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By "scary equations", I only meant the mathematical models that rely on unattainable precision. (actually you basically expanded much of what I had in mind. Thank you very much!!) The only time, I think, when an equity analyst can be trusted is when they have an investment track record, in which they have significant skin in the fund. While equity research uses public information, it does depend on how that information is used. Also there is a subtle link between 2 and 3. Namely the herd mentality. The vast majority is wrong when little to no time is actually spent on analysing the annual reports. To disagree with the general consensus of analysts takes courage. Just as proposing contrarian actions. ¥»½g¤å³¹¤w³Q willyho ©ó Jul 2 2019, 06:35 ½s¿è¹L |
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willyho | |
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Just to flesh out things a little more, finance despite the appearances, is not a science. This is despite the mathematical models/equations used. True, the data collected is often empirical, but even so, empirical data is not the only defining feature of science. (In the same way that economics is not a science, since there always seems to be a way to explain away predictions that went wrong) Science experiments have results that can be repeated, and apply to relatively closed systems. Think chemistry for example. If A + B => C, then A and B will always give C under standard conditions. This is taking into consideration the possibilities of side products D and E, and a non-complete yield. IE the yield of C is not 100%. (Standard conditions mean room temperature, atmospheric pressure, humidity etc) Hess's Law is a law because it can predict outcomes, even if the experimental outcomes are not 100% identical to calculations/theoretical expectations.
The issue with finance is that it involves applying closed models to an open system. Even with the most charitable interpretations, that empirical data collected is free from bias, that there is no conflicts of interest and so forth, the data applies to a period in the past/historical. To construct a model using a set of historical data, presumes that past conditions apply to the present. (Similar to predicting population trends) In other words, the financial model assumes no technological changes, no regulation changes, no societal changes etc. The point is that such changes have no effect on scientific "laws", because they are independent of observation. What a financial model can offer, is a framework for thinking about the financial data. In other words, it is a framework for constructing a narrative about the company, and based on the numbers, the operations of the company. And this is where subjectivity is unavoidable. The various models become the scaffolding of a narrative, but it is down to the analyst on how the narrative is finalised. This becomes more apparent when a model is used to make projections. And this is where finance becomes more like glorified fortune-telling at best, and snake-oil at worse. The point is that all the data outputs from financial projections are assumptions. A model based on careful assumptions at best can only suggest the direction of a trend, as opposed to predicting the direction. (Think how sports bets are constructed. For example, based on past performances, Arsenal tends to choke when facing teams that are more "gritty", or just simply better. But completely over run a weaker team in terms of skill and physicality. However, occasionally, they can force that vital draw to clinch a top 4 spot. So based on past performances, if Arsenal is playing (say) Barcelona or Real Madrid, then it is almost certain that betting on either Barca or RM is safer, not excluding the chances of an upset) Hence using statistical techniques, the probabilities of events occurring under pre-specified conditions are recorded and used as case studies. (For example if the financial ratio of a firm is above a certain number, then events A B and C could result. Again based on historical data) That is still not taking into account that the required data is often seen as sensitive and thus difficult to gather. (This perhaps partly explains why the emphasis on "hand-collected" data in finance journal papers, which in turn often requires the researcher's own connections.) In conclusion, as in Benjamin Graham's The Intelligent Investor, the returns of an investment is proportional to the intellectual effort applied. In plain words, critical thinking, in addition to understanding the numbers, is more key to returns than a fine-tuned finance model. ¥»½g¤å³¹¤w³Q willyho ©ó Jul 8 2019, 09:11 ½s¿è¹L |
Pearltea | |
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Your arguments seem to resemble a lot of Nassim Taleb¡¦s (the author of The Black Swan) criticisms of the approaches and methodologies that experts and economists utilized in predicting the markets. Despite logical assumptions and empirical approaches, most found themselves wrong because of their failure to account for unforeseen, catastrophic events. (Sort of like risks that have extremely low probability of occurrence, but the potential impact could cripple the world).
I agree pretty much with what you said, I wish I could have more contribution to the discussion but the world of financial markets is not my area of expertise (property & casualty insurance and financial contracts are). However, I do have some mixed opinions about the use of statistics over the years, in an industry that relies heavily on the law of large numbers (predictive modeling). Here are some of my scattered thoughts regarding these models: -They really do work, but in order to yield a positive return, you do need a sound model with high credibility factor and realistic expectations. Then stick with it 'pretty' religiously. For example, one time I made a financial decision based on 'high probability of success' in the 3rd quarter, but a low probability event occurred in the 4th quarter, resulting in a complete wiped-out profitability target and the 'bet' I placed with the insurer resulted in an economic loss of a high five-figure amount instead. I would be lying if I said I didn't feel like a loser at that time, but the important thing was to understand the the theory of the model was not 'broken', but rather the odds did not work out in my favor. I did stick with the approach and over time and it yielded a pretty healthy return. (broke even and more) -They do require tweaks. Like you said, critical thinking is crucial. It would be silly to assume that one approach would work perpetually. There are times when using historical data might seem to be the right thing to do, but a little bit of 'common sense' would tell you a whole different story. One example would be the wildfire events in the state of California in recent years. Historically there were areas (with super high value homes) where insurers believed that would have very little to almost no chance of being impacted by wildfires. And some insurers over-expanded by having more lenient underwriting guidelines, offering more competitive prices, and doing little loss control. Little did they know the 2017 wildfire was so hard to contain and suppress that even some of those homes (along with hundreds of millions of dollars worth of jewelry, fine art collections, collectibles and antique vehicles) were burned down in flames in just a matter of minutes. Sure that might be a ¡¥black swan¡¦ event, but why would any company blindly believe in a model and put that much financial exposure to one or few risks without proper risk control was just mind-boggling. ¥»½g¤å³¹¤w³Q Pearltea ©ó Jul 14 2019, 03:34 ½s¿è¹L |
willyho | |
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How did you know that I read "The Black Swan"? Although I was thinking more about the books on gfc. I think I'm more of a dabbler in finance, and especially with financial history. (Paper money, come to think of it, is the closest thing to alchemy. Who would've thought that bits of paper can have the same influence as gold did)
If the question is why companies take excessive risk by exposure to extremely unlikely events, part of the reason is easy money, and perhaps the attitude of "it probably won't happen to me, because I can get out of it/I am smarter than average etc" A case in point is the bankruptcy of pellegrin ¦Ê´I¶Ô. Based on the investigation report, the top management ignored the warnings of their risk department, and placed their trust on their superstar fund manager, who they headhunted from Lehman Brothers (I think). The problem was that the firm was in transition from a boutique firm to a large corporation. This also meant that what would work at a large US investment bank, may not apply to the firm. Hence despite the short term profits, when the Asian financial crisis hit, the firm was in no shape to survive the storm. |
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