The founder and Chairman of Dreman Value Management (est. 1977) shares his views on how investors can beat the market with this book (written in 1998). In reference to the efficient market hypothesis (EMH), Dreman writes "Nobody beats the market, they say. Except for those of us who do." More on this book is availablehere. One of his earlier books (from 1982) has already been summarized here.
Chapter 4
Chapter 4
Dreman quotes many "experts" from military to political to corporate leaders that have made famous predictions that went terribly wrong. Experts in the stock market are no different. While people gather en masse to hear the opinion of experts, Dreman argues that this actually prevents investors from achieving superior returns. Like a bad golf swing that must be unlearned, investors must unlearn the way they currently use "experts" if they have any hope of earning superior returns.
Also check out: The psychological research explaining the apparent inability of experts to forecast the future is discussed. The way humans best handle problems is in a linear fashion (e.g. Step 1 do this, Step 2 do that etc.). But more complex problems require interactive reasoning, whereby the interpretation of one input can change depending on how the other inputs are evaluated. Research suggests that humans tend to apply linear approaches to solving problems that are optimally solved interactively.
The more complex the problem (i.e. as more inputs need to be evaluated), the more this "line" of thinking can lead to poor results. But exacerbating the problem is that the more inputs that are provided, the more confident the decision-maker becomes. Studies testing respondent predictions for events that are highly uncertain show that when a decision-maker is provided with more information, his ability to predict stays flat (or rises moderately at best) but his confidence increases with every piece of new information, which can be a dangerous combination.
Dreman argues that stock research requires similar interactive thinking, and that the problems that must be solved (predicting a future stock price etc.) are complex, requiring the simultaneous evaluation of scores of data points. Despite the availability of unimaginable amounts of information, the outcome is nevertheless very difficult to predict, leading to low predictability, but overconfidence on the part of experts.
Chapter 5
Chapter 5
Dreman compares research analysts to dealers at the casino. The players ask the dealers, who seem to know the game well, what to play; unfortunately, the players are still destined to lose.
Investors rely on these "dealers" for their estimates in formulating their own investment decisions. However, in a paper Dreman himself authored, Dreman showed that analysts are off in their EPS estimates by about 40% per quarter. The dramatic difference is there even after removing companies with low earnings (to avoid large percentage effects just because earnings were small on an absolute basis).
Investors rely on these "dealers" for their estimates in formulating their own investment decisions. However, in a paper Dreman himself authored, Dreman showed that analysts are off in their EPS estimates by about 40% per quarter. The dramatic difference is there even after removing companies with low earnings (to avoid large percentage effects just because earnings were small on an absolute basis).
Furthermore, the effect is prevalent even across industries, from cyclical to non-cyclical alike. The trend is also getting worse, with analysts in the most recent decades actually performing worse, despite their seeming informational advantages.
Analysts also tend to be overly optimistic. EPS growth from 1982 to 1996 was about 8% per year, but analyst forecasts taken at the beginning of each year suggested predictions over this period were for 21% per year.
Other studies have confirmed these findings, noting other interesting tidbits in the process. One study found that analyst estimates would be more accurate if they simply blindly assumed a 4% rise in earnings every year for every company.
Why might this be? For one thing, analysts are overconfident in their own research. Many expect their findings to be accurate within 5%, but they are not. Despite the research suggesting analysts are not accurate, each individual analyst seems to believe he is better at predicting than he really is. Furthermore, not a lot of emphasis is placed on being accurate. Analyst pay/bonus structures are often based on how much trading brokerage the analyst brings in. This helps explain why there are so many more buys than sells. (By comparison, few brokerage commissions are brought in for “sell” recommendations.) So, it’s not about being right; instead, it’s about bringing in clients.
This helps explains why one study showed that analysts that work at firms that also have investment banks issue 25% more “buy” recommendations and 46% fewer sell recommendations than their counterparts at firms without investment banks. (Investment banking clients have been known to shun firms that make negative recommendations about their stock.)
Dreman also discusses anecdotal evidence that further suggests analysts are not paid for their accuracy but for their clients. In one example, Donald Trump once requested that an analyst be fired after he issued a sell recommendation on Trump’s company. The analyst was fired, and won a few years’ worth of salary in court as a result, shortly after Trump’s company filed for bankruptcy.
Saj Karsan
http://www.barelkarsan.com
Analysts also tend to be overly optimistic. EPS growth from 1982 to 1996 was about 8% per year, but analyst forecasts taken at the beginning of each year suggested predictions over this period were for 21% per year.
Other studies have confirmed these findings, noting other interesting tidbits in the process. One study found that analyst estimates would be more accurate if they simply blindly assumed a 4% rise in earnings every year for every company.
Why might this be? For one thing, analysts are overconfident in their own research. Many expect their findings to be accurate within 5%, but they are not. Despite the research suggesting analysts are not accurate, each individual analyst seems to believe he is better at predicting than he really is. Furthermore, not a lot of emphasis is placed on being accurate. Analyst pay/bonus structures are often based on how much trading brokerage the analyst brings in. This helps explain why there are so many more buys than sells. (By comparison, few brokerage commissions are brought in for “sell” recommendations.) So, it’s not about being right; instead, it’s about bringing in clients.
This helps explains why one study showed that analysts that work at firms that also have investment banks issue 25% more “buy” recommendations and 46% fewer sell recommendations than their counterparts at firms without investment banks. (Investment banking clients have been known to shun firms that make negative recommendations about their stock.)
Dreman also discusses anecdotal evidence that further suggests analysts are not paid for their accuracy but for their clients. In one example, Donald Trump once requested that an analyst be fired after he issued a sell recommendation on Trump’s company. The analyst was fired, and won a few years’ worth of salary in court as a result, shortly after Trump’s company filed for bankruptcy.
Saj Karsan
http://www.barelkarsan.com
