Asset price random walk theory. Can’t win so diversify

I agree. There are many questionable practices in all of trading and investment. It is difficult for an outsider to win.

A good theory to learn is the random walk model of stock prices taught in intro finance classes.

Most professionals cannot forecast asset prices very well despite their frequent assertions to the contrary (they are scheming to fleece the sheeple). There is no systematic audits of forecasting prowess (for good reason).

Lay persons cannot forecast at all. It is not even worth the effort to try unless one has inside information or something special. (Illegal?)

Investors cannot forecast so they should just hold a well diversified portfolio so that if one or more assets gets clobbered then hopefully the other assets will do ok. That is the way herd behavior works —
they sell one asset and buy another. So when the price of one asset goes down, the price of another will go up. So by buying lots of different kinds of assets some are bound to be winners. On average prices are going up so the winners will on average outpace the losers.

I have given up on playing the commodities market 20 years ago when I found out that the CBOT was giving their floor buyers preferences on time of commodity purchase contracts over and above what the normal investor has. This created organizations that were uncontrolled that had agents on the floor. I am sure that this practice is still going on in the New York ComEx also.

The random walk hypothesis is a financial theory stating that stock market prices evolve according to a random walk and thus cannot be predicted. It is consistent with the efficient-market hypothesis.

The concept can be traced to French broker Jules Regnault who published a book in 1863, and then to French mathematician Louis Bachelier whose Ph.D. dissertation titled “The Theory of Speculation” (1900) included some remarkable insights and commentary. The same ideas were later developed by MIT Sloan School of Management professor Paul Cootner in his 1964 book The Random Character of Stock Market Prices.[1] The term was popularized by the 1973 book, A Random Walk Down Wall Street, by Burton Malkiel, a Professor of Economics at Princeton University,[2] and was used earlier in Eugene Fama’s 1965 article “Random Walks In Stock Market Prices”,[3] which was a less technical version of his Ph.D. thesis. The theory that stock prices move randomly was earlier proposed by Maurice Kendall in his 1953 paper, The Analytics of Economic Time Series, Part 1: Prices.[4]

Testing the hypothesis[edit]

Random walk hypothesis test by increasing or decreasing the value of a fictitious stock based on the odd/even value of the decimals of pi. The chart resembles a stock chart.

Burton G. Malkiel, an economics professor at Princeton University and writer of A Random Walk Down Wall Street, performed a test where his students were given a hypothetical stock that was initially worth fifty dollars. The closing stock price for each day was determined by a coin flip. If the result was heads, the price would close a half point higher, but if the result was tails, it would close a half point lower. Thus, each time, the price had a fifty-fifty chance of closing higher or lower than the previous day. Cycles or trends were determined from the tests. Malkiel then took the results in a chart and graph form to a chartist, a person who “seeks to predict future movements by seeking to interpret past patterns on the assumption that ‘history tends to repeat itself’”.[5] The chartist told Malkiel that they needed to immediately buy the stock. When Malkiel told him it was based purely on flipping a coin, the chartist was very unhappy.[citation needed] Malkiel argued that this indicates that the market and stocks could be just as random as flipping a coin.

The random walk hypothesis was also applied to NBA basketball. Psychologists made a detailed study of every shot the Philadelphia 76ers made over one and a half seasons of basketball. The psychologists found no positive correlation between the previous shots and the outcomes of the shots afterwards. Economists and believers in the random walk hypothesis apply this to the stock market. The actual lack of correlation of past and present can be easily seen. If a stock goes up one day, no stock market participant can accurately predict that it will rise again the next. Just as a basketball player with the “hot hand” can miss the next shot, the stock that seems to be on the rise can fall at any time, making it completely random.[citation needed]

A non-random walk hypothesis[edit]

There are other economists, professors, and investors who believe that the market is predictable to some degree. These people believe that prices may move in trends and that the study of past prices can be used to forecast future price direction. There have been some economic studies that support this view, and a book has been written by two professors of economics that tries to prove the random walk hypothesis wrong.[6]

Martin Weber, a leading researcher in behavioral finance, has performed many tests and studies on finding trends in the stock market. In one of his key studies, he observed the stock market for ten years. Throughout that period, he looked at the market prices for noticeable trends and found that stocks with high price increases in the first five years tended to become under-performers in the following five years. Weber and other believers in the non-random walk hypothesis cite this as a key contributor and contradictor to the random walk hypothesis.[7]

Another test that Weber ran that contradicts the random walk hypothesis, was finding stocks that have had an upward revision for earnings outperform other stocks in the following six months. With this knowledge, investors can have an edge in predicting what stocks to pull out of the market and which stocks — the stocks with the upward revision — to leave in. Martin Weber’s studies detract from the random walk hypothesis, because according to Weber, there are trends and other tips to predicting the stock market.

Professors Andrew W. Lo and Archie Craig MacKinlay, professors of Finance at the MIT Sloan School of Management and the University of Pennsylvania, respectively, have also presented evidence that they believe shows the random walk hypothesis to be wrong. Their book A Non-Random Walk Down Wall Street, presents a number of tests and studies that reportedly support the view that there are trends in the stock market and that the stock market is somewhat predictable.[citation needed]

One element of their evidence is called the simple volatility-based specification test, which is an equation that states:

X_t = \mu + X_{t-1} + \epsilon_t\,

X_t is the price of the stock at time t
\mu is an arbitrary drift parameter
\epsilon_t is a random disturbance term.
With this equation, they report that they have been able to put in stock prices over the last number of years, and figure out the trends that have unfolded.[8] They have found small incremental changes in the stocks throughout the years. Through these changes, Lo and MacKinlay believe that the stock market is predictable, thus contradicting the random walk hypothesis. Lo and MacKinlay have authored a paper, the Adaptive Market Hypothesis, which puts forth another way of looking at the predictability of price changes.

Adaptive market hypothesis

The adaptive market hypothesis, as proposed by Andrew Lo,[1] is an attempt to reconcile economic theories based on the efficient market hypothesis (which implies that markets are efficient) with behavioral economics, by applying the principles of evolution to financial interactions: competition, adaptation and natural selection.[2]

Under this approach, the traditional models of modern financial economics can coexist with behavioral models. Lo argues that much of what behaviorists cite as counterexamples to economic rationality—loss aversion, overconfidence, overreaction, and other behavioral biases—are, in fact, consistent with an evolutionary model of individuals adapting to a changing environment using simple heuristics.

According to Lo,[3] the adaptive market hypothesis can be viewed as a new version of the efficient market hypothesis, derived from evolutionary principles:

Prices reflect as much information as dictated by the combination of environmental conditions and the number and nature of “species” in the economy.

By species, he means distinct groups of market participants, each behaving in a common manner—pension fund managers, retail investors, market makers, hedge fund managers, etc.

If multiple members of a single group are competing for rather scarce resources within a single market, then that market is likely to be highly efficient (for example, the market for 10-year U.S. Treasury notes, which reflects most relevant information very quickly indeed). On the other hand, if a small number of species are competing for rather abundant resources, then that market will be less efficient (for example, the market for oil paintings from the Italian Renaissance).

Market efficiency cannot be evaluated in a vacuum, but is highly context-dependent and dynamic. Shortly stated, the degree of market efficiency is related to environmental factors characterizing market ecology, such as the number of competitors in the market, the magnitude of profit opportunities available, and the adaptability of the market participants.


The adaptive market hypothesis has several implications that differentiate it from the efficient market hypothesis:

To the extent that a relation between risk and reward exists, it is unlikely to be stable over time.

There are opportunities for arbitrage.

Investment strategies—including quantitatively, fundamentally and technically based methods—will perform well in certain environments and poorly in others.

The primary objective is survival; profit and utility maximization are secondary.

The key to survival is innovation: as the risk/reward relation varies, the better way of achieving a consistent level of expected returns is to adapt to changing market conditions.


One thought on “Asset price random walk theory. Can’t win so diversify

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