Can you believe the efficient market hypothesis?

(ORDO NEWS) — Understanding how to outperform the stock market is the surest path to wealth. In an effort to achieve this goal, investors turn to the work of scientists. But in doing so, they simultaneously change both markets and the way researchers see them.

The idea of ​​financial market efficiency was at its peak among theorists in the 60s and 70s. This hypothesis states that all information related to the value of an asset is instantly reflected in the price – thus, it is pointless to trade based on this data. The price will change only under the influence of what may happen in the future – but news for that and news that it is impossible to know about them in advance. Stock prices move “at random”. No wonder the book called “A Accidental Walk on Wall Street” became a bestseller.

This idea inspired index funds, which simply buy up all benchmark stocks from the S&P 500 list. Since the 70s, index funds have gradually gained momentum, increasing their market share. Today they manage about 20% of all assets.

But the efficient market hypothesis has been challenged many times. When the American stock market crashed overnight by 23% in October 1987, it was completely unclear why investors so dramatically and dramatically changed their minds about the fair value of assets. Robert Schiller, a professor at Yale University, won the Nobel Prize in economics for his work on how the stock market becomes more volatile than it should be when traders make adequate predictions about the cash flow that goes into the pockets of investors.

Another example of divergence between theory and practice is the foreign exchange market. When Sushil Wadhwani left the hedge fund for the monetary policy committee of the Bank of England in 1999, he was very surprised by the bank’s approach to forecasting currency movements. The largest bank relied on a theory called “uncovered interest parity,” which states that the expected change in the exchange rate is proportional to the difference in interest rates between the two countries. This suggests that the forward rate was the safest bet in predicting exchange rate fluctuations in the market.

Wadhwani was at a loss: he knew many people who practice carry trade – borrowing money in a low-income currency and then investing in a more profitable currency. But if the truth is on the side of the bank, then such a trade cannot be profitable. As a result of lengthy negotiations, the Bank of England made a classic British compromise: it began to only partially predict the movement of the currency based on the forward rate.

Many financial professionals still believe they can outperform the market. After all, there is a potential flaw at the heart of efficient market theory. For information to be reflected in prices, a trading process must take place. But why should traders get involved in trades if their efforts are doomed to fail?

Anti Ilmanen, a former market researcher who now works for the asset management company AQR, expressed a view of the “effective inefficiency” of the market. In other words, the average investor will not be able to outperform the market. But if you are ready to invest a large amount and make friends with powerful software, then you have a chance of success.

This largely explains the growth in the number of quantitative investors. They try to speculate on anomalies that cannot be explained by the efficient market hypothesis. One example is the momentum effect: stocks that have been above market performance in the recent past do not give up. Another example is the effect of low volatility: stocks that are more resilient than a constantly fluctuating market generate higher risk-adjusted returns than theory predicts.

In order to capitalize on such anomalies, new types of funds have emerged, known in stock jargon as “smart beta”. In a sense, these funds are simply trying to systematically copy the traditional approach of fund managers, who interview CEOs and study balance sheets in an attempt to watch out for winning stocks.

The prosperity of these funds depends on the reasons for the profitability of past anomalies. There are three possible options.

First, anomalies are statistic quirks: if you look at the data long enough, stocks might be taking off, for example, on rainy April Mondays. But this does not mean that this will continue in the future.

The second option is that the increased profitability is compensation for the risk. Small businesses may have negative earnings, but their shares will be less liquid in this case, and therefore difficult to sell if necessary – they are likely to go broke. Two academics, Eugene Fama and Kenneth French, argued that most of the anomalies can be attributed to three factors: the size of the company, its valuation relative to its assets, and their volatility.

The third option is based on the fact that the revenue reflects some specific features of the behavior. The change in earnings from impulse stocks may be that investors were in no rush to believe the good news and did not realize that the company’s budget was on the mend. But behavior can change. Wadhwani says that stock price movements are more pronounced today on the day the earnings announcement is read compared to subsequent days than 20 years ago. In other words, investors are reacting faster. The carry trade is also less profitable than it used to be. Ilmanen says it is likely that smart beta revenues are declining due to the proliferation of various strategies.

If the market changes, then the specialists studying it must adapt to these changes. Much modern research work focuses on anomalies and behavioral oddities that can cause investors to make irrational decisions. The adaptive market hypothesis, invented by Andrew Lo of MIT, suggests that market development is akin to evolution. Traders and fund managers adhere to strategies that are profitable from their point of view. The successful ones stay in the game, and the unprofitable ones, respectively, drop out of it.

The results can be overwhelming. In August 2007, there was a major disruption when software-based automated operations suddenly stopped working: there were suspicions that one of the managers was urgently selling off his positions after losses in the mortgage market. This episode demonstrates the risks of a quantitative approach: if computers process the same information, then they can buy the same stocks.

Today, the value of shares of emerging American companies, primarily from the technological spectrum, is higher than the value of shares around the world – the same picture was observed during the dot-com bubble. What if the trend changes direction? No clever mathematical formula will make people buy positions when panic reigns around.


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