Predicting stock market returns from news photos

Or it might just be that this is already known to the algorithmic traders, who already use headlines (text) as an input to their systems.

It 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?

> 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".

Ah, 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-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?

Sure. 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.

Assuming 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.

They're writing about predict stock returns not just simply matching the current state. But yeah, I'm also skeptical about it.

That’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.

I’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.