{"id":13968,"date":"2026-02-09T12:37:04","date_gmt":"2026-02-09T12:37:04","guid":{"rendered":"https:\/\/mengossdit.com\/muhuma\/?p=13968"},"modified":"2026-02-09T15:34:04","modified_gmt":"2026-02-09T15:34:04","slug":"reimagining-investment-portfolio-management-with-4","status":"publish","type":"post","link":"https:\/\/mengossdit.com\/muhuma\/2026\/02\/09\/reimagining-investment-portfolio-management-with-4\/","title":{"rendered":"Reimagining Investment Portfolio Management With Agentic Ai"},"content":{"rendered":"<div id=\"toc\" style=\"background: #f9f9f9;border: 1px solid #aaa;display: table;margin-bottom: 1em;padding: 1em;width: 350px;\">\n<p class=\"toctitle\" style=\"font-weight: 700;text-align: center;\">Content<\/p>\n<ul class=\"toc_list\">\n<li><a href=\"#toc-0\">1 Metaheuristics For Portfolio Optimization<\/a><\/li>\n<li><a href=\"#toc-2\">Why Do Modern Ml Models In Finance Often Appear To Underperform?<\/a><\/li>\n<li><a href=\"#toc-3\">Ai Gives Informed Investment Decisions<\/a><\/li>\n<li><a href=\"#toc-4\">Analyze And Pick Stocks<\/a><\/li>\n<li><a href=\"#toc-6\">See How Agentic Ai Transforms Investment Decision-making<\/a><\/li>\n<\/ul>\n<\/div>\n<p>AI continuously monitors the asset price fluctuation patterns, FX, interest, yield, and inflation  rates, on-balance <a href=\"https:\/\/www.mouthshut.com\/product-reviews\/everestex-reviews-926207002\">Everestex reviews<\/a> volumes, and other capital market performance indicators. Such an architecture is built around a deep learning analytics engine and can be flexibly extended with LLM-based agents to automate judgement-heavy investment operations end to end. Below, our consultants share a sample layered architecture of AI software for wealth management and investment, describing its key components and data flows. ScienceSoft creates AI-powered investment solutions with scalable and secure architecture that enables smooth processing of financial data. Real-time detection of fraudulent investment patterns to timely take protective measures and prevent financial losses.<\/p>\n<p><img decoding=\"async\" class=\"aligncenter\" style=\"display: block;margin-left:auto;margin-right:auto;\" 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\" width=\"300px\" alt=\"AI driven portfolio management\"\/><\/p>\n<h2 id=\"toc-0\">1 Metaheuristics For Portfolio Optimization<\/h2>\n<div style=\"text-align:center\"><iframe width=\"562\" height=\"319\" src=\"https:\/\/www.youtube.com\/embed\/glgHYXz7cjI\" frameborder=\"0\" alt=\"AI driven portfolio management\" allowfullscreen><\/iframe><\/div>\n<p>Specifically, Mohagheghi et al. (2019) suggested how MCDM should deal with uncertainty-related issues and which optimization techniques could be useful for project portfolio construction. Later, Munhoz Arantes and Cesar Ribeiro Carpinetti (2019) published a review (with more than 110 papers cited) of how MCDM can be used for risk assessment. However, they found only one publication, namely (Vetschera and Almeida, 2012), related to the portfolio selection problem. A comprehensive review of MCDM techniques was presented in the study Mardani et al. (2015), where a list of publications (more than 460) with different applications in many fields of science, engineering and management was provided.<\/p>\n<ul>\n<li>In capital markets, time series are often used to store time-based trading data and market data.<\/li>\n<li>These models analyze up to three years of lagged observations to predict financial outcomes with greater accuracy than traditional methods.<\/li>\n<li>One adaptable method is threshold rebalancing, using range-based mechanisms to reallocate assets when they exceed predefined thresholds swiftly.<\/li>\n<li>ScienceSoft\u2019s team of 20 data scientists created custom algorithms for technical pattern recognition, stock price forecasting, and autonomous trading.<\/li>\n<\/ul>\n<p>Nonetheless, it&#8217;s essential to note that the effectiveness of these strategies may vary depending on factors such as portfolio size, investment objectives, and prevailing market conditions. They encompass dynamic portfolio rebalancing through reinforcement learning (RL), utilizing its algorithms to maximize portfolio returns, and applying lag-optimized trading indicators in conjunction with genetic algorithms. Dynamic rebalancing, as articulated by Ilmanen and Maloney (2015), is an active investment approach where investors adjust their portfolios not confined to fixed schedules or specific percentage deviations.<\/p>\n<div itemScope itemProp=\"mainEntity\" itemType=\"https:\/\/schema.org\/Question\"><\/div>\n<div itemProp=\"name\">\n<h2>What are the 4 pillars of AI?<\/h2>\n<\/div>\n<div itemScope itemProp=\"acceptedAnswer\" itemType=\"https:\/\/schema.org\/Answer\"><\/div>\n<div itemProp=\"text\">\n<p>Fairness, efficacy, transparency, and accountability are the four pillars of responsible AI, but translating these concepts into real-world processes and controls can be challenging.<\/p>\n<\/div>\n<h3 id=\"toc-1\">Latest News<\/h3>\n<div itemScope itemProp=\"mainEntity\" itemType=\"https:\/\/schema.org\/Question\"><\/div>\n<div itemProp=\"name\">\n<h2>How is AI used in portfolio management?<\/h2>\n<\/div>\n<div itemScope itemProp=\"acceptedAnswer\" itemType=\"https:\/\/schema.org\/Answer\"><\/div>\n<div itemProp=\"text\">\n<p>AI is transforming portfolio management by enhancing asset allocation, risk management, and investment strategies through advanced machine learning, predictive analytics, and real-time data processing.<\/p>\n<\/div>\n<p>Assessing risk and return within a target asset allocation often relies on a rebalancing strategy. Recognizing the diversity of rebalancing methods is crucial; some strategies are well-documented for their simplicity and effectiveness, while others, though less familiar, offer innovative perspectives. Whether targeting a 50\/50, 70\/30, or 40\/60 allocation, portfolio rebalancing involves reshuffling assets to achieve a predefined composition (Chen J. et al., 2020). This section focuses on executing portfolio orders and aligning them with investor objectives while considering market impact and asset price dynamics.<\/p>\n<p><img decoding=\"async\" class=\"aligncenter\" style=\"display: block;margin-left:auto;margin-right:auto;\" 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\" width=\"302px\" alt=\"AI driven portfolio management\"\/><\/p>\n<h2 id=\"toc-2\">Why Do Modern Ml Models In Finance Often Appear To Underperform?<\/h2>\n<div style=\"border: grey solid 1px;padding: 14px;\">\n<h3>IRONVALE CAPITAL Expands Global AI Asset Management Footprint, Accelerates Entry into Japan &#8211; Ag Plus, Inc.<\/h3>\n<p>IRONVALE CAPITAL Expands Global AI Asset Management Footprint, Accelerates Entry into Japan.<\/p>\n<p>Posted: Sat, 25 Oct 2025 07:00:00 GMT <a href=\"https:\/\/news.google.com\/rss\/articles\/CBMi-AFBVV95cUxPbkpfOUZPU0dwYlFlM2QxbG9UM0pIZ2lUQzEtN1JWQ3hiSFh5UGExOU9rd1Z5UU01WHJqYmdmN1pTendEeUZDSF9leWkwZFNHYnpsMmJtSV9tRFp6VXVPLXVnenZ1MnpUc0FHNXAxVFRLSkdMc1I3QmxBZUVUazBVRGR1S0l5QS1fVHNWVVREbjJSTkltU2xZd0FOSHBBWWpLM0ZHSElKUUQ3RXlTVlF6TTBLd1ZKTlhnLW1id2xUdmlxaGxrRzd6eDNwYktIb3JMcm5MaTNJMUZiMEZteDB5XzhlVlVtcWpuMzFnd25KRkEzQmR3ZlUteA?oc=5\" rel=\"nofollow\">source<\/a><\/p>\n<\/div>\n<p>In the wealth management space, poor investment decisions can cause huge financial and reputational losses. Based on the AI-generated suggestions, wealth managers can create tailored investment roadmaps for their clients, and investors can self-design trading journeys. It reveals dependencies between the indicators, predicts the market moves, considers investor sentiment, and delivers insights into emerging opportunities.<\/p>\n<ul>\n<li>Moreover, to emphasize the need for transparency and fairness of decisions, laminable artificial intelligence (XAI) area approaches are briefly reviewed, and a case study of post-hoc explanations for portfolio construction is presented.<\/li>\n<li>\u2022 High-frequency trading gains microsecond advantages \u2014 AI-powered systems achieve sub-microsecond latency and statistical arbitrage strategies with Sharpe ratios of 4.0, far exceeding human capabilities.<\/li>\n<li>Specifically, XAI techniques are essential for portfolio allocation decisions and for predicting returns using machine learning.<\/li>\n<li>In non-discretionary portfolio management, the portfolio manager provides investment advice, but clients make the final decisions.<\/li>\n<li>Portfolio construction has been a significant task since 1952 when Markowitz introduced the mean-variance model.<\/li>\n<\/ul>\n<h3 id=\"toc-3\">Ai Gives Informed Investment Decisions<\/h3>\n<p>It contains the client&#8217;s needs, circumstances, and constraints to achieve a particular reward goal at a given risk level. Finally, we discuss recent regulatory developments in the European investment business and highlight specific aspects of this business where explainable artificial intelligence could advance transparency of the investment process. Moreover, as the use of artificial intelligence in finance is challenged by transparency, fairness and explainability requirements, the case study of post-hoc explanations for asset allocation is demonstrated. The future lies in blending AI\u2019s analytical power with human expertise to create smarter, more dynamic strategies. For AI to drive real value, collaboration between tech developers, financial institutions, and regulators is key.<\/p>\n<div itemScope itemProp=\"mainEntity\" itemType=\"https:\/\/schema.org\/Question\"><\/div>\n<div itemProp=\"name\">\n<h2>Which AI is best for making portfolios?<\/h2>\n<\/div>\n<div itemScope itemProp=\"acceptedAnswer\" itemType=\"https:\/\/schema.org\/Answer\"><\/div>\n<div itemProp=\"text\">\n<p>Use Lovable AI to create an elegant portfolio that highlights your skills, projects, and testimonials. Define your portfolio categories and personal brand. Lovable AI generates a visually appealing portfolio site. Customize project galleries, case studies, and CTAs.<\/p>\n<\/div>\n<p>They run based on a daily schedule in a fully automated fashion, producing the <a href=\"https:\/\/www.mouthshut.com\/product-reviews\/everestex-reviews-926207002\">https:\/\/www.mouthshut.com\/product-reviews\/everestex-reviews-926207002<\/a> expected output and storing it in MongoDB. The data storage and retrieval are pivotal to AI agent effectiveness and can be advanced by embedding and vector search capabilities. Memory leverages both long and short-term contextual data for informed decision-making and continuity of the interactions.<\/p>\n<div style=\"display: flex;justify-content: center;\"><iframe width=\"609\" height=\"451\" src=\"https:\/\/maps.google.com\/maps?q=&#038;t=&#038;z=15&#038;ie=UTF8&#038;iwloc=&#038;output=embed\"><\/iframe><\/div>\n<h2 id=\"toc-4\">Analyze And Pick Stocks<\/h2>\n<div itemScope itemProp=\"mainEntity\" itemType=\"https:\/\/schema.org\/Question\"><\/div>\n<div itemProp=\"name\">\n<h2>What are the 5 biggest AI fails?<\/h2>\n<\/div>\n<div itemScope itemProp=\"acceptedAnswer\" itemType=\"https:\/\/schema.org\/Answer\"><\/div>\n<div itemProp=\"text\">\n<ul>\n<li>Volkswagen&apos;s Cariad Billion-Dollar AI Fail.<\/li>\n<li>Taco Bell&apos;s Drive-Thru AI Gone Wrong.<\/li>\n<li>Google AI Overviews: The Hallucination Problem.<\/li>\n<li>Arup Deepfake Heist: $25 Million Stolen.<\/li>\n<li>Replit &quot;Rogue Agent&quot;: Complete Database Deletion.<\/li>\n<li>McDonald&apos;s &amp; Paradox.ai: 64 Million Records Exposed.<\/li>\n<li>UnitedHealth &amp; Humana: Algorithmic Care Denial.<\/li>\n<\/ul>\n<\/div>\n<div><\/div>\n<p>These methods reveal underlying structures, simplify visualization, and introduce a form of ordering in the market space. The multitude of market constituents and their interrelationships, coupled with specific structures, motivate the application of unsupervised machine learning techniques. Comparatively, Ridge, LASSO and Elastic net methods focus more on shrinkage, moving the model coefficients to zero. In this way, the complexity of portfolio selection is reduced if there are no correlations among the assets.<\/p>\n<p><img decoding=\"async\" class=\"aligncenter\" style=\"display: block;margin-left:auto;margin-right:auto;\" 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\" width=\"303px\" alt=\"AI driven portfolio management\"\/><\/p>\n<h3 id=\"toc-5\">Enhancing Portfolio Management Using Artificial Intelligence: Literature Review<\/h3>\n<p>This ensures optimal timing and reduces unnecessary trading activity. Suboptimal returns and emotionally-driven decisions that compound over time. Counterparty risk assessment \u2014 Natural language processing analyzes news across multiple languages to identify risks that might only be published in certain regions. The success persisted even after accounting for trading costs\u2014a crucial test for practical implementation. The hierarchical risk parity method demonstrates this approach effectively.<\/p>\n<ul>\n<li>Integrating these tools into their practices is essential for those aiming to navigate the complexities of today\u2019s financial landscape successfully.<\/li>\n<li>\u201cAXYON AI developed comprehensive Deep Learning investment strategies based on various data for SMBC &#8211; Global Investment &amp; Consulting.<\/li>\n<li>This shift aligns with the broader move toward hyper-customization in financial services.<\/li>\n<li>\u2022 AI-driven asset allocation outperforms traditional methods \u2014 Machine learning models reduce forecast errors by up to 27% and create more accurate risk assessments than conventional approaches.<\/li>\n<\/ul>\n<ul>\n<li>First, AI impacts information efficiency by reducing the marginal cost of information acquisition and processing for portfolio managers.<\/li>\n<li>Credit deterioration detection \u2014 Machine learning algorithms identify subtle patterns in financial statements, market behavior, and external data that precede credit issues.<\/li>\n<li>A significant milestone is the Hierarchical Risk Parity (HRP) approach (L\u00f3pez de Prado, 2016) aimed to improve the robustness of Risk Parity schemes in markets with fluctuating covariances.<\/li>\n<li>Lo (2004) and Lo (2017a) suggest that behavioral aspects in the portfolio decision-making process align with an evolutionary model with a perspective of adaptation, and this new approach combining economy and psychology is called the \u201cAdaptive Market Hypothesis\u201d.<\/li>\n<\/ul>\n<p>The methods for performance evaluation can be broadly categorized into conventional and risk-adjusted methods. However, Grinblatt and Titman (1989) introduces a comprehensive model designed to offer a nuanced perspective on diverse aspects of portfolio performance measurement. The advent of AI\/ML tools has ushered in a new era of dynamic portfolio rebalancing strategies. This approach aims to optimize investment performance while effectively managing risk (Gaivoronski et al., 2005). This approach underscores the importance of regular portfolio review and rebalancing only when asset allocations surpass a predetermined minimum rebalancing threshold.<\/p>\n<p><img decoding=\"async\" class=\"aligncenter\" style=\"display: block;margin-left:auto;margin-right:auto;\" 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\" width=\"305px\" alt=\"AI driven portfolio management\"\/><\/p>\n<p>HRP models, part of the hierarchical approach, demonstrate robust out-of-sample properties without requiring a positive-definite return covariance matrix\u2014a notable weakness in mean-variance-based portfolios. The hierarchical tree structure corresponds to diversification aspects in portfolio optimization models, where assets in the classic Markowitz portfolio are consistently located on the outer leaves of the tree (Onnela et al., 2002). For instance, D&#8217;Urso et al. (2013) and D&#8217;Urso et al. (2016) utilized a model-based approach with various fuzzy cluster variations and different distance metrics in financial markets. Deep learning-based methods for time series forecasting are prevalent in the literature and will continue to give state-of-the-art results in the foreseeable future. Convolutional Neural Networks (CNNs), traditionally employed for images and videos, find application in forecasting financial time series data (Kirisci and Cagcag Yolcu, 2022). This approach is crucial in determining influential predictors, such as industry or market output, preventing overfitting, and controlling model complexity in machine learning methods (Li, 2015; Gu et al., 2020).<\/p>\n<div style=\"text-align:center\"><iframe width=\"560\" height=\"314\" src=\"https:\/\/www.youtube.com\/embed\/P3730dYhZ3U\" frameborder=\"0\" alt=\"AI driven portfolio management\" allowfullscreen><\/iframe><\/div>\n<p>Poor quality data leads to unreliable AI outputs, financial waste, and increased risk. Despite these benefits, AI portfolio management faces real challenges you can&#8217;t ignore. Research confirms that sentiment derived from Twitter data can predict <a href=\"https:\/\/techbullion.com\/everestex-review-platform-features-for-digital-asset-traders\/\">https:\/\/techbullion.com\/everestex-review-platform-features-for-digital-asset-traders\/<\/a> short-term stock price movements. News sentiment serves as a powerful predictor of market movements, with studies confirming a causal relationship between market sentiment and stock returns. These systems operate independently of conventional risk factors and market movements. Financial institutions now process market data and execute trades at speeds that make human reaction times irrelevant.<\/p>\n<div style=\"text-align:center\"><iframe width=\"562\" height=\"314\" src=\"https:\/\/www.youtube.com\/embed\/0eGFQaHvrEg\" frameborder=\"0\" alt=\"AI driven portfolio management\" allowfullscreen><\/iframe><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Content 1 Metaheuristics For Portfolio Optimization Why Do Modern Ml Models In Finance Often Appear To Underperform? Ai Gives Informed Investment Decisions Analyze And Pick Stocks See How Agentic Ai Transforms Investment Decision-making AI continuously monitors the asset price fluctuation patterns, FX, interest, yield, and inflation rates, on-balance Everestex reviews volumes, and other capital market [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[31],"tags":[],"class_list":["post-13968","post","type-post","status-publish","format-standard","hentry","category-6-best-online-trading-platforms-of-2026-3"],"_links":{"self":[{"href":"https:\/\/mengossdit.com\/muhuma\/wp-json\/wp\/v2\/posts\/13968","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mengossdit.com\/muhuma\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mengossdit.com\/muhuma\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mengossdit.com\/muhuma\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/mengossdit.com\/muhuma\/wp-json\/wp\/v2\/comments?post=13968"}],"version-history":[{"count":1,"href":"https:\/\/mengossdit.com\/muhuma\/wp-json\/wp\/v2\/posts\/13968\/revisions"}],"predecessor-version":[{"id":13969,"href":"https:\/\/mengossdit.com\/muhuma\/wp-json\/wp\/v2\/posts\/13968\/revisions\/13969"}],"wp:attachment":[{"href":"https:\/\/mengossdit.com\/muhuma\/wp-json\/wp\/v2\/media?parent=13968"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mengossdit.com\/muhuma\/wp-json\/wp\/v2\/categories?post=13968"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mengossdit.com\/muhuma\/wp-json\/wp\/v2\/tags?post=13968"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}