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.
An empirical study of joint bivariate probability distribution of two consecutive price increments for a set of stocks at time scales ranging from one minute to thirty minutes reveals asymmetric structures with respect to the axes y=0, y=x, x=0 and y=-x. All four asymmetry patterns remarkably resemble a four-blade mill…
Study compares price patterns of cryptocurrencies and stocks using machine learning.
New model predicts energy prices under different scenarios.
This paper presents a continuous-time model of intraday trading, pricing, and liquidity with dynamic TWAP and VWAP benchmarks. The model is solved in closed-form for the competitive equilibrium and also for non-price-taking equilibria. The intraday trajectories of TWAP trading targets cause predictable intraday pattern…
A novel method uses blockchain transaction graphs for Bitcoin price prediction.
Model predicts Bitcoin's future movements using multimodal pattern matching.
Study finds recurring patterns in cryptocurrency volatility and liquidity.
The paper combines Bitcoin price models with expert corrections for better predictions.
Adaptive TFTs improve cryptocurrency price prediction accuracy.
The paper proposes a method to identify high-quality financial patterns using entropy.
Enhances trading signals using image analysis and weighted moving averages.
The paper analyzes how market prices respond to information processing and non-linear dynamics.
The paper analyzes financial market turbulence using mathematical physics.
Model shows how heterogeneity in strategies and risk tolerance affects financial market stability.
Grid-scale batteries' bid patterns in price uncertainty markets
The statistical properties of the bid-ask spread of a frequently traded Chinese stock listed on the Shenzhen Stock Exchange are investigated using the limit-order book data. Three different definitions of spread are considered based on the time right before transactions, the time whenever the highest buying price or th…
The position of the EWS (economy-wide substitution)-ratio vector determines the Rybczynski sign pattern, which expresses the factor endowment--commodity output relationships, and the Stolper-Samuelson sign pattern, which expresses the commodity price--factor price relationships in a three-factor two-good general equili…
A new multiagent model of the stock market is formulated that contains four states in which the agents may be located. Next, the model is reformulated in the language of the functional integral containing fluctuations of prices and quantities of cash flows. It is shown that in the functional integral of that type descr…
NFTs with diverse rare attributes sell at higher prices.
Novel approach predicts long-term seasonal component of electricity prices for improved forecasting.
This paper develops a European option pricing formula for fractional market models. Although there exist option pricing results for a fractional Black-Scholes model, they are established without accounting for stochastic volatility. In this paper, a fractional version of the Constant Elasticity of Variance (CEV) model …
Framework explains deep learning candlestick recognition.
ReVol normalizes stock price features to mitigate distribution shifts, improving prediction accuracy.
We seek to deepen understanding of the micro-foundations of institutionalization while contributing to a sociological theory of markets by investigating the puzzle of price bubbles in financial markets. We find that such markets, despite textbook conditions of high efficiency -- perfect information, atomistic agents, n…
A new data-driven model forecasts electricity prices efficiently.
We demonstrate that minority mechanisms arise in the dynamics of markets because of effects of price impact; accordingly the relative importance of minority and delayed majority mechanisms depends on the frequency of trading. We then use minority games to illustrate that a vanishing price return auto-correlation functi…
LSTM networks improve stock price prediction accuracy.
Optimal energy trading strategy for intraday markets using Hawkes processes.
The study reveals distinct patterns in retail investors' holding periods affecting stock returns.
Study analyzes price change patterns across different market capitalizations using Markov chains.
We find empirically a characteristic sharp peak-flat trough pattern in a large set of commodity prices. We argue that the sharp peak structure reflects an endogenous inter-market organization, and that peaks may be seen as local ``singularities'' resulting from imitation and herding. These findings impose a novel strin…
A simple trading model based on pair pattern strategy space with holding periods is proposed. Power-law behaviors are observed for the return variance , the price impact and the predictability for both models with linear and square root impact functions. The sum of the traders' wealth displays a positive v…
Study on price fluctuations and persistence in European electricity spot markets.
New method separates model and non-model risks for more practical asset pricing.
Market Mill is a complex dependence pattern leading to nonlinear correlations and predictability in intraday dynamics of stock prices. The present paper puts together previous efforts to build a dynamical model reflecting the market mill asymmetries. We show that certain properties of the conditional dynamics at a sing…
QGMS framework detects market endpoints using geometric patterns.
Hybrid model predicts stock prices using ML, DL, and NLP.
In this paper we outline initial concepts for an immune inspired algorithm to evaluate price time series data. The proposed solution evolves a short term pool of trackers dynamically through a process of proliferation and mutation, with each member attempting to map to trends in price movements. Successful trackers fee…
Based on the Log-Periodic Power Law (LPPL) methodology, with the universal preferred scaling factor , the negative bubble on the oil market in 2014-2016 has been detected. Over the same period a positive bubble on the so called commodity currencies expressed in terms of the US dollar appears to take place w…
In Europe, Germany is taking the lead in the switch from the conventional to renewable energy. This poses new challenges as wind and solar energy are fundamentally intermittent, weather-dependent and less predictable. It is therefore of considerable interest to investigate the evolution of price volatility in this post…
The main aim of this work is to incorporate selected findings from behavioural finance into a Heterogeneous Agent Model using the Brock and Hommes (1998) framework. Behavioural patterns are injected into an asset pricing framework through the so-called `Break Point Date', which allows us to examine their direct impact.…
This paper uses neural networks to predict stock prices more accurately.
We introduce and study a simple model of a limit order-driven market. Traders in this model can either trade at the market price or place a limit order, i.e. an instruction to buy (sell) a certain amount of the stock if its price falls below (raises above) a predefined level. The choice between these two options is pur…
We propose a new indicator for technical analysis. The indicator emphasizes maximums and minimums in price series with inherent smoothing and has a potential to be useful in both mechanical trading rules and chart pattern analysis.
In order to understand the origin of stock price jumps, we cross-correlate high-frequency time series of stock returns with different news feeds. We find that neither idiosyncratic news nor market wide news can explain the frequency and amplitude of price jumps. We find that the volatility patterns around jumps and aro…
Strategy evaluation schemes are a crucial factor in any agent-based market model, as they determine the agents' strategy preferences and consequently their behavioral pattern. This study investigates how the strategy evaluation schemes adopted by agents affect their performance in conjunction with the market circumstan…
This paper compares machine learning models for pricing European options.
This study predicts stock prices using various machine and deep learning models.