ST-GAN predicts stock trends using financial news and data.
problem Predicting financial trends in stock markets.
method ST-GAN combines NLP and technical indicators using GAN technology.
result Significant improvement over existing models in stock price forecasting.
Model predicts S&P 500 IT sector index prices with high accuracy.
problem Predicting S&P 500 IT sector index prices accurately.
method Non-linear model using financial and economic indicators.
result Predictive accuracy of 99.4% for S&P 500 IT sector index.
MarketSenseAI uses LLMs to improve stock analysis and outperforms benchmarks.
problem Improving accuracy in stock analysis and selection.
method Combining LLMs with SEC filings, earnings calls, and macroeconomic reports.
result Significant improvement in fundamental analysis accuracy and outperformance of benchmarks.
In an analysis of the US, the UK, and the German stock market we find a change in the behavior based on the stock's beta values. Before 2006 risky trades were concentrated on stocks in the IT and technology sector. Afterwards risky trading takes place for stocks from the financial sector. We show that an agent-based mo…
Application of fuzzy support vector machine in stock price forecast. Support vector machine is a new type of machine learning method proposed in 1990s. It can deal with classification and regression problems very successfully. Due to the excellent learning performance of support vector machine, the technology has becom…
LLMs show biases in investment analysis, leading to unreliable recommendations.
problem LLMs face conflicts between pre-trained knowledge and real-time market data, leading to biases in investment analysis.
method Experimental framework to investigate emergent behaviors in LLMs, analyzing sector, size, and momentum biases.
result Distinct, model-specific biases observed, including a tendency to prefer technology stocks, large-cap stocks, and contrarian strategies.
Advanced ML/DL models predict stock prices using technical analysis.
problem Accurately predicting stock prices in a complex market.
method Use of deep learning models for stock price prediction.
result Deep learning models can predict stock prices with high accuracy.
AI models predict stock trends using historical data and public sentiment.
problem Improving stock market prediction accuracy using AI.
method Employed regression and classification ML algorithms for technical and fundamental analysis respectively.
result Median performance suggests AI is not yet superior to stock markets.
A new framework forecasts stock trends by mining shared information from concepts.
problem Forecasting stock trends using static concept information limits accuracy.
method Proposes a graph-based framework that mines concept-oriented shared information from both predefined and hidden concepts.
result Improves stock trend forecasting performance through dynamic concept relevance and hidden concept information.
ChatGPT predicts stock market reactions from news headlines without financial training.
problem Predicting stock price movements using non-financial data.
method Used post-knowledge-cutoff headlines to train ChatGPT-4, which forecasts stock market reactions.
result ChatGPT-4 can predict stock market reactions with high accuracy, especially for small stocks and negative news.
AI enhances quantitative investment for better returns and risk control.
problem Achieving stable returns through AI in quantitative investment.
method Application of AI technology in quantitative investment strategies.
result AI improves investment performance and risk management.
In this article we review several techniques to extract information from stock market data. We discuss recurrence analysis of time series, decomposition of aggregate correlation matrices to study co-movements in financial data, stock level partial correlations with market indices, multidimensional scaling and minimum s…
Margin trading and short selling boost green tech innovation in China.
problem Encouraging green technology innovation in Chinese companies.
method Quasi-experimental research using panel data of Chinese listed companies, double difference model.
result Margin trading and short selling increase green tech innovation significantly.
Study uncovers financial trends from cross-lingual news data.
problem Understanding financial dynamics across diverse global economies.
method Sentiment analysis, NER, and semantic textual similarity for news articles.
result Meaningful correlation between stock price movements and cross-linguistic news sentiments.
This paper uses cointegration to identify profitable pair-trading strategies for Indian stocks.
problem Finding profitable pair-trading opportunities in Indian stock market.
method Cointegration analysis to identify co-movement stocks, forming pairs, evaluating portfolios.
result Pairs from auto and realty sectors generally yielded the highest returns, while IT sector pairs had negative returns.
In this research the technology of complex Markov chains is applied to predict financial time series. The main distinction of complex or high-order Markov Chains and simple first-order ones is the existing of aftereffect or memory. The technology proposes prediction with the hierarchy of time discretization intervals a…
Predict stock prices using financial news sentiment analysis.
problem Predicting stock market trends for better investment returns.
method Deep Learning (MLP, LSTM, FinBERT-LSTM) integrating news sentiment.
result FinBERT-LSTM model predicts stock prices more accurately.
Predicts stock volatility using Twitter data and random forests.
problem Predicting stock implied volatility using Twitter data.
method Random forests with ablation study on different predictors, including Twitter attention and sentiment features.
result Certain sectors like Consumer Discretionary, Technology, Real Estate, and Utilities are easier to predict.
Study compares price patterns of cryptocurrencies and stocks using machine learning.
problem Investor behavior in cryptocurrencies vs. stocks.
method Machine learning models (LR, RF, SVM) classify price time series of cryptocurrencies and stocks.
result Cryptocurrencies and stocks have distinct price patterns, explained by various statistical features.
Study evaluates digital transformation impact on financial performance using LLMs.
problem Measuring and understanding the impact of digital transformation on financial performance.
method Constructed DT indicators from company reports; analyzed effects of different digital technologies.
result Digital transformation improves financial performance, but varies by technology.
Decentralized finance uses blockchain for $70B in assets, differing from traditional finance.
problem Ensuring compliance and security in decentralized finance.
method Systematic analysis of legal, economic, security, and privacy aspects.
result Decentralized finance offers unique economic effects and security features.
Study compares information flow between Chinese and US stock sectors.
problem Analyzing how information flows between sectors in Chinese and US stock markets.
method Daily sector indices, transfer entropy of daily returns, comparing 2000-2017.
result Most active sectors in information exchange differ between China and US, reflecting market dynamics.
Weak predictability of stock price movement 2 days after annual report disclosure.
problem Predicting stock price movement after annual report disclosure.
method Used various models including decision tree, logistic regression, random forest, neural network, prototypical networks; used financial indicators from EastMoney.
result Maximum accuracy and precision of stock price movement prediction is around 59.6% and 0.56 respectively, with random forest performing best.
A novel SVR parameter optimization method using GSA outperforms other meta-heuristics in stock market forecasting.
problem Optimizing SVR parameters for reliable regression performance on small sample sizes.
method Golden Sine Algorithm (GSA) for parameter tuning of SVR.
result The GSA-based SVR outperforms eleven other meta-heuristics in terms of accuracy and computing time.
The algorithmic trading comes from digitalisation of the processing of trading assets on financial markets. Since 1980 the computerization of the stock market offers real time processing of financial information. This technological revolution has offered processes and mathematic methods to identify best return on trans…
Quantum-enhanced method improves stock return prediction accuracy.
problem Improving precision of stock return forecasting.
method Quantum Gramian Angular Field (QGAF) combining quantum computing and CNNs.
result Significantly improved prediction accuracy (25% MAE, 48% MSE reduction).
Stock market prediction is one of the most attractive research topic since the successful prediction on the market's future movement leads to significant profit. Traditional short term stock market predictions are usually based on the analysis of historical market data, such as stock prices, moving averages or daily re…
A deep learning strategy outperforms traditional methods in stocks portfolio management.
problem Optimizing stock portfolio performance using machine learning.
method Deep Deterministic Policy Gradient framework with neural networks.
result Compound annual return rate of 14.12% compared to 7 other strategies.
Study shows market quality improves with larger orders, not smaller tick sizes or higher trading frequencies.
problem Impact of order book tick sizes, metaorders, and trading frequencies on market quality.
method Multi-agent reinforcement learning model to simulate stock market dynamics.
result Market quality benefits from larger orders but not from smaller tick sizes or higher trading frequencies.
The study predicts stock volatility using LSTM and GARCH models.
problem Accurately predicting stock price volatility is challenging.
method Multiple volatility models (GARCH, GJR-GARCH, EGARCH, LSTM) applied to three sectors.
result LSTM outperformed other models in pharma sector volatility prediction.
Study shows market volatility affects optimal communication design for trading strategies.
problem Investigating how communication impacts trading strategy performance in multi-agent systems.
method 5-agent LLM-based trading systems across 450 experiments spanning 21 months, comparing 5 organizational structures.
result Communication improves performance but depends on market characteristics, with competitive conversation excelling in volatile tech stocks.
Paper proposes a deep learning model to predict stock prices using sentiment analysis.
problem Predicting future stock movement using financial textual and numerical data.
method A blending ensemble deep learning model with two levels of RNNs, LSTM, and GRU followed by a fully connected neural network.
result The model improves prediction accuracy compared to traditional methods.
Proposes LSR-IGRU for improved stock trend prediction.
problem Challenges in stock price prediction due to complex relationships and nonlinear dynamics.
method Long short-term relationships matrix and improved GRU input for better temporal and relationship integration.
result Significantly improved accuracy in predicting stock trend changes.
New model predicts financial market abnormalities using stock index uncertainties.
problem Forecasting abnormal financial fluctuations in the market.
method Quantitative analysis of mean and volatility uncertainties, constructing early warning indicators.
result Established a new abnormal fluctuations warning model.
Digitwashing gap boosts stock crash risk, study finds.
problem The gap between companies' digital promises and actual performance increases stock crash risk.
method Empirical analysis of Shanghai and Shenzhen A-share companies from 2010 to 2021, robustness tests conducted.
result GDT significantly increases stock price crash risk, confirmed by robust tests.
A non-Bayesian time-varying model is developed by introducing the concept of the degree of market efficiency that varies over time. This model may be seen as a reflection of the idea that continuous technological progress alters the trading environment over time. With new methodologies and a new measure of the degree o…
The study distills news sources to analyze stock reactions, finding sentiment has asymmetric and sector-specific effects.
problem Analyzing the influence of financial text sources on stock reactions.
method Mixed text sources from professional platforms, blogs, and message boards were distilled using different lexica to analyze sentiment variables.
result Sentiment has an asymmetric and sector-specific effect on stock reactions.
Improved stock trading model using sentiment analysis and machine learning.
problem Enhancing reinforcement learning models for high-frequency stock trading.
method Combining deep Q network with ARBR sentiment indicator, applying PCA and LSTM, incorporating market sentiment.
result Significantly improved performance in stock trading, achieving a maximum annualized rate of return of 54.5%.
Study tests 11 stylized facts for modern stock markets, finding support for 8.
problem Whether stylized facts from 2001 still hold for modern markets.
method Replicated 11 stylized facts for intraday returns of Dow 30 stocks using authoritative data.
result 8 of 11 stylized facts supported, 3 not supported.
This paper considers a portfolio trading strategy formulated by algorithms in the field of machine learning. The profitability of the strategy is measured by the algorithm's capability to consistently and accurately identify stock indices with positive or negative returns, and to generate a preferred portfolio allocati…
Forecasting stock market direction is always an amazing but challenging problem in finance. Although many popular shallow computational methods (such as Backpropagation Network and Support Vector Machine) have extensively been proposed, most algorithms have not yet attained a desirable level of applicability. In this p…
New algorithm predicts ranked stock lists for long-short portfolios.
problem Constructing effective long-short stock portfolios using machine learning.
method Proposes a new listwise learn-to-rank loss function to emphasize top and bottom of a rank list.
result Demonstrates superior performance in constructing long-short portfolios with a 38% annual return.
Out of the companies, Dolby is the company with the best overall financial and operation health. According to the table that accounted its financial statements for the past three years, Dolby has stable profit margins that generates a revenue in the billions, the only company in ten figures. Corporate competition to ga…
When the full stock of a new product is quickly sold in a few days or weeks, one has the impression that new technologies develop and conquer the market in a very easy way. This may be true for some new technologies, for example the cell phone, but not for others, like the blue-ray. Novelty, usefulness, advertising, pr…
News items have a significant impact on stock markets but the ways are obscure. Many previous works have aimed at finding accurate stock market forecasting models. In this paper, we use text mining and sentiment analysis on Chinese online financial news, to predict Chinese stock tendency and stock prices based on suppo…
Recent research in economic theory attempts to study optimal economic growth and spatial location of economic activity in a unified framework. So far, the key result of this literature - asymptotic convergence, even in the absence of decreasing returns to capital - relies on specific assumptions about the objective of …
The paper examines Bitcoin's nature using fractal geometry and finds it highly persistent, affecting predictability and decentralization.
problem Understanding the nature and predictability of Bitcoin prices.
method Statistical analysis of Bitcoin returns using fractal geometry.
result Bitcoin exhibits high persistence in prices, reducing efficiency but increasing predictability.
Our society has been computerised and globalised due to emergence and spread of information and communication technology (ICT). This enables us to investigate our own socio-economic systems based on large amounts of data on human activities. In this article, methods of treating complexity arising from a vast amount of …