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.
We derive asset pricing formula for markets with incomplete information and subjective views.
AlphaMLDigger predicts excess returns in fluctuating markets.
Proponents of behavioral finance have identified several "puzzles" in the market that are inconsistent with rational finance theory. One such puzzle is the "excess volatility puzzle". Changes in equity prices are too large given changes in the fundamentals that are expected to change equity prices. In this paper, we of…
AlphaZeroBeta uses deep reinforcement learning for market-neutral portfolios, outperforming traditional methods.
Study tests if equity factors explain Bitcoin's risk and returns.
Study finds significant premium for low-beta stocks in firm-level idiosyncratic return distributions.
Regression Trees analyze stock returns, revealing market excess return as the most informative factor.
Sparse portfolio strategy from mutual funds' favorite stocks in China A share market.
Pairs trading strategy fails to outperform market benchmarks, but performs well during bear markets.
This study uses NLP to predict stock performance based on analyst reports.
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…
How an investor invests in the market is largely influenced by the market efficiency because if a market is efficient, it is extremely difficult to make excessive returns because in an efficient market there will be no undervalued securities i.e. securities whose value is less than its assumed intrinsic value, which of…
Study finds key investing characteristics for success in equity markets.
New method uses asymmetric Tsallis relative entropy for better risk assessment in financial portfolios.
Deep learning models improve stock market portfolio returns.
We decompose returns for portfolios of bottom-ranked, lower-priced assets relative to the market into rank crossovers and changes in the relative price of those bottom-ranked assets. This decomposition is general and consistent with virtually any asset pricing model. Crossovers measure changes in rank and are smoothly …
Study finds option volume imbalance predicts equity market returns.
Market inefficiencies arise from density-dependent returns in a noisy environment.
The LLS stock market model is a model of heterogeneous quasi-rational investors operating in a complex environment about which they have incomplete information. We review the main features of this model and several of its extensions. We study the effects of investor heterogeneity and show that predation, competition, o…
The paper derives market-based correlations between asset prices and returns.
Earlier studies have shown that stock market distributions can be well described by distributions derived from Tsallis entropy, which is a generalization of Shannon entropy to non-extensive systems. In this paper, Tsallis relative entropy (TRE), which is the generalization of Kullback-Leibler relative entropy (KLRE) to…
By adopting Multifractal detrended fluctuation (MF-DFA) analysis methods, the multifractal nature is revealed in the high-frequency data of two typical indexes, the Shanghai Stock Exchange Composite 180 Index (SH180) and the Shenzhen Stock Exchange Composite Index (SZCI). The characteristics of the corresponding multif…
We present a simple model of a stock market where a random communication structure between agents gives rise to a heavy tails in the distribution of stock price variations in the form of an exponentially truncated power-law, similar to distributions observed in recent empirical studies of high frequency market data. Ou…
Novel ML approach optimizes large portfolios without covariance matrix issues.
Deep neural network learns meaningful factors to predict stock returns.
Paper uses news data to model asset correlations without market data.
We explore a decomposition in which returns on a large class of portfolios relative to the market depend on a smooth non-negative drift and changes in the asset price distribution. This decomposition is obtained using general continuous semimartingale price representations, and is thus consistent with virtually any ass…
The paper assesses how equity tail risk impacts US Treasury bond returns.
The paper reports the construction of artificial stock market that emerges the similar statistical facts with real data in Indonesian stock market. We use the individual but dominant data, i.e.: PT TELKOM in hourly interval. The artificial stock market shows standard statistical facts, e.g.: volatility clustering, the …
The main objective is to present a some variant of the Black - Litterman model. We consider the canonical case when priori return is determined by means such excess return from the CAPM market portfolio which is derived using reverse optimization method. Then the a priori return is at risk quantified uncertainty. On th…
We discuss the finding that cross-sectional characteristic based models have yielded portfolios with higher excess monthly returns but lower risk than their arbitrage pricing theory counterparts in an analysis of equity returns of stocks listed on the JSE. Under the assumption of general no-arbitrage conditions, we arg…
AI-Trader benchmarks LLMs in live financial markets, revealing poor trading performance.
This paper proposes an empirical test of financial contagion in European equity markets during the tumultuous period of 2008-2011. Our analysis shows that traditional GARCH and Gaussian stochastic-volatility models are unable to explain two key stylized features of global markets during presumptive contagion periods: s…
We present extensive evidence that ``risk premium'' is strongly correlated with tail-risk skewness but very little with volatility. We introduce a new, intuitive definition of skewness and elicit an approximately linear relation between the Sharpe ratio of various risk premium strategies (Equity, Fama-French, FX Carry,…
We find economically and statistically significant gains when using machine learning for portfolio allocation between the market index and risk-free asset. Optimal portfolio rules for time-varying expected returns and volatility are implemented with two Random Forest models. One model is employed in forecasting the sig…
In this paper, we study the dynamics of absolute return, trading volume and bid-ask spread after the trading halts using high-frequency data from the Shanghai Stock Exchange. We deal with all three types of trading halts, namely intraday halts, one-day halts and inter-day halts, of 203 stocks in Shanghai Stock Exchange…
We present a simple model that uses time series momentum in order to construct strategies that systematically outperform their benchmark. The simplicity of our model is elegant: We only require a benchmark time series and several related investable indizes, not requiring regression or other models to estimate our param…
Currency volatility shocks predict lower excess returns, and buying weak transmitters outperforms selling strong ones.
We establish the existence of anomalous excess returns based on trend following strategies across four asset classes (commodities, currencies, stock indices, bonds) and over very long time scales. We use for our studies both futures time series, that exist since 1960, and spot time series that allow us to go back to 18…
Quantum neural network and tensor network models outperform classical models in Japanese stock market predictions.
The geometric Lévy model (GLM) is a natural generalisation of the geometric Brownian motion model (GBM) used in the derivation of the Black-Scholes formula. The theory of such models simplifies considerably if one takes a pricing kernel approach. In one dimension, once the underlying Lévy process has been specified, th…
This note investigates the causes of the quality anomaly, which is one of the strongest and most scalable anomalies in equity markets. We explore two potential explanations. The "risk view", whereby investing in high quality firms is somehow riskier, so that the higher returns of a quality portfolio are a compensation …
I prove that if markets are weak-form efficient, meaning current prices fully reflect all information available in past prices, then P = NP, meaning every computational problem whose solution can be verified in polynomial time can also be solved in polynomial time. I also prove the converse by showing how we can "progr…
The paper analyzes competition among fund managers using excess logarithmic returns and constructs games to find optimal allocations.
MarketSenseAI uses AI to select stocks with 10-30% excess alpha.
We develop a theoretical trading conditioning model subject to price volatility and return information in terms of market psychological behavior, based on analytical transaction volume-price probability wave distributions in which we use transaction volume probability to describe price volatility uncertainty and intens…
Crypto simulations show HODL strategy loads risk onto most investors, with macro-sentiment affecting returns.
The financial crisis of 2008 generated interest in more transparent, rules-based strategies for portfolio construction, with Smart beta strategies emerging as a trend among institutional investors. While they perform well in the long run, these strategies often suffer from severe short-term drawdown (peak-to-trough dec…