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-------------------- 𝑨𝒔𝒔𝒆𝒕 𝑴𝒂𝒏𝒂𝒈𝒆𝒎𝒆𝒏𝒕 𝑺𝒆𝒓𝒊𝒆𝒔: 𝑩𝒐𝒐𝒌 2 ------------------- 🚀 "𝑴𝒂𝒄𝒉𝒊𝒏𝒆 𝑳𝒆𝒂𝒓𝒏𝒊𝒏𝒈 𝒇𝒐𝒓 𝑨𝒔𝒔𝒆𝒕 𝑴𝒂𝒏𝒂𝒈𝒆𝒎𝒆𝒏𝒕: 𝑵𝒆𝒘 𝑫𝒆𝒗𝒆𝒍𝒐𝒑𝒎𝒆𝒏𝒕𝒔 𝒂𝒏𝒅 𝑭𝒊𝒏𝒂𝒏𝒄𝒊𝒂𝒍 𝑨𝒑𝒑𝒍𝒊𝒄𝒂𝒕𝒊𝒐𝒏𝒔" edited by Emmanuel Jurczenko, and it's a treasure trove for anyone in finance! 📊🤖 This comprehensive volume brings together leading financial economists and industry experts to explore the latest advancements in applying machine learning to asset management. The book covers a range of critical topics, offering both theoretical insights and practical applications. 💡 𝑲𝒆𝒚 𝑻𝒂𝒌𝒆𝒂𝒘𝒂𝒚𝒔 💡 𝑹𝒆𝒕𝒖𝒓𝒏 𝒂𝒏𝒅 𝑹𝒊𝒔𝒌 𝑭𝒐𝒓𝒆𝒄𝒂𝒔𝒕𝒊𝒏𝒈 📈 Innovative machine learning methods for predicting stock returns and managing risk are thoroughly examined. These techniques help in refining traditional forecasting models to improve accuracy and performance. 𝑷𝒐𝒓𝒕𝒇𝒐𝒍𝒊𝒐 𝑪𝒐𝒏𝒔𝒕𝒓𝒖𝒄𝒕𝒊𝒐𝒏 🛠️ The book introduces new approaches to building robust portfolios using machine learning, highlighting the advantages of these methods in optimizing asset allocation and enhancing investment strategies. 𝑷𝒆𝒓𝒇𝒐𝒓𝒎𝒂𝒏𝒄𝒆 𝑨𝒕𝒕𝒓𝒊𝒃𝒖𝒕𝒊𝒐𝒏 𝒂𝒏𝒅 𝑻𝒓𝒂𝒏𝒔𝒂𝒄𝒕𝒊𝒐𝒏 𝑪𝒐𝒔𝒕𝒔 💵 Detailed chapters discuss the application of machine learning in performance attribution and modelling transaction costs, providing valuable tools for portfolio managers to better understand and manage the factors influencing portfolio performance. 𝑷𝒓𝒂𝒄𝒕𝒊𝒄𝒂𝒍 𝑨𝒑𝒑𝒍𝒊𝒄𝒂𝒕𝒊𝒐𝒏𝒔 𝒂𝒏𝒅 𝑪𝒂𝒔𝒆 𝑺𝒕𝒖𝒅𝒊𝒆𝒔 📚 Real-world examples and case studies illustrate how machine learning algorithms can be implemented in various aspects of asset management, from stock selection to multi-asset allocation and factor investing. #MachineLearning #Finance #AssetManagement #Innovation #DataScience #AI #Investing

Generative AI is more than generating texts We're thrilled to introduce the latest version of **DeepFeatTimeGPT**, our cutting-edge proprietary generative multi-modal forecasting model designed exclusively for long-horizon forecasting in corporate finance. And yes, in the world of business, ""long-horizon"" takes on a whole new dimension! 😉 📊 **Astonishing Precision:** Our model achieves unparalleled precision in zero-shot evaluations, even for predictions up to an astounding 10 years into the future. That's right, it anticipates market dynamics that would leave most algorithms scratching their heads. 🎯 **Causality Integration:** What sets us apart? DeepFeatTimeGPT seamlessly integrates our Neuro-Symbolic Controller, ensuring the incorporation of causality into predictions. No more vague or improbable results – our model prevents hallucination for the most accurate forecasts. 🌐 **Massive Training Data:** Trained on a colossal dataset of nearly 5 billion financial data points, our model reigns as the most comprehensive financial forecasting pretrained model available today. When it comes to harnessing insights from data, we've got you covered. 📊 **Data-Driven Outperformance:** Ready to experience exceptional results right out of the box? Our DeepFeatTimeGPT is poised to elevate your decision-making game. If you're seeking to surpass benchmarks and make impactful business decisions, look no further. Curious to explore the realm of data-driven excellence? Drop me a direct message and let's dive into how DeepFeatTimeGPT can work wonders with your data. Get ready to revolutionize your approach to forecasting in corporate finance! 💼📈 #DataDrivenDecisions #FinanceForecasting #AIInnovation #decisionmaking #decisionintelligence #finance #cfo https://lnkd.in/gJSYqePJ

Struggling to choose between fine-tuning and RAG for your LLM projects? I've been deep in the trenches with this for months. Here's what I've learned: 1. Assess your data situation: • Got a rich, labeled dataset? Fine-tuning might be your jam. • Dealing with dynamic, frequently updated info? RAG could be the way to go. 2. Consider your model size: • Working with smaller models? Fine-tuning can give them a serious boost. • Got a big boy LLM? RAG can leverage its existing knowledge while adding fresh context. 3. Think about your specific needs: • Need to modify behavior or writing style? Fine-tuning excels here. • Want to tap into external knowledge sources? RAG is your best bet. 4. Evaluate your resources: • Fine-tuning can be computationally expensive but might save you in the long run. • RAG requires less training but needs a solid retrieval system in place. 5. Don't forget about hallucinations: • Fine-tuned models can still make stuff up. • RAG systems are generally more grounded in retrieved facts. 6. Consider a hybrid approach: • Combine fine-tuning for core behavior with RAG for up-to-date info. • This can give you the best of both worlds. 7. Implement, test, and iterate: • Start with prompt engineering and simple RAG. • If that doesn't cut it, move to fine-tuning. • Keep refining based on performance and user feedback. Remember, there's no one-size-fits-all solution. The key is understanding your specific use case and being willing to experiment. Pro tip: Keep an eye on emerging techniques. I've been playing with the idea of using intelligent agents to dynamically choose between database and web searches for RAG. The potential is mind-blowing. What's your experience been with fine-tuning vs. RAG? Drop your thoughts below! #AI #MachineLearning #LLM #FineTuning #RAG Read More: https://lnkd.in/d8GfZCuu

Discover the power of decision trees in machine learning and data analysis—simple, interpretable, and effective for classification and regression challenges across industries. #DecisionTrees #MachineLearning #DataAnalysis #AI https://lnkd.in/eNQHrAUS

Revolutionizing Finance with Machine Learning: A Path to Informed Investment 📈💼 In the ever-evolving world of finance, making prudent investment decisions is paramount. But traditional approaches struggle with vast data and complex patterns. Enter machine learning, your secret weapon! 🧠 Discover how machine learning deciphers intricate financial data, unveiling priceless insights for smarter investments.This article is your guide to the future of finance. Let's dive in! 💡 The future of finance is data-driven. Are you ready to harness the potential? Let's explore together. 💡 To read the full article and dive deeper into the world of data science, click on the following link https://lnkd.in/gCdriHfT. 📖✨ Like, share, and comment below with your thoughts and questions! Let's engage in this exciting conversation together. 👇💬 #MachineLearning #Finance #Investment #AI #DataAnalytics #PredictiveModeling #PortfolioOptimization #EthicalAI #SmartDataAnalytic #SmartDataLearning

The urgent need to move from technical to business value measures of AI project value. "When evaluating ML models, data scientists focus almost entirely on technical metrics like precision, recall, and lift. But these metrics are critically insufficient. They tell us the relative performance of a predictive model but provide no direct reading on the absolute business value of a model. Instead, the focus should be on business metrics - such as revenue, profit, savings, and number of customers acquired. These straightforward, salient metrics gauge the fundamental notions of success. They relate directly to business objectives and reveal the true value of the imperfect predictions ML delivers. They are core to building a much-needed bridge between business and data science teams." Via MIT.

Harnessing MLFlow with RAG can transform your data analysis 🧮 — leading to quicker decisions🚀. Ever grappled with large-scale data science tasks? 🕵️♂️Let's talk RAG pipeline with MLFlow. For those not in the know, a RAG (Retrieval Augmented Generation) is a process of optimizing the output of a large language model📊. It provides an effective way to visualize and quantify uncertainties in data processing and decision-making processes🤔. Now, blend that with the power of MLFlow - a remarkable open-source platform for managing the end-to-end machine learning lifecycle🔄. It's robust, versatile, and designed to streamline workflow. But how do you combine the two? The first step is to leverage MLFlow's tracking functionalities to log metrics and parameters during the data processing phase📈. This allows for real-time monitoring👀. Next, decide your parameters for RAG - a choice that will vary depending on the specifics of your project. For example correctness, groundedness, similarity, and context. Lastly, use MLFlow's rich visualizing capabilities to represent these metrics on a RAG scale. This visual representation enables at-a-glance understanding of your projects🙌. Like when I worked on a predictive model for a major healthcare provider 🏥 - using MLFlow and RAG helped us track anomalies in real time. This led to quicker, more effective decision-making, directly impacting patient outcomes👏. Share your experience with RAG pipeline and MLFlow. Let's learn together👥. #rag #mlflow #modelevaluation #ai

Hello Connections!! 👉Cross-validation is a cornerstone of robust machine learning models. Here’s a quick breakdown of different types of cross-validation, along with their pros, cons, and best use cases: 👉 K-Fold Cross-Validation 💡 Pros: Provides a good balance between bias and variance, works well with a moderate-sized dataset. 💡 Cons: Computationally intensive, especially with a large number of folds. 💡 When to Use: General-purpose, especially when you have enough data to avoid overfitting. 👉 Stratified K-Fold Cross-Validation 💡 Pros: Maintains class distribution across folds, ideal for imbalanced datasets. 💡 Cons: Slightly more complex to implement. 💡 When to Use: When your data has imbalanced classes and you want to ensure each fold is representative of the overall distribution. 👉 Leave-One-Out Cross-Validation (LOOCV) 💡 Pros: Maximum utilization of data, low bias. 💡 Cons: Very high variance, extremely computationally expensive. 💡 When to Use: When the dataset is very small and you want to ensure that each data point gets to be in the test set once. 👉 Leave-P-Out Cross-Validation 💡 Pros: More flexibility in testing multiple points. 💡 Cons: Exponentially more expensive with increasing p, rarely used in practice. 💡 When to Use: Very specific scenarios where you need to test exactly p points and have the computational power to do so. 👉 Time Series Split 💡 Pros: Maintains the temporal order of data, preventing data leakage. 💡 Cons: Only suitable for time series data, does not work with randomly ordered data. 💡 When to Use: For time series forecasting and when the order of data points matters. Each of these techniques has its unique strengths and is suited to different scenarios. Choosing the right cross-validation method can significantly impact the performance and reliability of your model. Happy modeling! 🚀 #MachineLearning #DataScience #CrossValidation #AI #ML #DataScienceCommunity #TimeSeries #ImbalancedData #ModelEvaluation

Adapting Static Fairness Notions to Sequential Decision Making: Achieving Equal Long-term Benefit Rate The importance of considering long-term fairness in machine learning models is emphasized in this content. It is noted that simply imposing fairness criteria in static settings can worsen bias over time. To address biases in sequential decision-making, recent works have introduced long-term fairness notions in the Markov Decision Process - MDP - framework. However, it is shown that summing up the step-wise bias without considering the importance difference of different time steps can create a false sense of fairness. To address this, a new long-term fairness notion called Equal Long-term Benefit Rate - ELBERT - is introduced. ELBERT takes into account the varying temporal importance and adapts static fairness principles to the sequential setting. Additionally, it is demonstrated that the policy gradient of Long-term Benefit Rate can be analytically reduced to the standard policy gradient, allowing for the application of standard policy optimization methods to reduce bias. This results in the proposed bias mitigation method ELBERT-PO. Experimental results on three sequential decision-making environments show that ELBERT-PO effectively reduces bias while maintaining high utility. The code for ELBERT-PO is available at https://lnkd.in/d3dpNGNS. https://lnkd.in/d5yD6w2s

𝑪𝒂𝒏 𝑾𝒆 𝑷𝒓𝒆𝒅𝒊𝒄𝒕 𝑻𝒐𝒅𝒂𝒚? In the ever-evolving world of finance, the quest to predict market trends is a challenge that captivates many. Recently, I embarked on an exciting journey to enhance our understanding and prediction of stock price trends using machine learning techniques. Leveraging historical data, I applied the wavelet trend finder method to identify the underlying patterns in stock prices. Wavelet transformation, specifically the discrete wavelet transform (DWT) with the 'db24' wavelet, allows us to decompose the time series data and capture the subtle fluctuations that are often missed by traditional methods. This transformation aids in distinguishing between 'up', 'down', and 'sideways' trends by analyzing the approximation coefficients. Once the trends were identified, the next step was to predict the current candlestick trend based on these historical patterns. Here, the MLPClassifier, a powerful neural network model, came into play. By extracting key features such as percentage changes and candlestick patterns from the historical data, we trained the MLPClassifier to recognize these intricate patterns and predict future trends. The model was trained on historical data and tested on more recent data to ensure a realistic backtesting scenario. This approach allowed us to simulate real-world trading conditions and validate the effectiveness of our predictions. The results were near promising. The model demonstrated an ability to predict 'up' and 'down' trends, although predicting 'sideways' trends remains a challenge, as these patterns are often less distinct. The overall accuracy and performance metrics suggest that our approach is semi-effective, and there's potential for further refinement and optimization. The journey doesn't stop here. As I continue to explore and refine these techniques, the goal is to develop even more robust models that can provide valuable insights and support informed trading decisions. Interested in the detailed methodology and results? Let's connect and discuss how advanced machine learning techniques can revolutionize trend prediction in financial markets! Remember that the AI needs numbers to crunch! Download link: https://lnkd.in/eu4HivCA ***If you found any mistake in the code, contact me*** #Finance #MachineLearning #StockMarket #DataScience #WaveletTransform #MLPClassifier #TrendPrediction