04 Aug Using News Sentiment Data for Investment Decisions
We are going behind the scenes to discover how our in-house data scientist Alexander Pieper uses News Sentiment data to create Buy/Sell Signals for stocks. Transforming unstructured news data into structured signals! A Step-by-Step Guide to transform raw data into an AAPL Trading Signal in Python:
Summary and Outlook
What did we do in this article? We went from having raw News Sentiment data to having a daily executable investment signal for the Apple stock in only 14 lines of code (excluding backtesting (16 lines) and plots (45 lines)). A portfolio using this signal is outperforming the stock by only disinvesting in bearish times. These bearish times are detected using only information on how Apple is currently represented in the news landscape.
How can this signal be adjusted to personalize it? This signal requires some parameters, like the rolling time frames for the sentiment (30 & 90 days), the scaling and smoothing of the Z-score, and the interpretation of said Z-Score for the signal. Risk-friendly investors could make the signal more reactive and fast by lowering the rolling timeframes and smoothing. One could also short the stock, if
Z-Score < 15, go out of the market if
Z-Score < 35 & Z-Score >= 15, and otherwise go long. This would probably yield higher returns, but would as well be riskier.
Will this strategy work for every stock? That is a harder question to answer. In my analysis, it works well for a large number of stocks but might also underperform for other stocks, as stock-independent market timing is a notoriously hard challenge. This article serves as a proof of concept for the robustness of using sentiment data to augment trading decisions.