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 evaluates financial anomaly detection methods on Canadian stock market.
Study finds mixed evidence of monthly stock market anomalies in Turkey and US.
Useful alpha returns vanished in modern stock markets.
New methods evaluate stock market anomalies for prospect investors.
New study finds day-of-the-week effects in stock market returns using multifractal analysis.
Detects anomalies in stock and crypto data with high accuracy.
Midterm stock price prediction is crucial for value investments in the stock market. However, most deep learning models are essentially short-term and applying them to midterm predictions encounters large cumulative errors because they cannot avoid anomalies. In this paper, we propose a novel deep neural network Mid-LS…
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…
Topological anomaly scores predict return curves in S&P 500 stocks
Paper uses machine learning to analyze stock market anomalies, predicting drift direction and portfolio performance.
The paper explains stock market predictability through a model of heterogeneous beliefs.
We find that when measured in terms of dollar-turnover, and once -neutralised and Low-Vol neutralised, the Size Effect is alive and well. With a long term t-stat of , the "Cold-Minus-Hot" (CMH) anomaly is certainly not less significant than other well-known factors such as Value or Quality. As compared to marke…
The study finds solar terms significantly impact China's stock market returns and volatility.
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 …
Proposes BA method for unbiased time series anomaly detection evaluation.
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…
Method detects insider trading using trading data and dimensionality reduction.
Adaptive framework predicts stock prices better during volatile periods.
We uncover a large and significant low-minus-high rank effect for commodities across two centuries. There is nothing anomalous about this anomaly, nor is it clear how it can be arbitraged away. Using nonparametric econometric methods, we demonstrate that such a rank effect is a necessary consequence of a stationary rel…
Study detects anomalies in financial markets using GNN and nonextensive entropy.
DeepTrust uses NLP to quickly identify and verify financial anomalies on Twitter.
Study reveals holiday effect on China's time-honored brands, especially alcoholic beverages.
Enhances anomaly detection in financial markets using AI agents.
ReVol normalizes stock price features to mitigate distribution shifts, improving prediction accuracy.
The aim of this study is to investigate quantitatively whether share prices deviated from company fundamentals in the stock market crash of 2008. For this purpose, we use a large database containing the balance sheets and share prices of 7,796 worldwide companies for the period 2004 through 2013. We develop a panel reg…
We report on the occurrence of an anomaly in the price impacts of small transaction volumes following a change in the fee structure of an electronic market. We first review evidence for the existence of a master curve for price impact on the Johannesburg Stock Exchange (JSE). On attempting to re-estimate a master curve…
Study reveals the 2020 U.S. stock crash was endogenous, not caused by COVID.
It seems to be very unlikely that all relevant information in the stock market could be fully encoded in a geometrical shape. Still,the present paper will reveal the geometry behind the stock market transactions. The prices of market index (DJIA) stock components are arranged in ascending order from the smallest one in…
In the standard equilibrium and/or arbitrage pricing framework, the value of any asset is uniquely specified from the belief that only the systematic risks need to be remunerated by the market. Here, we show that, even for arbitrary large economies when the distribution of the capitalization of firms is sufficiently he…
A simple and elegant arrangement of stock components of a portfolio (market index-DJIA) in a recent paper [1], has led to the construction of crossing of stocks diagram. The crossing stocks method revealed hidden remarkable algebraic and geometrical aspects of stock market. The present paper continues to uncover new ma…
The paper predicts and explains the decay of stock anomaly performance over time.
Stock market indices are one of the most investigated complex systems in econophysics. Here we extend the existing literature on stock markets in connection with nonextensive statistical mechanics. We explore the nonextensivity of price volatilities for 34 major stock market indices between 2010 and 2019. We discover t…
Survey on deep learning methods for stock market prediction.
Geography effect is investigated for the Chinese stock market including the Shanghai and Shenzhen stock markets, based on the daily data of individual stocks. The Shanghai city and the Guangdong province can be identified in the stock geographical sector. By investigating a geographical correlation on a geographical pa…
Investor expectations shifted pessimistically during the 2020 stock market crash and recovery.
The study examines network analysis for predicting stock market performance.
We study the temporal evolution of the market efficiency in the stock markets using the complexity, entropy density, standard deviation, autocorrelation function, and probability distribution of the log return for Standard and Poor's 500 (S&P 500), Nikkei stock average index, and Korean composition stock price index (K…
Study examines how economic policy uncertainty impacts stock markets.
Study shows stock market efficiency varies over time and can be networked.
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 …
Study finds long memory in some emerging Asian stocks but not in developed markets.
In a stock market, the numeraire portfolio, if it exists, is the portfolio with the highest expected logarithmic growth rate at all times. A numeraire market is a stock market for which the market portfolio is the numeraire portfolio. We study open markets, markets comprising the higher capitalization stocks within a b…
We investigate the strength and the direction of information transfer in the U.S. stock market between the composite stock price index of stock market and prices of individual stocks using the transfer entropy. Through the directionality of the information transfer, we find that individual stocks are influenced by the …
This paper surveys NLP techniques for predicting stock market movements.
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…
A surprising image of the stock market arises if the price time series of all Dow Jones Industrial Average stock components are represented in one chart at once. The chart evolves into a braid representation of the stock market by taking into account only the crossing of stocks and fixing a convention defining overcros…
Model shows stock markets can be inefficiently mispriced.
Study shows similarities and differences in crypto and equity dynamics during pandemic.