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Predicting stock market returns from news photos
- java-man 5y ago$39.95 (for academic use). Excellent! I am sure the research was paid for by the Australian taxpayers, but it's Elsevier who makes money from publication.
- Animats 5y agoFrom the article: "It works by analysing the ‘top lists’ of editorial pictures on stock image website Getty. Using machine learning, the algorithm produces a daily score based on the types of photos used in global news reports. Lead author Dr Angel Zhong said investors could use this information to better predict daily stock market returns, based on the mood of investors worldwide." Not, "Angel Zhong Fund announces 142% gain for year."
- quickthrowman 5y agoExactly. If you could accurately predict returns, you wouldn’t publish, you’d profit. Profiting from the strategy would be a much better form of proof than a paper. Although.. maybe this is the secret sauce that powers Jim Simons’ Medallion Fund
- betterunix2 5y agoOr it might just be that this is already known to the algorithmic traders, who already use headlines (text) as an input to their systems.
- deleted 5y ago[deleted]
- ggm 5y agoIt probably had implicit status as post hoc reasoning since the images include post closure photos for at least some markets since some news images come after closure. They'd have to work awfully hard to demonstrate better than random with only prior data. A confounding problem is that news sources change stories and use a/b testing to pick story winners. This could even be a meta analysis of emerging sentiment on delay, which means you'd do better timewise on the origin sentiment than the second differential. Except you can't access the same pool of opinion so at best, it's a clever trick to access emerging sentiment through reactive reasoning on news sites. Why is that better as a driving function to invest across the market than other sources?
- capableweb 5y ago> Exactly. If you could accurately predict returns, you wouldn’t publish, you’d profit While it's sometimes fun to assume things, it gets hard when you talk about people. In this case, not everyone is driven by profits and money, so it's most likely incorrect to assume "They didn't use this way of gaining profit themselves, so it's probably not profitable".
- agumonkey 5y agoAlso nothing says he didn't try and profit enough (to fund more research) from his idea already.
- jaredwiener 5y agoAlso -- wouldn't it be a safe assumption that photo editors are choosing the pictures that match the tone of the news they are reporting on? This seems like a roundabout way of saying that financial news seems to mostly accurately report the state of financial markets....
- nanis 5y agoAh, that pesky reverse causation thing ... In fact, one might get even better results if one trains the model on the daily market charts newspapers publish such as this: https://thumbs.dreamstime.com/x/stock-market-newspaper-background-charts-13413217.jpg https://thumbs.dreamstime.com/x/stock-market-newspaper-backg... Oh, that's harder to get than the top-10 list at Getty, I see. Yeah, convenience sampling is king. Is one of the authors "Not Sure!" by any chance?
- splitstud 5y agoSure. That would be why using this or a similar method as a synthesis or proxy to aid prediction could have value, right? An issue is that methods of money management one might use in conjunction with this type of system can themselves have determinism for total returns. Measuring effectiveness would be hard.
- hammock 5y agoAssuming the news gets printed before market open (not really a safe assumption in the digital age), for this to have predictive power one would have to be looking at the intraday difference from market open to close, or the difference from previous close to today's open, not from previous close to today's close. It's not clear from the abstract/article which they looked at.
- spyder 5y agoThey're writing about predict stock returns not just simply matching the current state. But yeah, I'm also skeptical about it.
- secondaryacct 5y agoThe first rule of investing though is once everyone knows the martingale, nobody can profit off of it anymore. So now this won't work.
- qeternity 5y agoThat’s OP’s point: if this were truly predictive then you would just go raise a few billion dollars and quietly implement this strategy…you wouldn’t write a paper about it. The fact that we’re reading about a paper, and not a headline about a wildly successful systematic fund, suggests this isn’t all that valuable.
- not-elite 5y agoYou're probably correct in this case, but not in general. The authors of [1] could have quietly run a money printer but elected to seek reforms instead. [1] https://online.wsj.com/public/resources/documents/1029sechft.pdf https://online.wsj.com/public/resources/documents/1029sechft...
- danielmarkbruce 5y agoi don't think that's right. People were already using the information quickly weren't they?
- bumby 5y agoI’m not sure that’s the case in all circumstances. There’s at least a few financial anomalies that could be taken advantage of, but human biases constrain their actual use. One example is the low volatility anomaly, where low risk assets outperform the market, when time and again, fund managers struggle to do so without leverage. But fund managers generally won’t employ such a strategy because their investors don’t want to see themselves in low-risk assets when high-risk ones are going gangbusters because of FOMO.
- Animats 5y agoOne word. Vanguard.
- yellow_lead 5y agoI didn't read the whole study and this is a pre-proof but I'm not very optimistic about claims like this. There are often many data biases that are hard to root out. https://sci-hubtw.hkvisa.net/10.1016/j.jfineco.2021.06.002 https://sci-hubtw.hkvisa.net/10.1016/j.jfineco.2021.06.002
- personjerry 5y agoThis is silly because the photo selections are predicated on the news articles. Which implies the articles for that day are already published. Which means this classifier basically "reads the market news correctly based on the pictures within".
- paulpauper 5y agoI dunno if this is related to the article because its pay-walled, but elon tweeting a picture of dogs does seem to make doge-themed coins go up
- cjbenedikt 5y agoDownload available here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3841844 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3841844
- m3kw9 5y agoIs probably another case of “you think you are good, but it was just luck”
- black_13 5y agoBoy everyone forgets 2001 and 2008 and the devastation
- deleted 5y ago[deleted]
- ta988 5y agoNews photos could be used to predict price changes of pork pie.
- anonu 5y agoPeople often claim to "find" new market factors - but in reality they're just re-hashes of some other very well known and mathematically defined factor. I think in this case, its just called "momentum". Sentiment analysis is like alchemy. People have searched for it forever in hopes of some sort of pot of gold. Turns out, sentiment is directly caused by the markets. If you're in the red you're unhappy... its that simple. Then what happens? Your favorite WSJ reporter will write up some pessimistic article with a stock photo of some long-lost stock broker dinosaur with his face in his hands. Market might go down another few days... momentum. I haven't read the whitepaper - its paywalled. But probably not worth a read.
- sinuhe69 5y agoNo test? No validation whatsoever? Ah, found this: “Consistent with behavioral models, Photo Pessimism predicts market return reversals and trading volume. The relation is strongest among stocks with high limits to arbitrage and during periods of elevated fear.” Anybody who is a bit familiar with the stock market knows that highly arbitraged stocks tend to fluctuate greatly with bad news. The effect here is magnified so it’s easy to say there is a correlation. But by no way it can represent the market sentiment as a whole. And IMO it doesn’t speak for the value of the model.