201–210 of 232 posts
Also, not all trading is liquid, nor is it short term, both of which are targets for automated algorithmic trading. Long terms investments are typically human decisions.
Earlier quoted context omitted.
Machine learning is "learning from data." It is not the assumption that there are no dynamics, and that the future will simply be a repetition of the past. To the extent that the future is predictable, learning from data is the best that can be done. The reality is that speech recognition, language translation, face recognition, object classification and detection, semantic segmentation, speech and image synthesis ha…
I bet Google "could" write some algorithms that can predict if a stock will go up or down seconds before change based on live search data. All it takes is for an article or something to come out, then watch people search for "Will IBM stock drop" - and perform live sentiment analysis across all such live queries involving the stock name. In a sense, it's all about what information you have access to.
1. Traders generally exert an advisory/supervisory role on the set of prices that the bank offers, prices which are based on a formula or other fairly automatic means, with an added human adjustment. They already extensively use technology.
2. Traders are therefore most involved where profit can be made but simple algorithms don't work. For example, pricing big deals in illiquid markets, like when a company issues a large complex bond. As this contract is by definition not traded yet and not the same as others, there is necessarily limited applicable training data, so that there is no way to learn by example - i.e., use deep learning techniques (what I assume the question is asking about). In this case, trust and relationships are extremely important as both sides of the deal have limited information.
3. Markets change dynamics, often very rapidly. Traders have to react intelligently to events: like interest rates hitting the zero lower bound, wars breaking out or industrial accidents. They need to anticipate the actual consequence to future cash flows and also to sense the appetite of the market after the event. Publicly announced AI techniques are very far away from this kind of complex general reasoning.
The days of manual trading are long gone: of open outcry traders, yelling in bullpits and making handsignals, when banks would hire big imposing ex-football players. The question is a "why do you feel you can get away with beating your wife"?
Earlier quoted context omitted.
Do you consider several hedge funds consistently beating the market with significant margins for over 20 years to be "anecdotal" evidence? The evidence shows that it is very difficult for traders to consistently beat the market. The evidence does not show that their performance, as a profession, is random.
Yes, I consider them anecdotal. If you have a billion people flipping coins, a few are for sure getting 20 tails in row, but that is no evidence that they would be better coin flippers than the others. > The evidence does not show that their performance, as a profession, is random. I think you are right, if I recall correctly, the evidence points to a conclusion that the performance of their profession is worse than…
1. As stated, it's not falsifiable. So you start with a conception of the market as entirely random, and you observe that participants are consistently beating this market. Each time you observe someone beating the market, you chalk it up to the probability distribution. "Well, that's just a two-sigma event." Then you see it happen again. "Well, that's just a three-sigma event." Then again, and again, and again. How many sigmas from the average market performance are you willing to accept before you agree that someone is legitimately and purposely beating the market with a skill-based mechanism, not a chance-based mechanism?
Furthermore, do you have the numbers to turn this into a falsifiable claim? What is your time interval? Daily, weekly, monthly or annually? How many correct forecasts do they have to make ("how many sigmas from the average"), compared to the chance expectation of coin flipping over the same timescale? If you don't have these numbers handy, then it's purely a thought experiment. Subsequently, the observation that funds like Berkshire Hathaway, Bridgewater, Renaissance Technologies, Baupost Group, Citadel, DE Shaw, etc. consistently beat the market for at least 20% net of fees over 20-30 years suggests that, per Occam's Razor, people can beat the market due to skill.
2. The analogy is not comparable to active trading. You don't need to hit 20 heads in a row to beat the market consistently, you just need to hit a p-value number of x heads correct for y coin flips greater than chance would suggest. We don't assume that basketball is a game of chance if the players can't make all their shots in a row; nor do we assume that baseball players with a 0.3 batting average aren't clearly better than the average high school dugout. If your trading interval is weekly or monthly, and you're consistently up over the market (even net of fees!) for 240 months or 360 months, it doesn't matter if every single month was a winner.
3. Have you ever read Warren Buffet's response to the EMH assertion, as postulated by Fama?[1] He outlined an excellent rebuttal in his 1984 The Superinvestors of Graham and Doddsville. Essentially, if you assume that the coin flipping analogy does map to trading, then you should expect to see a normal distribution of the winners, given that the market is inherently random and no one is achieving superior coin flips through skill. However, if you observe that the winning coin-flippers consistently hail from a small village with standard coin-flipping training, then it is more reasonable to assume that there is something unique about those particular flippers. This is what we see in reality - yes, most amateur traders fail miserably, and yes, most hedge funds underperform the market over time. But there is a relatively small concentration of extremely successful funds and traders in an uneven distribution.
4. Even Fama has walked back on Efficient Market Hypothesis, and no longer espouses the view that the market is inherently random. It is deeply complex, yes, but it is not efficient, nor entirely random. Several studies have been conducted to empirically examine EMH, and the results in favor of the hypothesis are dubious.[2][3][4] A much more charitable retelling of EMH is the weak position, which essentially states that any obvious alpha will be quickly arb'd out of real utility, but that non-obvious alpha, or alpha which is technically public but not easily accessible will retain utility until it becomes obvious. This also maps more cleanly to reality, in which trading on e.g. news reports is mostly unprofitable (everyone can get a news report at around the same time, for the same level of skill) whereas mathematically modeling pricing relationships can be extremely profitable (doing so accurately requires public, but mostly unclean data and a great deal of skill).
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1. The Superinvestors of Graham and Doddsville - http://www8.gsb.columbia.edu/rtfiles/cbs/hermes/Buffett1984....
2. Investment Performance of Common Stocks in Relation to Their Price-Earning Ratios: A Test of the Efficient Market Hypothesis - http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.1977....
3. The Cross-Section of Expected Stock Returns - http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.1992....
4. International Stock Market Efficiency and Integration: A Study of 18 Nations - http://onlinelibrary.wiley.com/doi/10.1111/1468-5957.00134/a...
because traders are used to seeing such predictions fail. Reuters was trading FX electronically since the early 1990s. At the tier one IB I worked for the IT budget was 500m USD a year (across products), and that was in 1997! Huge resources were thrown at automation. However, to this day, large trades in FX (> 10m USD notional) are still almost exclusively performed by humans over a telephone or over the bloomberg me…
Machine learning is "learning from data." It is not the assumption that there are no dynamics, and that the future will simply be a repetition of the past. To the extent that the future is predictable, learning from data is the best that can be done. The reality is that speech recognition, language translation, face recognition, object classification and detection, semantic segmentation, speech and image synthesis ha…
What do speech recognition, language translation, face recognition, object classification/detection have in common that is not true about predicting the future price of a security?
Earlier quoted context omitted.
Yes, I consider them anecdotal. If you have a billion people flipping coins, a few are for sure getting 20 tails in row, but that is no evidence that they would be better coin flippers than the others. > The evidence does not show that their performance, as a profession, is random. I think you are right, if I recall correctly, the evidence points to a conclusion that the performance of their profession is worse than…
Yes, that's the classical coin flipping example from the strong position on Fama's Efficient Market Hypothesis. There are several problems with the coin-flipping analogy: 1. As stated, it's not falsifiable. So you start with a conception of the market as entirely random, and you observe that participants are consistently beating this market. Each time you observe someone beating the market, you chalk it up to the pro…
Now, I fully agree that there have been certain anomalies (value premium anomaly in case of Buffett) that pretty much align with the strategies of these long term successful investors. Question is, has it been their skill to pick right anomaly as a basis for their strategy, or luck? Again, in the world of investment strategies, there for sure is someone trying almost anything. And if that anomaly disappears[3], do they have the skill to change their strategy?
But we are a bit off topic here. The original question was "Why do traders in investment banks..." that is a different species from the warrenbuffetts.
[1] Asset markets can be right somewhat like a clock that has stopped is right twice a day.
[2] Yes, Keynes said "The market can stay irrational longer than you can stay solvent."
[3] I have actually bet my money that the value premium anomaly is not disappearing, but that anomaly is not something investment bank traders can enjoy, as the anomaly is far too long term anomaly for them.
Earlier quoted context omitted.
> because traders are used to seeing such predictions fail. There was a joke from the 80s: soon the whole trading floor will be replaced by a computer, a man and a dog. The man presses the button to turn on the computer every morning. The computer operates all of the transactions and settlements automatically. And the dog is there to bite the man if he touches any other button. 30 years later, still no dog on the flo…
The NYSE floor traders are only there for window dressing to be seen by the TV cameras. They are entirely redundant and watch movies for much of the day when there is no need to show a flurry of activity after opening or before closing. The entire building is no longer of any real importance.
> "Would you trust purchasing a house from a seller, without meeting/talking to them, or a single person before and throughout the purchase?" You mean I can get an unbiased look at a house in peace, compare the numbers, look at the plans, measure the humidity and do my due diligence without a sales person breathing down my neck? Hell yea. I'd pay premium for that.
This is the classical hn bias. Normal people would like to talk to humans. Hn users would rather get info, plans, etc from houses and eventually buy them and sign the contract using a REST API with a node.js client..
I don't know anyone who wants to pay that fee.