Construction of confidence interval for a univariate stock price signal predicted through Long Short Term Memory Network

Text Mining of Stocktwits Data for Predicting Stock Prices

Stock price prediction can be made more efficient by considering the price fluctuations and understanding the sentiments of people. A limited number of models understand financial jargon or have labelled datasets concerning stock price change. To overcome this challenge, we introduced FinALBERT, an ALBERT based model trained to handle financial domain text classification tasks by labelling Stocktwits text data based on stock price change. We collected Stocktwits data for over ten years for 25 different companies, including the major five FAANG (Facebook, Amazon, Apple, Netflix, Google). These …

Stock Price Forecasting and Hypothesis Testing Using Neural Networks

In this work we use Recurrent Neural Networks and Multilayer Perceptrons to predict NYSE, NASDAQ and AMEX stock prices from historical data. We experiment with different architectures and compare data normalization techniques. Then, we leverage those findings to question the efficient-market hypothesis through a formal statistical test.

PriceSeer：实时股票预测中大型语言模型的评估

PriceSeer: Evaluating Large Language Models in Real-Time Stock Prediction

PriceSeer通过实时动态数据评估LLMs在股票预测中的性能，提供数据无污染的基准测试及六种先进模型的多时间范围预测分析。

使用半参数分位数回归研究库存对农作物产量与价格间相关性及收入保险保费的影响

The Impact of Stocks on Correlations between Crop Yields and Prices and on Revenue Insurance Premiums using Semiparametric Quantile Regression

本文提出使用带惩罚B样条的半参数分位数回归估计农作物产量与价格的库存条件联合分布，发现相关性随库存增加负值减弱，并证明忽略该关系会导致收入保险保费定价偏差。

Metaheuristics optimized feedforward neural networks for efficient stock price prediction

The prediction of stock prices is an important task in economics, investment and making financial decisions. This has, for decades, spurred the interest of many researchers to make focused contributions to the design of accurate stock price predictive models; of which some have been utilized to predict the next day opening and closing prices of the stock indices. This paper proposes the design and implementation of a hybrid symbiotic organisms search trained feedforward neural network model for effective and accurate stock price prediction. The symbiotic organisms search algorithm is used as a…

Neural networks for stock price prediction

Due to the extremely volatile nature of financial markets, it is commonly accepted that stock price prediction is a task full of challenge. However in order to make profits or understand the essence of equity market, numerous market participants or researchers try to forecast stock price using various statistical, econometric or even neural network models. In this work, we survey and compare the predictive power of five neural network models, namely, back propagation (BP) neural network, radial basis function (RBF) neural network, general regression neural network (GRNN), support vector machin…