Let's Get Back to the Fundamentals

Asset class investing is a science. It is about taking advantage of the most up to date academic thinking available today and designing portfolios that successfully implement this information to achieve the highest possible level of return commensurate with the level of risk. The techniques available for applying a lot of this research have improved dramatically over the years. Studies have been vindicated with further proofs and evidence and increasingly this information is being applied with great success. Initially, it was thought that the information only worked in theory and couldn’t be transferred from ‘gown to town’ but processes have proven that this wealth of data is indeed applicable to individual portfolios. Applying this technology will allow you to separate the noise that clutters so much of the investment landscape from the information that determines return.

The key is to understand the importance of portfolio construction and the inter relationships that create a portfolio designed to be suitable for your particular requirements. There is a need for using a disciplined investment framework to ensure successful wealth creation. Sound processes and empirical data that evince confidence in the desired outcome serve to protect you from the whims that destroy so many market participants. Staying up to date with current thinking in these fields is just one aspect of a professional financial planner’s role

Our firm has spent considerable time and resources implementing breakthrough collaborative research conducted over the years by Professors Eugene Fama from the University of Chicago Booth School of Business and Ken French from Dartmouth College Tuck School of Business. This research built upon extensive work undertaken by many others over the years. Research into capital markets has been greatly enhanced by the computational analysis of massive amounts of data. The combination of technology and huge databases have been critical in proving or disproving theories and determining the attributes of the factors that determine a portfolios return. It is no coincidence that the Centre for Research in Securities Prices (CRSP) Database is housed at the Booth School of Business where a lot of the original breakthrough work has been done on portfolio theory.

Prior to the advent of the computer, work was already being done on the efficiency of markets. Some of this research dates back to the work of Louis Bachelier, a French mathematician, who presented his dissertation in 1900 on “The Theory of Speculation” for his degree of Doctor of Mathematical Sciences at the Sorbonne in Paris. In this paper he stated that “the mathematical expectation of the speculator is zero.” He describes this condition as a “fair game.” He arrived at this conclusion because “it seems that the market, the aggregate of speculators, at a given instant can believe in neither a market rise nor a market fall, since, for each quoted price, there are as many buyers as sellers.” The logic is irrefutable, “Clearly the price considered most likely by the market is the true current price: if the market judged otherwise, it would quote not this price, but another price higher or lower.” Over time of course, prices will move in either direction, when the market as a group changes its’ mind about “what the price considered most likely “is going to be. The size of the fluctuation tends to get larger as the time horizon lengthens.[1]

There is a capital market rate of return and this is determined by the level of income generated and the growth of that income. The rest is pure speculation as markets endeavor to react to news and events that have not yet happened. In constructing an investment portfolio the aim is to capture as much as possible of the market return in the most efficient manner and to ensure the optimum reward for taking risk is achieved. A well designed portfolio will still suffer periods of negative returns during a major market correction but diversification of risks will reduce the effects of this, prevent permanent capital loss and with adequate rebalancing, ensure that full benefit can be achieved when markets inevitably recover.

The methodology of Portfolio Theory can be distilled into some basic concepts: Firstly, markets are broadly efficient. This does not mean that prices of investment assets always correctly reflect their intrinsic value, merely that the price reflects the consensus view of intrinsic value at a given time, based upon the available information. Indeed prices are often “wrong”, but in a random and therefore unpredictable way. This leads to the conclusion that the probability of persistently outperforming the market is not significantly greater than would occur by mere chance. In academic theory this is referred to as the “efficient market hypothesis” which is a term Professor Fama coined in his doctoral research paper in 1965.[2]

There is considerable confusion and misunderstanding in relation to the Efficient Market Theory (EMT). This is understandable when you consider that a broad understanding of EMT by individual investors would undermine many of the investment practices and opportunity for profit in many of the brokerage firms and fund management organizations that exist to extract the highest level of fees and charges by forecasting, stock picking and market timing strategies. There is a determination by many in the investment community to discredit this information and ‘muddy the waters’ because their very livelihood depends on maintaining the status quo.

EMT holds that stocks are always correctly priced since everything that is publicly known about the stock is reflected in its market price. It is not about perfect pricing but rather imperfect pricing in a random manner. The point of capitalism is that capital markets work and that risk is rewarded. The market is the sum total of all participants estimations and all known information. One manager or one investor cannot consistently out-predict or out-forecast every other participant. There are indeed times when, with the benefit of hindsight, we see that the sum total of investors was behaving in a manner that seemed hard to justify. An example of this was when the NASDAQ technology index in the United States in 2000 was trading at a price/earnings ratio (P/E) of 150 which was unsustainable. It was just as unsustainable when it was trading at 100 or even 50 times earnings.

In 1992, Professors Fama and French developed a paper that helped to explain the relative out-performance of some managers by defining the different dimensions of risk. “The Cross Section of Expected Stock Returns” was a breakthrough paper that revolutionized the understanding of how markets were priced and how returns were generated. They documented the three-factor model, which explained market risk, the smaller companies’ effect and the value or book to market effect evident in all markets around the world. Value and size were identified as dimensions of risk in addition to the market itself. In a logically functioning efficient market, those investors willing to take on the increased risk of small companies or distressed, out of favor companies should be rewarded with a higher rate of return over time than the average participant in the market.

It should cost more for a riskier company to raise capital, via a higher interest rate on borrowings or a lower share price on equity capital. Therefore, the out-performance of small and value companies is a function of their increased cost of capital and not as a result of any inefficiency as described by some. Managers that have a higher proportion of value or small company stocks in their portfolio should out-perform the average over time due to the higher risk taken. Some fund managers are likely to present this as evidence of skill rather than capturing what is in effect an asset class return.

Unfortunately, due to something called ‘style drift,’ very few managers are able to consistently capture the full benefit of the asset class that is available. The returns of all managers and market participants can be charted on a graph and you find the normal bell curve of distribution will exist, with most managers falling close to the index and a few outliers at the extremities. We all know who the good outliers are. They get massive inflows of new funds after they have achieved good performance and have the profits to swamp us with advertising spend about their past performance. The badly performing outliers go out of business or are merged with better performing funds so we end up with a skewed picture. This is a case of survivorship bias reflecting a better situation than is actually the case in reality.

The trouble is that no one can successfully predict the good outliers in advance because this is a random occurrence driven more by style predominance over a particular period than manager skill. If we were to look back to the nineties, the last five years of the decade the standout performer in the Australian funds management arena was Colonial First State Funds Management and for the five years prior to that the standout performer was Bankers Trust. Both these institutions received the bulk of investor inflows prior to a period of takeovers, restructuring and subsequently had periods of underperformance. Regrettably, we have not seemed to learn our lesson as investors are still led by a sales driven advisory culture focused on seeking out the next ‘guru’ fund manager or institution that will ‘shoot the lights out’ and justify the cost of their advice.

Extreme volatility is a consequence of conflicting information and market uncertainty. As new information is processed quickly there can be violent swings one way then the other. There is no evidence of any fund manager or brokerage house that was able to participate in the tech boom and bust of 1999/2000 and successfully exit their positions prior to the peak in March 2000. The extreme returns being achieved entailed extreme risks. No one was able to systematically benefit to a degree that could be explained by anything other than luck. On June 30, 2001 there was a swing of 70 points in the ASX 200 index a few minutes after closing. It was corrected within minutes of opening the next day. No one was able to systematically profit from this or predict it accurately in advance. On October 20, 1987, the market was 25% lower at the end of the day than the beginning. There was no fund manager that was able to profit from this or even if there was, there was no one who could systematically gain this market intelligence in advance on a consistent basis to profit at the next downturn.

The Efficient Market Hypothesis view is that all of these were the result of temporary market mania and all were unable to be profited from, as all were unpredictable in advance. They were the rapid re-evaluation of circumstance by millions of investors reacting independently to new and evolving information and no one individual was able to consistently predict when the sum total of all other participants (the market) had under or over reacted. In 1987, the collective total of all participants in the US share market over reacted to known information. As a result of these over reactions, the prices of stocks quickly recovered in 1988 and over the subsequent years. Previously, in the crash of 1929, the total of all participants under reacted to the known information and another bigger crash was required to adjust prices to an appropriate level and this occurred in mid 1930.

The Efficient Market Hypothesis postulates that around half the crashes that occur are likely too small and half are likely to be too big for markets to be operating efficiently in the long term. Unpredictable economic outcomes and news items generate frequent price changes. The distribution of these changes is around the mean (the expected return that people require to hold stocks). Share markets have booms and busts and as a result there is a market risk that cannot be diversified away. Over the very long term there is a market risk premium of approximately 7% above inflation in order to reward people for the market risk of these fluctuations. The distribution of returns has outliers, where there is sometimes too much adjustment to new information and sometimes too little adjustment but the adjustment is always unpredictable in advance.

One of the reasons the average investor consistently makes poor buy/sell timing decisions is that they are constantly being advised to transfer to a fund or stock that has been outperforming it’s peers without a comprehensive understanding as to why that is the case. Usually, this transfer sets up unrealistic expectations and it is done just prior to it regressing to the mean or even under performing for a period. Saying markets are efficient is not saying the fund managers and analysts that make up the market are stupid. To the contrary, it is because they are so very good as a group at processing all known information that there is little room to profit in advance from glaring inconsistencies. There is a capital market rate of return and that return will be greater over the long term depending on the portfolio’s exposure to small or distressed companies held. For many years the cost of capital for these companies has been misinterpreted as these shares being undervalued originally when in effect they had been priced for risk.

[1] Bachelier, Louis.1900. “Theory of Speculation”. Paris: Gauthier-Villars. Translated by A. James Boness and reprinted in Cootner, loc. cit.

[2] Fama, Eugene F. 1965 “The Behaviour of Stock Prices,” Journal of Business (January 1965), pp34-105