This study tackles mutual fund portfolio prediction, focusing on novel items.
problem Predicting novel items in mutual fund portfolios is challenging and less explored.
method Created a comprehensive benchmark dataset and evaluated various recommender system models.
result Autoencoder-based approaches outperform state-of-the-art models in predicting novel items.
In this paper we derive the exact solution of the multi-period portfolio choice problem for an exponential utility function under return predictability. It is assumed that the asset returns depend on predictable variables and that the joint random process of the asset returns and the predictable variables follow a vect…
Enhanced LSTM predicts equity trends, outperforming traditional methods.
problem Nonstationary and nonlinear market regimes challenge trend forecasting.
method LSTM-based framework for forecasting equity trend differences.
result LSTM framework outperforms traditional methods in terms of overall PNL.
Predicting market volatility from financial news and tweets.
problem Quantifying future volatility and returns in financial modeling.
method Topic modeling and sentiment analysis of financial news and tweets.
result Positive sentiment in tweets is negatively correlated with market volatility.
DQN outperforms traditional stock market strategies by 30%.
problem Optimizing portfolio management in the stock market.
method Deep Q-Network applied to portfolio management, with discretization and neural network enhancements.
result DQN strategy yields 30% higher profit and lower risk compared to traditional strategies.
Study uses machine learning to predict stock prices, finds Kalman filter works well for low-volatility stocks.
problem Predicting stock prices using machine learning.
method Applied recursive machine learning techniques including linear Kalman filters and LSTM architectures to historical stock prices.
result Simple linear Kalman filter performs well for low-volatility stocks, while LSTM architectures outperform for high-volatility stocks.
How to forecast next year's portfolio-wide credit default rate based on last year's default observations and the current score distribution? A classical approach to this problem consists of fitting a mixture of the conditional score distributions observed last year to the current score distribution. This is a special (…
The study evaluates nine machine learning regressors for predicting NASDAQ stock opening prices.
problem Predicting stock market opening prices for profitable trading strategies.
method Nine different machine learning regressors were applied to NASDAQ stock market data.
result The study found that certain regressors outperform others in predicting stock opening prices.
A new model forecasts optimal portfolio weights from high-frequency data.
problem Forecasting optimal portfolio weights from high-frequency data.
method Dynamic Conditional Weights (DCW) model for portfolio weights dynamics.
result DCW model outperforms other models in portfolio allocations and measures.
Using an artificial neural network (ANN), a fixed universe of approximately 1500 equities from the Value Line index are rank-ordered by their predicted price changes over the next quarter. Inputs to the network consist only of the ten prior quarterly percentage changes in price and in earnings for each equity (by quart…
CPPS selects portfolios using conformal prediction for better returns.
problem Optimizing portfolio returns with predictive models and uncertainty.
method Conformal prediction framework for portfolio selection.
result CPPS outperforms simpler strategies in delivering superior returns.
Investigates how options can control systemic risk in portfolios.
problem Systemic risk in optioned portfolios.
method Correlation hedging, extreme loss hedging, and SOCP formulation.
result Options can make systemic risk controllable and enhance return-risk performance.
Research optimizes a small RES utility's portfolio by dynamically trading in German electricity markets.
problem Managing risks in RES producers and electricity traders in changing electricity markets.
method Uses SVAR model to estimate market relationships and data-driven trading strategies to optimize revenue and reduce risk.
result Data-driven trading strategies increase utility revenue and reduce trading risk.
The paper analyzes equity market dynamics and optimal portfolios using time-varying optimization.
problem Analyzing the time-varying structure of equity markets, particularly market capitalization inequality and concentration.
method The study employs mathematical functionals of time-varying portfolios and a Sharpe optimization procedure.
result Optimal portfolios exhibit varying market capitalization exposure over time.
Proving that next-token prediction makes language models generate coherent long documents.
problem Understanding why language models generate coherent documents despite focusing on next-token prediction.
method Proving the power of next-token prediction in learning longer-range structure using Recurrent Neural Networks (RNN).
result Optimizing next-token prediction in RNNs yields a model that closely approximates the training distribution, even for long-range coherence.
The paper proposes a new model using financial big data to improve portfolio risk analysis.
problem Addressing potential information loss in portfolio risk measurement.
method Uses financial big data to incorporate out-of-target-portfolio information and overcomes the curse of dimensionality.
result The use of financial big data improves small portfolio risk analysis.
Bayesian method predicts asset returns for better portfolio optimization.
problem Uncertainty in financial markets makes traditional portfolio optimization methods unreliable.
method Bayesian predictive synthesis (BPS) combined with dynamic linear models.
result Predicted distribution information improves portfolio performance.
Large language models predict stock market returns better than traditional methods.
problem Predicting stock market returns using financial news sentiment analysis.
method Analysis of large language models (LLMs) including BERT, OPT, FINBERT, and Loughran-McDonald dictionary model.
result OPT model shows highest accuracy (74.4%) in predicting stock market returns.
This study explains and mitigates inflated returns and turnover in SPO-based portfolio optimization.
problem Inflated returns and excessive turnover in SPO-based portfolio optimization.
method KKT-based interpretation of portfolio decisions as ranking over adjusted scores, empirical evaluation of stabilization mechanisms.
result Realistic output constraints and portfolio-level turnover control improve SPO-based strategies.
Project predicts stock prices for robust portfolio design in Indian sectors.
problem Precise stock price prediction for robust portfolio design.
method Minimum variance and optimal risk portfolio optimization using past stock prices.
result Backtesting shows improved performance of optimized portfolios over equal weight portfolio.
Enhanced portfolio selection using sentiment data and LSTM.
problem Improving portfolio selection through sentiment analysis and price prediction.
method Semantic Attention Model for sentiment prediction, LSTM for price prediction, mean-variance strategy for portfolio optimization.
result Sentiment-aware portfolio strategies outperform non-sentiment aware models on average.
Which song will Smith listen to next? Which restaurant will Alice go to tomorrow? Which product will John click next? These applications have in common the prediction of user trajectories that are in a constant state of flux over a hidden network (e.g. website links, geographic location). What users are doing now may b…
LSTM model predicts stock prices for optimized portfolios.
problem Accurate stock price prediction for optimized portfolio design.
method Past stock prices from 2016-2020, LSTM model for prediction.
result High accuracy of LSTM model in predicting stock returns.
Reinforcement learning after next-token prediction aids in learning from diverse sequence lengths.
problem Learning from sequences of varying lengths and complexity.
method Introducing a framework to study reinforcement learning with autoregressive transformers, focusing on next-token prediction and mixture distributions of short and long sequences.
result Reinforcement learning after next-token prediction enables autoregressive transformers to generalize from long sequences, even when they are rare.
The paper optimizes portfolios using clustering and Sharpe ratio-based optimization.
problem Optimizing portfolio performance in financial modeling.
method Combines K-Means clustering for asset segmentation and Sharpe ratio-based optimization.
result Optimized portfolios outperform traditional equal-weighted benchmarks.
Investment returns naturally reside on irregular domains, however, standard multivariate portfolio optimization methods are agnostic to data structure. To this end, we investigate ways for domain knowledge to be conveniently incorporated into the analysis, by means of graphs. Next, to relax the assumption of the comple…
The paper uses LSTM to predict stock prices and optimize portfolio weights.
problem Accurate prediction of stock prices and designing optimized portfolios.
method Built sector-wise portfolios and an LSTM model for stock price prediction.
result The LSTM model accurately predicts stock prices with high accuracy.
Maximizes stock portfolio predictability using machine learning.
problem Improving stock portfolio performance through predictive modeling.
method Optimal constrained weights in the MPP constructed using Elastic Net, Random Forest, and Support Vector Regression models.
result MPP portfolios can outperform or underperform the index based on the time period.
This study optimizes stock portfolios using LSTM for historical data analysis.
problem Optimizing stock portfolios with predicted future prices and risks.
method Historical stock price data from Indian market sectors, LSTM model for prediction.
result LSTM model predicts high returns and low risks for optimized portfolios.
Deep learning predicts cross-sectional stock prices for practical investment.
problem Predicting stock prices using cross-sectional factors.
method Deep learning model for daily stock price prediction.
result Profitable investment framework demonstrated in Japanese stock market.
Develops BPDS for better financial portfolio decisions.
problem Model uncertainty in financial time series forecasting.
method Bayesian dynamic modelling and predictive decision synthesis.
result Improved predictive and decision outcomes compared to traditional Bayesian analysis.
A new approach predicts next observations without explicit decoding for better control.
problem High-dimensional observations and unknown dynamics in real-world control tasks.
method Proposes a novel information-theoretic LCE approach using predictive coding to develop a decoder-free model.
result The model reliably learns a controllable latent space leading to superior performance.
In this paper we extend the existing literature on xVA along three directions. First, we enhance current BSDE-based xVA frameworks to include initial margin in presence of defaults. Next, we solve the consistency problem that arises when the front-office desk of the bank uses trade-specific discount curves (CSA discoun…
Project predicts stock performance and builds an efficient portfolio for six Indian sectors.
problem Predicting stock prices accurately for optimal portfolio design.
method Analysis of time series, machine learning, and deep learning models; Modern Portfolio Theory; minimum variance and optimal risk portfolio optimization.
result Built and tested an efficient portfolio for six Indian sectors using historical stock prices.
MACE optimizes stock portfolios for maximal predictability.
problem Maximizing risk-adjusted profitability through predictable stock returns.
method Developed a machine learning algorithm (MACE) using Random Forest and Ridge Regression.
result Significant increases in predictability and profitability with minimal conditioning information.
EXAMM evolves RNNs for stock return prediction and portfolio trading.
problem Predicting stock returns for optimal portfolio trading.
method Evolutionary Neural Architecture Search (EXAMM) for evolving RNNs.
result Evolving RNNs outperform traditional benchmarks in stock trading.
PredACGAN optimizes portfolios by balancing returns and risk.
problem Difficulty in considering portfolio risk with deterministic deep learning models.
method PredACGAN uses ACGAN structure for probabilistic predictions and risk measurement.
result PredACGAN portfolios outperform non-PredACGAN portfolios in terms of returns and risk metrics.
A framework for eliciting utility functions from investor preferences.
problem Hard elicitation of specific utility functions in portfolio selection.
method Preference-fitting method using probability-wealth pairs and PHARA approximation.
result Fitted utility function converges to the optimal one as more data is used.
This paper optimizes portfolios of thematic sector stocks using LSTM models.
problem Designing an optimized portfolio of stocks to maximize return and minimize risk.
method Extracted stock prices from Jan 2016 to Dec 2020, used LSTM model for prediction, designed portfolios based on critical stocks.
result LSTM model accurately predicted future stock returns, indicating high accuracy.
Paper proposes SPO paradigm for better portfolio optimization in real markets.
problem Real-world trading frictions and constraints affect portfolio optimization quality.
method SPO paradigm with decision-focused training using surrogate loss and linear predictors.
result Decision-focused training improves risk-adjusted performance and robustness.
Law explains how LLMs learn to predict next tokens.
problem Understanding how LLMs process input data internally.
method Introduced a precise law governing token embeddings in LLMs.
result Each layer equally contributes to next-token prediction accuracy.
Paper integrates LLMs into portfolio optimization to improve decision quality.
problem Suboptimal portfolio decisions due to mismatch between prediction and decision quality.
method Integrates LLMs with decision-focused learning, using attention mechanism to process asset relationships and macro variables.
result Model consistently outperforms state-of-the-art deep learning models in portfolio optimization.
The study explores how Transformers predict the next token in a sequence.
problem Understanding the mechanism behind Transformers' autoregressive learning ability.
method Exploring the approximation ability of Transformers for next-token prediction through specific instances and a causal kernel descent method.
result Transformer models can learn context-dependent functions f for next-token prediction based on past and current observations. This paper examines how different decoding algorithms for LLMs align with various goals.
problem Consistency of decoding algorithms with different goals in LLMs.
method Analysis of greedy, lookahead, random sampling, and temperature-scaled random sampling algorithms.
result Random sampling is consistent with the true probability distribution, but other goals require optimal algorithms for specific probability distributions.
Model predicts next destination for users based on past trips and features.
problem Predicting the next destination in multi-destination trips.
method Used Cleora for city graph embedding and EMDE for prediction.
result Achieved 2nd place in Booking Data Challenge.
We introduce a microscopic model of interacting financial agents, where each agent is characterized by two portfolios; money invested in bonds and money invested in stocks. Furthermore, each agent is faced with an optimization problem in order to determine the optimal asset allocation. The stock price evolution is driv…
A new model optimizes portfolios by learning stock return distributions conditioned on factors.
problem Optimizing portfolios with high-dimensional asset-specific factors.
method Conditional Diffusion Transformer architecture linking each asset's return to its factor vector.
result The model outperforms benchmarks in mean-variance and mean-CVaR optimization.
Investors who optimize their portfolios under any of the coherent risk measures are naturally led to regularized portfolio optimization when they take into account the impact their trades make on the market. We show here that the impact function determines which regularizer is used. We also show that any regularizer ba…