A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study analyzes Airbnb booking lead times during global crises using a new metric.
Optimizes Airbnb pricing to increase revenue and reduce booking regret.
Study analyzes Airbnb lead-time distributions for Nights Booked and Gross Booking Value, finding divergent shapes and tail behavior.
Research shows a significant increase in stay lengths for digital nomads in the U.S. during and after the pandemic.
This paper analyzes Airbnb listings in the city of San Francisco to better understand how different attributes such as bedrooms, location, house type amongst others can be used to accurately predict the price of a new listing that optimal in terms of the host's profitability yet affordable to their guests. This model i…
Bayesian models predict evolving guest origin markets in tourism.
Bayesian model predicts evolving guest origin markets in tourism.
A commonly expressed concern about the rise of the peer-to-peer rental market Airbnb is that hosts---those renting out their properties---impose costs on their unwitting neighbors. I consider the question of whether apartment building owners will, in a competitive rental market, set a building-specific Airbnb hosting p…
Deep learning is a form of machine learning for nonlinear high dimensional pattern matching and prediction. By taking a Bayesian probabilistic perspective, we provide a number of insights into more efficient algorithms for optimisation and hyper-parameter tuning. Traditional high-dimensional data reduction techniques, …
Paper tackles diversity in Airbnb search results.
Improves deep learning for Airbnb search ranking.
The application to search ranking is one of the biggest machine learning success stories at Airbnb. Much of the initial gains were driven by a gradient boosted decision tree model. The gains, however, plateaued over time. This paper discusses the work done in applying neural networks in an attempt to break out of that …
Pricing a rental property on Airbnb is a challenging task for the owner as it determines the number of customers for the place. On the other hand, customers have to evaluate an offered price with minimal knowledge of an optimal value for the property. This paper aims to develop a reliable price prediction model using m…
We study platforms in the sharing economy and discuss the need for incentivizing users to explore options that otherwise would not be chosen. For instance, rental platforms such as Airbnb typically rely on customer reviews to provide users with relevant information about different options. Yet, often a large fraction o…
The paper examines the reliability of limit order book representations in the face of data perturbation.
The study connects periodic surface homeomorphisms to contact structures using rational open books.
The paper introduces foliated open books for contact 3-manifolds with boundary foliations.
The existing literature provides evidence that limit order book data can be used to predict short-term price movements in stock markets. This paper proposes a new neural network architecture for predicting return jump arrivals in equity markets with high-frequency limit order book data. This new architecture, based on …
Latent order book models have allowed for significant progress in our understanding of price formation in financial markets. In particular they are able to reproduce a number of stylized facts, such as the square-root impact law. An important question that is raised -- if one is to bring such models closer to real mark…
Study integrates deep learning with financial data for improved trading strategies.
Modeling high-frequency order book data with Hawkes-Markovian process.
This paper employs machine learning algorithms to forecast German electricity spot market prices. The forecasts utilize in particular bid and ask order book data from the spot market but also fundamental market data like renewable infeed and expected demand. Appropriate feature extraction for the order book data is dev…
Paper improves deep learning models for limit order book data.
Deep learning predicts Bitcoin spot price movements from order books.
The latent order book of \cite{donier2015fully} is one of the most promising agent-based models for market impact. This work extends the minimal model by allowing agents to exhibit mean-reversion, a commonly observed pattern in real markets. This modification leads to new order book dynamics, which we explicitly study …
Paper examines GCHP for mid-price prediction in financial data.
Through the analysis of a dataset of ultra high frequency order book updates, we introduce a model which accommodates the empirical properties of the full order book together with the stylized facts of lower frequency financial data. To do so, we split the time interval of interest into periods in which a well chosen r…
Graph Neural Network improves volatility forecasting for 500 S&P stocks.
The Tick library simulates and learns Hawkes processes with latency effects.
We introduce the notion of a nested open book, a submanifold equipped with an open book structure compatible with an ambient open book, and describe in detail the special case of a push-off of the binding of an open book. This enables us to explicitly describe a natural open book decomposition of a fibre connected sum …
Forecasting the movements of stock prices is one the most challenging problems in financial markets analysis. In this paper, we use Machine Learning (ML) algorithms for the prediction of future price movements using limit order book data. Two different sets of features are combined and evaluated: handcrafted features b…
Generative tools mimic stock market traders using synthetic data.
Model uses statistical physics principles to predict financial market volatility and returns.
The paper considers a general semi-Markov model for Limit Order Books with two states, which incorporates price changes that are not fixed to one tick. Furthermore, we introduce an even more general case of the semi-Markov model for LimitOrder Books that incorporates an arbitrary number of states for the price changes.…
Deep learning predicts stock price changes in Limit Order Books.
Handles decompositions reveal new open book structures.
Simulates realistic execution and costs in limit order books.
Adaptive learning model forecasts financial prices using order book data.
Financial markets can be described on several time scales. We use data from the limit order book of the London Stock Exchange (LSE) to compare how the fluctuation dominated microstructure crosses over to a more systematic global behavior.
We examine the dynamics of the bid and ask queues of a limit order book and their relationship with the intensity of trade arrivals. In particular, we study the probability of price movements and trade arrivals as a function of the quote imbalance at the top of the limit order book. We propose a stochastic model in an …
The abstract discusses embedding manifolds in open books and contact structures.
We study a coverings of open books and virtually overtwisted contact manifolds using open book foliations. We show that open book coverings produces interesting examples such as transverse knots with depth grater than 1. We also demonstrate explicit examples of virtually overtwisted open books.
Commonly used limit order book attributes are empirically considered based on NASDAQ ITCH data. It is shown that some of them have the properties drastically different from the ones assumed in many market dynamics study. Because of this difference we propose to make a transition from "Statistical" type of order book st…
A new Hawkes process model captures order book dynamics in high-frequency trading.
We show that the statistics of spreads in real order books is characterized by an intrinsic asymmetry due to discreteness effects for even or odd values of the spread. An analysis of data from the NYSE order book points out that traders' strategies contribute to this asymmetry. We also investigate this phenomenon in th…
In this paper, we establish a fluid limit for a two--sided Markov order book model. Our main result states that in a certain asymptotic regime, a pair of measure-valued processes representing the "sell-side shape" and "buy-side shape" of an order book converges to a pair of deterministic measure-valued processes in a c…
An open book decomposition of a 3-manifold induces a Heegaard splitting for , and the minimal genus among all Heegaard splittings induced by open book decompositions is called the \emph{open book genus} of . It is conjectured by Ozbagci \cite{O} that the open book genus is additive under the connected sum of …
New 'book links' generalize braids and plats, proving Markov's theorem.