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.
This study reviews techniques to estimate volatility and price Variance Swaps.
Historical returns depend on historical closing prices and distributions. We describe how to compute adjusted closing prices from closing price/distribution data with an emphasis on spreadsheet implementation. Then the growth of a security from one date to another (1 + total return) is just the ratio of the correspondi…
Data-driven method for option pricing using historical asset prices.
Analyzes how rough volatility affects stock pricing and risk premium.
Optimizes bidding strategy for Maker Protocol auctions.
Empirical study on long-term discount rates using historical bond prices.
The paper reviews historical and modern approaches to asset pricing probability measures.
Contextualizing financial news improves stock price predictions.
We investigate whether it is possible to formulate option pricing and hedging models without using probability. We present a model that is consistent with two notions of volatility: a historical volatility consistent with statistical analysis, and an implied volatility consistent with options priced with the model. The…
Study predicts stock prices using historical data and sentiment analysis.
Paper proposes a model to predict stock prices using historical and sentiment data.
In this study, we present a simple stochastic order-book model for investors' swarm behaviors seen in the continuous double auction mechanism, which is employed by major global exchanges. Our study shows a characteristic called "fat tail" is seen in the data obtained from our model that incorporates the investors' swar…
Hybrid model combines PCA and RNN for better aerospace stock price prediction.
This study optimizes stock portfolios using LSTM for historical data analysis.
This paper investigates the impact of pre-existing offline data on online learning, in the context of dynamic pricing. We study a single-product dynamic pricing problem over a selling horizon of periods. The demand in each period is determined by the price of the product according to a linear demand model with unkn…
The study evaluates nine machine learning regressors for predicting NASDAQ stock opening prices.
Study uses machine learning to predict stock prices, finds Kalman filter works well for low-volatility stocks.
Predicts Bitcoin price using Twitter sentiment analysis.
The importance of considering the volumes to analyze stock prices movements can be considered as a well-accepted practice in the financial area. However, when we look at the scientific production in this field, we still cannot find a unified model that includes volume and price variations for stock assessment purposes.…
We revisit the problem of pricing options with historical volatility estimators. We do this in the context of a generalized GARCH model with multiple time scales and asymmetry. It is argued that the reason for the observed volatility risk premium is tail risk aversion. We parametrize such risk aversion in terms of thre…
In this work we are concerned with valuing optionalities associated to invest or to delay investment in a project when the available information provided to the manager comes from simulated data of cash flows under historical (or subjective) measure in a possibly incomplete market. Our approach is suitable also to inco…
Modeling house prices in Australia reveals supply limitations as the primary driver of extreme trends.
The paper uses GRU and self-attention for SPY option pricing.
AI models predict stock trends using historical data and public sentiment.
Research predicts healthcare index movements using historical OHLC data.
The paper develops loss functions for pricing models using observational data.
Statistical dynamics of financial systems is investigated, based on a model of a randomly coupled equation system driven by a stochastic Langevin force. Anticorrelations of price returns, and subdiffusion of prices is found from the model, and and compared with those calculated from historical $/EURO exchange rates.
Deep learning models predict stock prices with high accuracy.
Predict stock price movements using financial data and news articles with LLMs.
Global oil price is an important factor in determining many economic variables in the world's economy. It is generally modeled as a stochastic process and have been studied through different techniques by comparing the historic time series of demand, supply and the price itself. However, there are many historic events …
In this work we use Recurrent Neural Networks and Multilayer Perceptrons to predict NYSE, NASDAQ and AMEX stock prices from historical data. We experiment with different architectures and compare data normalization techniques. Then, we leverage those findings to question the efficient-market hypothesis through a formal…
The paper proposes machine learning models for option pricing without using historical or implied volatility.
In this chapter, we consider volatility swap, variance swap and VIX future pricing under different stochastic volatility models and jump diffusion models which are commonly used in financial market. We use convexity correction approximation technique and Laplace transform method to evaluate volatility strikes and estim…
This research predicts stock market movements using Vision-Language models.
Deep learning models predict stock prices with high accuracy.
The long-term dependence of Bitcoin (BTC), manifesting itself through a Hurst exponent , is exploited in order to predict future BTC/USD price. A Monte Carlo simulation with geometric fractional Brownian motion realisations is performed as extensions of historical data. The accuracy of statistical inferen…
A new data-driven model forecasts electricity prices efficiently.
The paper calculates extreme measures in continuous time conic finance.
It has been recently shown that spot volatilities can be very well modeled by rough stochastic volatility type dynamics. In such models, the log-volatility follows a fractional Brownian motion with Hurst parameter smaller than 1/2. This result has been established using high frequency volatility estimations from histor…
We propose that predictability is a prerequisite for profitability on financial markets. We look at ways to measure predictability of price changes using information theoretic approach and employ them on all historical data available for NYSE 100 stocks. This allows us to determine whether frequency of sampling price c…
Proposes a multi-modal attention network for better stock price prediction.
In this paper we apply active learning algorithms for dynamic pricing in a prominent e-commerce website. Dynamic pricing involves changing the price of items on a regular basis, and uses the feedback from the pricing decisions to update prices of the items. Most popular approaches to dynamic pricing use a passive learn…
The model predicts stock price trends and opening, minimum, and maximum prices with reasonable accuracy.
We extend the classical Cox-Ross-Rubinstein binomial model in two ways. We first develop a binomial model with time-dependent parameters that equate all moments of the pricing tree increments with the corresponding moments of the increments of the limiting Itô price process. Second, we introduce a new trinomial model i…
The study uses historical revenue data to forecast music catalog cashflows and multipliers.
We model bond's price curves corresponding to the sovereign uruguayan debt nominated in USD, as an alternative to the official bond prices publication released by the Central Bank of Uruguay (CBU). Four different gaussian models are fitted, based on historical data issued by the CBU, corresponding to some of the more f…
Paper compares stock price prediction models using Heston and Geometric Brownian Motion.
Cryptocurrency and NFT prices are highly correlated, mirroring historical bubbles.