It began with a handful of academics, notably William Sharpe.

Sharpe won the Nobel Memorial Prize in Economic Sciences in 1990, based on his research on Capital Asset Pricing Model (CAPM). The CAPM attempts to explain the expected returns of a security (stock, bond, etc.).

I will avoid the technicalities as much as possible and make it comprehensible for the layman. I wanted to share this because it could have a significant impact on your investment strategy. So be sure to read on.

First, we know that most, if not all, governments issue debts. For example, the Singapore Government issue bonds of varying maturity dates. These bonds pay out coupon rates to bondholders and are deemed as the least risky asset in the entire country. Typically, the government’s 10-year bond interest rate is used as an indication of the risk-free rate for the country.

The significance of the risk-free rate is that any investments of a riskier nature are expected to give a higher return. Else, why take the risk? If the 10-year interest rate is 2.5%, you should expect to get more than 2.5% when investing in stocks.

Second, we understand a security such as a stock, can fluctuate in price. We also understand a stock index is used to indicate the general movements of the stock market of a particular country. We could then benchmark the movement of a stock against the stock index, to tell us if the stock is more, or less, volatile than the index.

If we set the index at a value of 1, a stock would be more (less) volatile if it has a value more (less) than 1. This value is known as Beta.

Those are the first two factors of the CAPM equation. The expected returns of a stock are hence, based on the prevailing risk-free rate and the volatility against the index. Since the risk-free rate is the same for all securities, the only difference is the degree of volatility of a stock. The more volatile the stock, the higher the expected returns.

But this isn’t useful at this stage.

The next significant breakthrough was when Eugene Fama and Kenneth French broke down the Beta into two more factors – Value and Size. It started a chain reaction of further divisions of Beta found by other notable academics. The findings were significant for finance quants who could tap on these anomalies to make above average investment returns. I would like to share these Factors with you now.

Value investing is a popular strategy among investor, and many excellent results have been achieved. But what defines value differs from person to person.

Most would agree that buying good earnings as cheap as possible is a value play. That is true. But not useful enough if it cannot be quantified.

Fama and French were very precise with their definition, and that was to rank stocks based on the book-to-market ratio, or more commonly known as Price-to-Book (PB) Ratio. The study found that the lowest PB ratio stocks (cheapest) tend to give higher returns than high PB ratio stocks (expensive).

You probably cannot imagine just one PB ratio could increase your investment returns.

It is simple but not easy.

The problem with this Value Factor is that you will face mostly unfamiliar stocks. Moreover, they are likely to have problems going on with their businesses. It is counter-intuitive since our belief is to invest in good businesses. Hence you will find it uncomfortable to invest in them and shun this approach even though it gives you potentially higher returns.

Fama and French also discovered that smaller-size companies tend to give higher returns than bigger-size companies.

That is again, counter-intuitive, as most people would believe larger companies are going to perform better than the smaller ones as the former are well capitalised and have more power to determine pricings and grow earnings. They are more stable too.

Fama and French explained that smaller companies are indeed riskier and hence investors have to be compensated with higher returns.

I would prefer the reasoning from the angle of behavioural finance. Most investors prefer big companies because of the perception we described above. When more investment capital flows to the bigger companies, it created an unequal distribution resulting in price discrepancies. Stock prices of big companies tend to trade at a premium because more investors buy them.

Investing in smaller companies would make most investors uncomfortable and hence, shun this group of stocks despite potential higher returns.

Another group of academics found the Momentum Factor. One of the most cited ones was Mark Carhart. He found that stocks that have gone up (down) the most in the past 12 months tend to go even higher (lower). These suggested stock prices have momentum.

That is contrary to the Value Factor. I believe that the Momentum Factor happens in shorter periods (months) while the Value Factor happens in longer periods (years).

The Momentum Factor was well captured by a trading strategy called trend following.

The problem with the Momentum Factor is that it can be fleeting. It is known that trend following is a low accuracy but high payoffs strategy. It means that most traders have to cut losses most of the time to preserve capital. That is very unnatural, and many people do not have the discipline and emotional detachment to do it persistently. Most would give up before the strategy starts working.

Why momentum exists? I would reason that investors have the herd instinct and would find it easy to invest when a stock has gone up in price. A recent example would be the red hot property market in Singapore, whereby more investors, not less, piled capital into houses.

4. Beta – Low Volatility

Beta can be put simply as a relative volatility of a stock to an index. A low Beta stock has a low correlation to the market index and lesser degree of price fluctuations. It was found that low Beta stocks tend to outperform the high Beta stocks.

Of the Factors discussed in this article, this is the most unexplainable Factor because academics have long said that volatility is a measurement of risk. Higher Beta stocks are essentially having higher volatility and hence should have higher returns to compensate for additional risk. But the findings have flipped the whole argument around.

A paper was attributing part of Buffett’s superior performance to his exposure to low Beta stocks.

Last but not least is the Quality Factor. One indicator of quality was Gross Profitability (Gross Profits / Total Assets) as expounded by Robert Novy-Marx.

By buying the highest Gross Profitable stocks will give you higher returns than the lower Gross Profitability stocks.

That is intuitive as investors prefer profitable stocks than less profitable ones.

I had heavily summarised and simplified the last 40 years of research on investment returns. What we have gone through is known as Factor Based Investing, and it has been gaining acceptance within the finance industry.

The more important question is whether it can work in reality? Can investments be reduced to a few Factors?

I strongly believe in these Factors and getting exposed to at least one of them would provide a higher chance of etching out higher returns than most people. The studies are evolving, and of course, history is not a good predictor of future returns. It is equally important to ask if some of these Factors could disappear. I think behavioural finance has an answer – as long as the anomalies are caused by humans, and humans continue to make investment decisions, we can have a higher degree of confidence that such Factors will continue to work.

It began with a handful of academics, notably William Sharpe.

Sharpe won the Nobel Memorial Prize in Economic Sciences in 1990, based on his research on Capital Asset Pricing Model (CAPM). The CAPM attempts to explain the expected returns of a security (stock, bond, etc.).

I will avoid the technicalities as much as possible and make it comprehensible for the layman. I wanted to share this because it could have a significant impact on your investment strategy. So be sure to read on.

First, we know that most, if not all, governments issue debts. For example, the Singapore Government issue bonds of varying maturity dates. These bonds pay out coupon rates to bondholders and are deemed as the least risky asset in the entire country. Typically, the government’s 10-year bond interest rate is used as an indication of the risk-free rate for the country.

The significance of the risk-free rate is that any investments of a riskier nature are expected to give a higher return. Else, why take the risk? If the 10-year interest rate is 2.5%, you should expect to get more than 2.5% when investing in stocks.

Second, we understand a security such as a stock, can fluctuate in price. We also understand a stock index is used to indicate the general movements of the stock market of a particular country. We could then benchmark the movement of a stock against the stock index, to tell us if the stock is more, or less, volatile than the index.

If we set the index at a value of 1, a stock would be more (less) volatile if it has a value more (less) than 1. This value is known as Beta.

Those are the first two factors of the CAPM equation. The expected returns of a stock are hence, based on the prevailing risk-free rate and the volatility against the index. Since the risk-free rate is the same for all securities, the only difference is the degree of volatility of a stock. The more volatile the stock, the higher the expected returns.

But this isn’t useful at this stage.

The next significant breakthrough was when Eugene Fama and Kenneth French broke down the Beta into two more factors – Value and Size. It started a chain reaction of further divisions of Beta found by other notable academics. The findings were significant for finance quants who could tap on these anomalies to make above average investment returns. I would like to share these Factors with you now.

Value investing is a popular strategy among investor, and many excellent results have been achieved. But what defines value differs from person to person.

Most would agree that buying good earnings as cheap as possible is a value play. That is true. But not useful enough if it cannot be quantified.

Fama and French were very precise with their definition, and that was to rank stocks based on the book-to-market ratio, or more commonly known as Price-to-Book (PB) Ratio. The study found that the lowest PB ratio stocks (cheapest) tend to give higher returns than high PB ratio stocks (expensive).

You probably cannot imagine just one PB ratio could increase your investment returns.

It is simple but not easy.

The problem with this Value Factor is that you will face mostly unfamiliar stocks. Moreover, they are likely to have problems going on with their businesses. It is counter-intuitive since our belief is to invest in good businesses. Hence you will find it uncomfortable to invest in them and shun this approach even though it gives you potentially higher returns.

Fama and French also discovered that smaller-size companies tend to give higher returns than bigger-size companies.

That is again, counter-intuitive, as most people would believe larger companies are going to perform better than the smaller ones as the former are well capitalised and have more power to determine pricings and grow earnings. They are more stable too.

Fama and French explained that smaller companies are indeed riskier and hence investors have to be compensated with higher returns.

I would prefer the reasoning from the angle of behavioural finance. Most investors prefer big companies because of the perception we described above. When more investment capital flows to the bigger companies, it created an unequal distribution resulting in price discrepancies. Stock prices of big companies tend to trade at a premium because more investors buy them.

Investing in smaller companies would make most investors uncomfortable and hence, shun this group of stocks despite potential higher returns.

Another group of academics found the Momentum Factor. One of the most cited ones was Mark Carhart. He found that stocks that have gone up (down) the most in the past 12 months tend to go even higher (lower). These suggested stock prices have momentum.

That is contrary to the Value Factor. I believe that the Momentum Factor happens in shorter periods (months) while the Value Factor happens in longer periods (years).

The Momentum Factor was well captured by a trading strategy called trend following.

The problem with the Momentum Factor is that it can be fleeting. It is known that trend following is a low accuracy but high payoffs strategy. It means that most traders have to cut losses most of the time to preserve capital. That is very unnatural, and many people do not have the discipline and emotional detachment to do it persistently. Most would give up before the strategy starts working.

Why momentum exists? I would reason that investors have the herd instinct and would find it easy to invest when a stock has gone up in price. A recent example would be the red hot property market in Singapore, whereby more investors, not less, piled capital into houses.

4. Beta – Low Volatility

Beta can be put simply as a relative volatility of a stock to an index. A low Beta stock has a low correlation to the market index and lesser degree of price fluctuations. It was found that low Beta stocks tend to outperform the high Beta stocks.

Of the Factors discussed in this article, this is the most unexplainable Factor because academics have long said that volatility is a measurement of risk. Higher Beta stocks are essentially having higher volatility and hence should have higher returns to compensate for additional risk. But the findings have flipped the whole argument around.

A paper was attributing part of Buffett’s superior performance to his exposure to low Beta stocks.

Last but not least is the Quality Factor. One indicator of quality was Gross Profitability (Gross Profits / Total Assets) as expounded by Robert Novy-Marx.

By buying the highest Gross Profitable stocks will give you higher returns than the lower Gross Profitability stocks.

That is intuitive as investors prefer profitable stocks than less profitable ones.

I had heavily summarised and simplified the last 40 years of research on investment returns. What we have gone through is known as Factor Based Investing, and it has been gaining acceptance within the finance industry.

The more important question is whether it can work in reality? Can investments be reduced to a few Factors?

I strongly believe in these Factors and getting exposed to at least one of them would provide a higher chance of etching out higher returns than most people. The studies are evolving, and of course, history is not a good predictor of future returns. It is equally important to ask if some of these Factors could disappear. I think behavioural finance has an answer – as long as the anomalies are caused by humans, and humans continue to make investment decisions, we can have a higher degree of confidence that such Factors will continue to work.