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geov

So the tscore on this is 11. This is for a singlesample ttest. pvalue? I cannot easily find any programs that will print out anything that small even using exponential notation. I may look later and amend this if I do. Jim You can do it in excel. t Stat= 11.09546279 P(T<=t) onetail= 1.04972E17 


Jrinne

So the tscore on this is 11. This is for a singlesample ttest. pvalue? I cannot easily find any programs that will print out anything that small even using exponential notation. I may look later and amend this if I do. Jim You can do it in excel. t Stat= 11.09546279 P(T<=t) onetail= 1.04972E17 WHOA!!!!! Jim From time to time you will encounter Luddites, who are beyond redemption. de Prado, Marcos López on the topic of machine learning for financial applications 

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Jrinne
at Dec 10, 2019 11:54:15 AM

judgetrade

Jim, sorry to answer so late. My post relates not to black box systems. I know I shoot me in the foot here, but I personally would never trade a black box system and I actually would not recommend it. I might trade one but only if the provider would give almost everything away (like on a 1:1 ZOOM session where he or she shows me at least the ranking system and reveils it and does the robustness tests life together with me). The reason: you need to have 100% trust in your system if you want to trade it and you have at least I need to have test it on robustness (1, 3, 5, 10, 20, 50, 100, 200 Stocks, different caps (nano, small, mid, big), evenid =1, evenid = 0, different universes (US, Canada, Nasdaq 100, Sp500), at least all sectors, if not at least 20 industries and sub industries) and then looking at the capital curve (no statistics, bc. they do not capture the tails, at least the once I know). Also on every factor I use, I need to find at least a dozen researching papers from akademia (my favorite OSAM), If they can not backtest it for much longer timeframes (like 1870). Also, I always look, that my system that I trade has a lot of degrees of freedom. That means as less buy and sell rules as possible and a lot of stocks left in the universe (I look at the screen of the buy rules, if there are less then 1000 Stocks I am not using it and get rid of buy rules). For example: I have a trading system that works great on the overall market and this trading system works almost perfectly on the sector healthcare, but I do not use it (the healthcare version) simply bc. the overall stock count on healtcare is to low for me, I am to scared that something might happen to the sector and I am dead in the water. I give up performance bc. I do not want to get even near to overoptimisation. I know I might let something on the table, but the closer I get to optimisation, the less confident I will be to trade it. Last step is to find an assumption on why the factor works, and if this is not assumptionable by cycle behaiviour or simply emotions, I do not use it no matter how good the backtest and the robustnes test was (though this step is highly subjective). That is also the reason why I would never trade a system where I do not understand the methods that are used. For example AI stuff where even the backtest change from run to run. the Job of my coach is not to understand all this, but to keep me in the process. he asks: ok. lets go through the process and make sure you have done everything concerning to your rules. Then he asks, o.k., trust your sellf (in an event of a DD that is hard for me). My main point is: For me, following above rules, P123 is already perfect (I might want to have a function where the system buys more stocks when it is 20% down, if you know how to do this help me :)), also bc. its already a lot to do to follow this rules. Something new I would probably not use until I have mastered the fuctionality set of todays P123 and I have not yet, maybe I am at 30% (though looking forward to international stocks and how my modells do there, bc. its perfect for robustness testing). I want to master what is in front of (and that is 80% my mindset and only 20% P123) me. it is me, not the function that is missign on P123, that is my point. This process is 100% based on what I learned since 2010 here with the p123 Comunity. Best Regards Andreas 

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judgetrade
at Dec 11, 2019 5:54:51 AM

Jrinne

I know I shoot me in the foot here, but I personally would never trade a black box system and I actually would not recommend it. Andreas I love you but I think you do "shoot yourself in the foot." Ethically, I think you should (at P123) give a disclaimer. I really do. You certainly have a financial incentive since you have retired and financial disclosures are require almost everywhere. Since P123 does not require it yet, here it is: The mean 2year excess returns of all of your systems: 20.5. I have not looked at your 5year returns (as Georg has averaged for all Designer Models). I look forward to outofsample results on your new system. You surely have fitted the backtestincluding timing. Maybe the results will be good. We will see. Believe me, I understand that a tscore of 11 (as Georg verifies) is hard to overcome (pvalue < 10^17). I wish you the best with your efforts in this regard. The pvalue of someone winning the lottery 2 times in a lifetime is greater than this. I will add that I really like your systems and even hope mine are similar. Yours is one of the (group of) systems that I do not believe I can beat long term. Sure I could get lucky for a year or two but the law of averages will eventually catch up with me. One or the reasons I have cut my positions significantly. Jim From time to time you will encounter Luddites, who are beyond redemption. de Prado, Marcos López on the topic of machine learning for financial applications 

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Jrinne
at Dec 11, 2019 9:55:58 AM

geov

Believe me, I understand that a tscore of 11 (as Georg verifies) is hard to overcome (pvalue < 10^17). The pvalue of someone winning the lottery 2 times in a lifetime is greater than this. Jim That pvalue is just to match SPY. For a 5year return 20% higher than SPY excel calculates a t Stat = 16.16 and a twotail pvalue= 5.45E26 for the 75 models under consideration. I think a better measure to calculate the probability of a model performing better than SPY is to derive it from the number of models. Number performing better = 6 Number performing worse = 69 Probability that a model performs better than SPY = 6/75 = 8.0% 95% Confidence Interval: 3.0% to 16.6%. For models performing 20% better than SPY: Number performing better = 3 Number performing worse = 72 Probability that a model performs 20% better than SPY = 3/75 = 4.0% 95% Confidence Interval: 0.8% to 11.3%. 

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geov
at Dec 11, 2019 2:00:30 PM

Jrinne

Georg, Thanks for the other way to look at it. Jim From time to time you will encounter Luddites, who are beyond redemption. de Prado, Marcos López on the topic of machine learning for financial applications 

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Jrinne
at Dec 11, 2019 3:04:54 PM

geov

Forgetting about the benchmark, there are 17 models showing a negative total return; the average being 21.2% over 5 years. That means the probability of a model losing money is 17/75= 22.7%, with a 95% confidence interval of 13.79% to 33.79%. Therefore the upper probability that a DM will lose money over 5 years is 34%. That's not a good bet. We have to rethink the design process of our models. Any good suggestions would be welcome. 

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geov
at Dec 11, 2019 8:28:50 PM

yuvaltaylor

I made the exercise to check the 5year returns of the Designer Models. There are 75 DMs with inception 5 years ago or earlier. The average total return over 5 years of the 75 models= 23.4%. and the excess 5year return over SPY= 43.8% There are only 6 models which outperformed SPY over 5 years. For the 6 models the average excess 5year return over SPY= 25.8% That is not great. We have to do better than that. Perhaps good market timing is the answer. Excel file is attached. In the discussion so far we've been placing the blame for the failure of the designer models on overoptimization. So I decided to create a simulation without ANY optimization at all to see how that would have done over the last five years. You can find it here: it's public: https://www.portfolio123.com/port_summary.jsp?portid=1592851 It's a very simple system: it just buys the top 25 stocks ranked by the old QVGM ranking system and holds until the rank goes below 95; the universe is the Prussell 3000. Well, lo and behold, it underperformed the S&P 500 by 42.25% over the last five years. EXACTLY the same as the designer models. Draw your own conclusions. I would hazard the following guesses: 1. The old, triedandtrue factors like ROE and P/E have largely been arbitraged away, and stocks are priced in such a way that there are very few values to be found by using those factors. Investors are now so used to pricing stocks by using these metrics that it's hard to find any advantage in using them. 2. We may be in a regime that resembles the late 1990s, when overpriced highgrowth tech stocks ruled the market and S&P slaughtered all other strategies in sight. As we all know, that was not meant to last. 3. While there certainly have been some egregiously overoptimized designer models, given the "base rate" I have established, we cannot blame overoptimization entirely for their overall failure to perform. Some of the "failed" models may actually be very good ones that are just going through a blue streak. After all, every single triedandtrue strategy and every single great investor has had some long periods of underperformance. [Edit: somehow the chart below got lopped off at the right. The 2019 results are 13.95 for model, 27.20 for benchmark, and 13.25 for excess.] Screenshot_20191211 Performance  Statistics  Simulated Strategy.png (10113 bytes) (Download count: 74) Yuval Taylor Product Manager, Portfolio123 invest(igations) Any opinions or recommendations in this message are not opinions or recommendations of Portfolio123 Securities LLC. 

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yuvaltaylor
at Dec 11, 2019 8:56:23 PM

Jrinne

In the discussion so far we've been placing the blame for the failure of the designer models on overoptimization. Thank you Yuval. EVERYTHING gets blamed on over optimization. Yuval presents evidence that the answer may be more nuanced. Nuanced enough that I am sure I do not have an answer now. Maybe someone else does. But it should be looked at rationally as Yuval is doing here. If P123 can do it they should run the Designer Models WITH NO SLIPPAGE and see if that takes care of the problem. It could be that our result are fairly random with the guaranteed drag of slippage. It may be a simple answer. Yuval posted an excellent reason that timing should not work most of the time (the market is usual up). P123 should see if they can look at this as a potential problem. We now have some pretty convincing evidence that optimization of sims has some sort of problem—whatever that problems is. GEORG IS JUST CORRECT ABOUT THIS, PERIOD. Yuval presents anecdotal evidence that optimization may not be the problem or at least not the entire problem. Please correct me if that is not what this study shows or if there is another more important lesson from the study. P123 hired an AI expert who looked at Random Forests. They were understandably frustrated with the fact that this was no better than Random. RANDOM IS LOOKING GOOD NOW AS THE EVIDENCE WE NOW HAVE SHOWS THAT RANDOM FORESTS ARE BETTER THAN SIMS. Sims are not Random but are statistically inferior. The "null hypothesis" that sims are equivalent to a random selection of stocks from the universe can probably be rejected. This specific experiment should be done to be sure this statement is true. Someone with knowledge of this LIKE GEORG should do a paired comparison of sims with equal weigh of factors that seem good and compare this to a random selection of stocks from the universe. Perhaps P123 could randomly select from the 50 factors that they gave to the AI expert for this. Do this enough times to get an answer (this is NOT rocket science). P123 was going to look at support vector machines (hire the AI expert to do this). I think this can work ALONG WITH FEATURE SELECTION. The SVM must look a nonlinear solutions for this to work. P123 should look for proof of concept. See if machine learning can work. Once it is shown let the P123 members find the best solutions and sell them as an expanded version of Designer Models. Of course, proof of concept may be desired first. The first attempt at machine learning—with a Random Forest—seems to be better than sims already. At least it was not shown to be a way to steadily lose money. I have few answers. I have some anecdotal evidence that machine learning can outperform sims. I certainly do not have access to enough data to prove it. If P123 could find something that is proven to work with an intelligent selection of factors it would be helpful to their business model. This may be a proprietary method owned by Renaissance Technologies at this point. Maybe, knowledge is not owned by anyone. But it is NOT rocket science. I do not think hiding the fact that a port is generally a method of gradually giving away your money is a good business model. A tscore of 11 is a tough handicap. People will eventually leave P123—because they have no money to invest if for no other reason. I miss Oliver Keating. Some of his GREAT IDEAS are performing terribly. Why did he leave? He is smart enough to know when something does not work. I would have bet that DennyHawles’ models would have work. Why didn’t they? Just market timing? I had extreme respect for Oliver Keating!!! If his sims cannot perform then mine cannot either (long term). This is simple fact. Since he is gone now, because he has a large number of sims (24), and because there is no recent survivorship bias we could look at his stuff (with other good samples). 2year excess returns 26. I have not looked at his 5year excess returns. Denny’s models speak for themselves with out any statistics. I have been a big fan of P123 but I am not going to chase a bunch of cherrypicked backtests. If the sims I have now (that have done okay outofsample up until now ) do not rebound in a year or two I will move completely to a method that uses price data to select Sector ETFs. I will probably stay with P123 to get this data. But Yahoo has this data. I would check slippage and timing first. I am not saying I know here in December of 2019. But I will know at some point (it is NOT rocket science). I do not know if P123 will be with me then. So far they have been hostile toward finding out—with Yuval’s study here being (perhaps) sign of a change at P123. It is a first step. Jim From time to time you will encounter Luddites, who are beyond redemption. de Prado, Marcos López on the topic of machine learning for financial applications 

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Jrinne
at Dec 12, 2019 5:06:59 AM

