In the world of finance, data-driven decision making has become increasingly important. As a result, techniques that provide robust and reliable predictions are of immense value to investors . One such technique is Conformal Prediction . Conformal Prediction is a machine learning approach designed to offer valid predictions under a given level of confidence . Unlike traditional prediction methods that provide a point estimate, Conformal Prediction provides a prediction i

Graph Attention Networks (GATs) are an exciting frontier in the domain of machine learning , specifically in the realm of graph -based deep learning . They are designed to handle data structured as graphs , and their unique attention mechanism allows them to prioritize the most relevant information during the learning process. For investors , understanding GATs can offer insights into potential future trends in technology and business applications. What is a Graph ? Before

In finance and investing , the quest for understanding causal relationships between variables is paramount. Whether it's understanding the impact of interest rates on asset prices or the effect of corporate governance on stock returns, causal inferences drive insightful decision-making. One powerful method to untangle causality is Instrumental Variable Analysis. This article elucidates the core principles of IV Analysis and its application in causal inference with a l

In an era where Artificial Intelligence has become a linchpin for various sectors, the financial realm is no exception. The deployment of AI in investment strategies has shown a new pathway to how assets are allocated, risks are assessed, and returns are optimized. One of the AI frameworks that has been gaining interest among investors is Active Inference . This article elucidates what Active Inference is, and how it can be a game-changer in the investment landscape. Und

In the vast field of financial investment, it's essential to understand the underlying relationships between variables, especially when multiple variables interact in complex ways. Structural Equation Modeling (SEM) serves this exact purpose. This statistical technique can be particularly useful for investors seeking to gain deeper insights into the factors that influence a company's value, profitability , and other financial metrics. What is Structural Equation Modeling (SE

In the world of artificial intelligence and machine learning , the Transformer architecture has made significant strides in various domains, from natural language processing (NLP) to image recognition. Combining this architecture with knowledge graphs , which encode structured information about the world, gives rise to the concept of Knowledge Graph Transformers . For investors interested in the cutting-edge of AI research and its applications, understanding this paradigm

In the realm of Artificial Intelligence, the dawn of Transformer architecture has delineated a new epoch of advancements. Introduced in...

In the realms of statistics and causal inference, biases are unwanted distorters of reality that can mislead investors into making...

Systematic compositionality is a critical aspect of cognitive ability, both in humans and, as it's now being discovered , in artificial intelligence (AI) as well. It's a principle that governs how simpler parts can be combined to form more complex expressions and understandings. Recent advancements in AI have brought to light a method known as Meta-Learning for Compositionality (MLC), which seems to play a pivotal role in enabling systematic compositionality in neural netwo

Investing involves a myriad of decisions based on assessments of risks , rewards, and comparisons between different options. An investor might wonder: “ What would have been the outcome had I chosen another investment? ” Answering such questions can be challenging due to confounding factors . This is where Propensity Score Matching (PSM) comes in. PSM is a statistical method used to reduce bias by equating groups based on these confounding factors . What is Propensity Score

TimesNet represents a notable advancement in the domain of time series analysis, which is a fundamental area of study for investors. The...

The quest for causality in Artificial Intelligence is about understanding the 'why' behind patterns, enabling more informed and actionable insights. Graph Neural Networks (GNNs) and the Deep Graph Library (DGL) are promising avenues in this direction. While primarily designed for relational reasoning, their inherent structure and capabilities can be harnessed to get us closer to Causal AI . Unraveling the intricate web of causality in vast datasets has been a longstandin

In today's investment landscape , advanced analytics are vital. The ability to extract meaningful insights from vast datasets can offer investors an edge. One particular data source of immense value is the U.S. Securities and Exchange Commission (SEC) filings . By tapping into this reservoir of corporate data, investors can gain a more comprehensive understanding of a company's financial health, risk factors , management strategies, and much more. With the advent of Graph Ne

Quantitative bias analysis is a critical tool for investors . It refers to the process of quantifying the bias that may be present in data or analytical results. Every decision made by an investor, no matter how educated or sophisticated, is influenced by the data they utilize. However, if that data contains bias , their decisions might be fundamentally flawed. Understanding Bias Bias is a systematic error that can distort the true relationship or measurement in your data

In the burgeoning field of artificial intelligence, terms and concepts can often become jargon-heavy and complex. For investors looking...

In machine learning and artificial intelligence , Graph Neural Networks (GNNs) have emerged as a powerful tool to process structured data. A particularly interesting concept within GNNs is that of heterophilic graphs. In this article, we aim to provide investors with a comprehensive understanding of heterophilic and homophilic graphs, their significance in GNNs , and how they can reshape industries. Basics: What are Graph Neural Networks (GNNs) ? Graph Neural Networks are

In artificial intelligence , one of the most pressing challenges is ensuring that the decisions made by machine learning models are understandable by humans. This is where Explainable AI (XAI) comes in. In this article, we will look into what XAI is, why it's important, and provide real-world examples for investors . What is Explainable AI (XAI)? Explainable AI refers to methods and techniques used in the domain of artificial intelligence that make the outcomes of machine

In recent years, the field of deep learning has expanded beyond traditional data types like images and texts, venturing into more complex structures like graphs . This expansion has given birth to Graph Neural Networks (GNNs) . For investors, understanding GNNs and the relationship with Causal AI can provide insights into their potential applications and implications in various industries. What are Graph Neural Networks? A GNN is a type of neural network specifically desi

For many investors, the rise of artificial intelligence in decision-making processes, especially in fields like finance, healthcare, and...

In the world of investing , data analysis is crucial for making informed decisions. But what if the data you're analyzing leads you down a misleading path? This article delves into the concept of " spurious correlations " and the emergent field of " causal AI " to help investors avoid pitfalls and make better decisions. What are Spurious Correlations ? In statistics, a spurious correlation , or spuriousness , refers to a relationship between two variables that appears str