Why Vision-Language Models Struggle with Stock Predictions

Vision-language models (VLMs) had investors buzzing with excitement at their potential to predict stock prices using visual data. But if you thought they'd revolutionize your trading strategy, think again. The reality? They're not quite ready for prime time.

The False Promise of VLMs

VLMs have been touted as the future of stock price forecasting. They attempt to interpret candlestick charts, which are the bread and butter of most traders. But here's the kicker: these models struggle to genuinely improve predictive accuracy. One big problem is that they often can't distinguish whether their visual input analysis is actually enhancing prediction. It's like having a crystal ball that only works in perfect weather.

Most existing datasets are limited to single-period or tabular inputs. This is a far cry from the multi-scale candlestick charts human analysts rely on. Real traders use these charts to spot long-term trends and short-term shifts. Without the ability to integrate these multi-scale dynamics, VLMs are fighting with one arm tied behind their back.

New Dataset, Same Old Problems

To address this, a new multi-scale candlestick charts dataset was created. It aims to assess how well VLMs can use these signals. The evaluation combines diagnostics from confusion matrices and information coefficient time series, with XGBoost as a baseline. Sounds impressive, right? Yet, the results paint a less rosy picture. While VLMs shine in clear uptrends or downtrends, they falter in typical market conditions where the waters are murkier.

there's a glaring issue with prediction biases. The models show limited sensitivity to forecast horizons specified in prompts. It's a fancy way of saying they struggle with precise timing. And in the stock market, timing is everything. If you can't tell when to buy or sell, you're just guessing.

Why Should You Care?

So, what does this mean for traders and investors? If you're relying on VLMs for stock predictions, you might be in for a disappointment. These models aren't the game-changers they're hyped up to be. At least not yet. They're still learning the ropes of temporal reasoning, which is key for accurate stock forecasts.

In essence, while VLMs offer potential, they're not ready to replace seasoned analysts or strong trading algorithms. If nobody would play it without the model, the model won't save it. Until these models can handle the complexities of the stock market's ups and downs, they'll remain more of a novelty than a necessity.