Proposes a new model to measure trade impact and information content in fluctuating markets.
problem Measuring price impact and information content of trades in a time-varying market setting.
method Non-linear observation-driven model for dynamically estimating market impact and information content.
result Market impact shows intraday patterns with large fluctuations, some of which are exogenous.
Study shows monetary policy impacts digital assets like BTC and ETH.
problem Impact of monetary policy on digital assets and DeFi.
method Event study regressions and high-frequency price analysis.
result Monetary policy surprises negatively affect BTC and ETH but not other digital assets.
We study the role of co-jumps in the interest rate futures markets. To disentangle continuous part of quadratic covariation from co-jumps, we localize the co-jumps precisely through wavelet coefficients and identify statistically significant ones. Using high frequency data about U.S. and European yield curves we quanti…
We study the behavior of U.S. markets both before and after U.S. Federal Open Market Committee (FOMC) meetings, and show that the announcement of a U.S. Federal Reserve rate change causes a financial shock, where the dynamics after the announcement is described by an analogue of the Omori earthquake law. We quantify th…
Non-spanning identification of scheduled event risk in option pricing.
problem Separating continuous surface from scheduled jump in option pricing.
method Modeling FOMC decisions, CPI releases, and NFP reports as deterministic-time jumps in risk-neutral option pricing.
result Improves held-out event-spanning pricing with Gaussian and two-component mixture jumps.
Large financial dataset tracks FOMC communications and their impact.
problem Understanding how FOMC communications influence financial markets.
method Constructed a large annotated dataset of FOMC speeches, minutes, and transcripts. Developed a hawk-dove classification task. Evaluated various models on the dataset and used RoBERTa-large for monetary policy stance measurement.
result Monetary policy stance measures derived from FOMC documents predict market performance.
New tool uses computer vision to assess FOMC press conference complexity and its impact on equity returns.
problem Understanding the impact of FOMC press conferences on financial markets.
method Developed a measure of discussion complexity using video images and deep learning algorithms.
result Complex discussions during press conferences are associated with higher equity returns and lower volatility.
Proposes a Structural Matrix Autoregressive model for joint analysis of asset returns, realized volatility, and trading volume.
problem Joint analysis of asset returns, realized volatility, and trading volume
method Structural Matrix Autoregressive model
result Volatility is primary driver of trading activity, with informational shocks incorporated through price variability.
FedSight AI predicts federal funds rate using LLMs and multi-agent reasoning.
problem Predicting Federal Open Market Committee's decisions on federal funds rate.
method Multi-agent framework with large language models, structured and unstructured inputs, and CoD extension for efficient reasoning.
result Achieved 93.75% accuracy and 93.33% stability in predicting FOMC outcomes.
This study models FOMC policy decisions using debate-based LLMs.
problem Accurately predicting central bank policy decisions, especially FOMC's, is challenging.
method A novel framework that simulates FOMC's collective decision-making process through iterative rounds of LLMs interacting as agents.
result The debate-based approach significantly outperforms standard LLMs in prediction accuracy.
Study finds monetary policy uncertainty negatively impacts Bitcoin returns.
problem Impact of monetary policy and uncertainty on cryptocurrencies market.
method Markov Switching Means VAR (MSM-VAR) method.
result Monetary policy uncertainty leads to a decline in Bitcoin returns.
Enhanced regime shifts detection using unstructured text and financial data.
problem Detecting regime shifts in financial markets is challenging due to noisy and multicollinear data.
method Combines LLM reasoning on unstructured text and statistical validation on financial time series.
result Framework achieves F1 score of 0.82, outperforming pure data-driven methods.
Investor emotions predict earnings announcements, but excitement lowers returns.
problem The impact of investor emotions on earnings announcements and their returns.
method Social media data analysis over a decade to test the relationship between investor emotions and earnings announcements.
result Excitement about earnings announcements is associated with lower announcement returns.
This study examines how earnings announcements affect option volatility and pricing.
problem The impact of earnings announcements on option volatility and pricing.
method Analysis of extremely short-term options data to study bimodality and concavity in IV curves.
result Investors pay a premium to hedge against extreme volatility during earnings announcements in the presence of concave IV smiles.
Agol's announcement proved a full classification of certain Kleinian groups.
problem Classifying non-free Kleinian groups generated by two parabolic transformations.
method Full proof of Agol's announcement.
result Classification of non-free Kleinian groups generated by two parabolic transformations.
The study classifies policy announcements' impact on stock market volatility.
problem Evaluating the impact of Central Bank announcements on stock market volatility.
method Proposed a model-based classification method using Markov Switching dynamics and Multiplicative Error Model.
result Successful classification of 144 European Central Bank announcements on stock market volatility.
Predicts stock price changes based on clinical trial announcements.
problem Forecasting the impact of clinical trial results on pharma stock prices.
method BERT for sentiment analysis, Temporal Fusion Transformer for forecasting, graph convolution network for event relationships, gradient boosting for price change prediction.
result Identifies two crucial factors: drug portfolio size and network effect of related events.
This an announcement for the generalized asymptotic expansion of Tian-Yau-Zeldtich.
Media tone around earnings announcements predicts stock returns.
problem Determining if media tone around earnings announcements provides useful information for stock prices.
method Conducted an event study on media tone around earnings announcements for nonfinancial S&P 500 firms.
result Media tone around earnings announcements predicts abnormal stock returns.
The above title is the same, but with "semisimple" instead of "simple," as that of a notice by N. Kowalsky. There, she announced many theorems on the subject of actions of simple Lie groups preserving a Lorentz structure. Unfortunately, she published proofs for essentially only half of the announced results before her …
This study analyzes how the Indian stock market reacts to budget announcements using fractal methods.
problem Understanding the impact of Union Budget announcements on the Indian stock market.
method Utilizes fractal interpolation function and fractal dimensional analysis to study the NIFTY50 index over -15 to +15 days post-budget day.
result The budget announcements significantly affect the Indian stock market, as evidenced by average abnormal return and cumulative abnormal return.
We study an option pricing framework that accounts for the price impact of an earnings announcement (EA), and analyze the behavior of the implied volatility surface prior to the event. On the announcement date, we incorporate a random jump to the stock price to represent the shock due to earnings. We consider different…
This paper introduces a non-parametric framework to statistically examine how news events, such as company or macroeconomic announcements, contribute to the pre- and post-event jump dynamics of stock prices under the intraday seasonality of the news and jumps. We demonstrate our framework, which has several advantages …
We present a Hawkes model approach to foreign exchange market in which the high frequency price dynamics is affected by a self exciting mechanism and an exogenous component, generated by the pre-announced arrival of macroeconomic news. By focusing on time windows around the news announcement, we find that the model is …
This thesis identifies share buybacks and predicts their impact on stock performance.
problem Recognizing and predicting the impact of share buybacks on stock performance.
method NLP approaches for automated detection of share buybacks, machine learning models for prediction.
result Most companies underperform after a share buyback, but some significantly outperform.
Considered an important macroeconomic indicator, the Purchasing Managers' Index (PMI) on Manufacturing generally assumes that PMI announcements will produce an impact on stock markets. International experience suggests that stock markets react to negative PMI news. In this research, we empirically investigate the stock…
Study predicts stock price direction on earnings announcement days using multi-modal deep learning.
problem Predicting stock price movements during earnings announcements is challenging due to market noise and discontinuities.
method Constructed a multi-modal feature space combining fundamental metrics, technical indicators, and sentiment scores from financial news articles. Evaluated LSTM and Transformer models against a baseline.
result Transformer model outperforms LSTM in identifying volatile movements, achieving higher macro F1-score.
Study finds financial YouTube channel 3PROTV predicts stock market performance and sentiment changes.
problem Determining the informational value of financial YouTube channels.
method Analyzing 3PROTV's content and its impact on stock market performance and sentiment.
result 3PROTV's content, particularly negative sentiment, predicts stock market performance and sentiment changes.
New framework predicts earnings announcements using press release content, surpassing earnings surprises.
problem Predicting stock returns based on earnings press releases.
method Compared traditional and BERT-based embeddings of press releases, finding content as informative as earnings surprises.
result FinBERT yields highest predictive power for earnings announcement returns.
The attack intensity of distributed denial of service (DDoS) attacks is increasing every year. Botnets based on internet of things (IOT) devices are now being used to conduct DDoS attacks. The estimation of direct and indirect economic damages caused by these attacks is a complex problem. One of the indirect damage of …
This paper quantifies the impact of Dow Jones Sustainability Index listing on stock returns.
problem Measuring the impact of listing on the Dow Jones Sustainability Index.
method A matching portfolio approach to analyze pre- and post-listing returns.
result Cumulative abnormal returns are significantly positive in the weeks leading to the official announcement.
Green bond leaks impact equity markets, altering investor reactions.
problem Green bond leaks affect equity market reactions.
method Identified 259 instances of pre-announcement leaks in 2,036 green bond headlines.
result News leaks significantly alter equity trading dynamics and investor reactions.
This is a research announcement of the theory of orbifold quantum cohomology.
The purpose of this note is to announce complete answers to the following questions. (1) For an essential simple loop on a 2-bridge sphere in a 2-bridge link complement, when is it null-homotopic in the link complement? (2) For two distinct essential simple loops on a 2-bridge sphere in a 2-bridge link complement, when…
The study reveals distinct patterns in retail investors' holding periods affecting stock returns.
problem Understanding the impact of retail investors' investment horizons on stock returns.
method Using self-reported holding periods from StockTwits, the study categorizes retail investors into long-horizon and short-horizon groups and analyzes their return patterns.
result Long-horizon retail investors exhibit underreaction to earnings announcements, while short-horizon investors show overreaction.
This paper gives a complete proof of the result announced in the title.
Paper uses machine learning to analyze stock market anomalies, predicting drift direction and portfolio performance.
problem Capturing dynamics of Post-Earnings-Announcement Drift (PEAD) using machine learning.
method Uses Extreme Gradient Boosting (XGBoost) with genetic algorithm optimization to analyze PEAD dynamics.
result Demonstrates how PEAD dynamics are influenced by different factors across sectors and quarters.
In this paper, we prove that each automorphism of the Torelli group of a surface is induced by a diffeomorphism of the surface, provided that the surface is a closed, connected, orientable surface of genus at least 3. This result was previously announced by Benson Farb for genus at least 4 and has recently been announc…
In a unified framework we study equilibrium in the presence of an insider having information on the signal of the firm value, which is naturally connected to the fundamental price of the firm related asset. The fundamental value itself is announced at a future random (stopping) time. We consider two cases. First when t…
We give a complete proof of results announced by Hirasawa and Sakuma describing explicitly the Kakimizu complex of a non-split, prime, special, alternating link.
In this paper we announce the following result: ``Every manifold of dimension ≥3 admits a complete negatively Ricci curved metric.'' Furthermore we describe some sharper results and sketch proofs.
This is the first of a series of three papers which provide proofs of results announced recently in arXiv:1210.7494.
We outline a framework which generalizes Felix Klein's Erlanger Programm which he announced in 1872 after exchanging ideas with Sophus Lie.
Study generalizes finiteness theorem using Lie theory.
problem Finiteness theorem in Hadwiger-Alesker theory.
method Generalization through Lie theory.
result Generalization of finiteness theorem.
Despite the robust structure of the Internet, it is still susceptible to disruptive routing updates that prevent network traffic from reaching its destination. Our research shows that BGP announcements that are associated with disruptive updates tend to occur in groups of relatively high frequency, followed by periods …
In this paper, which is the continuation of [EFW2], we complete the proof of the quasi-isometric rigidity of Sol and the lamplighter groups. The results were announced in [EFW1].
Skewness dispersion predicts future stock market returns, especially in months with monetary policy announcements.
problem Predicting future stock market returns using skewness dispersion.
method Cross-sectional analysis of firm-level realized skewness and stock market returns.
result Skewness dispersion is a significant predictor of future stock market returns, robust to various estimation methods.
Study shows SEC crypto classification led to significant market reactions.
problem Impact of SEC classification of crypto assets as securities.
method Event study methodology focusing on explicitly named crypto assets.
result Significant adverse market reactions, with returns plummeting 12% over one week.