Why You Should NOT use AI for Stock Price Predictions?
In this episode of Quant Lab, we explore why using AI to predict stock prices is a flawed approach. While AI can enhance trading, the unpredictability of markets, reliance on past performance, model complexities, and limitations of black box algorithms present major challenges. We also examine the effects of high-frequency trading, data quality issues, and the critical role of human sentiment in market dynamics. Don't miss our live webinar this Saturday at 9 PM Indian time for more insights on trading and market analysis.
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Tags
#trading #quants #technicalanalysis #quantlab #algorithmictrading #stockmarket #aitradingbot #aitrading #technology #tradingtechnology #tradingtech #community #learning #tradingview #fundamentalanalysis #quantfinance #quantitativefinance #algotrading #financialmarkets #finance #ai #artificialintelligence #machinelearning #computationalfinance #quantitativeanalysis #highfrequencytrading #prop trading #python #datascience #machinelearningintrading #deeplearningintrading #statisticalarbitrage #meanreversion #trendfollowing #volatilitytrading #pairstrading #arbitrage #rsi #macd #bollingerbands #stochastic #adx #metatrader #ninjatrader #lightspeed #interactivebrokers #tdameritrade #nlp #reinforcementlearning #generativeadversarialnetworks #llm
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Chapters
00:00 Introduction to Quant Lab
00:16 The Misconception: AI Predicting Stock Prices
01:09 Why Stock Markets Are Unpredictable
02:33 Challenges with AI Models in Trading
04:39 Community and Resources
05:34 The Black Box Nature of AI Models
06:18 High-Frequency Trading and Market Impact
07:22 Data Quality and Availability Issues
09:30 Conclusion: The Limitations of AI in Trading
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