Investment risk is the probability of a substantial and permanent loss of capital. We buy a stock at 100 expecting to earn a return, consisting of appreciation and possibly a stream of dividends. But our expectation may be disappointed: the price may go down rather than up and we may decide to sell the stock at a loss, either because we need the money or because we come to realise, rightly or wrongly, that we made a mistake and the stock will never reach our expected level.

How does investment risk relate to volatility – the standard deviation of past returns, measuring the extent to which returns have been fluctuating and vibrating around their mean? Clearly, we prefer appreciation to be as quick and smooth as possible. If our expected price level is, say, 150, we would like the stock to reach the target in a straight line rather than through a tortuous rollercoaster. On the other hand, if we are confident that the price will get there eventually, we – unimpressionable grownups – may well endure the volatility. In fact, if on its way to 150 the price dropped to 70 it would create an inviting opportunity to buy more.

Volatility increases investment risk only insofar as it manages to undermine our confidence. We might have rightly believed that Amazon was a great investment at 85 dollars in November 1999, but by the time it reached 6 two years later our conviction would have been brutally battered. Was there any indication at the time that the stock could have had such a precipitous drop? Sure, the price had been gyrating wildly until then, up 21% in November, down 12% in October, up 29% in September and 24% August, down 20% in July, and so on. The standard deviation of monthly returns since the IPO had been 33%, compared to 5% for the S&P500, suggesting that further and possibly more extreme gyrations were to be expected. But to a confident investor that only meant: tighten your seatbelt and enjoy the ride. A 93% nosedive, however, was something else – more than enough to break the steeliest nerves and crush the most assured resolve. ‘I must be wrong, I’m out of here’ is an all too human reaction in such circumstances.

Therefore, while volatility may well contribute to raise investment risk, it is not *the same as* investment risk. It is only when – rightly or wrongly – conviction is overwhelmed by doubt and poise surrenders to anxiety that investment risk bears its bitter fruit.

Amazon is a dramatic example, but this is true in general. Every investment is made in the expectation of making a return, *together with* a more or less conscious and explicit awareness that it may turn out to be a flop. Every investor knows this, in practice. So why do many of them ignore it in theory and keep using financial models built on the axiom that volatility equals investment risk? As we have seen, the reason is the intellectual dominance of the Efficient Market Theory.

So the next question is: Why is it that, according to the EMT, investment risk coincides with volatility? The answer is as simple as it is unappreciated. Let’s see.

If the EMT could be summarised in one sentence, it would be: The market price is right. Prices are always where they should be. Amazon at 85, 6 or 1000 dollars. The Nasdaq at 5000, 1400 or 6400. At each point in time, prices incorporate all available information about expected profits, returns and discount rates. Prices are never too high or too low, except with hindsight. Therefore, an investor who buys a stock at 100 because he thinks it is worth 150 is fooling himself. Nobody can beat the market. If the market is pricing the stock at 100, then that’s what it’s worth. The price will change if and only if *new *information – unknown and unknowable beforehand and therefore not yet incorporated into the current price – prompts the market to revise its valuation. As this was true in the past as it is true in the present and in the future, past price changes must also have been caused by no other reason than the arrival of information that was new at the time and unknown until then. Thus all price changes are unknowable and, by definition, unexpected. And since price changes are the largest components of returns – the other being dividends, which can typically be anticipated to some extent – we must conclude that past returns are largely unexpected. At this point there is only one last step: to identify risk with the unexpected. If we define investment risk as anything that could happen to the stock price that is not already incorporated into its current level, then the volatility of past returns can be taken as its accurate measure.

Identifying investment risk with volatility *presupposes* market efficiency. This is part of what Eugene Fama calls the *joint hypothesis problem*. To be an active investor, thus rejecting the EMT in practice, while at the same time using financial models based on the identification of investment risk with volatility, thus assuming the EMT in theory, is a glaring but largely unnoticed inconsistency.

So the next question is: what is it that practitioners know and makes them behave as active investors, and EMT academics ignore and leads them to declare active investment an impossible waste of time and to advocate passive investment?

Again, the answer is simple but out of sight. In a nutshell: Practitioners know by ample experience that investors have different priors. EMT academics assume, by theoretical convenience, that investors have common priors.

Different priors is the overarching theme of the entire Bayes blog. People can and do reach different conclusions based on the same evidence because they interpret evidence based on different prior beliefs. This is blatantly obvious everywhere, including financial markets, where, based on the same information, some investors love Amazon and some other short it. In the hyperuranian realm of the EMT, on the other hand, investors have common priors and therefore, when faced with common knowledge, cannot but reach the same conclusion. As Robert Aumann famously demonstrated, *they cannot agree to disagree*. This is why, in EMT parlance, prices reflect all available information.

Take the assumption away and the whole EMT edifice comes tumbling down. This is what Paul Samuelson was referring to in the final paragraphs on the Fluctuate and Vibrate papers. More explicitly, here is how Jonathan Ingersoll put it in his magisterial Theory of Financial Decision Making, immediately after ‘proving’ the EMT:

In fact, the entire “common knowledge” assumption is “hidden” in the presumption that investors have a common prior. If investors did not have a common prior, then their expectations conditional on the public information would not necessarily be the same. In other words, the public information would properly also be subscripted as φ_{k} – not because the information differs across investors, but because its interpretation does.

In this case the proof breaks down. (p. 81).

Interestingly, on a personal note, I first made the above quotation in my D.Phil. thesis (p. 132). A nice circle back to the origin of my intellectual journey.