- Factors are features of securities that can be used to explain risk and return.
- Investors can use factor ETFs to boost their returns, improve their investment performance, and reduce risk.
- Factor ETFs are not pure trackers; they stray from the market’s up and down movement to some extent.
- Because these ETFs are so new, it’s difficult to say how effective they are. The rationale, on the other hand, is good enough that a wise investment could pay dividends.
What exactly are factor ETFs?
As it has become common knowledge that traditional stock picking does not always work, trackers and exchange-traded funds (ETFs) that follow a simple, passive technique of following a set market or index have been immensely popular in recent years.
A simple tracker with no frills “Purchasing a market index, such as the S&P 500 or the FTSE in the United Kingdom, has drawbacks. Investors are entirely exposed to the market in question and all of its fluctuations, despite the fact that it is highly transparent. As a result, it’s not unexpected that hybrid ETFs have emerged that are still tracker ETFs but are intentionally biased in one or more ways. They’re frequently referred to as “ETFs with a factor.”
What is a value factor ETF, exactly?
Overview. The goal of the US Value Factor ETF is to deliver long-term capital appreciation by investing in US stocks with lower market valuations compared to fundamentals such book value of equity and earnings.
What exactly is a factor fund?
What are “factor based” funds, and how do they work? Factor-based funds are an active management strategy. They have the potential to meet specific risk and return objectives by “tilting” portfolios toward certain stock characteristics, such as recent momentum, greater quality, or lower stock prices, on purpose and openly.
What exactly is factor investing?
Factor investing is a strategy for investing that focuses on unique determinants of performance across asset classes. Macroeconomic and stylistic variables are the two basic categories of factors. Factor investing can improve portfolio performance, reduce volatility, and increase diversification.
So, what exactly is a factor strategy?
Factor investing is a strategy for selecting assets based on characteristics linked to greater returns. Macroeconomic factors and style factors are the two main categories of factors that have influenced stock, bond, and other asset returns. The former seeks to explain returns and risks within asset classes, whereas the latter aims to capture broad concerns across asset classes.
The rate of inflation, GDP growth, and the unemployment rate are all common macroeconomic parameters. Creditworthiness of a corporation, share liquidity, and stock price volatility are all microeconomic elements to consider. Growth versus value stocks, market capitalization, and industrial sector are all style considerations.
What’s the difference between factor investing and smart beta investing?
We could allocate to a combination of the stock market and a long–short multi-factor portfolio instead of designing an equity portfolio as smart beta by selecting stocks ranked by factors. ETFs or liquid alternative mutual funds could be used to create an alpha + beta strategy. The costs of beta ETFs, such as the S&P 500, are nearly nothing, and long–short multi-factor ETFs are priced well below 1%, resulting in total costs comparable to smart beta ETFs.
- To distinguish between beta and factor returns, a smart beta portfolio requires constant performance attribution analysis. It’s easy to tell if you’re getting outperformance with an alpha + beta portfolio.
- When it comes to portfolio creation, smart beta and factor investing are vastly different. Allocating to a long–short multi-factor portfolio produces returns that are more in accordance with the core scholarly research on factor investing.
- Stock market correlations in smart beta ETFs are more than 0.9. A long–short multi-factor portfolio, on the other hand, shows no link with beta. As a result, alpha could be used to supplement bonds in a well-balanced portfolio. This is an intriguing issue in a low-interest-rate situation. And the alpha’s portfolio weight might be customized to the risk preferences of the investor: the less risk averse the investor, the lower the beta exposure.
We built a number of alpha + beta portfolios that included exposure to the US stock market as well as a long–short multi-factor portfolio that included the Size, Value, and Momentum factors. Despite the fact that transaction costs are not included, the portfolios are rebalanced annually to reduce them.
The smart beta portfolio has the highest CAGR, followed by the market (beta) and other alpha + beta combinations. In a gradually increasing stock market, this illustrates the strength of compounding returns.
Investors would have had to continue with the smart beta portfolio, whatever the benefits of compounding were in hindsight. That would have been difficult in the face of maximum drawdowns of above 80%. The alpha + beta portfolios were spared from such precipitous drops. Investors aim to maximize their profits in theory, but in practice, we need a smooth ride to stay committed.
Is the size of an ETF important?
When comparing similar ETFs, the rule of thumb is that bigger is better. Larger ETFs can take advantage of economies of scale to reduce expenses and are less likely to be liquidated, which can negatively impact your returns. To be viable, ETFs must grow to a certain size.
What is the formula for factor returns?
This framework can be used to look at the exposures of a hypothetical long-only equities portfolio that tries to capture value, momentum, and size style premia. 3 In practice, an investor may not know his or her portfolio exposures ahead of time, but because our purpose is to show how to use the analysis effectively, we’ll proceed as if we do.
We employ the well-known long/short academic factors HML, UMD, and SMB as explanatory variables.
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To examine the drivers of portfolio returns, we employ a regression model. We calculate the contribution of each factor to portfolio returns by multiplying its beta by its corresponding average risk premium during the sample period (see Exhibit 2).
What do factor portfolios entail?
A factor portfolio is a diversified collection of equities with varied levels of risk exposure to factors such as inflation, interest rates, and oil prices. An investor can create a more diversified portfolio with a better probability of outperforming the market by combining the correct risk factors (also known as premiums). A portfolio is a collection of investment options.
The stocks in a factor portfolio are chosen so that they have a beta of 1.0 on one factor and a beta of 0 on all other factors. Countries, industries, and styles are used as explanatory variables, and stocks are assigned a 0 or 1 exposure.
A beta of one indicates that the price of the securities will move with the market. The security will be less volatile than the market if the beta is less than one. A beta of more than one indicates that the price of the investment will be more volatile than the market.
Univariate regressions that effectively treat the component in isolation produce simple factor portfolios.
Pure factor portfolios are the result of multivariate regressions that include all components at the same time.
“A well-diversified portfolio with a beta on one factor of 1.0 and a beta of zero on all other variables.”
Experts say the key is to choose elements with a lengthy track record of producing positive results and low (or even negative) correlations between them. Then, if one of the factors fails, there’s a strong likelihood that one of the others is keeping things in check.
What are equities that use Factor Analysis?
Investors can determine which aspects provide the best risk-adjusted returns by examining the underlying exposures of stocks, ETFs, and strategies. Factor analysis is a method that allows investors to target the inherent risks that they believe will provide the best profits.
A factor analysis of a fund can reveal whether its returns are due to general market exposure or not (relating to the market risk premium)
This is critical since it might be difficult to determine why a stock or fund outperforms the market. To put it another way, this procedure aids in determining the source of returns. Factors can affect the performance of both passive index-tracking and actively managed portfolios.
Factor analysis serves as the foundation for semi-passive quantitative investment techniques such as smart beta, which selects stocks using rules-based methodologies. These factor investing techniques seek to profit from market anomalies or hazards with larger risk premiums than the market as a whole (the market itself trades at a premium to risk-free alternatives).
Instead of investing into broader market exposure afforded by standard size-based indices, smart beta strategies, which are based on factor analysis approaches, target specific risks in the building of alternative indices. A smart beta exchange-traded fund (ETF) with a momentum bias, for example, tracks the performance of equities with high momentum. The ETF’s performance would be compared to a standard index such as the S&P 500.
Proprietary indices, often known as “self-indexing,” are used to implement the smart beta approach.
The underlying value aspect seen in many stocks is a common characteristic that has been shown in statistical analyses to provide greater risk-adjusted returns. This factor is based on the idea that in the long run, cheap equities outperform overpriced ones.
Quality is another important consideration for both active fund managers and quantitative techniques. However, some believe that because this is a subjective and difficult to define aspect, it is better suited to the active-management strategy, which employs fundamental analysis and human stock selection.
The size factor, on the other hand, shows that smaller equities do better over time than larger ones, though the rationale for this is sometimes disputed. Some experts feel that higher returns are attributable to smaller stocks’ higher risk premiums, which are attributed in part to information ambiguity, making stock analysis and due diligence more difficult.
The low volatility component, on the other hand, seeks out less volatile investments that outperform on a risk-adjusted basis, while the momentum factor says that stocks that have gained momentum beat those that have lost it.
Other elements that investors and experts feel are responsible for higher long-term returns have been identified.
Factors, or individual return drivers, tend to be highly uncorrelated from one another and hence they perform in diverse market conditions and cycles. As a result, a multifactor approach to portfolio creation can reduce volatility and smooth returns.
While a standard diversified portfolio model would contain exposure to both stocks and bonds, a factor investing approach can be diversified by combining styles that react differently under different market scenarios. Instead of diversifying an equity portfolio with bonds and cash, a small-cap fund can diversify risks within a portfolio by containing large stocks. Similarly, combining growth and value strategies is another option.
Indeed, a lot of market observers have claimed that diversifying among equities and bonds is not as varied as previously thought, because these asset classes frequently move in the same manner during market collapses and increases.
Some proponents claim the factor analysis paradigm promotes more diversified portfolios than traditional methods by focusing on risk components, particularly those that move in different ways amid shifting market conditions. Essentially, this demonstrates a shift away from asset class diversification and toward underlying factor diversification.
For decades, factor analysis approaches have been employed to try to decode stock returns by identifying underlying investment traits. The value component, for example, was discovered by Graham and Dodd in 1934 in a work titled Security Analysis.
As with risk premia methods, a factor-based approach can be applied in a variety of ways, including employing leverage or short selling a fund or index. A basket of long-short investments is used in the risk premia model to aim absolute returns. In the meantime, an alpha overlay technique helps to diversify a fund by focusing on diverse underlying causes.
Long-only proprietary indexes can identify specific elements that contribute to higher risk-adjusted returns.
