Tag: sentiment

Ever since Typesafe’s Jev came out, people have been raving about its “one-shot” classification capability. We decided to put our news archives through Qwen (a “regular” llm) and Jev (a classifier) to bucket the sentiment expressed in each of them into Positive, Neutral and Negative.

Is Jev lazy? Picking “Neutral” is the path of least resistance, isn’t it? What if we split it by media type?

It looks like Jev loves being neutral. Either Indian financial media is mostly unbiased or Jev is not as “one-shot” as claimed to be. Thanks to Jev’s neutrality, the two models agree only 43% of the time.

Given their differences, are they useful?

There is no evidence that extreme media sentiment reliably predicts subsequent index direction. The positive post-event paths may simply reflect the general upward drift of the indices.

It appears that sentiment is in the eye of the analyzer and largely useless in predicting low frequency market direction. Details about the methodology and results are here.

Code and charts are on github.

Markets trend, until they don’t. Markets consolidate until they start trending again. Since the beginning of markets, traders have been trying to figure out when these turning points occur. At StockViz, we have approached this problem from a risk-management angle rather than a prediction angle. We assume that we cannot predict, but we can definitely prepare.

Risk-management, however, is not free. Whether you hedge or employ stop-losses, there is a cost involved. What if we can reduce this cost by employing market sentiment as an input to our risk-management models? One approach could be to widen stop-loss levels when the market is “extremely bearish” and tighten them when they get “extremely bullish.”

To get things started, we have setup a weekly sentiment roundup that looks at a few popular news sources ranked using different lexicons. The first step would be create a time series of these values to see how they relate to market returns.

Stay tuned!