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-----------------  𝑨𝒔𝒔𝒆𝒕 𝑴𝒂𝒏𝒂𝒈𝒆𝒎𝒆𝒏𝒕 𝑺𝒆𝒓𝒊𝒆𝒔 - 𝑩𝒐𝒐𝒌 1 ---------------------- 📘 Exploring "𝙈𝙖𝙘𝙝𝙞𝙣𝙚 𝙇𝙚𝙖𝙧𝙣𝙞𝙣𝙜 𝙛𝙤𝙧 𝘼𝙨𝙨𝙚𝙩 𝙈𝙖𝙣𝙖𝙜𝙚𝙢𝙚𝙣𝙩 𝙖𝙣𝙙 𝙋𝙧𝙞𝙘𝙞𝙣𝙜" by Henry Schellhorn 📘 🔍 𝙄𝙣𝙨𝙞𝙜𝙝𝙩𝙛𝙪𝙡 𝙊𝙫𝙚𝙧𝙫𝙞𝙚𝙬 🔍 Henry Schellhorn's "𝙈𝒂𝙘𝒉𝙞𝒏𝙚 𝙇𝒆𝙖𝒓𝙣𝒊𝙣𝒈 𝒇𝙤𝒓 𝑨𝙨𝒔𝙚𝒕 𝑴𝙖𝒏𝙖𝒈𝙚𝒎𝙚𝒏𝙩 𝙖𝒏𝙙 𝙋𝒓𝙞𝒄𝙞𝒏𝙜 " is a must-read for finance professionals and tech enthusiasts keen on merging the realms of finance and machine learning. Schellhorn adeptly navigates the complex intersection of these two fields, offering both theoretical foundations and practical applications. 💡 𝙆𝒆𝙮 𝙏𝒉𝙚𝒎𝙚𝒔 💡 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 𝗼𝗳 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: The book kicks off with a solid introduction to machine learning principles, covering essential algorithms and their applications. Schellhorn ensures readers grasp the core concepts, making advanced topics more approachable. 𝑨𝒔𝒔𝒆𝒕 𝑴𝒂𝒏𝒂𝒈𝒆𝒎𝒆𝒏𝒕 𝑨𝒑𝒑𝒍𝒊𝒄𝒂𝒕𝒊𝒐𝒏𝒔: Schellhorn delves into various machine learning techniques and their direct applications in asset management. From portfolio optimization to risk assessment, the book highlights how machine learning can revolutionize traditional asset management practices. 📈 𝙋𝙧𝙞𝙘𝙞𝙣𝙜 𝙈𝙤𝙙𝙚𝙡𝙨: A significant portion of the book is dedicated to pricing models. Schellhorn demonstrates how machine learning can enhance pricing accuracy for a variety of financial instruments, including derivatives and bonds. The integration of machine learning methods provides a fresh perspective on pricing strategies. 💰 𝙋𝙧𝙖𝙘𝙩𝙞𝙘𝙖𝙡 𝙄𝙢𝙥𝙡𝙚𝙢𝙚𝙣𝙩𝙖𝙩𝙞𝙤𝙣𝙨: One of the standout features of this book is its focus on practical implementations. Schellhorn provides detailed case studies and examples, allowing readers to see machine learning techniques in action. This hands-on approach makes the concepts more tangible and applicable to real-world scenarios. 🛠️ #MachineLearning #Finance #AssetManagement #Pricing #Innovation #DataScience #AI

GPT4 outperforms financial analysts in predicting earnings changes. This works by only feeding GPT4 numbers, with no text-based context. Predictions have predictive power for future stock returns. “Chain-of-Thought (CoT) prompt that effectively ‘teaches’ the model to mimic a financial analyst.” - if you know how to prompt well, you can get great results. “GPT shows a remarkable aptitude for financial statement analysis…” 📉 What This Means for Real Estate: • Strategic Advantage: This is another piece of evidence showing that by integrating GPT4 into our financial analysis processes, we can enhance our strategic planning and investment decisions. I wouldn’t replace humans with AI but would get all my analysts trained in using it. • Market Forecasting: Improved accuracy in predicting market movements and property values, helping us stay ahead of trends and make smarter investments. Research gives mixed evidence on how good GPT4 is for forecasting but it’s clear in showing that it helps forecasters be more accurate. • Risk Management: Better predictive capabilities translate to more effective risk management strategies, I really think qualitative and quantitative analysis abilities of GPT4 are a game changer for risk management. I think this would be a great addition to the presentation I gave to EPRA (European Public Real Estate Association) in March! #RealEstate #AI #FinancialAnalysis #GPT4 #InvestmentStrategies #Innovation

RAG in Financial Analysis: Transforming Market Insights with AI 💼📈 Excited to delve into how Retrieval-Augmented Generation (RAG) is revolutionizing financial analysis! This advanced AI technology is reshaping how analysts gather and interpret market data, providing deeper insights and more accurate forecasts. 📈💼 RAG integrates the generative capabilities of AI with powerful data retrieval systems, offering a new level of precision and efficiency in financial analysis. Key Advantages of RAG in Financial Analysis: 1️⃣ Enhanced Data Accuracy: RAG retrieves up-to-date and relevant data from extensive financial databases, ensuring that analyses and forecasts are based on the latest information. 2️⃣ Improved Efficiency: By automating data retrieval, RAG allows financial analysts to focus on interpreting data and making strategic decisions rather than spending time on manual data collection. 3️⃣ Dynamic Insights: With RAG, analysts can quickly adapt to market changes by accessing real-time data, enabling more responsive and informed decision-making. Real-World Applications: * Investment Strategy: RAG can provide investment managers with detailed reports on market trends and company performance, helping them to make more informed investment decisions. * Risk Management: Financial institutions can use RAG to identify potential risks by retrieving and analyzing relevant economic indicators and market data. * Regulatory Compliance: RAG helps ensure compliance by retrieving the latest regulations and guidelines, enabling financial firms to stay updated with legal requirements. Best Practices for Implementing RAG in Financial Analysis: 💡 Curate Diverse Data Sources: To maximize the benefits of RAG, ensure your system has access to a wide range of financial data, including market trends, economic reports, and regulatory updates. 💡 Optimize Retrieval Algorithms: Focus on fine-tuning your retrieval mechanisms to prioritize accuracy and relevance. This enhances the quality of your financial analyses and insights. 💡 Continuous Model Training: Regularly update your models to incorporate new data and trends. Continuous learning is crucial for maintaining the effectiveness and accuracy of your RAG system. Let’s Connect and Innovate! #AI #FinancialAnalysis #MachineLearning #DeepLearning #RAG #ArtificialIntelligence #Finance #TechInnovation #FutureOfFinance #AIApplications

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

Learnings from Finance & how this matters for #ecommerce brands: Fascinating: In finance models exceed human performance, but the best outcomes are achieved through adding additional human context into the model. We see the same dynamic with Autopilot. Listing optimization automations outperform human optimizations on a portfolio of products. Yet, again adding human context to complement the model drives superior outcomes to the AI-only use case. That's why we combine automation, AI & human-listing-expert-input for high scale product listing optimizations. Brands see the results within 3 days of optimizing as it start delivering an immediate traffic lift on #amazon.

GPT-4 beating professional financial analysts. What does this mean for finance? A recent study, by researchers with the University of Chicago, has demonstrated the financial capabilities of LLMs. Here are some key takeaways: >>Superior Performance GPT-4 surpasses human analysts, especially in challenging scenarios, and matches the accuracy of state-of-the-art (narrow) machine learning models. The model excels in analyzing financial statements, a traditionally complex and judgment-intensive task. >>Narrative Insights Unlike traditional models, GPT-4 generates valuable narrative insights about a company’s future performance, offering a unique edge in financial analysis. The model's analysis is not reliant on memory but rather on its ability to understand trends, financial ratios, and economic reasoning. >>Trading Success Strategies based on GPT-4’s predictions yield higher Sharpe ratios and alphas, highlighting its potential for superior investment returns. >>Complementary to Human Analysts Interestingly, the study finds that GPT-4 and human analysts are complementary. While the model excels in scenarios where analysts might exhibit bias or disagreement, human analysts add value with additional context and industry-specific knowledge. >>Future of Financial Statement Analysis (FSA) Traditionally, FSA involves critical thinking and complex judgments. Other than for CPA-exam performance, the model cannot rely on its memory to perform this task. The study suggests that LLMs could take a central role in future decision-making, driving efficiency and innovation in the financial industry. Read the full paper here: https://lnkd.in/e5_MBkQx #AI #FinTech #Investment #Innovation #FinancialAnalysis

When we explain the Asset Assess FDD analytics platform to prospective clients and partners, one curious question is popping up more and more: Do you do A.I.? Since artificial intelligence (A.I.) has gathered lots of buzz and transformed other industries in recent years, these types of questions are understandable. And our answer? It’s an emphatic YES … but it’s not what you might think. Find out more in the below article, and contact us if you would like to see how our platform can transform your property portfolio. https://lnkd.in/gnzitBMh

A.I.? It is a big brush, but there is a special stroke which the Asset Assess platform fits nicely into! Read the article linked below to find out more.

Completed "Mastering Finance Fundamentals" from The Wharton School To solve problems in AI and Data Science, it's always essential to be as close to business as possible. If the solutions align with business goals and objectives, they are more likely to get implemented. This course provided great overview of: 1. Uncover a Company’s Financial Strengths and Challenges • Analyze Balance Sheets, Income Statements, and Statements of Cash Flows • Compare statements across companies • Glean insights relating to a company’s financial position 2. Analyze Ratios for Deeper Understanding • Assess risk accurately within an organization’s financial activities • Leverage multiple profitability measures for a holistic view • Compare ratios across companies for greater context 3. Document a Business Improvement for Your Organization • Synthesize cross-company analysis of financials to identify an actionable business improvement • Make a case for your improvement: what it is, why it is important, and supporting evidence This definitely helped me to get little closer to understand business needs. Thanks to professor Richard Lambert for the very nice explanation throughtout. #DataScience #AI #BusinessUnderstanding #BusinessConsulting #wharton Wharton Online

📈 Exciting Industry Insight: Enhancing Stock Analysis with Machine Learning 🚀 In today's fast-paced financial landscape, harnessing the power of technology is paramount. Machine Learning techniques are revolutionizing stock analysis, offering deeper insights and smarter decision-making. Traditionally, stock analysis relied heavily on historical data and human interpretation. Enter Machine Learning – an innovation that thrives on data patterns and predictive models. By leveraging ML algorithms, we can now analyze vast datasets with lightning speed, identifying hidden trends and correlations that might escape the human eye. **Key Benefits:** 🔹 **Predictive Insights:** ML models can forecast stock price movements, helping investors make informed decisions. 🔹 **Risk Assessment:** ML techniques quantify risk factors, aiding in constructing more robust portfolios. 🔹 **Real-time Analysis:** Rapid data processing enables quick reactions to market fluctuations. However, it's crucial to remember that ML isn't a crystal ball – it's a tool. Human expertise still plays a vital role in contextualizing results and making strategic judgments. As we embrace this exciting evolution in stock analysis, collaboration between finance professionals and data scientists becomes paramount. Together, we can harness the true potential of Machine Learning to navigate the complex world of investments. #StockAnalysis #MachineLearning #FinancialInnovation #DataDrivenDecisions #InvestmentStrategy