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 finds stocks with common firm fears earn lower returns.
The paper integrates behavioral finance into asset pricing using subordinated models.
The value of stocks, indices and other assets, are examples of stochastic processes with unpredictable dynamics. In this paper, we discuss asymmetries in short term price movements that can not be associated with a long term positive trend. These empirical asymmetries predict that stock index drops are more common on a…
We investigate the relationships of the VIX with US and BRIC markets. In detail, we pick up the analysis from the point left off by (Sarwar, 2012), and we focus on the period: Jan 2007 - Feb 2018, thus capturing the relations before, during and after the 2008 financial crisis. Results pinpoint frequent structural break…
We analyze four structured products that have caused severe losses to investors in recent years. These products are: return optimization securities, yield magnet notes, reverse exchangeable securities, and principal-protected notes. We describe the basic structure of these products, analyze them probabilistically using…
Corporate transparency reduces investors' disposition effect by increasing confidence in holding profitable and losing stocks.
Study finds social media investor emotions predict stock prices.
Estimates crypto risk premia using hidden factors and finds significant integration with traditional markets.
In the spirit of behavioral finance, we study the process of opinion formation among investors using a variant of the 2D Voter Model with a tunable social temperature. Further, a feedback acting on the temperature is introduced, such that social temperature reacts to market imbalances and thus becomes time dependent. I…
Researchers infer firm-level supply chain networks from sector-level data to assess systemic risk.
This paper develops a model of reference-dependent assessment of subjective beliefs in which loss-averse people optimally choose the expectation as the reference point to balance the current felicity from the optimistic anticipation and the future disappointment from the realisation. The choice of over-optimism or over…
There are some statistical anomalies in the Chinese stock market, i.e., positive return skewness, anti-leverage effect (positive returns induce higher volatility than negative returns); and reverse volatility asymmetry (contemporaneous return-volatility correlation is positive). In this paper, we first confirm the exis…
Crypto simulations show HODL strategy loads risk onto most investors, with macro-sentiment affecting returns.
Investors in stock market are usually greedy during bull markets and scared during bear markets. The greed or fear spreads across investors quickly. This is known as the herding effect, and often leads to a fast movement of stock prices. During such market regimes, stock prices change at a super-exponential rate and ar…
Fed-FEARE model extracts rules from multiple agencies' data securely.
The Chicago Board Options Exchange (CBOE) Volatility Index, VIX, is calculated based on prices of out-of-the-money put and call options on the S&P 500 index (SPX). Sometimes called the "investor fear gauge," the VIX is a measure of the implied volatility of the SPX, and is observed to be correlated with the 30-day real…
We introduce a dynamic credit portfolio framework where optimal investment strategies are robust against misspecifications of the reference credit model. The risk-averse investor models his fear of credit risk misspecification by considering a set of plausible alternatives whose expected log likelihood ratios are penal…
Many practical environments contain catastrophic states that an optimal agent would visit infrequently or never. Even on toy problems, Deep Reinforcement Learning (DRL) agents tend to periodically revisit these states upon forgetting their existence under a new policy. We introduce intrinsic fear (IF), a learned reward…
Spatial ABM predicts housing market trends in Sydney.
New algorithms learn and interpret asymmetry-labeled DAGs for COVID-19 fear.
Study shows COVID-19 increases stock market crash risk in China.
This paper introduces forward-looking measures of the network connectedness of fears in the financial system, arising due to the good and bad beliefs of market participants about uncertainty that spreads unequally across a network of banks. We argue that this asymmetric network structure extracted from call and put tra…
Cryptocurrency markets show higher spreads during extreme fear and greed phases.
Unified framework linking firm signals and cross-asset spillovers for SDF estimation.
Study finds significant premium for low-beta stocks in firm-level idiosyncratic return distributions.
Using a recently introduced rational expectation model of bubbles, based on the interplay between stochasticity and positive feedbacks of prices on returns and volatility, we develop a new methodology to test how this model classifies 9 time series that have been previously considered as bubbles ending in crashes. The …
Study assesses climate risks on supply chains and financial systems using detailed firm emissions data.
This paper uses machine learning to improve VIX index calculation and detect market manipulation.
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…
We explain the main concepts of Prospect Theory and Cumulative Prospect Theory within the framework of rational dynamic asset pricing theory. We derive option pricing formulas when asset returns are altered with a generalized Prospect Theory value function or a modified Prelec weighting probability function and introdu…
Study on systemic risk in European insurance sector, showing insurer connections during stress.
We take a look the changes of different asset prices over variable periods, using both traditional and spectral methods, and discover universality phenomena which hold (in some cases) across asset classes.
Study examines how industrial emissions evolve over time in response to various factors.
Study uses AI to simulate stock market behavior, revealing how trader psychology affects market stability.
Model assesses how supply chain disruptions affect financial stability.
ML helps select variables for minimum-variance portfolios, reducing risk and improving performance.
The study finds that supply chain information from LLM embeddings improves stock returns predictions.
Investment behavior in wine industry influenced by profitability and capitalization.
In this paper, we establish a link between quantum stochastic processes, and nonlocal diffusions. We demonstrate how the non-commutative Black-Scholes equation of Accardi & Boukas (Luigi Accardi, Andreas Boukas, 'The Quantum Black-Scholes Equation', Jun 2007, available at arXiv:0706.1300v1) can be written in integral f…
Study uses machine learning to analyze Twitter sentiments about COVID-19.
Bilateral CVA as currently implement has the counterintuitive effect of profiting from one's own widening CDS spreads, i.e. increased risk of default, in practice. The unified picture of CVA and liquidity introduced by Morini & Prampolini 2010 has contributed to understanding this. However, there are two significant om…
Zipf's law states that the number of firms with size greater than S is inversely proportional to S. Most explanations start with Gibrat's rule of proportional growth but require additional constraints. We show that Gibrat's rule, at all firm levels, yields Zipf's law under a balance condition between the effective grow…
Higher CEO career breadth correlates with better firm performance.
This paper investigates the effect of cross-shareholding on stock price synchronicity, as a measure of price informativeness, of the listed firms in the Chinese stock market. We gauge firms' levels of cross-shareholdings in terms of centrality in the cross-shareholding network. It is confirmed that it is through a nois…
NoLBERT avoids lookback and lookahead biases for better econometric inference.
The working mathematician fears complicated words but loves pictures and diagrams. We thus give a no-fancy-anything picture rich glimpse into Khovanov's novel construction of `the categorification of the Jones polynomial'. For the same low cost we also provide some computations, including one that shows that Khovanov's…
The waiting time needed for a stock market index to undergo a given percentage change in its value is found to have an up-down asymmetry, which, surprisingly, is not observed for the individual stocks composing that index. To explain this, we introduce a market model consisting of randomly fluctuating stocks that occas…
Bitcoin volatility can be predicted from price and alternative data.