Browse: B
Browse all entries across every topic.
Entries starting with "B"
Barber and Odean show that individual investors who trade more frequently earn significantly lower returns, establishing that overconfidence leads to excessive trading which harms portfolio performance.
Barberis and Thaler survey behavioral finance's two pillars: limits to arbitrage (why mispricing persists) and psychology (systematic biases in beliefs and preferences), showing how they explain asset pricing anomalies.
Explore the behavioral psychology principles that make learning platforms like Duolingo, Khan Academy, and Brilliant effective — and how you can apply Fogg's Behavior Model, habit loops, and gamification to your own learning practice.
Understand how cognitive biases distort financial decision-making, how modern portfolio theory provides a rational framework, and how to combine both for better investment outcomes.
Behavioral finance challenges the efficient market hypothesis by incorporating psychological factors into financial decision-making. This module covers cognitive biases (overconfidence, anchoring, confirmation bias, loss aversion, herding), prospect theory, market anomalies (momentum effect, January effect, post-earnings-announcement drift), and 2025-2026 trends including behavioral ESG investing, the impact of retail trading platforms on market efficiency, and the use of NLP to measure investor sentiment at scale.
The Black-Scholes-Merton model provides a closed-form solution for European option prices via a no-arbitrage argument based on continuous delta-hedging, founding the modern options market.
How to structure self-study from Remember to Create: retrieval practice, worked examples, and teach-back at every level.
Bollen, Mao and Zeng show that the 'Calm' mood dimension from Twitter feeds can predict DJIA movements with 87.6% accuracy, demonstrating the predictive power of social media sentiment.
Bueche tracks the FATF's extension of AML regulation to virtual assets, including the Travel Rule for crypto transfers, and analyzes implementation challenges in the decentralized finance landscape.
Complete open source data stack: Dagster + dbt + Iceberg + Trino + DuckDB + Superset. Cost analysis against Snowflake and Databricks. Deployment patterns with Docker Compose, Terraform, and Kubernetes.
The Modern Open Source Data Stack The 2026 open source data stack is modular, composable, and cloud-agnostic. Teams assemble best-in-class tools for each layer rather than buying monolithi
Learn dbt fundamentals (models, tests, docs, sources), Dagster's software-defined asset approach, and how to build end-to-end pipelines combining both tools with best practices and a comparison of orchestration frameworks.
Combine Spark's processing power with dbt's transformation layer to build production-grade, tested, documented data pipelines.