IN THIS LIST

A Performance Analysis of Variable Annuities With Risk Control

The Value of Research: Skill, Capacity, and Opportunity

The S&P/BMV IPC Turns 40

Rotation Strategies and Their Role in the Australian Market

Earnings Revision Overlay on Fundamental Factors in Asia

Research - Nov 15, 2018

A Performance Analysis of Variable Annuities With Risk Control

EXECUTIVE SUMMARY

A variable annuity is a tax-deferred retirement vehicle with account values linked to the performance of underlying investment options, typically mutual funds.
A variable annuity with risk control framework has the added feature of providing caps and floors to the investment performance, which in turn is linked to the performance of the underlying investment options, typically a price index.
We construct hypothetical portfolios that allocate between a variable annuity with a risk control mechanism and a blended portfolio of stocks and bonds. Historical performance for the hypothetical portfolios with allocation to products with a risk control feature showed better downside protection than a stock portfolio or a traditional 60/40 stock/bond portfolio in some scenarios.

INTRODUCTION OF VARIABLE ANNUITIES WITH RISK CONTROL

A variable annuity is a tax-deferred retirement vehicle with account values linked to the performance of the investment options chosen by the market participant. The investment options for a variable annuity are typically mutual funds that invest in stocks, bonds, money market instruments, or some combination of the three.

A variable annuity that uses a risk control framework has the added feature of providing caps and floors to investment performance that are linked to the performance of the underlying investment options. If the underlying index delivers returns greater than the cap level or lower than the floor level, market participants will receive guaranteed payments at the cap or floor level, respectively. Therefore, variable annuities with risk control offer downside protection to investors at the expense of forgoing a degree of upside return. They offer market participants better visibility and predictability on future cash flows from annuities by effectively incorporating risk management tools in investment products.

A variable annuity with risk control features shares the same concept as and similar structure to those of risk control indices. Risk control indices are designed to measure the performance of underlying equity or futuresbased indices at specified volatility levels. As a benchmark provider of risk control indices, S&P Dow Jones Indices finds it relevant and meaningful to investigate the impact of incorporating a risk control framework into investment products, such as variable annuities, in a portfolio context.

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Research - Oct 30, 2018

The Value of Research: Skill, Capacity, and Opportunity

Tim Edwards

Managing Director and Global Head of Index Investment Strategy

S&P Dow Jones Indices

Craig Lazzara

Managing Director, Index Investment Strategy

S&P Dow Jones Indices

Hamish Preston

Head of U.S. Equities

S&P Dow Jones Indices

Anu R. Ganti

Head of U.S. Index Investment Strategy

S&P Dow Jones Indices

EXECUTIVE SUMMARY

How much should a portfolio manager be willing to pay for research?

The question is of importance to any manager, but has become particularly pertinent since newly imposed European rules require that the costs of investment research—previously offered by many investment banks as an in-kind consideration in return for brokerage business—be unbundled from trading.

Unfortunately, attempts to determine a fair value for research in the most general circumstances are doomed to fail. Even if we only consider direct recommendations to buy or sell certain securities, the value of such recommendations to a portfolio manager will vary according to the absolute size of positions taken in response. Instead, we provide a framework for estimating relative research values across markets and constituents, under certain stylized (but reasonable) assumptions.

Exhibit 1 provides a summary of our main result—comparing the putative value of recommendations in selected markets, expressed as a multiple of the equivalent measure applied to stock-based recommendations within the S&P 500® .

INTRODUCTION: THE IMPACT OF “UNBUNDLING” RESEARCH COSTS

The Markets in Financial Instruments Directive (MiFID) II is an updated version of a regulation that has been in force throughout the European Union (EU) since November 2007.¹ The update came into effect on January 3, 2018, and seeks “to reform market structures, bring more transparency to the trading of financial instruments, and strengthen investor protection.”²

For our purposes, the relevant regulatory change is that execution costs and charges must be separated, or “unbundled,” from the cost of research, and that investment managers must either absorb research costs or explicitly pass them on to their clients under pre-agreed terms.³ Since investment managers were formerly allowed to pay for research by the allocation of client trading commissions, MiFID II has the potential to produce major changes in the economics of research sales.

While these rules are of most immediate concern to institutions operating in the EU, MiFID II has potential global implications: the updated directive applies to all firms that conduct business in Europe, and many expect the legislation to be extended to other regions.^4,5

From a practical perspective, MiFID II requires managers to set research budgets and to decide where to spend them. Obviously, the size of a particular research budget will depend on idiosyncratic factors, such as a firm’s assets under management. But when it comes to allocating resources, the relative value of research is likely to be comparable—if I find one analyst’s recommendations to be worth double those of other analysts, it is reasonable to hypothesize that these recommendations would also prove to be twice as valuable to anyone else.

This paper argues that the relative value of research is driven by a combination of three things: the information content of the research, the dispersion within the market where recommendations are made and implemented, and the capacity of each market to allow for active positions of varying sizes. While we do not claim to offer a universally applicable framework for setting research budgets, we hope to offer a practical and useful way to think about the value of signals for markets of varying size, concentration, and risk levels.

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Research - Oct 18, 2018

The S&P/BMV IPC Turns 40

A benchmark index represents the performance of a specified securities market, market segment, or asset class. The S&P/BMV IPC seeks to measure the performance of the largest and most liquid stocks listed on the Mexican Stock Exchange (Bolsa Mexicana de Valores; BMV). The index is designed to provide a representative, investable, and replicable measure of the Mexican equities market.

The S&P/BMV IPC was launched on Oct. 30, 1978, as part of a Mexican market revolution, and it has been the icon of the Mexican equity market ever since. In the middle of a long inflationary period, between the devaluation of the Mexican peso and changes in monetary policy regarding the exchange rate, 1978 was a decisive year for the Mexican economy. The country’s central bank implemented new economic and financial policies, marking a new regulatory environment that included tax exemption on capital gains, the approval of the revaluation of certain assets to support listed companies facing a delicate financial situation, and the “Mexicanization” of foreign capital through the stock exchange.¹

INDEXING IN MEXICO

The need to measure the evolution of the Mexican equity market existed even before the launch of the Índice de Precios y Cotizaciones (IPC), which is now the S&P/BMV IPC. The first attempt of one of the S&P/BMV IPC’s predecessors, the Promedio de hechos de la Bolsa index from 1910, was calculated as the annual arithmetic average of the values traded by each listed company. However, this first approach was unstable due to the lack of liquidity in the market and the frequent change of listings.

Later on in 1958, the universe was restricted to 11 industrial companies, and the calculation was based on the daily average price. As the capital market grew, the sample of 11 companies became rather unrepresentative. As a result, in 1966, the sample size was changed to 30 constituents, and the calculation methodology was changed to link the average price with the previous value and to introduce adjustments for corporate actions such as splits.²

In October 1978, the BMV started the calculation of the IPC in parallel, making it public on September 1980. Over the course of the past 40 years, the index has evolved, keeping its representation and objectives aligned with the market. In this paper, we highlight the four decades of index history and major milestones, in addition to examining the characteristics of the S&P/BMV IPC as the iconic symbol of the Mexican equity market.

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Research - Sep 07, 2018

Rotation Strategies and Their Role in the Australian Market

Priscilla Luk

Managing Director, Global Research & Design, APAC

S&P Dow Jones Indices

Sector allocation is one of the main pillars in equity portfolio management. In this paper, we examine how the sector price momentum strategy and the cyclical and defensive sector rotation strategy performed in Australia based on the S&P/ASX 200 Global Industry Classification Standard (GICS^®) sector indices.

EXECUTIVE SUMMARY

As measured by the S&P/ASX 200, the Financials and Materials sectors have accounted for 50% of the Australian market since December 1989, but since sectors can fall in and out of favor, mimicking benchmark sector weighting may not be an optimal way to maximize portfolio returns.
Our study on sector price momentum shows that strong momentum sectors in recent months tend to outperform in coming months, and the opposite holds for the weak momentum sectors, suggesting that sector price momentum can be exploited in sector allocation.
An unoptimized portfolio with quarterly allocation to the top three sectors based on 12-month price momentum generated an annualized excess return of 4.5% compared with the market between December 1989 and June 2018.
Cyclical sectors in Australia represented 80% of the total market, but they underperformed defensive sectors on average over the entire period studied, and they only outperformed defensive sectors in 7 of the past 28 years.
Our study on cyclical and defensive sector performance across global and domestic economic cycles shows the global economic cycle is a stronger driver of relative performance of cyclical versus defensive sectors than the domestic economic cycle in Australia.
A dynamic allocation strategy that equal weights cyclical sectors during global economic up cycles and rotates to defensive sectors during global economic down cycles achieved an excess return of 5.2% per year compared with the Australian market.

SECTOR DIVERSIFICATION IN THE AUSTRALIAN MARKET

In a previous paper, “Is There Value in Asia Ex-Japan Sector Rotation Strategies?”, we examined how sector allocation based on price momentum and economic cycles performed in Asia, excluding Japan, and we concluded that these two strategies delivered better returns than the benchmark. In this paper, we will study how these two strategies performed in the context of Australian sectors.

Our study focuses on the 11 broad sectors classified by GICS: Energy, Materials, Industrials, Consumer Discretionary, Consumer Staples, Health Care, Financials, Real Estate, Information Technology, Telecommunication Services, and Utilities. The analysis covers the period between December 1989 and June 2018. We use the S&P/ASX 200 and S&P/ASX 200 GICS sector indices to represent the Australian market and sector performances, respectively, since March 2000, which is when the S&P/ASX 200 GICS sector returns data history begins. For dates before the beginning of the S&P/ASX 200 GICS sector index return data, we use S&P Australia BMI and S&P Australia BMI GICS sector index data.

As of month-end June 2018, the S&P/ASX 200 was diversified into 11 GICS sectors, and some of these sectors can be traded through exchangetraded funds (ETFs; see Appendix for full list of S&P/ASX sector ETFs). Financials was the largest sector in the index by weight (33.1%), consisting of 26 stocks with a combined value of more than AUD 2.2 billion traded daily. Utilities was the smallest sector in the index, weighing merely 2.0% and containing five stocks with a combined value of AUD 114 million traded daily.

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Research - Sep 04, 2018

Earnings Revision Overlay on Fundamental Factors in Asia

EXECUTIVE SUMMARY

In this research paper, we explore the effectiveness of overlaying earnings revision strategies on traditional fundamental value and quality factors across seven Pan Asian markets—Australia, China, Hong Kong, India, Japan, South Korea, and Taiwan—between March 31, 2006, and March 31, 2018.

HIGHLIGHTS

The earnings revision-screened factor portfolios outperformed their respective comparable factor portfolios across the majority of Pan Asian markets for the value and quality factors.
Our screening approach did not introduce a large increase in portfolio turnover or strong sector or size biases to the fundamental factor portfolios historically.
Among various Asian markets, the earnings revision overlay generated the most significant excess return in Australia and Hong Kong for a majority of fundamental factors.

INTRODUCTION

In our previous research paper “Do Earnings Revisions Matter in Asia?”, we concluded that stock prices tended to move in the same direction as their earnings revisions in the majority of Pan Asian markets, and the earnings revision strategies delivered excess returns in a majority of the markets. Due to the high portfolio turnover of this strategy, using earnings revision as an overlay to fundamental factors may be more practical.

The goal of this research is to evaluate the effectiveness of earnings revision strategies over the fundamental factor portfolios. We overlaid two earnings revision screens (EPS change and EPS diffusion) on the fundamental value and quality factor portfolios, respectively. Specifically, we examined if the earnings revision screens were historically effective in generating return alpha or reducing the risk of the factor portfolios by filtering out the stocks with relatively poor earnings revisions from the factor portfolios.^¹

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