18 ms·

I've reproduced 130 research papers about “predicting the stock market”

• starpilot 7y ago
  Spoilers: > Literally every single paper was either p-hacked, overfit, or a subsample of favourable data was selected (I guess ultimately they're all the same thing but still) OR a few may have had a smidge of Alpha but as soon as you add transaction costs it all disappears. > I should caveat that I was a profitable trader at multiple Tier-1 US banks so I can say with confidence that I made a decent attempt of building whatever the author was trying to get at.
  • DennisP 7y ago
    But also: > Almost every instrument is mean-reverting on short timelines and trending on longer timelines. i.e. he confirms the momentum factor, which isn't surprising since there's more solid evidence for it than anything else, going back hundreds of years. He doesn't say what fundamental factors he looked at, so it's possible that value, size, and profitability/quality would hold up as well. All those have been studied pretty extensively in academia, in papers going back decades. The author took only a fairly random sampling of recent papers.
    • edoceo 7y ago
      All the finance folks I know use "fundamentals" to mean some class of attributes, that they all know what they are. Like when I say "GPL3" to someone who has a-priori knowledge. Not exactly sure but I think the "fundamentals" are top-line revenue, unit cost, unit margin, yoy-growth, EBTDA, cash-on-hand, default-alive - and likely the ratios those values produce.
      • DennisP 7y ago
        These sort of papers don't look at all the fundamentals at once. They'll isolate one factor, or a small related group, and see if it's predictive. Two simple factors that are well supported: on a risk-adjusted basis, stocks of small companies do better than big ones, and value stocks do better than growth stocks. ("Value" means the stock is cheap relative to some simple fundamental measure like the company's book value.)
      • MR4D 7y ago
        You’re pretty correct. A simpler way to think of it is any data that comes off the Income Statement, Balance Sheet, or Cash Flow Statement - or can be directly calculated using such data.
      • lootsauce 7y ago
        Fundamentals mean zilch for trading unless your'e talking earnings announcements or like news.
        • H8crilA 7y ago
          Please don't downvote this comment. There's a huge difference between short term trading and investing. And yes for short term trading (minutes/hours/few days) fundamentals matter rather litte - after all you can be a successful trader of Bitcoin, which has pretty much no fundamentals. Except on volatility events like macro data releases, earnings, FED decisions, surprise news (looking at you GE) etc.
          • Retric 7y ago
            > fundamentals matter rather litte Having a small impact is not the same a having zero impact. Traders operate over longer timeframe even if they are holding an individual stock for minutes there is a limited pool of stocks. Keep playing the game and longer term impacts add up.
            • derefr 7y ago
              But do the fundamentals of an individual investment matter in the short term compared to the fundamentals of the market as a whole? I would suspect that the “longer term impacts add[ing] up” would just be market health. Day-trading randomly-picked stocks with random buys and sells is a Markov approximation of an index fund :)
              • Retric 7y ago
                I think that depends on how stocks are chosen when you are day trading. Limiting things to high volatility stocks for example creates a bias in your index fund approximation.
      • derefr 7y ago
        I know as little about this as you do; my own gathering is that “fundamentals” are any signal you can get from a public company’s mandated quarterly reporting. So, every piece of data on the published balance sheet, plus maybe any reported board actions.
    • bitL 7y ago
      Look up technical vs fundamental analysis. It's trading 101.
  • albertshin 7y ago
    Discredits a whole field -- doesn't post any papers that he attempted to replicate.
• paultopia 7y ago
  Interesting but it would be nice if the author would, you know, write up his/her own detailed analysis with replication steps and post on arxiv or something.
  • defertoreptar 7y ago
    I got the feeling this was more of a case of "I did all this work for myself. Nothing useful came up, so here's what I found." The author may be ok with spending an hour sharing the findings, but doesn't want to spend more time than that.
    • DennisP 7y ago
      Which is mildly interesting, but not something other people should use for investment decisions.
    • guitarbill 7y ago
      Maybe he could work with someone else to get it to a publication stage? It's super interesting, and combats a well-known problem that you get a lot more out of publishing positive cases (we found a correlation) vs negative cases (we found no correlation), although they're both valuable knowledge. Look, I know writing stuff up sucks. But it could be a great opportunity to learn a lot of things from a very knowledgable person. With the right prof, it could be a great undergrad project.
    • michaelbuckbee 7y ago
      Which honestly feels like a reasonable position. Though the untrusting, conspiracy-minded part of my brain wonders if I did review 130 different research papers and found 1 that worked if I'd keep it a secret and just tell the world that they were all crap.
    • throwawaywego 7y ago
      I use the "everything is at least 10x greater on the internet" rule.
    • numakerg 7y ago
• rossdavidh 7y ago
  This is one of those cases where I would have guessed this would be the case, but it's nice that somebody else spent their time to verify, since I'm unwilling to spend my time to do so. Also nice that they shared their experience with the rest of us. If it worked, it wouldn't be published, or at least not until it stopped working.
  • lucb1e 7y ago
    > Also nice that they shared their experience with the rest of us. Except they didn't share the results with the rest of us. I know you said "experience" not "results", but when disproving papers, the least you can do is write down three sentences about each paper as you go along reproducing them, noting what you are seeing, perhaps with a snapshot (just a zip file or so) of the code. This is calling a whole field nonsense (that everyone expects to be full of nonsense) without giving enough evidence for anyone else to dispute your claims.
• lidHanteyk 7y ago
  This is unsurprising. P likely is not equivalent to NP [0], and predicting the market is NP-hard [1]. It's nice to see empirical work in the field, though, and especially nice to see reproductions of published papers. [0] https://www.scottaaronson.com/papers/pnp.pdf https://www.scottaaronson.com/papers/pnp.pdf [1] https://arxiv.org/abs/1002.2284 https://arxiv.org/abs/1002.2284 Edit for downvoters and repliers: If enough market participants are irrational, then it can still be possible for people to predict other people, instead of the market, and make money that way. NP-hardness indeed doesn't rule out heuristic approaches, but experience with 3-SAT and other NP-complete problems suggest that there will be arbitrarily bad times, and that in those times, the amount of loss can be exponential in the length of time that the heuristic poorly predicts the market.
  • dvt 7y ago
    Philip Maymin seems like a serious guy... but that EMH ↔ P=NP paper is absolutely not even remotely a proof. Was genuinely very curious and it's at best an intuition. Some claims, e.g. Knapsack and 3SAT are (almost?) isomorphic to the efficient market hypothesis, are pretty bold. And the justification is hand-wavy at best.
    • kd0amg 7y ago
      It's not just hand-wavy. It's outright wrong. The size of the search space isn't just exponential in the amount of history -- that's the number of possible histories. The number of mappings from histories to positions is doubly exponential, so they can't even be uniquely described in less than exponential space.
    • pzone 7y ago
      He's not a serious guy at all, he's a nutjob who likes listening to himself talk.
      • deleted 7y ago
        [deleted]
      • pmaymin 7y ago
• paulpauper 7y ago
  Literally every single paper was either p-hacked, overfit, or a subsample of favourable data was selected (I guess ultimately they're all the same thing but still) OR a few may have had a smidge of Alpha but as soon as you add transaction costs it all disappears. I could have told you that without testing. If anyone had a lucrative strategy would they disclose it in a paper to the general public? I think not.
  • lucasmullens 7y ago
    Proof can be more useful than a hunch.
    • numakerg 7y ago
      And the author has shown none.
  • mruts 7y ago
    CAPM, Fama French Three Factor, or five factor? Are you serious? They don’t work anymore but they did at one point. The foundation of modern finance is built on published Chicago school papers.
    • DennisP 7y ago
      From what I've read, they don't work quite as well but they do still seem to work. Economists have spent a lot of time trying to figure out why. Generally, they come up with behavioral explanations, like recency bias, and structural ones, like agency issues.
• riku_iki 7y ago
  TLDR, no results, no code, no details, just admission of failure from anonymous redditor with SEO link on his unrelated crypto-trading project.
  • dvt 7y ago
    Why is this getting downvoted? The original post is clearly marketing clickbait. It's trivially true that most "predict the stock market" papers are going to be bunk.
  • mxcrossb 7y ago
    It’s baffling to me that anyone would believe this post. Do people here not have an ounce of skepticism?
    • javert 7y ago
      I think people aren't skeptical about this because it lines up with what they already expect. That's the case for me. If the guy is lying and it's all made up, I'd still wager that if you assembled a collection of 130 papers on this topic, at most 10% would be valid [1]. So even if he's wrong, he's probably not wrong. [1] I have an academic background, and I think invalid papers make it through peer review constantly. Peer review is not what people think it is.
• scarmig 7y ago
  Wouldn't any given approach rapidly lose efficacy as soon as its published? I would even guess that a paper being published means that, at the point the paper started to be written, its alpha had already decreased to zero. Otherwise the writers of the paper would still be using that approach. That's how it can appear to provide no value even if you extrapolate it back in time.
  • SubiculumCode 7y ago
    The author mentions this, and said he tested for "alpha decay" by applying method to datasets that preceded the data on which the model was tested/trained.
• deleted 7y ago
  [deleted]
• zazagura 7y ago
  Some of the papers I've seen are ridiculously obviously over-fitted. For example published in 2018, but "tested" on 3 months of 2010 prices of GBP/USD, USD/SEK and USD/THB. Quality forex data is so easy to get freely, that picking 3 months from 8 years ago on one major pair and two other random minor ones just stinks.
  • quirmian 7y ago
    Where can I get good quality forex data from?
    • capnrefsmmat 7y ago
      I used TrueFX for an assignment in a statistics-for-finance class I taught: https://www.truefx.com/ https://www.truefx.com/
  • jobigoud 7y ago
    They don't test the predictive power of their models?
• ddxxdd 7y ago
  >Literally every single paper was either p-hacked, overfit, or a subsample of favourable data was selected including methods that use: >News Text Mining. - This is where they'd use NLP on headlines or the body of news as a signal. I have to call this out. Is this author suggesting that you couldn't have made money by shorting Enron stocks milliseconds after the scandal was made public? Is it impossible to make money by buying a stock in a small company, seconds after an acquisition is announced? If a CEO gets sent to prison, will that company's stocks not be affected? And then there are other methods that use: >Fundamental data. So ratios from the income statement/balance sheet So buying stocks in companies with good financial health is not profitable? Something's being left out here.
  • huac 7y ago
    they're probably not accounting for HFT. that's not unreasonable to expect. > So buying stocks in companies with good financial health is not profitable? everybody has the same common sense, prices reflect all available information (at least, if you believe the efficient market hypothesis, which I do to some extent). so you shouldn't expect the method to be profitable in excess of the overall market profit -- what we call alpha.
    • ddxxdd 7y ago
      The market is not perfectly efficient. Small cap stocks outperform large cap stocks; the S&P500 outperforms leaving money under your mattress. I am more than willing to bet that if you combine these methods with an algorithm that estimates a stock's "proper price" with the information, a sophisticated algorithm should be able to at least outperform a layman's "Buy-and-Hold" strategy.
      • roguecoder 7y ago
        You can put your money on that, but you would pretty consistently, it turns out, be wrong. Which you would know if you had read the OP. Profiting off of other people's tendency to trade too much and too confidently is some of the surest money in the market, because regardless of evidence people want to believe that they can positively effect the outcome. Nest eggs are more like soufflés than caramels.
• leet_thow 7y ago
  The stock market is a complex adaptive system where the agents are constantly changing their strategies so that even if you were to find inefficiencies or patterns, they are only ephemeral.
  • alanbernstein 7y ago
    From the post: > The easiest way to test whether it was truly Alpha decay or just overfitting by the authors is just to reproduce the paper then go further back in time instead of further forwards. For the papers that I could reproduce, all of them failed regardless of whether you go back or forwards. :)
  • deepnotderp 7y ago
    This is why the truly successful quant groups like Renaissance continuously adjust their strategies and come up with new ones. Renaissance in particular has invested heavily into their data processing pipeline which enables them to have a significant advantage over the rest of the field.
    • leet_thow 7y ago
      Yes, if I remember correctly from an interview I saw with the founder he mentioned that the barrier to entry used to be high and that commodities markets 'used to' trend.
• lettergram 7y ago
  I had the same result from most papers. My personal conclusion is that when people get a winning strategy they don’t publish. I personally put my money where my mouth was for a few years: https://austingwalters.com/backtesting-our-100-yoy-profit-generating-strategy/ https://austingwalters.com/backtesting-our-100-yoy-profit-ge... That being said, I try to be honest too. This can disappear any time and the model I use may only be good in this environment. I do not know. I think that’s the challenge with papers, you don’t honestly know when or if the strategy works. It clearly won’t forever regardless. That’s why I don’t share my exact method. And after doing all the research myself AND trying to sell my algorithm. I honestly don’t think the industry knows what it’s doing either. People are worried about sharpe ratios and all this BS stuff. The reality is for these models you mitigate risk via temporary and ever changing methods. Can’t really publish on that.
  • vkou 7y ago
    I hope that your personal implementation of your strategy takes into account 2008 ;). Because, hooo, boy, your 20% YoY returns sound great, but may not be taking enough risk of a similar collapse into account.
    • lettergram 7y ago
      It does take those risks into account and I tested it on 2006-2008 previously. However, there is little point in testing a collapse with the same model. If the stock market collapses anyone investing would be out of luck. Your standard models won’t be able to track that, because it’s usually something like a “black swan” event[1]. Instead you’d want some sort of meta model. In either case. I’m getting 100% YoY returns when I augment my model, in real life. I think even a 50% loss one year wouldn’t be the end of the world. [1] https://en.m.wikipedia.org/wiki/Black_swan_theory https://en.m.wikipedia.org/wiki/Black_swan_theory
      • joeblow9999 7y ago
        ill take the warren buffett position
  • 7y ago
• stevespang 7y ago
  Incredible work. Back in the 90's I traded with TradeStation, yes, always running my various algo's on "historical data" - - - which was ALWAYS just curve fitting. I once had formula that made ton's of $$$$ on 4 years of backtested historical data, S&P futures, but once I started paper trading it it failed within one month.
• jmpman 7y ago
  Has anyone tried a simple approach - trying to predict which of the S&P 500 will have the lowest 10% returns, and build an (S&P 500 - 10%) index? It seems obvious that the S&P is stacked with some great companies and some old dogs. Does that method not work?
  • dwohnitmok 7y ago
    Intuitively the problem you run into is that occasionally those obviously bad companies have amazing comebacks and you completely miss out on those, so you can still end up underperforming the S&P 500.
  • smnrchrds 7y ago
    Since it's obvious, everyone knows it. And since everyone knows it, it's already priced in. You cannot find an edge by acting on widely-known public information.
    • jmpman 7y ago
      I’m acting on the fact that almost all my investments are in the S&P 500, and I don’t have a (easy) way to pick my favorite 450 out of those 500. How would knowing the worst 50 be already priced in? They would be priced lower than they should? Good let’s get them out of my index. How else?
      • nostrademons 7y ago
        They'd be priced low. There is no "should" when it comes to markets - the market price is whatever people will transact at. The problem is that stock price movements depend upon future events - making money in the markets is effectively a future-prediction problem. So if your strategy is to discard the bottom 10%, great, you got rid of Foot Locker and Sears. You also would've gotten rid of Apple in 1998, which was responsible for a good portion of the index's gains over the last 20 years. And you would've kept losers like PG&E, which went bankrupt over a black-swan event (they were doing fine until they burned down a town).
      • 7y ago
• rolltiide 7y ago
  yeah but you can make a lot of money preaching technical analysis to your congregation a few times per week protip: a fibonacci retracement from a randomly selected extreme will always tell you something protip: it takes 5 months for your congregation’s account to get eaten up from transaction costs when their stop limits keep getting hit in the mean time you can just play TA roulette and they’ll always be impressed by your “uncanny” perceptive abilities
  • throwawaywego 7y ago
    If you know that 5.6% of the users (automated, or parroting the preacher) of an exchange will use fibonacci retracements, and that 15% of the amateur market will follow the market price change caused by either buying or selling activity, then you can play roulette with a decent edge. Of course, not as much as the preacher, who is allowed to bet before (s)he will speak to his or her congregation. When you gather enough of these commonly used technical analysis, it's like having to predict in which startup Ron Conway will invest, but you can calculate Conway in a Python one-liner, and keep up-to-date by going to weekly sermon.
    • rolltiide 7y ago
      yes, this is possible, with more trades becoming a self fulfilling prophecy, and have stop limits in place all the times it didn't work. I wish the technical analysis flock would merely incorporate different things. TA takes a time series chart and imagines time series patterns. It primarily neglects what didn't get printed on a time series chart, and why. How big are the orders at the resistance level, do you have a record of the order sizes that appeared at the last resistance level? who is selling at the resistance level and why? The TA answer is "just because its a psychologically round number for the resistance price" or "because thats how high the last high candle was", but you can greatly improve your win rate by understanding who is in the market and why, which is possible to understand and a large portion of my trading strategies. It can be much more data intensive though so I can see why 1980s gurus did not do it.
• WheelsAtLarge 7y ago
  I could bet a ton that most people will make excuses as to why the papers failed. There's something within us that wants to hit the stock market lottery. I truly believe that there are streeks to profits in the stock market in the same way you will find streeks in any set of random numbers but they are impossible to find in a consistent manner. The road to wealth for most in the stock market is time and investing in a basket of good stocks. Whoever thinks that they have found a system to profits in the stock market. Test and retest your method a few times. It's unlikely you have a winning system.
  • tomp 7y ago
    No, there are trading strategies that do work, over multiple years, and can then be adjusted and refined to work even longer. But obviously nobody is going to publish such strategies. You’re looking at a negative selection of papers.
    • WheelsAtLarge 7y ago
      Describe one. I know if there are any current ones no one will talk about them. But have there been any consistent ones in the last 100 years? I'm sure someone would have at least written about them in their memoirs.
      • batterseapower 7y ago
        https://www.aqr.com/Insights/Research/Journal-Article/A-Century-of-Evidence-on-Trend-Following-Investing https://www.aqr.com/Insights/Research/Journal-Article/A-Cent...
      • tomp 7y ago
        I don’t know any good ones... AFAIK value, size signals, merger arbitrage, and other kinds of predictionsused to work well in context of statistical arbitrage... but yeah their existence is evidenced by successful companies / hedge funds (Renaissance, Citadel, Jump, Two Sigma, ...)
  • bjourne 7y ago
• bitL 7y ago
  Hasn't there recently been shown that only strategies using simple momentum-derived technical indicators were able to consistently bring returns in the stock market?
  • DennisP 7y ago
    Not just recently, it's been known for a while. The author actually confirmed it: > Almost every instrument is mean-reverting on short timelines and trending on longer timelines. This has held true across most of the data that I tested. (But momentum isn't the only thing that appears to work.)
• RickJWagner 7y ago
  I tend to follow Jack Bogle's advice. Jack made many people wealthy. His advice? "Nobody knows nothing." (Read Bogleheads.org to see how to leverage this into wealth.)
• omarhaneef 7y ago
  How did you do 130 papers in 7 months? That’s just over a paper every 2 days. What was the setup, how did you set up a pipeline? Was it R or Python? What was the data source? I am more surprised by your productivity than anything else.
  • deleted 7y ago
    [deleted]
  • bcherny 7y ago
    It was answered in a comment in that thread. Python+Pandas+Keras. I’d also love source code + data for this; without it, it’s a claim with nothing to back it, yet.
  • bwilsonkey 7y ago
    I don’t think he actually did it. He linked to a crypto scam medium post lol...
    • semi-extrinsic 7y ago
      As long as he's not even willing to publish the list of those 130 papers, I think it's OK to be a bit skeptical, yes.
    • hobofan 7y ago
      > crypto scam medium post Could you expand on that? I've only skimmed the article, but I don't see any "crypto scams" pushed in the article. The article is just about something the author seems to know about (algorithmic trading), applied to the cryptocurrency market. It does promote the authors project (why else would you write a Medium post), but in the worst case, that would be a normal scam and not a crypto scam.
      • FiberBundle 7y ago
        The whole article just seems like an attempt to steal money from uninformed people. He starts by giving vague information about trading strategies in general, then linking to an article about Renaissance Technologies as an example for successful algorithmic trading, then stating that most trading bots aren't successful and that the crucial differentiator for deciding whose bots to trust is the person's professional experience, which is obviously a reasonable thing to do, however the picture at the beginning of the article of him at a trading desk and him repeatedly mentioning his 7 year experience as a trader combined with the complete lack of any actual proof that his bots are actually profitable, make it seem as if he just tries to profit off his previous experience. He ends the article writing this: "All you’ll need to get started is: 1. $1000 2. To press a single button to get the bots started" Furthermore in the reddit comments in response to the following question: "130 papers re-implemented in 7 months? I'm blown away. Write a software engineering book about how you did it so quickly. Then write a self-help book about having enough motivation to see it through." he writes: "100-hour weeks and a desire for a better life for the ones you love will get you there pretty quickly" This guy seems like a complete fraud, I find it sort of sad that this has landed on the frontpage of HN with that money upvotes.
• whoisninja 7y ago
  more important than this study if done correctly is the fact that he built a framework that can ingest all this data and that he had access to all these datasets typical hedge fund spends millions of dollars in order to build such frameworks and buying datasets, sure most academic papers fail if you replicate but the framework and datasets are very valuable because you can eventually find something on your own or an improvement on existing ideas if you keep trying hard enough + there are other sources of ideas like quant research from brokers, ideas from platforms like quantopian etc. but yea in general if you have an outstanding idea that works - you would have very less or no incentive to publish it. why would jim simons have his researchers publish anything when they can make money for him all day long everyday ... just my 2 cents.
  • whoisninja 7y ago
    i asked the author where did he source his data, his reply was "i scraped data" how can you scrape pricing data? not every data in this is on public domain, otherwise there would be no Bloombergs, CapitalIQs selling data for millions(sure they're overrated and overpriced but still!). Or in other words, if he is right - he can sell data and make millions. no need of looking for an investment strategy. just my skeptical side saying :) you need clean data to accurately test ideas. for instance getting tick data is quite expensive. most universities have free access to Bloomberg, CapitalIQ etc. datasets the reason professors can test and also the reason some smart guys in the industry work for university on the side
    • lettergram 7y ago
      You can get data for free from places such as alpha vantage https://www.alphavantage.co/ https://www.alphavantage.co/
      • whoisninja 7y ago
        it's not enough data to test all of those 130 papers, he said he has also tested short term reversion trends. for that you need tick data(or atleast minute/hourly data)
• kart23 7y ago
  Buy low, sell high. Focus on reliable, proven stocks. It's not hard. Buy on bad days, sell on good days. Dont overcomplicate things.
• whoisninja 7y ago
  the author is trying to sell crypto trading bot ! looks shady to me if anything guarantees you profit, run away from it as fast as you can : https://credium.io/ https://credium.io/ https://towardsdatascience.com/crypto-trading-bots-a-helpful-guide-for-beginners-60decb40e434?source=friends_link&sk=6c68390cbf6ae7f464ad1666d55dba4b https://towardsdatascience.com/crypto-trading-bots-a-helpful...
• linux_devil 7y ago
  "The most frustrating paper: I have true hate for the authors of this paper: "A deep learning framework for financial time series using stacked autoencoders and long-short term memory". Probably the most complex AND vague in terms of methodology and after weeks trying to reproduce their results (and failing) I figured out that they were leaking future data into their training set (this also happens more than you'd think)." - Not sure how author tried to implement it , but is this not how you train LSTM networks by feeding t+1 data back into the cell again to predict t+2 data. It will be easier if author made it open source as well
  • laichzeit0 7y ago
    Leaking future data in would be using t+1 for t, e.g. something like a bi-directional LSTM. I assume he means the actual training dataset had some kind of signal in the data that was also in the test data.
    • sgt101 7y ago
      People do this by doing things like testing that their features contain information in both the training and test set. Because they are not exposing the data directly to the classifier they think that they haven't compromised the test set - but what they have done is increased the chances of a chance correlation.
  • jensgrud 7y ago
    I wrote my thesis last year comparing different RNNs against each other using this exact paper as baseline and basically concluded that you would be better off predicting the price yesterday than using their results. Authors did not respond when prompted for implementation details or comments. Overall, concluded that amongst RNNs the GRU architecture proved most favorable but still would not outperform simple stochastic models of the financial industry toolbox. You can check it out here https://github.com/jensgrud/financial-forecasting-lstm/tree/master/report https://github.com/jensgrud/financial-forecasting-lstm/tree/...
• lootsauce 7y ago
  Best survey of the subject I have found (most are bullshit) is Finding Alpha by Eric Falkenstein which he has graciously offered for free on his blog. By the subject I mean, finding an edge in trading. Spoiler alert there is no system to follow. He wrote algos for a local options market maker, had a significant Econ PHD thesis. The basic premise of the book is that real Alpha is rare, idiosyncratic and gets exploited by those in the know and eventually the edge goes away after several years. He gives several classic historic examples which are what made this book so interesting and unique to me. http://falkenblog.blogspot.com/2016/08/finding-alpha-pdf.html http://falkenblog.blogspot.com/2016/08/finding-alpha-pdf.htm...
• xondono 7y ago
  Everyone can drive a car in a straight line looking into the back mirror. Trouble only starts on the first turn.
• m3kw9 7y ago
  He didn’t state the time frame things are predicting. Going long is still the best way to beat any hedge funds or “pros”
• deleted 7y ago
  [deleted]
• SheinhardtWigCo 7y ago
  I wish it wasn’t this easy to get crypto-scam SEO articles to the front page of Reddit and HN.
  • numakerg 7y ago
    Wait you DON'T want to invest your money into a black box crypto trading bot system with guaranteed returns? For a community that prides itself on its critical thinking, HN is quick to laud a post with barely any substance when it confirms their preconceptions.
    • pembrook 7y ago
      You think this one was clever, take a closer look at the next couple of posts that blow up here having to do with HN’s favorite category of “privacy hysteria.” One thing you learn when you start your own side projects, everything on the internet is content marketing. Everything. And that’s not necessarily a bad thing since it aligns incentives. Expecting people to produce great content for you for free makes no sense. In this instance however, the post is obviously made up and the product this guy is hawking is a scam. I wouldn't have a problem with the self promotion if he provided real value, but he didn’t.
  • comatosesperrow 7y ago
    THANK YOU. I feel like I'm taking crazy pills watching everyone eat up the article where HE ASKS FOR YOUR MONEY.
    • lucb1e 7y ago
      Wait, what did I miss? I thought the writing was bad (both contents and style) so I didn't click through to the medium article, is that where they ask you to invest in their stuff instead?
      • comatosesperrow 7y ago
        That's where it is. He then calls it a "set it and forget it" crypto bot. Yes, please forget about the $1,000 you just gave me...
• throwaway156503 7y ago
  Technical analysis is pseudoscience.
• ricklamers 7y ago
  I think this is not rigorous enough to draw any real conclusions. If he had done a proper job of reproducing he would have created a write-up of his work explaining his reproduction methodology. The next step would be to get his work peer reviewed. I think only then you have come close to the amount of analysis and rigour necessary to discredit so many authors of (possibly peer reviewed scientific articles) academic research. The fact that he mentions that he doesn't know what a meta analysis is in the comments suggests that possibly _his_ results might not be what he purports them to be.
  • gillesjacobs 7y ago
    Exactly, I also find it extremely dubious a "Tier 1 professional quant trader" implemented 130+ papers in 7 months. This means obtaining the same data, reproducing feature engineering and hyperparameters. Implementing learning algos. Maybe the guy is genius and god-like in NLP, finance, data science and machine learning but even then 7 months is too little time. I was amazed at how few people call out this obvious lie here.
• 1e-9 7y ago
  In my view, the predominant mistake made by those who seek to create profitable strategies is that they approach trading as if the market is a zero-sum game. In particular, doing things that harm the markets, like naively adding to existing momentum, is just promoting price overshoot and instability by reinforcing positive feedback loops. Such approaches hurt others and while they might make money for long periods of time, they will almost surely end up losing all that profit and more during a small number of extreme market events. If you want to be reliably profitable, you need to first understand how the markets are not a zero-sum game and then you need to construct methods to improve the markets with your trading. There are countless ways that markets deviate from truly efficient behavior. Find some and develop strategies in areas that can benefit from your cognitive, experiential, and educational strengths. General examples of how one might improve the markets include things like providing liquidity when it's needed, limiting price overshoots when it's warranted, and incorporating new information about instrument values. The market will pay you in return if you do such things in a sound way. As long as you also do a good job of estimating and limiting your risk, you can be consistently profitable. There will be no significant public disclosures of detailed ways to trade profitably. The markets rely on the robustness provided by many different points of view addressing market needs in a variety of ways. Any parties that overweight one of those points of view will ultimately lose money in the process of adding market instability. There's too much of that already. Don't trade until you figure out how to make the markets better.
  • elmersglue 7y ago
    This is fantastic advice.
  • anonu 7y ago
    > you need to construct methods to improve the markets with your trading What percentage of traders do you think will approach the market with such altruism? I think the markets are way too complex to rationalize "improving a market". Markets are by definition good markets when you have long term and short term traders mixed in with technical and fundamental traders all with different alpha time horizons. This is a healthy market. If you're a speculator, so be it. As you point out, the market may teach you a lesson at some point. Those guys go away but new ones will join. It's a beautiful virtuous cycle. Not to say there aren't bad actors. Most speculators are not IMHO. But they cross the line when they undertake certain market activities, like spoofing for example. This is why good markets also have good regulatory oversight (finra, sec... )
• bArray 7y ago
  Currently I remain skeptical, 130 pages in 7 months plus meaningful experiments is quite some going. A list of the papers (so at least the authors can defend themselves), the source code and data used (because some of these methods require social media inputs) would definitely help. After doing so much work though, why wouldn't you go the extra small few steps to publish? That way the work can be peer reviewed and the Scientific community has a chance to learn from it.
  • bem94 7y ago
    > That way the work can be peer reviewed and the Scientific community has a chance to learn from it. After trawling through so much peer reviewed work which in their view is utterly broken, I can understand why they'd be despondent/mistrustful of submitting it to a peer review process.
    • bArray 7y ago
      If you want to go that route, why not write a detailed blog post? Or a white paper in an archive? Or a GitHub repository? Not putting anything out there seems awfully unproductive.
  • lucb1e 7y ago
    Yeah when I got near the end of the post, I was quite surprised. You spent a boatload of time on this and all you do with it is make a PSA on reddit going "hey lol I looked at all these papers and they're all overfit". Someone in the comments asked about sharing the work and was brushed off with "nah, the code is a mess". This reeks of unscientific work, exactly what s/he describes the papers to be. While the results are in line with everyone's expectations (hence it getting upvotes despite offering zero proof, people already believed it and like it being confirmed), and so I don't really doubt they did try to reproduce papers and failed, it also isn't a reliable source in the slightest. If they were serious about disproving the papers, they could just have written down the steps, even if it's just a few lines of notes with snapshots of the code for each paper. Just a list of papers isn't going to help the authors defend themselves, the methodology (if it can be called that) is so vague that the authors would basically be starting a "he said she said" discussion.
• zallarak 7y ago
  Some of the smartest people I've ever known from college became professional stock market investors and traders. Not a single one of them has significantly outperformed the market. I think of stock market investing as a white collar gig economy job. Both depend on some central entity, which makes a killing (brokerages in this case), and have extremely low barriers to entry.
• sixtypoundhound 7y ago
  I'm not surprised by this finding; too many smart people are working on the same problem. When it comes to investing in public markets as an outside player, I've seen three moves which can work.... 1) Identify a consumer product category going through the technology adoption S-curve, ideally something which isn't very subject to short term innovation cycles or disruption. My wife spotted the trend of people spending more on pet drugs before we got married and invested. We've ridden it for well over 20 years... 2) Buy when the entire industry is a flaming wreck and there is blood in the boardroom; assuming there is a good reason that demand for their products to continue. I've done this several times in natural resources (gold in the early 2000's, oil in 2016) and it worked out decently. 3) One I haven't executed yet; certain firms are basically bets that a specific event will occur, at which point the demand for their product will go bananas. Buy in the quiet period and sell once the event happens. Requires great patience to execute well.
• grahamannett 7y ago
  I barely skimmed this https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0180944 https://journals.plos.org/plosone/article?id=10.1371/journal... for the "most frustrating paper" but how did he determine that they were leaking "future data"?
• edwardy20 7y ago
  Professional quant here. I have to say I strongly disagree with the conclusions of the OP. > They were all found by using phrases like "predict stock market" or "predict forex" or "predict bitcoin" and terms related to those. Yeah, searching for any finance papers with "predict" or "machine learning" is literally the lowest quality tier you can get. These papers are often written by grad students who can pump an easy paper out by "applying" some already known ML algorithm to financial markets. Of course it's not gonna work. It also kills me when I see ML models who need stationarity assumptions applied to non-stationary time series data. Yeah, good luck with that. THAT being said, there is lots of high quality research which has been replicated over and over, showing that alpha does exist in the market (and which funds have made billions off of). I would like to see the OP try to replicate some of these instead. To give some simple examples: 1. Try searching for papers with the keywords "and the cross section of expected returns". For example, the momentum factor which can be tested and replicated with only linear regression. > There is substantial evidence that indicates that stocks that perform the best (worst) over a three- to 12-month period tend to continue to perform well (poorly) over the subsequent three to 12 months. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=299107 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=299107 2. Statistical arbitrage strategies which were known to work well until the mid 2000s. Also been replicated many times, furthermore, you can see the gradual decline in profitability pointing to the theory that "alpha decay" in this case is real. https://www.math.nyu.edu/faculty/avellane/AvellanedaLeeStatArb071108.pdf https://www.math.nyu.edu/faculty/avellane/AvellanedaLeeStatA... 3. High frequency strategies. No way OP or any retail trader can replicate this, but firms make billions of dollars per year consistently doing this. In conclusion, to make a claim that there is no alpha in the market seems highly suspect, and perhaps just needs a more nuanced view of how trading firms make their profits.
  • retiredcoder 7y ago
    I have seen the same stationary overlook on other fields and always blows my mind that people involve would get away with that.
  • gillesjacobs 7y ago
    I also find it highly unlikely anyone is able to implement 130+ papers in 7 months. This would require insane productivity, implausible access to pricing and news data resources (which are often not freely available) and expertise in machine learning, natural language processing, finance, and data science. OP had to implement financial, time-series and linguistic feature engineering pipelines, as well infer the architecture and hyper-parameters used AND train all these models. He also claims he "web scraped" all the data which is highly unlikely as pricing datasets are often sold for a pretty penny and not publicly available in the detail described in several of these papers. OP must be a genius to pull this off, all the while being a trader at "a Tier 1 US bank" (in itself that description is ridiculous). All OP has to show for all this work is a hastily written Reddit post with dubious claims. There is no proof of the work done whatsoever, no code samples, not even result tables or graphs. And at the end OP chills his cryptotrading bot. What's worse HN seems to gobble it up naively. Seemingly because OP is critical of something that is popular to criticize.
• gingabriska 7y ago
  Let's say an event occurs and you know a particular stock will go up 20% but you pump enough money into the stock to make the stock go 30% and then you let others chase the stock and publish news about this market move through media houses / content spamming / fake accounts. Then you short the stock once you get the desired movement and finally you remove the money from this stock so it goes into free fall and all people start selling. Finally, you close the short position when you notice there is no room for stock going further down. Now let's say stock ended up at 10% so you buy more stock so it goes 15% just below what you initially predicted. Assuming you've billions to move the stock. Why such strategy will not work?
  • anastalaz 7y ago
    Such a strategy does work and it's also illegal. It is called market manipulation specifically 'pump and dump' https://en.wikipedia.org/wiki/Market_manipulation https://en.wikipedia.org/wiki/Market_manipulation
• gillesjacobs 7y ago
  OP makes highly dubious claims and does not pass a basic smell test: ALL CLAIMS MADE ARE DUBIOUS: Implementing 130+ papers in 7 months is highly implausible. This would require: - Insane productivity. - Implausible access to pricing and news data resources (which are often not freely available). - Expertise in machine learning, natural language processing, finance, and data science. OP had to implement financial, time-series and linguistic feature engineering pipelines, as well infer the architecture and hyper-parameters used in all papers AND train all these models. ALL WITHIN 7 MONTHS. All while previously being a trader professionally i.e. not likely an expert in many of these fields. - OP also claims he "web scraped" all the data which is highly unlikely as price datasets are often sold for a pretty penny and not publicly available in the detail described in several of these papers. - Down the thread, OP says he does not know what a "meta analysis" study is al the while being capable of implementing 130+ papers. So someone who is an expert in ML, statistics, data science and finance does not know one of the most basic types of scientific study. All the while essentially engaging in a meta analysis study. - OP describes himself as "a trader at a Tier 1 US bank" to lend credibility to his post: in itself that description is ridiculous and sounds like a naive attempt at instilling authority. - When others encourage OP to publish results, he answers evasively: "probably a bit deep for a public forum but I was kinda glad to see the back of that work. It was an awesome learning experience but it's pretty soul destroying experimenting with tonnes of stuff that just doesn't work." EVIDENCE PROVIDED: Non-existent. All OP has to show for all this work is a hastily written Reddit post with dubious claims. There is no proof of the work done whatsoever, no code samples, not even result tables or graphs. The discussion of basic results are often made criticisms of this line of research. MOTIVATION: At the end OP shills his cryptotrading bot. This post was likely all just purely made-up to market his cryptotrading bot service. OP uses some common criticisms of market prediction research to garner authority as a wizz-kid to attract people to his crypto scam. What's worse many on HN and Reddit seem to gobble it up naively. Seemingly because OP is critical of something that is popular to criticize.
• SirSavary 7y ago
  Disappointed that this made it to the front-page of HN. Had it show up in my push feed because it was so popular; quite upset to click it and see the comments in here falling for someone who's a crypto scammer: - Redditor for 28 days; first post made to /r/investing and contained self-promotion for a website that functions as a search engine for stock market fundamentals (post removed by moderators, take a look using https://ceddit.com https://ceddit.com) - Has made a few, low impression comments across other trading subreddits (/r/algotrading and /r/securityanalysis) - Claims to have been a trader at "multiple Tier-1 US banks" for 7 years but a glance at their LinkedIn shows less than five and a half years (65 months) between two organizations - Claims that they found, analyzed, and reproduced "130+" academic papers in only "7 months". Even by superhuman standards, the outlook for completing this much work (at high quality) is grim. Here's some napkin math: 7 months * 30 days = 210 days 16 hours (superhuman) * 210 days = 3,360 hours 3,360 hours / 130 papers to reproduce = 25.8hrs per paper These numbers are intentionally rough to show that even if we're being generous, the idea that any one person could somehow recreate a full academic paper in less than two 16 hour working days is absurd. Add to this OP's claim of spending "weeks trying to reproduce [a paper's] results" and it the task becomes even more daunting. This is possibly the biggest red flag of all -- or OP is the world's first (and only) 100x developer. - Does not provide any empirical data to back their claims, only provides the name (not a link) of one paper, and when asked in comments to provide more information -- fails to deliver. This post desperately wants to come off as a research article but it's missing all the fundamentals to make it so. If OP's claims were true, why wouldn't they post their raw findings? We could make that argument that there's far too much to post, 130 papers would be a good bit of code, but there's no reason that OP couldn't provide a listing of the papers they "reproduced" at the bare minimum. OP has made extraordinary claims: most if not all "predicting the stock market" papers are fraudulent, but has failed to provide any supporting evidence to back this up beyond their own words. As someone who just analyzed well over a hundred papers and postures themselves as a data scientist, OP should know that citing yourself doesn't fly in this scenario. - OP ends their post with the following: "I try to write a bit on medium even though I'm not a great writer if you wanted to read more from me." There's absolutely nothing wrong with self-promotion, provided you're a quality creator and are transparent about what is being promoted. OP is neither of these things. Clicking the Medium.com link will take you to a blog post titled "Crypto Trading Bots · A helpful guide for beginners [2019]". If you're like me, you might assume this is a tutorial related to crypto trading bots. Perhaps information on setting one up, coding one yourself, or an overview of the landscape. It is none of these things. Much of the article explains how cryptocurrency trading bots work -- which is great, but quickly goes down the path of telling the reader that most bots are garbage and won't return a profit. Near the end of the post we're advised how to choose a viable trading bot and are provided with three questions to ask ourselves: 1. What is the professional experience level of the senior leaders of that firm? 2. Are their algorithms widely known and openly available to anyone? 3. Is their success aligned with your success? Immediately after these questions are the following lines: "Unfortunately, choosing a trading bot to go with isn’t as trivial as answering these three questions. In my opinion, everything ultimately comes down to people." What people, you ask? Perhaps someone like OP, who happens to be the founder of a trading bot platform. The post finishes off with a not-so-subtle advert for his company, along with the extraordinary claim that it will "take full responsibility for the profitability of our clients". According to the OP, all you need to get rolling on his platform is "$1000" and "to press a single button to get the bots started", never mind that the platform hasn't launched and AFAIK there's no start button to press. Clicking through to the platform's website will bring you to a scroll-jacked landing page full of marketing fluff. Scroll further down (or click the "Get Started" button) and you'll see a pricing table with only one option currently available: pre-order a "$145 single fee lifetime license". Compared to the two unavailable plans, $145 is a steal -- the next plan down would run you $660 ($55*12) a year. Combine this with the "First 90 days profitability or money back guarantee" and the whole damn thing sounds like an incredible deal. But you better act fast because this offer is only available to the "first 1000 members". OP's other trading bot articles aren't much better and in my opinion, directly promote his platform. ~~~ The whole thing sets off numerous alarm bells in my head -- and it should for you too. A trader who worked at "Tier 1 US banks" should know that guaranteeing the profits on your first 1,000 customers is not only ridiculous but so ambiguous as to be useless. Every developer on this site should know that not a single one of us would be remotely capable of maintaining a death-march level working pace for 7 months, launching a (credible) startup, followed by making non-zalgoized Reddit posts lacking any reference to the void. OP isn't an OG superhuman developer who worked for a bunch of big banks and learned all the secrets. They're a former trader turned wantrepreneur that's resorted to dirty tactics to promote their venture. As far as I'm concerned, and maybe it's inflammatory, but OP is a liar. Nothing more.
• unnouinceput 7y ago
  To OP (janny_kul@HN and chiefkul@RD): "Show me the code! Show me the code" - in obvious Cuba Gooding Jr. voice Until then I call this a fraud
• tripzilch 7y ago
  I read the post and .. he basically said he did it, and just claims the results (that are so tasty because they fit everyone's preconceptions). And that's all the information there is. Not even listing the papers that he supposedly reproduced. And then at the end of the post, you get a link to a blog post about crypto trading bots, which isn't even relevant in context, starts out reading like a intro/tutorial but it ends with "all you need to get started is $1000 and to press this button" ... Upvoted all the way to the front page? What the hell, HN. Flagged.
• maremp 7y ago
  Source OP deleted the post. Is there a mirror?