Agree with Larry, and thanks for the note about the book. Adding:
In addition to our own biases of "confirmation", "recency", "identity", etc., public markets express collective biases. The excess swings up or down illustrate this. And currently we have naive followers making bets based on a tweet or a text message from a friend. These gamblers will push stocks higher late in the cycle and likewise push assets down late in the cycle.
Another book on Doug's theme, "The 5 Mistakes Every Investor Makes," by Peter Mallouk.
Doug - I think this is your best posting yet. "Proving yourself wrong is the ultimate contrarian bet" - in a world where changing your mind is seen as weak, acknowledging there are multiple potential outcomes is confused and the phrase "I don't know" has been deleted from from discourse - you hit the nail on the head - if you have never read the book Sway, The irresistible Pull of Irrational Behavior by the Brafman brothers - which hilariously explores how unconscious bias impacts decision making - I will send to your office. Maybe I should start a substack called "The Unconscious Profit" :)
Let me propose a slightly different model: If you employ DCF, you need a timeframe of many years, let's say 15. To make an educated bet you need a lot of data, which belong by and large into two classes: (1) Hard data like debt, earnings, earnings growth, sales growth etc.; (2) Soft data like convictions, affinities, trends in technologies, markets, politics etc.
Doing your homework with (1), you have reason to believe to actually enhance the probability of choosing a company with a steeper returns trend. The downside is that everybody can do this kind of math because the data are easily available. So, to choose on the basis of this homework is not quite contrarian. Anything else equal, this strategy may earn a comfortable profit, a beta.
Unfortunately, guessing (2) is impossible because reality is way too complex compared to the capabilities of our brains (even if we assume to be living in a pure cause-and-effect-world, which we don't know). Convictions may change, people may change, markets may change, technologies may change, politics may change. So if we make a longterm-guess on (2) and our guess turns out to be right, we are just lucky.
Assumed we be lucky, then we may earn an uncomfortable profit, an alpha.
But can there be a be better way to alpha than by pure luck? Maybe. If we take for granted that the return we earn is a premium for the risk we take, then we might get to alpha by taking on higher risk. And, if in the stock market risk equals volatility, then, after having completed our homework with (1), we may choose the best-looking stocks with the highest volatilities.
As Robert Shiller has shown, stocks are not only driven by numbers but also by narratives. These narratives time and again lead to irrational exuberance, to prices driven by hope or fear. Let's assume the rolling longterm mean average of a stock as an expression of our homework (1; beta), and the relation of the current stock price to the rolling mean average (to be measured in standard deviations) as the stock's volatility. Then, (current price - StDev(1x, 2x, ..., nx) - rolling mean average > 0) will be our desired alpha.
Cautiously, over time, and dependent on our desired alpha, we may now buy the dips and sell the peaks and earn an uncomfortable profit.
More: (Disclaimer - pure opinion) The market's price declines over the last few months reflect a modest scenario for interest rate increases. The bear case is not priced in. A Warren Buffett's approach comes to mind, invest in companies that will do well over the next five years regardless of good or bad macro conditions.
Agree with Larry, and thanks for the note about the book. Adding:
In addition to our own biases of "confirmation", "recency", "identity", etc., public markets express collective biases. The excess swings up or down illustrate this. And currently we have naive followers making bets based on a tweet or a text message from a friend. These gamblers will push stocks higher late in the cycle and likewise push assets down late in the cycle.
Another book on Doug's theme, "The 5 Mistakes Every Investor Makes," by Peter Mallouk.
Doug - I think this is your best posting yet. "Proving yourself wrong is the ultimate contrarian bet" - in a world where changing your mind is seen as weak, acknowledging there are multiple potential outcomes is confused and the phrase "I don't know" has been deleted from from discourse - you hit the nail on the head - if you have never read the book Sway, The irresistible Pull of Irrational Behavior by the Brafman brothers - which hilariously explores how unconscious bias impacts decision making - I will send to your office. Maybe I should start a substack called "The Unconscious Profit" :)
Let me propose a slightly different model: If you employ DCF, you need a timeframe of many years, let's say 15. To make an educated bet you need a lot of data, which belong by and large into two classes: (1) Hard data like debt, earnings, earnings growth, sales growth etc.; (2) Soft data like convictions, affinities, trends in technologies, markets, politics etc.
Doing your homework with (1), you have reason to believe to actually enhance the probability of choosing a company with a steeper returns trend. The downside is that everybody can do this kind of math because the data are easily available. So, to choose on the basis of this homework is not quite contrarian. Anything else equal, this strategy may earn a comfortable profit, a beta.
Unfortunately, guessing (2) is impossible because reality is way too complex compared to the capabilities of our brains (even if we assume to be living in a pure cause-and-effect-world, which we don't know). Convictions may change, people may change, markets may change, technologies may change, politics may change. So if we make a longterm-guess on (2) and our guess turns out to be right, we are just lucky.
Assumed we be lucky, then we may earn an uncomfortable profit, an alpha.
But can there be a be better way to alpha than by pure luck? Maybe. If we take for granted that the return we earn is a premium for the risk we take, then we might get to alpha by taking on higher risk. And, if in the stock market risk equals volatility, then, after having completed our homework with (1), we may choose the best-looking stocks with the highest volatilities.
As Robert Shiller has shown, stocks are not only driven by numbers but also by narratives. These narratives time and again lead to irrational exuberance, to prices driven by hope or fear. Let's assume the rolling longterm mean average of a stock as an expression of our homework (1; beta), and the relation of the current stock price to the rolling mean average (to be measured in standard deviations) as the stock's volatility. Then, (current price - StDev(1x, 2x, ..., nx) - rolling mean average > 0) will be our desired alpha.
Cautiously, over time, and dependent on our desired alpha, we may now buy the dips and sell the peaks and earn an uncomfortable profit.
More: (Disclaimer - pure opinion) The market's price declines over the last few months reflect a modest scenario for interest rate increases. The bear case is not priced in. A Warren Buffett's approach comes to mind, invest in companies that will do well over the next five years regardless of good or bad macro conditions.