment methods. Adaptive Asset Allocation provides critical background information alongside a streamlined framework for improving portfolio performance.

Dynamic Asset Allocation

Dynamic Asset Allocation
Author: James Picerno
Publisher: Bloomberg Press
Total Pages: 256
Release: 2010-02-17
Genre: Business & Economics
ISBN: 9781576603598

Today’s modern portfolio theory is not your father’s MPT. It has undergone many changes in the past fifty years. Indeed, a new understanding of MPT has emerged, one that has a significant impact on managing asset allocation—especially in today’s turbulent markets. Dynamic Asset Allocation interprets and integrates the developments in modern portfolio theory: from the efficient-market hypothesis and indexing of decades past to strategies for building winning portfolios today. The book is filled with practical, hands-on advice for investors, including guidance on approaching investment as a risk-management task.

The New Science of Asset Allocation

The New Science of Asset Allocation
Author: Thomas Schneeweis
Publisher: John Wiley & Sons
Total Pages: 422
Release: 2010-02-12
Genre: Business & Economics
ISBN: 0470608390

A feasible asset allocation framework for the post 2008 financial world Asset allocation has long been a cornerstone of prudent investment management; however, traditional allocation plans failed investors miserably in 2008. Asset allocation still remains an essential part of the investment arena, and through a new approach, you'll discover how to make it work. In The New Science of Asset Allocation, authors Thomas Schneeweis, Garry Crowder, and Hossein Kazemi first explore the myths that plague this field then quickly move on to examine how the practice of asset allocation has failed in recent years. They then propose new allocation models that employ liquidity, transparency, and real risk controls across multiple asset classes. Outlines a new approach to asset allocation in a post-2008 world, where risk seems hidden The "great manager" problem is examined with solutions on how to capture manager alpha while limiting downside risk A complete case study is presented that allocates for beta and alpha Written by an experienced team of industry leaders and academic experts, The New Science of Asset Allocation explains how you can effectively apply this approach to a financial world that continues to change.

Dynamic Asset Allocation with Horizon Risk

Dynamic Asset Allocation with Horizon Risk
Author: Peter Mladina
Publisher:
Total Pages:
Release: 2017
Genre:
ISBN:

We compare the empirical distributions of equity and fixed-income returns at different time horizons and find that the risk of equities relative to fixed income is more acute at short time horizons than long time horizons, confirming previous research. This creates the opportunity to develop a dynamic asset allocation process that exploits the reduced horizon risk of equities relative to fixed income. We highlight key data on changing relative risk with time and leverage this information to introduce methods and concepts that inform glide path construction -- the building blocks for a dynamic asset allocation process that can support lifecycle, target date retirement, and goals-based investing frameworks.

Artificial Intelligence in Asset Management

Artificial Intelligence in Asset Management
Author: Söhnke M. Bartram
Publisher: CFA Institute Research Foundation
Total Pages: 95
Release: 2020-08-28
Genre: Business & Economics
ISBN: 195292703X

Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.