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.
Stock markets show unusual overnight and intraday returns.
Study finds mixed evidence of monthly stock market anomalies in Turkey and US.
Study examines how COVID-19 affected stock and crypto market efficiency.
China's stock market is the largest emerging market all over the world. It is widely accepted that the Chinese stock market is far from efficiency and it possesses possible linear and nonlinear dependence. We study the predictability of returns in the Chinese stock market by employing the wild bootstrap automatic varia…
Study finds long memory in some emerging Asian stocks but not in developed markets.
There is convincing evidence showing that the probability distributions of stock returns in mature markets exhibit power-law tails and both the positive and negative tails conform to the inverse cubic law. It supports the possibility that the tail exponents are universal at least for mature markets in the sense that th…
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…
Research shows SBP's tone impacts stock market returns positively or negatively.
Study uses Kalman-Filter to assess market efficiency in major stock markets.
Regression Trees analyze stock returns, revealing market excess return as the most informative factor.
The study explains stock return distributions using reaction functions.
Study finds investor sentiment has a significant positive relationship with stock returns in Moroccan and Tunisian markets.
Leveraged ETFs can boost returns but increase risk.
The paper analyzes return distribution of Chinese stock market indices over various time scales.
In this paper, we study the determinants of expected returns on the listed penny stocks from two perspectives. Traditionally financial economics literature has been devoted to study the macro and micro determinants of expected returns on stocks (Subrahmanyam, 2010). Very few research has been carried out on penny stock…
The paper tests stock return models and uses LSTM to predict stock returns.
This study empirically re-examines fat tails in stock return distributions by applying statistical methods to an extensive dataset taken from the Korean stock market. The tails of the return distributions are shown to be much fatter in recent periods than in past periods and much fatter for small-capitalization stocks …
This study examines return and risk of Puerto Rico stock market IRA products.
Modeling stock returns is not a new task for mathematicians, investors, and portfolio managers, but it remains a difficult objective due to the ebb and flow of stock markets. One common solution is to approximate the distribution of stock returns with a normal distribution. However, normal distributions place infinites…
Network analysis improves stock return forecasting.
LSTM model predicts stock returns with over 90% accuracy.
The validity of the Efficient Market Hypothesis has been under severe scrutiny since several decades. However, the evidence against it is not conclusive. Artificial Neural Networks provide a model-free means to analize the prediction power of past returns on current returns. This chapter analizes the predictability in …
In this paper we provide compelling evidence of cyclical mean reversion and multiperiod stock return predictability over horizons of about 30 years with a half-life of about 15 years. This implies that the US stock market follows a long-term rhythm where a period of above average returns tends to be followed by a perio…
Machine learning detects foreign stock market signals for U.S. companies.
Wide class of elliptically contoured distributions is a popular model of stock returns distribution. However the important question of adequacy of the model is open. There are some results which reject and approve such model. Such results are obtained by testing some properties of elliptical model for each pair of stoc…
We select the stocks traded in the New York Stock Exchange and we form a statistical ensemble of daily stock returns for each of the trading days of our database from the stock price time series. We study the ensemble return distribution for each trading day and we find that the symmetry properties of the ensem…
EXAMM evolves RNNs for stock return prediction and portfolio trading.
Study confirms Indian stock market is weak form inefficient.
Price fluctuations of commodities like cotton and wheat are thought to display probability distributions of returns that follow a Lévy stable distribution. Recent analysis of stocks and foreign exchange markets show that the probability distributions are not Lévy stable, a plausible result since commodity markets have …
This paper presents a quantitative analysis of the relationship between the stock market returns and corresponding trading volumes using high- frequency data from the Polish stock market. First, for stocks that were traded for suffciently long period of time, we study the return and volume distributions and identify th…
Study tests 11 stylized facts for modern stock markets, finding support for 8.
Study finds significant premium for low-beta stocks in firm-level idiosyncratic return distributions.
Study shows negative stock returns after Moroccan companies issue profit warnings.
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…
This paper examines the possibility of using derivative-implied risk premia to explain stock returns. The rapid development of derivative markets has led to the possibility of trading various kinds of risks, such as credit and interest rate risk, separately from each other. This paper uses credit default swaps and equi…
We present a finite-dimensional version of the quantum model for the stock market proposed in [C. Zhang and L. Huang, A quantum model for the stock market, Physica A 389(2010) 5769]. Our approach is an attempt to make this model consistent with the discrete nature of the stock price and is based on the mathematical for…
Skewness dispersion predicts future stock market returns, especially in months with monetary policy announcements.
News explains most overnight stock market gains.
Time-varying neural network improves stock return prediction.
Using a rolling windows analysis of filtered and aligned stock index returns from 40 countries during the period 2006-2014, we construct Granger causality networks and investigate the ensuing structure of the relationships by studying network properties and fitting spatial probit models. We provide evidence that stock …
Study compares cryptocurrency and stock markets using statistical equilibrium models.
Sparse portfolio strategy from mutual funds' favorite stocks in China A share market.
Python models predict stock sentiment for market-beating returns.
We shortly review the statistical properties of the escape times, or hitting times, for stock price returns by using different models which describe the stock market evolution. We compare the probability function (PF) of these escape times with that obtained from real market data. Afterwards we analyze in detail the ef…
In this paper, we address one of the main puzzles in finance observed in the stock market by proponents of behavioral finance: the stock predictability puzzle. We offer a statistical model within the context of rational finance which can be used without relying on behavioral finance assumptions to model the predictabil…
Study finds physical momentum portfolios in Indian stock market yield higher returns than benchmarks.
It is known that the impact of transactions on stock price (market impact) is a concave function of the size of the order, but there exists little quantitative theory that suggests why this is so. I develop a quantitative theory for the market impact of hidden orders (orders that reflect the true intention of buying an…
News novelty predicts negative stock market returns.