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 study finds that factor momentum is significant only at short lags compared to stock momentum.
This paper concentrates on the time series momentum or contrarian effects in the Chinese stock market. We evaluate the performance of the time series momentum strategy applied to major stock indices in mainland China and explore the relation between the performance of time series momentum strategies and some firm-speci…
Study finds physical momentum portfolios in Indian stock market yield higher returns than benchmarks.
MiM-StocR combines momentum indicators and adaptive ranking loss for better stock recommendation.
The paper identifies the minimum mean-variance spanning set and its importance in asset evaluation.
p-index approach shows efficient-contrarian strategy outperforms others in low-sentiment periods
DanSmp predicts stock movement using a hybrid-relational MKG and dual attention networks.
The paper explains stock market predictability through a model of heterogeneous beliefs.
We empirically test predictability on asset price by using stock selection rules based on maximum drawdown and its consecutive recovery. In various equity markets, monthly momentum- and weekly contrarian-style portfolios constructed from these alternative selection criteria are superior not only in forecasting directio…
NoxTrader predicts stock returns using LSTM for profitable trading.
We test the price momentum effect in the Korean stock markets under the momentum universe shrinkage to subuniverses of the KOSPI 200. Performance of the momentum strategy is not homogeneous with respect to change of the momentum universe. It is found that some submarkets generate the higher momentum returns than other …
This paper examines momentum spillover across multiple asset classes using only pricing data.
This study examines the presence of the day-of-the-week effect on daily returns of biotechnology stocks over a 16-year period from January 2002 to December 2015. Using daily returns from the NASDAQ Biotechnology Index (NBI), we find that the stock returns were the lowest on Mondays, and compared to the Mondays the stoc…
ChatGPT improves momentum strategies by analyzing news data.
Customer momentum is a positive relationship between a firm's returns and past returns of its customers.
This paper investigates the time-varying risk-premium relation of the Chinese stock markets within the framework of cross-sectional momentum and contrarian effects by adopting the Capital Asset Pricing Model and the French-Fama three factor model. The evolving arbitrage opportunities are also studied by quantifying the…
Investment strategy for NYSE stocks minimizes market correlation.
We propose a mathematical model of momentum risk-taking, which is essentially real-time risk management focused on short-term volatility of stock markets. Its implementation, our fully automated momentum equity trading system presented systematically, proved to be successful in extensive historical and real-time experi…
The paper shows how overreactions in stock prices can be predicted and used for trading.
Enhanced Momentum Transformer outperforms traditional trading strategies.
This paper focuses on the horse race of weekly idiosyncratic momentum (IMOM) with respect to various idiosyncratic risk metrics. Using the A-share individual stocks in the Chinese market from January 1997 to December 2017, we first evaluate the performance of the weekly momentum based on raw returns and idiosyncratic r…
We present a simple dynamical model of stock index returns which is grounded on the ability of the Cyclically Adjusted Price Earning (CAPE) valuation ratio devised by Robert Shiller to predict long-horizon performances of the market. More precisely, we discuss a discrete time dynamics in which the return growth depends…
Study examines stock price reactions to Texas winter storm power outages.
We provide complete source code for a front-end GUI and its back-end counterpart for a stock market visualization tool. It is built based on the "functional visualization" concept we discuss, whereby functionality is not sacrificed for fancy graphics. The GUI, among other things, displays a color-coded signal (computed…
StockGPT predicts stock returns using AI, outperforming traditional strategies.
Gradient boosting detects insider purchases predicting abnormal returns in microcap stocks.
Enhanced AI analysis predicts S&P 500 stock dynamics using various financial metrics.
The price of a given stock is exactly known only at the time of sale when the stock is between the traders. If we know the price (owner) then we have no information on the owner (price). A more general description including cases when we have partial information on both price and ownership is obtained by using the quan…
We consider the problem of neural network training in a time-varying context. Machine learning algorithms have excelled in problems that do not change over time. However, problems encountered in financial markets are often time-varying. We propose the online early stopping algorithm and show that a neural network train…
Trading strategy uses analyst coverage network to outperform markets.
The study of record statistics of correlated series is gaining momentum. In this work, we study the records statistics of the time series of select stock market data and the geometric random walk, primarily through simulations. We show that the distribution of the age of records is a power law with the exponent lyi…
Improved MACD trading strategies with other indicators for better performance.
Crowding is most likely an important factor in the deterioration of strategy performance, the increase of trading costs and the development of systemic risk. We study the imprints of \emph{crowding} on both anonymous market data and a large database of metaorders from institutional investors in the U.S. equity market. …
The starting point of this paper is the so-called Robust Positive Expectation (RPE) Theorem, a result which appears in literature in the context of Simultaneous Long-Short stock trading. This theorem states that using a combination of two specially-constructed linear feedback trading controllers, one long and one short…
This paper studies deep learning methodologies for portfolio optimization in the US equities market. We present a novel residual switching network that can automatically sense changes in market regimes and switch between momentum and reversal predictors accordingly. The residual switching network architecture combines …
LLMs show biases in investment analysis, leading to unreliable recommendations.
AI predicts stock winners with 2.43 Sharpe ratio, but returns are highly concentrated.
Study uses deep learning to predict stock trends with superior performance.
Researchers adaptively analyze market regimes to reveal investor behavior shifts.
PPO optimizes LLM-generated alpha weights for better trading performance.
Quantum effects improve stock option pricing model.
Introduces homotopy momentum sections on multisymplectic manifolds.
The paper analyzes how hyperparameters affect SGD with momentum's convergence rate.
This paper presents generalized momentum mappings for covariant Hamiltonian field theories. The new momentum mappings arise from a generalization of symplectic geometry to , the bundle of vertically adapted linear frames over the bundle of field configurations . Specifically, the generalized field momentum obs…
We give a detailed discussion about existence and uniqueness of Lu's momentum map. More precisely, we introduce the infinitesimal momentum map, and we study its properties. This allows us to describe the theory of reconstruction of the momentum map from the infinitesimal one. We provide the conditions for the uniquenes…
Momentum ResNets improve ResNets' memory efficiency.
We introduce various quantitative and mathematical definitions for price momentum of financial instruments. The price momentum is quantified with velocity and mass concepts originated from the momentum in physics. By using the physical momentum of price as a selection criterion, the weekly contrarian strategies are imp…
New algorithm Momentum-QNG improves optimization of quantum circuits.