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willyho
發表於: Jul 2 2019, 06:07  評價+1
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QUOTE (Pearltea @ Jun 29 2019, 13:23)
QUOTE (willyho @ Jun 26 2019, 08:06)
Equity research is really just a way of pitching sales of a stock, while trying to look like a science. Usually using "scary" equations.

Can you give some context on the "scary" equations?

I do see problems with these equity research reports, not only because they could be biased due to many obvious reasons, but the 'sound' approaches are hardly any indications of providing investors consistent investment return in the short run. Obviously some are better but the majority is mediocre at best:

1- biased/conflict of interest.  An analyst is unlikely to issue a "sell" rating on a publicly-traded company, if the firm that the analyst works for ever want the opportunity to raise capitals for the company down the road (investment banking).  One can claim that the equity research dept and investment banking dept run separately and do not collaborate, but I'm quite skeptical to believe that the equity research dept could remain neutral when considering the overall corporate strategy and the bigger opportunities.
  
2- majority is always (almost) wrong. If people make investing decisions/form opinions based on what everybody else knows (public information), then they'd most likely be following the mass, and the vast majority is usually wrong when it comes to investing. Equity research would fall under this category as they only use information made available to the public

3- emotional vs logics. Even the most prominent analysts on wall street, who boast themselves with prolific credentials, have no crystal ball when a stock would rise or fall and often find themselves simply following the price trends.  An example would be those covered tech/growth stocks in the past couple of years - they did nothing but raced each other into the oblivion in raising price targets because the market prices told them so. Nobody wants to look like an idiot by setting PT based on fundamentals if the price momentum is dominated by excessive pessimism/optimism. Nothing wrong with that, but then what value do they add by telling people to follow momentum?

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.

本篇文章已被 willyho 於 Jul 2 2019, 06:35 編輯過
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willyho
發表於: Jul 8 2019, 01:48  評價+2
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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.  
    


本篇文章已被 willyho 於 Jul 8 2019, 09:11 編輯過
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Pearltea
發表於: Jul 12 2019, 06:05  評價+1
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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.

本篇文章已被 Pearltea 於 Jul 14 2019, 03:34 編輯過
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willyho
發表於: Jul 16 2019, 03:56  評價+1
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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 百富勤. 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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耒戈氏
發表於: Jul 18 2019, 02:37  評價+1
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批評者永遠也是最大聲,默默支持的人不會有人理。
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雞仔嘜
發表於: Jul 18 2019, 08:06  評價+1
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元朗勁揪


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發表於: Jul 19 2019, 04:15  
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QUOTE (雞仔嘜 @ Jul 18 2019, 16:06 )
元朗勁揪

真。勇武 tongue.gif


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吳超
發表於: Jul 21 2019, 18:09  
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元朗勁揪,元朗好波,打得好!下之打得勁喲,威比人睇!

好波!!!


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吳超
發表於: Jul 21 2019, 18:33  
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給我打,給我往死裡打!


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neveryield
發表於: Jul 21 2019, 19:19  評價+1
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我正在考慮要不要明天開始帶器械上街,小棍子之類的,車子堜韺滮M?

帶有政治性質的暴力行為我雖然不認同,但還能理解;但這種無差別攻擊簡直是恐襲,人家台灣是一個鄭捷,子霸各樂馬這可是幾十個鄭捷,香港還有安全嗎?



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世間之事,惟鬥爭已。

既便你達成了那最高尚的目的,亦無法彌補因为你採用了最卑劣的手段所帶来的恶劣影響。

一碗醇酒拈手來,坐看洪流不復來
經年不見花已殘,舊日芳人何處尋
開醰陳酒香四溢,醉臥山河愁不還
倒酒為河,夾肉為林,有此佳肴,何以為憂?
眾人皆醒,唯我猶夢中,不知年日,問長城依舊?

一竹獨行,十木皆枯,百里無塵,千秋不還。
日月更年,星晨生息,西海東來,南松北往。
還看舊地,天移地去,綠葉無蹤,礫石為孤。
蒼蒼茫茫,滴水沉泥,青草既出,逝會歸回?

大雪連綿千幾里,孤房門角一窗櫺,
老湖中間一條狗,獨坐冰樹望烏雲。

杯中良酒回回香,甘甜酒辣酸辛苦,
佳陳何止千百變,喜愁哀樂豈無嚐?
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neveryield
發表於: Jul 31 2019, 18:39  評價+1
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颱風天,准時下班!!!!!


--------------------
世間之事,惟鬥爭已。

既便你達成了那最高尚的目的,亦無法彌補因为你採用了最卑劣的手段所帶来的恶劣影響。

一碗醇酒拈手來,坐看洪流不復來
經年不見花已殘,舊日芳人何處尋
開醰陳酒香四溢,醉臥山河愁不還
倒酒為河,夾肉為林,有此佳肴,何以為憂?
眾人皆醒,唯我猶夢中,不知年日,問長城依舊?

一竹獨行,十木皆枯,百里無塵,千秋不還。
日月更年,星晨生息,西海東來,南松北往。
還看舊地,天移地去,綠葉無蹤,礫石為孤。
蒼蒼茫茫,滴水沉泥,青草既出,逝會歸回?

大雪連綿千幾里,孤房門角一窗櫺,
老湖中間一條狗,獨坐冰樹望烏雲。

杯中良酒回回香,甘甜酒辣酸辛苦,
佳陳何止千百變,喜愁哀樂豈無嚐?
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Caesar
發表於: Aug 16 2019, 08:00  評價+1
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人生第一個專業試,
事,就是這樣成了


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Get busy living, or get busy dying.

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耒戈氏
發表於: Sep 4 2019, 02:37  評價+1
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反潮流才是王道!!
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當身邊所有人也反對你,不難想像所有人也想害你。

連颱風也死了,連個天不中意我。

突然想,不想活了。

連給我們一個喘息的機會也沒有。

---

連天混亂,觸發了多少抑鬱,不知有多少自殺案已經發生了。

我可否說,各方行徑已經殺害了一群人?

當然,他們只會說,對方錯!那些自殺的?管它的!

---

sad.gifsad.gifsad.gif
sad.gifsad.gifsad.gif
sad.gifsad.gifsad.gif

本篇文章已被 耒戈氏 於 Sep 4 2019, 02:39 編輯過
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neveryield
發表於: Sep 4 2019, 10:55  評價+3
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QUOTE
管它的!


新衣漂亮不?

附帶圖片
附帶圖片


--------------------
世間之事,惟鬥爭已。

既便你達成了那最高尚的目的,亦無法彌補因为你採用了最卑劣的手段所帶来的恶劣影響。

一碗醇酒拈手來,坐看洪流不復來
經年不見花已殘,舊日芳人何處尋
開醰陳酒香四溢,醉臥山河愁不還
倒酒為河,夾肉為林,有此佳肴,何以為憂?
眾人皆醒,唯我猶夢中,不知年日,問長城依舊?

一竹獨行,十木皆枯,百里無塵,千秋不還。
日月更年,星晨生息,西海東來,南松北往。
還看舊地,天移地去,綠葉無蹤,礫石為孤。
蒼蒼茫茫,滴水沉泥,青草既出,逝會歸回?

大雪連綿千幾里,孤房門角一窗櫺,
老湖中間一條狗,獨坐冰樹望烏雲。

杯中良酒回回香,甘甜酒辣酸辛苦,
佳陳何止千百變,喜愁哀樂豈無嚐?
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neveryield
發表於: Dec 25 2019, 13:15  評價+1
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聖誕都無人?有無搞錯?勤力蛇哪了?

祝 HKSAN 諸君 步步高陞, 萬事勝意,身體健康,恭喜發財。


--------------------
世間之事,惟鬥爭已。

既便你達成了那最高尚的目的,亦無法彌補因为你採用了最卑劣的手段所帶来的恶劣影響。

一碗醇酒拈手來,坐看洪流不復來
經年不見花已殘,舊日芳人何處尋
開醰陳酒香四溢,醉臥山河愁不還
倒酒為河,夾肉為林,有此佳肴,何以為憂?
眾人皆醒,唯我猶夢中,不知年日,問長城依舊?

一竹獨行,十木皆枯,百里無塵,千秋不還。
日月更年,星晨生息,西海東來,南松北往。
還看舊地,天移地去,綠葉無蹤,礫石為孤。
蒼蒼茫茫,滴水沉泥,青草既出,逝會歸回?

大雪連綿千幾里,孤房門角一窗櫺,
老湖中間一條狗,獨坐冰樹望烏雲。

杯中良酒回回香,甘甜酒辣酸辛苦,
佳陳何止千百變,喜愁哀樂豈無嚐?
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雞仔嘜
發表於: Dec 29 2019, 10:46  
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真正的天才是1%的天份+99%的努力
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聖誕快樂


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