Study predicts soccer player market values using machine learning and SHAP for interpretability.
problem Predicting accurate market values for professional soccer players.
method Ensemble machine learning models, SHAP for interpretability, Boruta for feature selection.
result GBDT model achieved high predictive accuracy (R-squared 0.901, RMSE 3,221,632.175).
Market forecasts converge to true values if some agents are correct.
problem Convergence of market forecasts in dynamic prediction markets.
method Dynamic model of prediction market with agents making forecasts.
result Aggregated market forecasts converge to conditional expectations.
We propose the development of a prediction market for forecasting prices for "toxic assets" to be transferred from Irish banks to the National Asset Management Agency (NAMA). Such a market allows market participants to assume a stake in a security whose value is tied to a future event. We propose that securities are cr…
Paper predicts stock market values using machine learning.
problem Predicting stock market values for Tehran stock exchange groups.
method Used machine learning algorithms including Decision Tree, Bagging, Random Forest, Adaptive Boosting, Gradient Boosting, XGBoost, Artificial neural network, Recurrent Neural Network, and Long short-term memory (LSTM).
result LSTM shows highest accuracy among all algorithms tested.
In this paper, we formulate a method for minimising the expectation value of the procurement cost of electricity in two popular spot markets: {\it day-ahead} and {\it intra-day}, under the assumption that expectation value of unit prices and the distributions of prediction errors for the electricity demand traded in tw…
Cubic predicts stock market indices by fusing stock latent embeddings and converting to binary classification.
problem Challenges in predicting stock market indices due to isolated time series treatment and simple regression.
method Fusion of stock latent embeddings, binary encoding classification, and confidence-guided prediction.
result Cubic outperforms state-of-the-art baselines in stock index prediction tasks.
The paper explains stock market predictability through a model of heterogeneous beliefs.
problem Understanding and predicting stock market behavior based on news and investor beliefs.
method A discrete-time model of heterogeneous beliefs where some agents receive noisy signals about asset fundamentals.
result Momentum and reversal in stock prices arise from investors' incorrect beliefs about signal accuracy and fundamental values.
Financial market prediction on the basis of online sentiment tracking has drawn a lot of attention recently. However, most results in this emerging domain rely on a unique, particular combination of data sets and sentiment tracking tools. This makes it difficult to disambiguate measurement and instrument effects from f…
The paper explores how market-based returns depend on past trade values.
problem Improving accuracy in forecasting market-based average and volatility of returns.
method Derives the dependence of market-based volatility and higher statistical moments of returns on statistical moments and correlations of current and past trade values.
result Market-based statistical moments can be approximated by a finite number of moments, improving forecast reliability.
MANA-Net improves market predictions by dynamically weighting news sentiments.
problem Aggregated Sentiment Homogenization in financial news data.
method Dynamic market-news attention mechanism to aggregate sentiments.
result MANA-Net outperforms recent market prediction methods by 1.1% Profit & Loss and 0.252 daily Sharpe ratio.
The study challenges the reliability of VaR due to market randomness.
problem Reliability and accuracy of VaR predictions are compromised by market randomness.
method Introduces market-based probabilities of price and return, dependent on trade values and volumes.
result Market-based price volatility is more accurate than frequency-based VaR predictions.
The paper examines how market trade values and volumes affect price autocorrelation.
problem Understanding the impact of market trade values and volumes on price autocorrelation.
method Derives the dependence of price statistical moments and volatility on trade values and volumes, and assesses statistical moments and correlations by conventional frequency-based probabilities.
result Highlights the impact of market trade randomness on price statistical moments and autocorrelation.
FS-GCLSTM predicts stock returns by leveraging value-chain relationships.
problem Traditional time series models fail to capture complex interdependencies in modern markets.
method FS-GCLSTM integrates value-chain networks and graph convolutions to predict stock returns.
result FS-GCLSTM consistently delivers superior portfolio performance compared to traditional models.
The paper assesses how equity tail risk impacts US Treasury bond returns.
problem The effects of equity tail risk on the US government bond market.
method Estimating equity tail risk using option-implied stock market volatility and assessing its predictive power in reduced-form regressions and a term structure model.
result Equity tail risk significantly predicts one-month excess returns on Treasuries.
Causal-NECO VaR improves financial risk assessment under market turbulence.
problem Inaccurate risk assessment in volatile markets.
method Causal Network Contagion Value at Risk (Causal-NECO VaR) using causal network framework.
result Robust and invariant predictive power in unstable financial environments.
QLSTM outperforms LSTM in predicting KSE 100 index movements.
problem Predicting stock market movement in uncertain economic conditions.
method Used LSTM and QLSTM models on monthly data of economic indicators.
result QLSTM provided more accurate predictions of KSE 100 index values.
This paper surveys NLP techniques for predicting stock market movements.
problem Predicting stock market movements with volatility, seasonality, and time dependence.
method Survey of recent literature in NLP and ML for stock market prediction.
result Illustrates recent trends in stock market prediction research.
ChatGPT predicts stock trends from Twitter sentiment, showing positive effects.
problem Predicting stock market trends using social media sentiment.
method Used ChatGPT for sentiment analysis of Twitter posts about Microsoft and Google.
result ChatGPT's predictions correlated positively with stock performance.
The DeepSurv model predicts purchase timing better than other survival models.
problem Predicting the exact purchase timing of consumers.
method Survival models (Kernel SVM, DeepSurv, Survival Random Forest, MTLR) were compared using various consumer attributes.
result DeepSurv model outperformed other models in predicting purchase completion.
This research predicts cryptocurrency price volatility using deep learning models.
problem Predicting the volatility of cryptocurrency prices to mitigate investment risk.
method Used CNN, LSTM, BiLSTM, and GRU models to predict the risk factor of 20 cryptocurrency parameters.
result Developed a new model with RMSE of 0.0089, significantly outperforming existing models.
Framework optimizes battery storage for markets by separating long-term degradation from short-term market dynamics.
problem Intractable computation due to timescale mismatch between battery degradation and market dynamics.
method Approximate dynamic programming with value function approximation and pseudo-time encoding.
result Policy outperforms benchmarks in real-time market scenarios.
Market-based asset price probability depends on trade volumes and values, improving forecasts and reliability.
problem Limited accuracy of frequency-based asset price statistical moments.
method Derive market-based variance and 3rd statistical moment from trade values and volumes, accounting for trade volume randomness.
result Market-based statistical moments improve price probability forecasts and reliability.
The study examines how market trade randomness influences price and return volatility.
problem The accuracy of predicting market-based volatilities and macroeconomic variables is limited.
method Analyzes time series of trade values and volumes, and develops econometric methodologies for predicting volatilities.
result Current macroeconomic models underestimate the accuracy of predicting market-based volatilities and macroeconomic variables.
Study finds financial YouTube channel 3PROTV predicts stock market performance and sentiment changes.
problem Determining the informational value of financial YouTube channels.
method Analyzing 3PROTV's content and its impact on stock market performance and sentiment.
result 3PROTV's content, particularly negative sentiment, predicts stock market performance and sentiment changes.
Private equity deals predict public market returns with up to 70% accuracy.
problem Predicting public market behavior from private equity transactions.
method Logit model using detailed analysis of private equity diligence process.
result Model predicts public market returns with up to 70% accuracy in specific sectors.
Proposes using diffusion models for probabilistic stock market predictions.
problem Uncertainties in financial data make deterministic models ineffective for stock market predictions.
method Utilizes Denoising Diffusion Probabilistic Models (DDPM) and Masked Relational Transformer (MRT).
result Achieves state-of-the-art performance in stock movement prediction and portfolio management.
This paper tackles missing data in Burundian bond market yield curves.
problem Missing data challenges accurate yield curve construction in Burundian sovereign bond market.
method Exploration of data limitations, proposing and testing various imputation methods (LR, Previous value, miss-Forest, Next value).
result Linear Regression method performs best across variables, approximating normal distribution for error values.
Study finds TVL doesn't predict cryptocurrency returns.
problem Assumption of TVL predicting returns in crypto markets.
method Examined TVL-sorted portfolios against crypto market returns, using various TVL measures.
result TVL-sorted portfolios' returns are linear functions of crypto market returns, replicable with standard tools.
The tick value is a crucial component of market design and is often considered the most suitable tool to mitigate the effects of high frequency trading. The goal of this paper is to demonstrate that the approach introduced in Dayri and Rosenbaum (2015) allows for an ex ante assessment of the consequences of a tick valu…
A financial market model uses spin variables to represent and predict agent behavior.
problem Predicting and understanding financial market behavior.
method Agent-based model with Potts model interpretation, focusing on spin variables representing opinions and actions.
result Model accurately predicts market behavior and statistical properties of financial returns.
The paper tackles fVaR prediction methods in finance.
problem Predicting future values at risk (fVaR) in finance.
method Various methods including Nested MC-empirical quantile, percentiles from distributions, quantile regressions, and limited inner simulations.
result Improved methods for predicting fVaRs, including those that are computationally efficient.
This paper improves fund net value prediction using ARIMA-LSTM hybrid model.
problem Inaccurate prediction of fund net value due to linear characteristics of models.
method ARIMA-LSTM hybrid model: ARIMA for linear characteristics, LSTM for nonlinear.
result Hybrid model shows higher accuracy in predicting fund net value.
Contextualizing financial news improves stock price predictions.
problem Predicting stock prices from financial news requires understanding historical context.
method Proposed a method using a large language model for main articles and a small model for historical context.
result Historical context significantly improves model performance across methods and time horizons.
This paper presents a novel adaptive-filter approach for predicting assets on the stock markets. Concepts are introduced here, which allow understanding this method and computing of the corresponding forecast. This approach is applied, as an example, through the prediction over the actual valuation of the PETR3 shares …
Bayesian GPR model predicts extreme stock market losses.
problem Forecasting rare but impactful extreme negative returns in equity markets.
method Developed a Bayesian Generalised Pareto Regression model linking scale parameter to market volatility.
result The Cauchy prior provides the best balance between predictive accuracy and model simplicity.
The uncertainties in future Bitcoin price make it difficult to accurately predict the price of Bitcoin. Accurately predicting the price for Bitcoin is therefore important for decision-making process of investors and market players in the cryptocurrency market. Using historical data from 01/01/2012 to 16/08/2019, machin…
In financial markets, the order flow, defined as the process assuming value one for buy market orders and minus one for sell market orders, displays a very slowly decaying autocorrelation function. Since orders impact prices, reconciling the persistence of the order flow with market efficiency is a subtle issue. A poss…
Blockchain technology shows significant results and huge potential for serving as an interweaving fabric that goes through every industry and market, allowing decentralized and secure value exchange, thus connecting our civilization like never before. The standard approach for asset value predictions is based on market…
This study investigates empirically whether the degree of stock market efficiency is related to the prediction power of future price change using the indices of twenty seven stock markets. Efficiency refers to weak-form efficient market hypothesis (EMH) in terms of the information of past price changes. The prediction …
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.
Paper uses conformal prediction for solar power forecasting in electricity markets.
problem Enhancing participation in electricity markets through accurate day-ahead PV power predictions.
method Combines machine learning for point predictions and conformal prediction for uncertainty quantification.
result CP with k-nearest neighbors and Mondrian binning outperforms linear quantile regressors in predicting PV power.
The study improves CLV predictions in retail banking with machine learning.
problem Estimating customer lifetime value in retail banking is challenging.
method Developed a novel framework for CLV predictions over arbitrary time horizons.
result 43% improvement in out-of-time CLV prediction error.
Study examines local extrema and crossing statistics in financial markets.
problem Understanding local extrema and crossing statistics in financial markets.
method Excursion set theory, numerical computation, theoretical prediction, clustering of geometrical measures, cross-correlation, Singular Value Decomposition.
result Excursion sets reveal statistical coherency and sensitivity to crises in financial markets.
Using a proprietary dataset of meta-orders and prediction signals, and assuming a quasi-linear impact model, we deconvolve market impact from past correlated trades and a predictable return component to elicit the temporal dependence of the market impact of a single daily meta-order, over a ten day horizon in various e…
The paper explores how market trade values and volumes affect price and return statistics.
problem Understanding the statistical properties of market trade, price, and return.
method Introduces secondary averaging procedure to describe statistical moments of market trades, price, and return.
result Predictions of market-based probabilities of price and return are limited by Gaussian distributions.
GPT model shows promise in financial market predictions.
problem Challenges in predicting stock market movements due to noisy data.
method Comparing GPT model with BERT model using Federal Reserve Beige Book data.
result GPT model has look-ahead bias, traditional models still perform better.
Market valuation duration is 175 years, but drops to 46 years during crises.
problem Understanding the duration of market valuation and its impact on returns.
method Comparing market valuation ratios and dividends to estimate duration, analyzing the discount rate effect.
result Valuation duration is negatively correlated with market returns, with a robust out-of-sample R2 of 15%.
The dynamics of market prices is described as the evolution of opinions in the trading community regarding future market behavior. The price then is a function of the voting process of the market players in favor to raise or reduce the value of a stock. The model presented in this paper is suited for pricing of options…