In relation to stockpicking I am reminded of the book, "Simple, But Not Easy" - Stockpicking is simple but its not easy to be successful. The Bayesian Doctor will calculate the updated belief based on this information using Bayes Theorem and update the chart of 'Updated Beliefs'. - He likes to invest in companies in which a number of directors are buying stocks in their own company using their own savings (as opposed to being granted options). If so, why? View all posts by kilian. PKA An example is scrutiny (and subsequent demolition) of Fortune 500 companies who hire or fire their CEO's for what turns out to be random short term financial success of failure. Student of Life The so-called Bayes Rule or Bayes Formula is useful when trying to interpret the results of diagnostic tests with known or estimated population-level prevalence, e.g. noted that research on the "base-rate fallacy" used an incomplete Bayesian analysis. One great example of the Bayes theorem and how it impacts our daily decision making is the base rate fallacy. So stockpicking for me its understanding that I have all the human bias's and need all the help I can get! Base Rates and Bayes’ Theorem. Consequently there are more Christians who look like satanists than there are satanists who look like satanists" In short, it describes the tendency of people to focus on case specific information and to ignore broader base rate information when making decisions involving probabilities. Get an intuition of what Bayes theorem is: One great example of the Bayes theorem and how it impacts our daily decision making is the base rate fallacy. A person receiving a positive test could be around 97.7% confident that it correctly indicates the development of the lactose intolerance. The problem is the broader the asset the more efficient the market and the harder it is to do selection... or should we all trade currencies? On the other hand, with Sensitivity at 70% the probability of infection, given a negative test result, is not zero, but depends on the Base Rate. Jun 8, 2020 epidemiology. Bayes' theorem for the layman. or the base rate fallacy?" We are told that if a person is actually drunk, the test will indicate so 100% of the time but, in addition to this, 5% of people tested will display a false positive – the test says they are drunk when they…. [Of course, some start-ups, biotechs and exploration stocks go onto doing extremely well, but the odds of selecting those in advance are small; by excluding such companies I think he improves his probability of out-performing the stock market as a whole.] Bayes Theorem is a mathematical equation where you can input the Base Rate for an event along with the probabilities associated with new information to get the actual overall probability for the event. I have been listening to an excellent audiobook in the car (also available as a book) called, "The Drunkard's Walk: How Randomness Rules" by Prof L. Mlodinow . I concluded that what was needed was a historically successful set (or sets) of screening criteria and an investment approach that suits your personality so you stick with it. Why are doctors reluctant to randomly test or screen patients for rare conditions? That all makes sense and in particular your 3rd paragraph clarifies nicely. He says this is a way of limiting the size of his loss if he has made a bad selection of a particular stock, thereby preserving capital for better use elsewhere. Tournesol wrote: "yes but what on earth does any of that have to do with Bayes Theorem? Have a good evening, This equation is completely fine like it this, but let me expand on P(E), the probability of seeing the evidence, a little bit more. Ian, P.S. But if the Base Rate is higher, it is well above zero. [It is well known that 'value' stocks and stocks with high dividend yields tend as a group to out-perform over the long-run.] 2. When evaluating the probability of an event―for instance, diagnosing a disease, there are two types of information that may be available. Why do knowers of Bayes's Theorem still commit the Base Rate Fallacy? To date my second best sector based calls have been in fixed income pref shares, where I arrived late but still in time to join in. 2 Review of Bayes’ theorem Recall that Bayes’ theorem allows us to ‘invert’ conditional probabilities. P( H | E ) = probability of H(ypothesis) given that E(vidence) [so “|” means “given that”] or in other words, the probability that the hypothesis holds, given that the evidence is true. Why would I be more likely to get it right just because I'm analysing a different aspect of the future? Bayes’Theorem and Base-Rate FallacyTheorem and Base-Rate Fallacy 3. Where do you stop with this line of thinking though? Conclusion5. Which might also strengthen the case for IT's or OEICs or ETF's which provide broad coverage of target sectors. This video by Julia Galef explains more about how you can use Bayes’ theorem, not just to avoid the base-rate fallacy, but also to improve your thinking more generally. In my opinion just a few successful calls which are used as the basis for significant investments and which are held for significant periods can deliver life changing returns. … Intuitively, one might think that it is not much different from the example above. A good stock picker may be better off shorting their sectors to get the relative perf of their stock picks if they want to avoid base risk. The axioms of probability are mathematical rules that probability must satisfy. You would be making a sector based decision. I was using Lord John Lee as an example of someone who been extremely successful at investing over many years, and whose success supports what Tom Firth wrote in that section. Bayes’ theorem: what it is, a simple example, and a counter-intuitive example that demonstrates the base rate fallacy. Hope that makes sense. We write that the probability of the event is . I do not claim any generalised success in other sectors but I'm working on it. Fill in your details below or click an icon to log in: You are commenting using your WordPress.com account. The rate at which something happens in general is called the base rate. He avoids start-ups and biotech or exploration stocks. Therefore, in practice we almost always have to expand: Bayesian theorem basically tells us to look at all the cases where the evidence is true and then looking at the proportion of these evidences, where the hypothesis is also true. $\begingroup$ @Semoi The base rate in this case is high enough, and the accuracy of the test good enough (at least when doing it twice in a row) that this doesn't … I have already explained why NSA-style wholesale surveillance data-mining systems are useless for finding terrorists. - He looks for moderately optimistic or better chairman's / CEO's most recent comments. This idea is linked to the Base Rate Fallacy. You could if you wished simply buy the sector in toto by using a collective or by buying a basket of shares. We have been oversold on the base rate fallacy in probabilistic judgment from an empirical, normative, and methodological standpoint. Our prior belief of having the disease is just the distribution of the disease in the population, so 65% or 0.65 (P (Li)). The evidence would suggest that experts and amateurs alike are poor forecasters whether it comes to company earnings or macro events - it seems the future just isn't all that clear, whatever the scale! Tom. In that case, each new ball (new information) updated his belief. really summarised the idea concisely and in very simple language - I may have to borrow your phrasing in the future! Such a statement would be so broad and so nebulous as to be of no value. Multiple sclerosis is one of the more common, rare diseases. The base rate fallacy, also called base rate neglect or base rate bias, is a formal fallacy.If presented with related base rate information (i.e. If I was to employ such a strategy, my worry would be that I've essentially replaced one forecasting problem (the stock picking problem) with another almost identical forecasting problem (the sector picking problem). Understand the base rate fallacy thoroughly. This updated belief (the resulting posterior probability) incorporates all the evidence of that claim. But, the big but in general, hospitals double check some positive results and you therefore could trust your hospitals. Is it easier? By looking in the table we can simply extract the data: posterior = (prior * probability of prior given new evidence) / all evidence. Thanks, Ultimately, most of us are in this game to protect and grow our capital...not to convince ourselves and others that we're great stock pickers! - He prefers companies that have had few changes in their directors and few changes in their auditors. The axioms of probability are these three conditions on the function P: 1. The base rate fallacy reconsidered: Descriptive, normative, and methodological challenges. It shows how a prior assumption (called prior probability) is updated in a light of new evidence. The description of John practically has the word Satanist on the tip of our tongues, and when the question comes, we are all too eager to declare that he is much more likely to be a Satanist than a Christian. We will begin to justify this view today. Suppose you came to the realisation that the oil sector was poised to outperform. In the appendix we work a similar example. Base Rate Fallacy: This occurs when you estimate P(a|b) to be higher than it really is, because you didn’t take into account the low value (Base Rate) of P(a). 2 Conditional Probability. I cannot find any of that reflected in your discussion of John Lee's approach that will help others to emulate it. This example, I’ve visualized from a video by Veritassium called “The Bayesian Trap”. Christians might possess the same characteristics only rarely but their numbers are big. Change ), You are commenting using your Google account. No shame in hedging your bets, it just helps to take the pressure off your own analysis after all. Let’s say we have two events and . Of course, John Lee's rules are not the only way to do that. If you are not comfortable with Bayes’ theorem you should read the example in the appendix now. Thomas Bayes and was first published in 1763, 2 years after his death. If so, why? Despite John’s appearance increasing the probability that he considers himself a Satanist, the fact is that there are around 2 billion Christians in the world and very few Satanists. This finding has been used to argue that intervi… Bayesian models are more intuitive to correctly specify than frequentist tests. In fact, each new experiment and new observation (given that the experimental parameters allow a deduction of a new direction) updates our beliefs, i.e. (GPAs) of hypothetical students. They know about it. Behavioral and brain sciences, 19(1), 1-17. On the other hand, with Sensitivity at 70% the probability of infection, given a negative test result, is not zero, but depends on the Base Rate. One great example of the Bayes theorem and how it impacts our daily decision making is the base rate fallacy. Conclusion As with the base rate fallacy, this process is best outlined with an example, for which I will use example 2 on the same Wikipedia page linked above. I am not saying that it is easy to figure out sectoral vectors (direction and magnitude of movement). or the base rate fallacy? Change ), You are commenting using your Facebook account. Example 1: Even if you are brilliant, you are not guaranteed to be admitted to Harvard: P(Admission|Brilliance) is low, because P(Admission) is low.
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