Benchmark selection and performance

Using regulatory data free of self-reporting bias for 2007–16, we decompose investment returns of 455 Dutch pension funds according to their key investment decisions, i.e., asset allocation, market timing and security selection. In extension to existing papers, we also assess the impact of benchmark selection. Over time, asset allocation explains 39% of the variation of returns, whereas benchmark selection, timing and selection explain 11%, 9% and 16%, respectively. Across pension funds, asset allocation explains on average only 19% of the variation in pension fund returns. Benchmark selection dominates this by explaining 33% of cross-sectional returns. We relate the choice for a specific benchmark to investment, risk and style preferences.

Analisa Algoritma Profile Matching dalam Menentukan Anggota Security Terbaik di PTPN IV Unit Usaha Marjandi

Security is a group of officers formed by agencies / projects / business entities to carry out physical security in the context of self-supporting security in their work environment. In the selection of the best security in Marjandi plantations still use the performance class data. This study discusses determining the best security member in PTPN IV marjandi business unit Data sources used from the Marjandi Business Unit PTPN IV office. This study uses the Decision Support System (SPK) technique in the data processing process using Profile matching method. Profile matching method is a mechanism for decision making by assuming that there is an ideal target value that must be met by the subject under study, rather than the minimum level that must be met or passed. The purpose of this study is to help the management to make it easier to do the best security selection at the Marjandi PTPN IV business unit.

Dynamic Alpha: A Spectral Decomposition of Investment Performance Across Time Horizons

The value added by an active investor is traditionally measured using alpha, tracking error, and the information ratio. However, these measures do not characterize the dynamic component of investor activity, nor do they consider the time horizons over which weights are changed. In this paper, we propose a technique to measure the value of active investment that captures both the static and dynamic contributions of an investment process. This dynamic alpha is based on the decomposition of a portfolio’s expected return into its frequency components using spectral analysis. The result is a static component that measures the portion of a portfolio’s expected return resulting from passive investments and security selection and a dynamic component that captures the manager’s timing ability across a range of time horizons. Our framework can be universally applied to any portfolio and is a useful method for comparing the forecast power of different investment processes. Several analytical and empirical examples are provided to illustrate the practical relevance of this decomposition. This paper was accepted by Gustavo Manso, finance.

From stock selection to multi-asset investment management

Purpose The purpose of this paper is to trace the evolution of the Archway Investment Fund (AIF) at Bryant University from its founding in 2005 as a portfolio focused exclusively on US equities to a multi-asset program that incorporates US equities, non-US equities, equity ETFs, REITs, individual bonds, fixed income ETFs and options. It also describes the explicit introduction of environmental, social and governance (ESG) considerations into the investment process. Design/methodology/approach The paper follows a case study approach. Findings The paper describes the programmatic changes that accompanied this evolution in these areas: finance department curriculum innovations; the investment guidelines and constraints that govern the AIF; the investment process utilized; the oversight and governance process; and the reporting, presentation, and publicity initiatives that keep critical constituencies (university administration, faculty, alumni and students) informed and engaged in this program to sustain its success. Originality/value The vast majority of student-managed funds are equity funds focused on individual stock selection. The AIF is a multi-asset fund with separate equity and fixed income sub-portfolios that explicitly incorporates ESG factors into the security selection process.

The learning experience continues: two decades and counting for CSULB’s SMIF program

Purpose The student-managed investment fund (SMIF) program at California State University, Long Beach (CSULB), was launched in 1995 with one portfolio worth $50,000. In the two decades since then, the program has grown to include three portfolios with a combined value of more than $700,000, managed on behalf of three different clients. The purpose of this paper is to describe the creation, evolution and growth of the program including the development of the new quantitative approach and its subsequent implementation. The paper also discusses the ongoing organizational, educational and investment-management challenges associated with the program. Design/methodology/approach The paper includes a description of the development and evolution of the program along with a discussion of the investment results for one of its three portfolios. Findings The paper finds: the new quantitative approach implemented in the program is effective as insurance against “black swan” events; and SMIF-type programs can provide learning experiences both for students and faculty members. Practical implications The paper explains the practical application of the new quantitative approach as well as the educational benefits of a SMIF-type program. Originality/value The paper provides insight into the structure of CSULB’s SMIF program and discusses a unique quantitative approach to asset allocation and security selection.