3-Factor ETF Model

Friday, 7 July 2017 Daniel Chow

Introduction

Exchange Traded Funds(ETFs) are marketable securities that are traded on the stock exchanges daily. There are multiple types of ETFs, each looking to accomplish different objectives such as tracking an index. Sector based ETFs look to give investors exposure to certain sectors and industries by allowing for a purchase of a basket of assets through the ETF.

This report will look towards building a 3-Factor model using historical returns, volatility, and sentiment to explain the future returns of ETFs. The model will then be used to select the best ETFs for a trading strategy.

Model

The following 3-Factor model will be considered in this report:

This model aims to predict the future performance of an asset using its past performance, past volatility, and past sentiment.

US Sectors

Firstly, we will attempt to test the model on US Industry/Sectors ETFs.

The following Sector ETFs will be used:

  1. Vanguard Energy ETF(VDE)
  2. Vanguard Financials ETF(VFH)
  3. Vanguard Health Care ETF(VHT)
  4. Vanguard Industrials ETF(VIS)
  5. Vanguard Information Technology ETF(VGT)
  6. Vanguard Materials ETF(VAW)
  7. Vanguard Telecommunication Services ETF(VOX)
  8. Vanguard Utilities ETF(VPU)

They represent 8 unique sectors in the US Market.

After fitting the model, at the start of each month, we will calculate the predicted future performance of all sectors and then long the ETF of the sector with a largest predicted return and hold for 1 month before we repeat the process.

For the sentiment, stocks from the S&P500 index are grouped into sectors and the daily sentiment of the stocks in each sector group is averaged to obtain the daily sentiment of each sector.

For the return and volatility data for each sector, price data from Vanguard Sectors ETFs are used as a proxy.

The model is then fitted using linear regression and the following model coefficients are obtained:

Interestingly, the negative coefficient for 3 Month Historical Return implies that better past performance leads to poorer future performance. A possible reason is that investors buy into sectors that have performed well in the past leading to the sector becoming overvalued and perform poorly in subsequent months.

Also, a larger volatility which is associated with more risk leads to better future performance which is somewhat consistent with the Capital Asset Pricing Model.

Lastly, better sentiment obviously leads to better performance and poor sentiment lead to poorer performance.

Here are the backtest results from Jan 2016 to June 2017:

**Benchmark is the S&P500 Index

 

There is some merit to selecting individual sectors over investing in the broad index, leading to larger returns over time.

 

European Indexes

The returns and sentiment of the following European Indexes will be used in this section:

  1. AEX
  2. CAC
  3. DAX
  4. IBEX
  5. STOXX50

The model is fitted using linear regression with the following results:

Again, we find that there is a negative coefficient for 3 Month Historical Return implying that better past performance leads to poorer future performance.

However, the significance of the volatility factor appears small and could possibly be eliminated to form the reduced model below.

Fitting the reduced model using the same data produced the following results:

Using the same strategy of investing in the ETF with the highest predicted return for 1 month, the following results were obtained[1]:

The performance of the model is extremely close to that of the benchmark. While the benchmark, Euro Stoxx 50 is one of the indexes being considered by the model, during the 1 year backtest period, the model did not select and invest in the Euro Stoxx 50 index.

This is evidence of the strong correlation between the countries that reside in the Eurozone. However, the model did however outperform the index slightly despite being handicapped by transaction costs which shows that while there is strong correlation, the model is able to pick out the better performers.

Asian Indexes

The returns and sentiment of the following Asian Indexes will be used for this report:

  1. Straits Times Index
  2. BSE SENSEX
  3. Shanghai Composite
  4. Nikkei
  5. Taiwan Stock Exchange Weighted Index
  6. KOSPI
  7. Jakarta Composite

The model is fitted using linear regression with the following results:

Again, we find that there is a negative coefficient for 3 Month Historical Return implying that better past performance leads to poorer future performance.

Using the same strategy of investing in the ETF with the highest predicted return for 1 month, the following results were obtained[2]:

 

Conclusion

The use of factor models to predict future returns can allow for the selection of ETFs with superior performance, leading to outperformance of benchmarks.

 

[1] These results were obtained from trading the indexes. Index ETFs should replicate the returns of the indexes and lead to similar performance barring ETF discounts and premiums. Commissions and slippage is simulated at 0.1% of trade value. The Euro Stoxx 50 index is used as the benchmark index. Risk Free Rate of 1% per annum is used for calculation of Alpha, Beta, Sharpe and Sortino.

[2] These results were obtained from trading the indexes. Index ETFs should replicate the returns of the indexes barring ETF discounts and premiums. Commissions and slippage is simulated at 0.1% of trade value. Straits Times Index is used as the benchmark index. Risk Free Rate of 1% per annum is used for calculation of Alpha, Beta, Sharpe and Sortino Ratios. Constant exchange rate is assumed.

Leave a Reply

Your email address will not be published. Required fields are marked *