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 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 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 …
Large and stable indices of the world wide stock markets such as NYSE and SP 500 together with NASDAQ -- the index representing markets of new trends, and WIG -- the index of the local stock market of Eastern Europe, are considered. Due to the relation between artificial insymmetrised patterns (AIP) and time series, st…
Study shows bifurcating price dynamics in ASME with traders.
AI models predict stock trends using historical data and public sentiment.
Sequential processing biases asset allocation in artificial stock markets.
AI algorithms outperform traditional trading methods in stock markets.
In this article, we established a stock market model based on agents' investing mentality. The agents decide whether to purchase the shares at the probability, according to their anticipation of the market's behaviors. The expectation of the amount of shares they want to buy is directly proportional to the value of ass…
Predicting the prices of stocks at any stock market remains a quest for many investors and researchers. Those who trade at the stock market tend to use technical, fundamental or time series analysis in their predictions. These methods usually guide on trends and not the exact likely prices. It is for this reason that A…
We demonstrate that the gain/loss asymmetry observed for stock indices vanishes if the temporal dependence structure is destroyed by scrambling the time series. We also show that an artificial index constructed by a simple average of a number of individual stocks display gain/loss asymmetry - this allows us to explicit…
Using virtual stock markets with artificial interacting software investors, aka agent-based models (ABMs), we present a method to reverse engineer real-world financial time series. We model financial markets as made of a large number of interacting boundedly rational agents. By optimizing the similarity between the act…
We applied Deep Q-Network with a Convolutional Neural Network function approximator, which takes stock chart images as input, for making global stock market predictions. Our model not only yields profit in the stock market of the country where it was trained but generally yields profit in global stock markets. We train…
An artificial stock market is established based on multi-agent . Each agent has a limit memory of the history of stock price, and will choose an action according to his memory and trading strategy. The trading strategy of each agent evolves ceaselessly as a result of self-teaching mechanism. Simulation results exhibit …
This paper evaluates random forest models for predicting stock price trends.
This research evaluates the performance of an Artificial Neural Network based prediction system that was employed on the Shanghai Stock Exchange for the period 21-Sep-2016 to 11-Oct-2016. It is a follow-up to a previous paper in which the prices were predicted and published before September 21. Stock market price predi…
This paper proposes a novel trading system which plays the role of an artificial counselor for stock investment. In this paper, the stock future prices (technical features) are predicted using Support Vector Regression. Thereafter, the predicted prices are used to recommend which portions of the budget an investor shou…
Weibo experts predict stock market better than non-experts.
Simulation reveals relationships in stock market pyramid schemes.
This paper compares LSTM, GRU, and Transformer models for stock price prediction.
A new concept, called balanced estimator of diffusion entropy, is proposed to detect scalings in short time series. The effectiveness of the method is verified by means of a large number of artificial fractional Brownian motions. It is used also to detect scaling properties and structural breaks in stock price series o…
Far-from-equilibrium models of interacting particles in one dimension are used as a basis for modelling the stock-market fluctuations. Particle types and their positions are interpreted as buy and sell orders placed on a price axis in the order book. We revisit some modifications of well-known models, starting with the…
Stock exchanges are considered major players in financial sectors of many countries. Most Stockbrokers, who execute stock trade, use technical, fundamental or time series analysis in trying to predict stock prices, so as to advise clients. However, these strategies do not usually guarantee good returns because they gui…
LSTM model predicts stock returns with over 90% accuracy.
Study shows maker-taker fees improve market efficiency but increase costs.
Paper predicts stock market values using machine learning.
The study evaluates various ML models for stock market prediction.
Study compares ANN and GARCH models for volatility prediction across sectors.
Study shows negative war news correlates with increased stock market volatility.
The paper compares advanced deep learning models for Indian stock price forecasting.
We review the recent approach of correlation based networks of financial equities. We investigate portfolio of stocks at different time horizons, financial indices and volatility time series and we show that meaningful economic information can be extracted from noise dressed correlation matrices. We show that the metho…
New attack targets speech-based AI models via stock market data.
Study uses OT to simulate markets, revealing power-law returns are driven by informational effect.
Paper proposes TDQN, a DRL strategy for optimal stock trading.
In this paper, we model the impact of oil price volatility on Tehranstock and industry indices in two periods of international sanctions and post-sanction. To analyse the purpose of study, we use Feed-forward neural net-works. The period of study is from 2008 to 2018 that is split in two periods during international en…
Model predicts stock market trends for better investment decisions.
A new trading model combines GARCH and PPO for better stock trading profits.
This paper proposes a trading strategy using TD3 for stock and cryptocurrency markets.
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…
Improved stock trading model using sentiment analysis and machine learning.
The model predicts stock price trends and opening, minimum, and maximum prices with reasonable accuracy.
Study uses AI to predict changes in international public finances based on US markets.
AI learns market manipulation through simulation, suggesting regulation.
We apply a simple trading strategy for various time series of real and artificial stock prices to understand the origin of fractality observed in the resulting profit landscapes. The strategy contains only two parameters and , and the sell (buy) decision is made when the log return is larger (smaller) than (…
Improved DRQN-ARBR model for better stock trading performance.
Study uses XAI and transformers for stock price prediction of top 100 BIST banks.
Study compares price limit and circuit breaker effects in stock markets.
CNNs identify stock market trend endpoints based on expert opinion.
AI enhances quantitative investment for better returns and risk control.