Review of Finance’s Post

"Market accessibility, bond ETFs, and liquidity" by Craig W. Holden and Jayoung Nam is now available in Review of Finance, Volume 28, Issue 5. Abstract We develop a stylized model that generates the following empirical predictions: the less (more) accessible the underlying market is ex ante, the more its liquidity improves (deteriorates) when basket trading becomes available. We empirically test these predictions using corporate bonds before and after the introduction of exchange-traded funds. Consistent with the model’s prediction, liquidity improvement is larger for highly arbitraged, low-volume, and high-yield bonds, and for 144A bonds to which retail investor access is prohibited by law. Our article leads to a more nuanced understanding of the impact of basket security introduction than previous research suggested. Dive into the article:https://lnkd.in/epa2wnX7 #reviewoffinance #finance

LLM Experiment on FOMC Transcripts 🧪🤖📊 Start to production: 2 weeks ⏰ Recently, I built a dashboard to test how well an LLM (Finbert & GPT4-o) would perform when guided through a set of iterative questions to make conclusions on FOMC meeting transcripts and what the economic outlook could be. 🎯 Results: • The LLM successfully extracted and understood 42 out of 45 analyzed transcript final Federal Reserve actions! • That's a 93.3% accuracy rate in interpreting nuanced financial decisions. 📈 🛠️ How It Works: • Fetches FOMC meeting transcripts • Utilizes GPT-4 and GPT-4-mini for in-depth Q&A analysis • Extracts expected actions and key discussion points • Detects recurring topics and rates them with sentiment scores using FinBert LLM • Displays relevant economic indicators discussed in each meeting 🔍 Additional Features: • Historical FOMC Meeting Analysis: Quickly browse through past meetings • AI-Powered Summaries: Get concise summaries • Expectation Tracking: Understand potential actions based on met/unmet expectations • Topic Sentiment Analysis: Gauge the tone of discussions on various economic issues • Immediately access the relevant datasets based on the discussed problems Feel free to check it out: https://lnkd.in/ekrXkUbX #AIExperiment #FinTech #DataEngineering #FinancialAnalysis #LLM

'Yield curve momentum' by Markus Sihvonen is now available in Review of Finance, Volume 28, Issue 3. I find that past month Treasury bond returns predict bond returns also in the following month. This time series momentum pattern is easiest to detect at shorter maturities but exists also for longer maturities. The momentum cannot be attributed to measurement error or other trivial frictions and can be profitably exploited e.g. using futures contracts. This momentum can be attributed to a change in the level factor of yields, even though the level of the level factor cannot explain the results. The FOMC drift, i.e. a short term drift pattern in yields after Fed meetings, can also explain some but not all of the observed momentum. Standard term structure models imply a so called spanning condition: no variable should predict bond returns after controlling for information in the term structure today. While these models can in principle generate momentum due to autocorrelation in model factors, they hence predict that the predictive ability of past return vanishes after controlling for sufficiently many yields. Such full spanning holds also in standard macrofinance and behavioral models. I find that past returns predict future returns also controlling for current yields. Moreover, they do so conditional on macroeconomic variables. This violation of the spanning condition poses challenges for standard theoretical explanations for momentum. Keep reading the digest: https://lnkd.in/ekTqGnkc Or read the full article here: https://lnkd.in/eeTQFkiD

Correlation in Financial Markets: A Double-Edged Sword Correlation, a statistical measure that quantifies the relationship between two variables, plays a crucial role in financial markets. When two assets are highly correlated, their prices tend to move together. This can be beneficial for diversification, as it reduces overall risk. However, correlation can also be a double-edged sword. Strengthening Correlation: Several factors can strengthen the correlation between two financial assets: Economic Factors: Economic events, such as interest rate changes or geopolitical tensions, can impact multiple assets simultaneously, increasing their correlation. Market Sentiment: Investor sentiment can drive prices of correlated assets in the same direction, particularly during periods of high volatility. Sectoral Influences: Assets within the same industry or sector often exhibit strong correlation due to shared economic factors and competitive dynamics. Breaking Correlation: Correlation can weaken or break down due to various factors: Diversification: Investing in assets from different sectors or regions can reduce correlation, as they are less likely to be affected by the same events. Company-Specific Factors: Individual company news or performance can cause a divergence in asset prices, even if they were previously highly correlated. Regulatory Changes: New regulations or policy changes can disrupt historical correlations and introduce new relationships between assets. A Famous Example: The Global Financial Crisis The Global Financial Crisis of 2008 provides a stark example of how correlation can break down. Prior to the crisis, mortgage-backed securities (MBS) were considered relatively safe investments, and their correlation with other asset classes was low. However, as the housing market collapsed and the subprime mortgage crisis unfolded, the correlation between MBS and other financial assets soared, leading to a widespread market meltdown.

Beating Financial BS How prevalent is what is known colloquially as financial ‘BS’? And how can you protect yourself against it? A fascinating academic research project, published in the Journal of Behavioural and Experimental Finance, looked at how easy it was to get people to part with their cash by using essentially meaningless jargon. Here is a summary of the findings: https://shorturl.at/inG67 #knightswoodhouse #financialadvice #investmentadvice

 I am pleased to share my article titled "Financial Markets: Classical and Bayesian Approach to Univariate Volatility Models." In this article aimed to examine the univariate volatility models within the framework of the Classical and Bayesian approaches. The study also contributes to the literature by guiding the selection of a better model that takes into account future fluctuations with applications in financial markets. https://lnkd.in/d8PEdveG

Continuing with our series of articles regarding the difficulty for individual traders to consistently beat the market over time, we will today explore how the Law of Large Numbers conspires from a mathematical point of view against that. We talked in our last article about the psychological aspects of trading and how these biases affect performance. But there’s more to that, not only our psyche plays against the attempt goal of consistently beat the market, but math also doesn’t help either. Here’s where the Law of Large Numbers (LLN) comes into play. The LLN is a powerful concept that underpins many statistical methods and real-world applications. It assures us that with enough data, the sample average will provide a reliable estimate of the population mean. There are two main forms of the LLN: the Weak Law of Large Numbers (WLLN) and the Strong Law of Large Numbers (SLLN) ...read more here: https://lnkd.in/dxgbM5dH

'In this paper, we begin to develop a seminal theoretical model, cast in an emotional finance framework, that has a first peek at relating investors’ conscious and unconscious emotions to stock market bubbles and crashes. As this will be the first model of this kind, we will speculate on whether this model, besides being ex post descriptive, can become ex ante predictive.' https://lnkd.in/ewQ7AMRh

I would like to share my recent paper published in Finance and Stochastics, coauthored with Dr. Kexin Chen. This paper is published as an open-access article for free download. Using a duality approach, we investigate under what conditions alternative data are useful for dynamic consumption-investment problems.

I've just finished the course “Algorithmic Trading and Stocks Essential Training” by Michael McDonald! I found the training both interesting and insightful, particularly the application of regression analysis concepts for modelling, which I covered in my first-year Statistics for Economics module. It's application proved to be highly effective at gauging the strength of relationships and  informing trading decisions. Additionally, the course expanded my knowledge of different investing approaches, with in-depth explanations of trading strategies such as factor models, high-frequency trading, and pairs trading. Moving forward, I hope this new knowledge will enhance my understanding of the financial sector on both a macro and microeconomic level. If you would be interested in the course,  a link  to the course can be accessed through my certificate. : https://lnkd.in/e3Ksp2HP #algorithmictrading #stocks.