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.
The paper uses DNN for electricity price forecasting and XAI for understanding the factors.
Liberalization of electricity markets has increasingly created the need for understanding the volatility and correlation structure between electricity and financial markets. This work reveals the existence of structural changes in correlation patterns among these two markets and links the changes to both fundamentals a…
StakeBench evaluates language understanding by linking comments to market commitments, improving model alignment with real-world outcomes.
The purpose of this article is to propose a new "theory," the Strategic Analysis of Financial Markets (SAFM) theory, that explains the operation of financial markets using the analytical perspective of an enlightened gambler. The gambler understands that all opportunities for superior performance arise from suboptimal …
Modeling market makers' quoting strategies to understand price impact.
Graph auto-encoders predict stock market instability by measuring graph structure changes.
Study shows tweets about COVID-19 can predict stock market performance.
Macroscopic price evolution models are commonly used for investment strategies. There are first promising achievements in defining microscopic agent based models for the same purpose. Microscopic models allow a deeper understanding of mechanisms in the market than the purely phenomenological macroscopic models, and thu…
This paper uses spectrum analysis to understand price behavior in the Indian stock market.
ABM simulates OTC government bond market dynamics, enhancing liquidity and stability.
Financial markets are well known for their dramatic dynamics and consequences that affect much of the world's population. Consequently, much research has aimed at understanding, identifying and forecasting crashes and rebounds in financial markets. The Johansen-Ledoit-Sornette (JLS) model provides an operational framew…
Catastrophic events, though rare, do occur and when they occur, they have devastating effects. It is, therefore, of utmost importance to understand the complexity of the underlying dynamics and signatures of catastrophic events, such as market crashes. For deeper understanding, we choose the US and Japanese markets fro…
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…
Understanding how funding and 4H context regulate crypto markets.
Paper models Bitcoin market dynamics using 1+1D field theory.
The ultimate value of theories of the fundamental mechanisms comprising the asset price in financial systems will be reflected in the capacity of such theories to understand these systems. Although the models that explain the various states of financial markets offer substantial evidences from the fields of finance, ma…
This study models AI traders' impact on financial markets using a multi-agent framework.
There is intense interest in understanding the stochastic and dynamical properties of the global Foreign Exchange (FX) market, whose daily transactions exceed one trillion US dollars. This is a formidable task since the FX market is characterized by a web of fluctuating exchange rates, with subtle inter-dependencies wh…
With the success of modern machine learning, it is becoming increasingly important to understand and control how learning algorithms interact. Unfortunately, negative results from game theory show there is little hope of understanding or controlling general n-player games. We therefore introduce smooth markets (SM-game…
Research simulates Lloyd's of London's specialty insurance market dynamics.
Dynamic factor analysis reveals insights into Philippine stock market dynamics.
We create a formal framework for the design of informative securities in prediction markets. These securities allow a market organizer to infer the likelihood of events of interest as well as if he knew all of the traders' private signals. We consider the design of markets that are always informative, markets that are …
Recently, several new pari-mutuel mechanisms have been introduced to organize markets for contingent claims. Hanson introduced a market maker derived from the logarithmic scoring rule, and later Chen and Pennock developed a cost function formulation for the market maker. On the other hand, the SCPM model of Peters et a…
We introduce a modular framework for market making. It combines cost-function based automated market makers with bandit algorithms. We obtain worst-case profits guarantee's relative to the best in hindsight within a class of natural "overround" cost functions . This combination allow us to have distribution-free guaran…
Modeling financial markets with sandpile model to understand price volatility and arbitrage constraints.
Monopoly breakup impacts AT&T and AMX's market value by 65% and 32%.
ClusterLOB clusters market events to identify different trading behaviors.
A new approach to the understanding of complex behavior of financial markets index using tools from thermodynamics and statistical physics is developed. Physical complexity, a magnitude rooted in Kolmogorov-Chaitin theory is applied to binary sequences built up from real time series of financial markets indexes. The st…
Study analyzes crude oil futures markets using visibility graphs to understand their structure and dynamics.
A new approach to the understanding of the complex behavior of financial markets index using tools from thermodynamics and statistical physics is developed. Physical complexity, a magnitude rooted in the Kolmogorov-Chaitin theory is applied to binary sequences built up from real time series of financial markets indices…
The understanding of complex systems has become a central issue because complex systems exist in a wide range of scientific disciplines. Time series are typical experimental results we have about complex systems. In the analysis of such time series, stationary situations have been extensively studied and correlations h…
The paper analyzes financial market turbulence using mathematical physics.
Combining neural networks and multiscale decomposition for financial market analysis.
We analyze impermanent loss in AMMs and show G3Ms are simplest.
Atoms and molecules are important conceptual entities we invented to understand the physical world around us. The key to their usefulness lies in the organization of nuclear and electronic degrees of freedom into a single dynamical variable whose time evolution we can better imagine. The use of such effective variables…
Study uses Granger causality to show investor sentiment influences stock prices.
Pari-mutuel markets are trading platforms through which the common market maker simultaneously clears multiple contingent claims markets. This market has several distinctive properties that began attracting the attention of the financial industry in the 2000s. For example, the platform aggregates liquidity from the ind…
The increasing richness in volume, and especially types of data in the financial domain provides unprecedented opportunities to understand the stock market more comprehensively and makes the price prediction more accurate than before. However, they also bring challenges to classic statistic approaches since those model…
Model simulates sparse order books in illiquid markets.
This paper contributes to the literature on international stock market comovements and contagion. The novelty of our approach lies in application of wavelet tools to high-frequency financial market data, which allows us to understand the relationship between stock markets in a time-frequency domain. While major part of…
Study examines how market dynamics affect emissions trading prices and abatement efforts.
Market makers play an important role in providing liquidity to markets by continuously quoting prices at which they are willing to buy and sell, and managing inventory risk. In this paper, we build a multi-agent simulation of a dealer market and demonstrate that it can be used to understand the behavior of a reinforcem…
In this work, we aim to gain a better understanding of the volatility smile observed in options markets through microsimulation (MS). We adopt two types of active traders in our MS model: speculators and arbitrageurs, and call and put options on one underlying asset. Speculators make decisions based on their expectatio…
Research predicts money market volume based on capital market and bank rates ratio.
New model uses symmetries and scaling laws to predict consumer advertising response.
This work models market regimes using CTMSTOU and simulates trading policies.
Visualizes futures markets using particle physics tools.
A phenomenon of the financial log-periodicity is discussed and the characteristics that amplify its predictive potential are elaborated. The principal one is self-similarity that obeys across all the time scales. Furthermore the same preferred scaling factor appears to provide the most consistent description of the mar…