*Warren Buffet: Passing on his secrets?*

If a poll was taken asking investors to name the greatest investor of all time, it is safe to assume that the vast majority would say “Warren Buffett”.

For years people have tried to figure out Buffet’s methods, hoping to replicate his investment success. So, what is Buffet’s “secret sauce”?

In this article, we try to find out!

We also explain how at ViniyogIndia.com, we use the same concepts to design our Core Investment Portfolios.

But before we begin, let’s take a step back and look at the ** factors** that make stock prices go up.

## Asset Pricing Models & Factors

Over the years, academic community have tried to come up with various models trying to explain asset returns. These models, or asset pricing models as they are usually called, describe expected returns of financial assets, such as stocks, bonds, etc. using various characteristics or factors.

Simply put, factor can be considered as a quantitative representation of a qualitative theme that can be used to explain asset returns.

### Capital Asset Pricing Model (CAPM)

An example is the Capital Asset Pricing Model or CAPM. In simple terms, it says, returns of an individual stock (or any other financial asset for that matter), is related to market returns. So if the market goes up, stocks in your portfolio are likely to go up (& vice versa) assuming a positive correlation.

This relationship is quantified by the following equation known as CAPM, which says expected return of an asset is Risk Free Rate, plus a constant times market risk premium (expected market returns minus risk free rate). This is constant or beta here is called the **Market Risk Factor**.

**R _{i} = R_{f }+β_{i}(R_{m }– R_{f})**

_{i}is the expected return on asset i; R

_{f}is the risk-free rate; β

_{i}is the beta of the investment; and R

_{m}is the expected return of the market.

### Fama-French Three Factor Model plus Carhart’s Momentum Factor

Despite its popularity, CAPM is simplistic and is often criticized for its inability of explain asset returns based on empirical data. In particular, multiple studies indicate that value stocks and small cap stocks tend to outperform growth and large cap stocks respectively.

*(Ben Graham, right, with young Warren Buffett)*

Foremost amongst these studies was the 1930s classic **Security Analysis** written by Buffet’s teachers Graham & Dodd almost 3 decades prior to publication of CAPM. This book had already laid the intellectual foundation for value investing.

These anomalies led Kenneth French and Eugene Fama to come up with a 3 Factor model that had **Value and Size as two additional factors**.

**R _{i }– R_{f }=α_{i} +β_{i,m}(R_{m }– R_{f})**

**+ β**

_{i,SMB}(SMB) + β_{i,HML}(HML) + ε_{i}In 1993, an IIT+IIM grad (& later Columbia University doc) named Narshiman Jegadeesh, along with Sheridan Titman (UCLA) published a seminal study showing that intermediate term momentum can be used to generate excess portfolio returns. Following their paper, which was described as a bombshell, and subsequent research, Mark Carhart expanded the model further by adding momentum as a fourth factor (1997).

*(Narshimhan Jegadeesh, left, with Sheridan Titman)*

Carhart’s Four Factor Model in regression form is represented as:

**R _{i }– R_{f }=α_{i} +β_{i,m}(R_{m }– R_{f})**

**+ β**

_{i,SMB}(SMB) + β_{i,HML}(HML) + β_{i,WML}(WML) + ε_{i}### Factor Zoo

Subsequently, many more factors have been identified to explain asset returns and newer factors are getting discovered by academia and industry alike.

For example, a paper published in 2014, examined 600 such factors from academic and practitioner literature. Another paper in 2015, reported 59 new factors discovered between 2010 and 2012 alone. So much so, that the situation of rapidly expanding factors led to coining of the term **Zoo of factors.**

## Buffet’s Secret Sauce

With this context in mind, lets return to our original question – **what is Warren Buffet’s secret sauce?**

A 2013 study titled “Buffett’s Alpha,” provides an interesting perspective.

The study says that Buffett’s success can be explained purely in terms of exposure to factors, and not by any unique stock-picking skills.

Once all the factors described in the study — namely, market beta, size, value, momentum, betting against beta (a strategy that takes leveraged long positions in low-beta assets and short positions in high-beta assets), quality, and leverage — are accounted for, a large part of Buffett’s performance is explained, and his alpha becomes statistically insignificant!

Perhaps Po, the panda and Mr. Ping, the duck, was right all along – there is no secret ingredient!!

On a different note, the movie ‘Kung-Fu Panda’ was so successful in China that it caused a national debate on why Westerners made a better film about Chinese culture than the Chinese themselves.

Be it Taosim or Yoga, Americans are rediscovering and repackaging ancient Asian concepts (of existing in harmony with the universe) better than us, while we, in particular the Indians, continue to ape the west.

Back to the topic, these findings however, does not in any way detract from the genius of the Buffets & Grahams of the world. Their genius lies in recognizing and applying the concepts of factor investing long ago, decades before modern financial theory could catch up.

## Factor Investing in Indian Markets

So, how well does factor investing work in India?

Below chart summarize Risk/Return Characteristics of Single-Factor Indices and Portfolios in Indian markets between October 2005 to June 2017.

*Source: S&P Dow Jones Indices LLC. Data from October 2005 to June 2017. Index performance based on total return in INR. Past performance is no guarantee of future results.*

Over the period from October 2005 to June 2017, portfolios for all factors, (low volatility, momentum, value, quality, dividend, and size) outperformed the S&P BSE LargeMidCap.

However, only low volatility, quality, and momentum delivered better risk-adjusted return (return per unit of risk) than the S&P BSE LargeMidCap.

## Factor Performance is Cyclical

Macroeconomic and market events affected each factor in different ways.

No single factor works all the time, instead, they are found to exhibit cyclicality with periods of outperformance and underperformance in different phases of the cycles.

Understanding the cyclical characteristic of factors across different macroeconomic regimes is vital for implementation of factor strategies.

Below chart summarizes performance characteristics of factors in different macroeconomic regimes, including market cycles, business cycles, and investor sentiment regimes in India

*Source: S&P Dow Jones Indices LLC. Data from October 2005 to June 2017. Index performance based on total return in INR. Past performance is no guarantee of future results. Table is provided for illustrative purposes. Note: Yellow, upward triangles represent favorable performance (positive excess return with outperformance probability not lower than 50%), while blue, downward triangles represent unfavorable performance (negative excess return with outperformance probability not higher than 50%) versus the S&P BSE LargeMidCap. The two factors with the highest information ratio in each of the market cycle phases are circled.*

#### Multifactor portfolios

While single factor portfolios can beat the market over long term, they are also likely to underperform over short /medium term due to factor cyclicality.

As an alternative, single factors could be blended into multifactor portfolios that aim to deliver smoother excess return across business and market cycles.

This is what we try to do in our Core Multifactor Portfolios.

## ViniyogIndia Core Multifactor Portfolios

ViniyogIndia.com offers three Core Portfolios based on Investor Risk Profile:

- Conservative Multifactor for Conservative Investors
- Balanced Multifactor for Moderate Investors, and
- Aggressive Multifactor for Aggressive Investors

The Core Portfolios are based on Multifactor, Multi-asset Regime-Switching Model and they offer a calibrated exposure to risk vs rewards.

Market states/regimes are modelled using a combination of proprietary + macro indicators (and also using statistical models such as Hidden Markov Models). This is combined with past performance to dynamically control factor exposures over market regimes.

As individual stock returns tend to be positively correlated with each other, further portfolio diversification is achieved using (asset classes negatively correlated to equities, such as) bonds (G-secs) and Gold, with asset allocation controlled based on market states.

Portfolio is balanced once every month to keep the percentage turnover low.

*Past performance of three core multi-factor portfolios, compared. Back-tested data.*

### Descriptive statistics

The following section presents the descriptive statistics for our (3 core) Multifactor Portfolios based on back-tests using past 11 years data.

First, we present the yearly returns data of our core strategies vis-à-vis Nifty 50 and Nifty 500 benchmarks for comparison. This is followed by an analysis of the sources of returns based on the three popular factor models described above.

The values shown in the tables, alpha & factor returns, are monthly values, while the numbers in braces represent the statistical significance (p-values)

**Interpretation of the data:**

The below tables show that all our models are generating significant alpha, that is statistically significant.

It also shows that, while small part of the returns is sourced from exposure to size and momentum factors, or strategic betas, significant source of returns are other factors. Returns from value factor is not always statistically significant. This is in-line with what we are actually doing.

For investors it is important to understand the sources of return in order to appreciate the value provided by fund managers against the fees they charge.

As we shall show in a separate article, many fund managers fail to generate alpha despite charging a significant management fee. In such cases, investors can easily replicate similar returns by investing in low cost, smart-beta (or strategic beta) strategies using ETFs, that provides exposure to these common factors (such as value, size, etc) at significantly lower cost.

### Conservative Multifactor for Conservative Investors

The conservative portfolio uses balanced asset allocation, with equity exposure varying between 60% and 40% while other assets (bond and gold combined) make up the rest.

Model | Alpha | MKT (Rm-Rf) | SMB | HML | WML |
---|---|---|---|---|---|

Carhart 4 Factor Model | 0.694 (0.01) | 0.346 (~0) | 0.199 (0.01) | 0.109 (0.08) | 0.21 (~0) |

Fama French 3 Factor Model | 1.093 (~0) | 0.236 (~0) | 0.199 (0.02) | 0.084 (0.19) | |

Capital Asset Pricing Model (CAPM) | 1.081 (~0) | 0.29 (~0) |

### Balanced Multifactor for Moderate Investors

The moderate version of the portfolio dynamically controls asset allocation & factor exposures based on market regimes, with peak equity exposure up to 80% and other assets making up the rest.

Model | Alpha | MKT (Rm-Rf) | SMB | HML | WML |
---|---|---|---|---|---|

Carhart 4 Factor Model | 0.887 (0.01) | 0.444 (~0) | 0.227 (0.00) | 0.135 (0.05) | 0.242 (~0) |

Fama French 3 Factor Model | 1.35 (~0) | 0.318 (~0) | 0.226 (0.01) | 0.107 (0.14) | |

Capital Asset Pricing Model (CAPM) | 1.328 (~0) | 0.387 (~0) |

### Aggressive Multifactor for Aggressive Investors

The aggressive portfolio invests primarily in equities, controlling factor exposures based on market regimes, except for extreme bear markets, when it allocates up to 50% of the assets in gold and bonds (G-Secs).

Model | Alpha | MKT (Rm-Rf) | SMB | HML | WML |
---|---|---|---|---|---|

Carhart 4 Factor Model | 1.077 (0.01) | 0.708 (~0) | 0.359 (0.00) | 0.184 (0.03) | 0.321 (~0) |

Fama French 3 Factor Model | 1.686 (~0) | 0.54 (~0) | 0.358 (0.002) | 0.146 (0.1) | |

Capital Asset Pricing Model (CAPM) | 1.666 (~0) | 0.635 (~0) |