Coronation Merchant Bank experts analyze the fundamentals of the Factor approach to investing, the various types of Factor Investing and its numerous benefits.
The concept of investing broadly covers the purchase or creation of assets with the use of funds or capital in order to obtain a return on the investment, also known as capital gain. It entails the purchase of a financial product with an expectation of favourable future returns. Investment assets comprising equities, fixed income instruments, derivative instruments, commodities, etc., are purchased with the sole aim of earning the maximum possible return.
Over the years, researchers have developed various approaches and models for driving optimal returns on various investment products. The two major approaches to investing include Factor Investing and Value Investing. These models were developed to ensure optimal asset allocation and maximum return on investment.
Understanding Value Investing
Value investing is an investment approach that seeks to leverage the value of underpriced or cheap stocks on the expectation that improving market conditions and strong corporate fundamentals will be priced into the value over time. The strategic asset allocation expectation is that the stock price will rise as its intrinsic value becomes more evident and widely acknowledged by investors. When the stock price rises to its intrinsic value over a given time frame, this translates to an increase in the value of the investment portfolio through capital gains and dividend cashflow. The greater the difference between the intrinsic value and the current stock price, the greater the margin of safety for value investors looking for investment opportunities.
Key financial metrics (such as earnings, revenue, or cash flow) are usually deployed to determine the value of a stock. Other characteristics considered during the stock valuation process include consistent profitability, stable revenue streams with reasonable growth, future earnings outlook and a long streak of established success history.
Understanding Factor Investing
Factor Investing is an Investment approach that involves targeting specific drivers of return across asset classes with the goal of achieving a given investment outcome or to improve long-term risk adjusted return. The drivers of return are called "Factors", and they form the foundations of factor investing. At its most basic level, factor-based investing is simply about defining and then systematically following a set of rules that produce diversified portfolios. The underlying assumption is that an investor can build a portfolio that consistently outperforms market by examining assets under defined characteristics.
The Role of Factors as Drivers of Risk and Return
The concept of factor investing would prove useful for fund managers in active portfolio management, corporate treasurers as part of liquidity management and individual investors. Investment decisions at all levels seek to determine the preferred asset allocation mix that satisfies risk and return constraints. With the aid of factor investing, investors seeking to maximize the return on their investment at a given level of risk exposure can achieve their objective.
Understanding the types of factors and their importance
Generally, two main factors drive portfolio returns: Macro Factors explain broad risks across asset classes like the pace of economic growth and inflation rate, which in turn provides an explanation for the returns on asset classes. On the other hand, Style Factors explain the risk and returns within these asset classes. For instance, low priced stocks are more likely to generate higher returns than high priced stocks.
B. Macroeconomic Factors
Key macroeconomic factors are outlined below;
Nigeria Policy rate versus 10Yr FGN Bond Yield
The graph above shows the relationship between Nigeria's policy rate and 10-year FGN bonds yield over the last 10 years. The trend shows some correlation, thus direction of interest rate will be a good factor for bond investing.
Nigeria Policy rate versus Stock Market Index
The chart above plots the relationship between Nigeria's policy interest rate and the stock market index over a 10year period. A close look at the graph shows low correlaton between the two factors and it can be deduced that other micro and macro factors beyond the movement of the policy interest rate were driving stock index prices.
Nigeria Policy rate versus Naira Exchange Rate
Background to the development of Factor Investing
The foundation of factor investing begins with the Capital Asset Pricing Model, a mathematical expression that tries to quantify the drivers of individual asset or portfolio returns while accounting for risk exposures. This is also known as the "One Factor" model as it accounts only for market risk. This was expanded further by Eugene Fama and Kenneth French when they accounted for size risk and value risk (Fama & French, 1992). The expanded CAPM model was able to explain asset or portfolio outperformance with a greater level of accuracy. This was also known as the three-factor model. Overall, the model was able to explain about 90% of the difference in asset returns.
Mark Carhart built on the Fama three-factor model by adjusting for momentum in explaining asset returns. In his paper "On Persistence in Mutual Fund Performance," (Carhart, 1997), he defined the momentum factor as the average return of the top 30% of stocks minus the average return of the bottom 30% as ranked by this measure. According to Berkin & Swedroe (2016), the addition of momentum to the three-factor model increased the explanatory power of the model by 5%Expanding on Mark Carhart's model, Robert Novy-Marx and Alan S. Zekelman added the quality or profitability factor to the model. Quality or profitability factor adopted by Benjamin Graham defines low quality in an enterprise as low debt, long history of paying dividend and earnings growth.
Applying factor Investing in Fixed Income
Despite the importance of factors, factor-based investing in fixed income has been slow to develop and remains a developing area of study. This is driven partly by the lack of data, relatively opaque pricing, and a relative lack of transparency in the asset class. Another challenge identified in applying factor investing to Fixed Income assets is that the various segments of fixed income markets make it difficult to create a one-size fits all factor investing model. However, factors may be even more critical in fixed income, as systematic risk constitutes a significant proportion of bond total risk.
Fixed income markets are, by nature, more reliant on systematic drivers than equity markets and the outperformance of a significant portion of fixed income portfolios have been driven by exposure to systemic risk (Khan, 2015). Soe & Xie (2016) identified that the combination of uncorrelated or low-correlated fixed income risk factors potentially allows for smooth return patterns and may offer portfolio diversification benefits over market cycles. All things being equal, the more volatile the bond yield is, the higher the yield needs to be in order to compensate for the volatility risk. Higher exposure to the value factor may be used to seek enhanced returns, while lower exposure to the low-volatility factor may be used to mitigate risk.
In assessing government and corporate bond indices, the usual weighting of securities based on market capitalization poses a peculiar challenge because it implies that high weightings are assigned to the most highly indebted countries and companies, respectively. Investors will therefore be disproportionately invested in issuers with the highest debt burden. In addition, debt indices are often less balanced than equity indices.
While there exists an extensive body of literature evaluating the use of factor-based models in developed markets, we have limited insight into its application in emerging markets like Nigeria. The following constraints have been identified for implementing factor investing in the Nigerian capital markets (Fixed Income and Equities)
As the market develops, the expectation is that investors would have access to larger data sets and diversity of the asset pool would increase. This would see to increased efficiency in decision making as well as a data driven approach to generating alpha returns.