By Shane Obata  @sobata416

Introduction

The purpose of this analysis was to examine the relationships between relative performance and ETF flows. Relative performance was defined as the excess returns of nine sector SPDR ETFs over the S&P 500. ETF flows were defined as the net weekly flows, adjusted for price movements.

From a practical standpoint, it was useful to examine relative performance because adept sector rotation can lead to alpha generation. For example, a portfolio manager (PM) who was overweight financials and underweight energy from 2009 to 2017 would have fared better than another PM whose allocations were in line with the S&P 500’s sector weights.

It was also useful to examine flow data because they often reflect sentiment. For example, net inflows into a top performing sector may indicate irrational exuberance and or performance-chasing.

This analysis was novel in that it considered relative performance instead of absolute performance. Moreover, it addressed some of the limitations of past research, which are covered in the Literature Review section of this paper. For example, past research focused on monthly and daily flows. Both of these frequencies may be suboptimal since the former are too sparse and the latter are too noisy. This analysis focused on weekly flows.

The results of the analysis were quite good. On average, the core models exhibited higher explanatory power than ones presented in past research. In addition, various market-based variables were considered. Some of them were highly significant and helped to add explanatory power to the core models.

Literary Review

Fund Flows and Underlying Returns: The case of ETFs” – Arsenio Staer – Q1, 2017

  • Model:

  • This paper looked at the effects of flows (X) on returns (Y)
  • ETF assets grew at >27% per annum since their introduction in 1993
  • ETFs exhibited a statistically significant and cross-sectionally consistent positive association with contemporaneous underlying index returns
  • Daily data showed contrarianism and monthly data displayed performance chasing
  • A 1SD positive flow shock corresponded to a positive impulse response in returns, followed by a meaningful reversal by t=5

  • A 1SD positive flow shock corresponded to a small positive impulse in flows, followed by a complete reversal by t=5
  • A 1SD positive return shock corresponded to a negative impulse response in flows, followed a stabilization period from t=5 onwards
  • A 1SD positive return shock corresponded to a negative impulse response in returns, followed a stabilization period from t=5 onwards

Conclusions:

– ETFs exhibited a significant positive relationship with contemporaneous index returns
– There is substantial evidence of price reversals following shocks in flows

Impact of rising interest rates on equity markets” – TIAA-CREF Asset Management – Q3, 2015

  • The last 12 rate tightening cycles led to mixed results for equities, which gained in half of the cycles
  • Interest rates were not enough. Core inflation levels and other variables were also key
  • There have been 8 cycles since 1972. Sub-groups that performed well more than 50% of the time were Non-US Developed Markets (n=8) and Emerging Markets (n=4)
  • REITs and Dividends also performed well (n=3)
  • Sector performance: Financials, particularly banks, and utilities tended to underperform during tightening cycles. Industrials tended to outperform
  • In this cycle, a period of sustained low rates has contributed to strong performance in “higher-yielding equity assets” such as Dividend Aristocrats, Utilities, REITs, MLPs, etc.

  • QE in Europe etc. has been holding down US rates, which have offered relative value vs. counterparts
  • Below “normal” rates should continue to artificially inflate demand for dividend paying stocks and US sovereign debt


Conclusions

– Rate tightening cycles yielded mixed results for equities. Important to consider other variables such as inflation
– Low interest rates have increased the demand for high-yield equity products

What drives ETF flows?” – Clifford et al. – Q3, 2013

  • Model:

  • This paper looked at the effects of returns (X) on flows (Y)
  • ETF flow chased past performance and this return chasing was particularly strong in broad market index funds
  • Flows did not correctly anticipate returns

Conclusions

– Flows chased returns in ETFs
– There was no convincing evidence “that flows are able to successfully anticipate future performance

Movements in the Term Structure of Interest Rates” – Robert B. Bliss – Q4, 1997

  • Movements in stock prices are largely idiosyncratic. In other words, it is hard to hedge one with another security with another
  • It is also hard to hedge stock price movements with index futures and other derivatives
  • Movements in bond prices are largely systemic. They are easy to hedge with a small number of bonds of different maturities or with rates derivatives
  • Explanatory models were more useful for interest rate changes than stock price changes. This speaks to the complexities of idiosyncratic risks
  • Using portfolios instead of individual securities helped to increase the explanatory power of factor based stock models
  • Factor 1 – a proxy for the level of rates – explained most of the variance in returns. Adding Factor 2 – the slope – also helped. Adding Factor 3 – the curve- added marginal explanatory power

Conclusions

– It is difficult to hedge against movements in stock prices due to idiosyncratic risks
– It is easy to hedge against movements in interest rates since movements in bond prices are largely systemic

Common Factors Affecting Bond Returns” – Robert Litterman and Jose Scheinkman – Q2, 1991

  • Most of the variation in returns on all fixed-income securities can be explained in terms of 3 factors: Level, Steepness and Curve

  • When rates are high, returns on long bonds will be closer to returns on short ones
  • When rates are low, returns on long bonds will be farther away from returns on short ones
  • For duration hedged portfolios, changes in values were attributable to changes in spreads. In other words, duration hedging eliminated systemic risk
  • “By increasing the number of instruments within the same maturity spectrum, we can make the dollar share of each security arbitrarily small…and hence make the variance of the return per dollar invested in the portfolio as small as we want…”
  • Factor 1 – a proxy for the level of rates – explained 89.5% of the variance in returns over the Risk Free Rate. Factor 2 explained 81% of the remaining variation
  • Curvature was highly correlated with implied volatility
  • Duration hedging greatly reduced dollar exposure to the level and steepness of rates (when considered in the hedging process). Remaining variance was largely explained by curvature

Conclusions

The level and steepness of rates explained the vast majority of the variance in returns
– Hedging with a portfolio of bonds was more effective than hedging with one bond

Analysis

Legend:

Inputs
Formulas
Data From Another Sheet
Data From Another Workbook

*Pricing data was obtained from Yahoo Finance, the St. Louis Fed, the CBOE and Investing.com. Flow data was obtained from Bloomberg. All data cover the March 6th, 2009 to February 10th, 2017 timeframe.

Absolute Performance

For this analysis, the nine SPDR sector ETFs were divided into three groups: Bond Proxies, Inflation Proxies and

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