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LLM-Driven Financial Forecasting Models in MATLAB

MATLABSolutions. Sept 13 2025 · 7 min read
LLM-Driven Financial Forecasting Models in MATLAB | AI-Power

The financial sector is witnessing a technological revolution with the rise of Large Language Models (LLMs). Traditionally used for text analysis, LLMs are now being integrated with powerful platforms like MATLAB to develop financial forecasting models that combine natural language insights with quantitative analysis. This fusion enables smarter decision-making, better risk management, and improved predictive accuracy.

What Are LLM-Driven Financial Forecasting Models?

LLM-driven financial forecasting refers to the use of natural language models (like GPT) to analyze financial news, reports, and sentiment, and then integrate those insights with quantitative models in MATLAB.

Instead of relying only on numerical time-series (like stock prices or exchange rates), these models also incorporate unstructured data such as:

By blending text-derived sentiment with traditional statistical and machine learning models, LLMs enhance forecasting accuracy.

Why MATLAB for LLM-Based Forecasting?

MATLAB provides a powerful environment to process, model, and simulate financial data. Its rich toolboxes and integration capabilities make it ideal for connecting LLM outputs to forecasting pipelines.

Key benefits include:

Applications of LLM-Driven Forecasting in Finance

  1. Stock Price Prediction – Sentiment from earnings reports + price data improves short-term predictions.

  2. Foreign Exchange Forecasting – Combine news sentiment with econometric models.

  3. Credit Risk Assessment – Use company filings and sentiment analysis for credit scoring.

  4. Market Volatility Forecasting – Predict volatility spikes by monitoring financial news and events.

Example Workflow in MATLAB

Here’s how a LLM-MATLAB forecasting workflow could look:

  1. Collect Financial Data: Import stock/forex data using MATLAB Datafeed Toolbox.

  2. Fetch Textual Data: Connect to APIs for news or use an LLM to summarize financial articles.

  3. Sentiment Scoring: Convert LLM outputs into quantitative sentiment scores.

  4. Feature Engineering: Combine sentiment features with historical prices, volatility, and technical indicators.

  5. Forecasting Model: Apply ARIMA, LSTM, or regression models in MATLAB.

  6. Backtesting & Validation: Assess performance using historical datasets.

  7. Deployment: Automate forecasts with live market feeds.

Future of LLMs in Finance with MATLAB

The integration of LLMs with MATLAB will reshape how forecasts are made:

As financial markets become more complex, this hybrid approach of language + numbers will be a cornerstone of next-generation forecasting.

Conclusion

LLM-Driven Financial Forecasting Models in MATLAB represent a paradigm shift in finance. By fusing AI-powered text analysis with MATLAB’s robust forecasting capabilities, institutions can unlock deeper insights, improve predictions, and manage risks with greater precision.

Model Setup

% LLM_Driven_Financial_Forecasting.m

% Example of integrating sentiment (from LLM) with price data in MATLAB

% -------------------------------------------------------------


clear; clc; close all;


%% Step 1: Load stock price data

% Example: Random walk to simulate stock prices

rng(42);

N = 300; % number of days

price = cumsum(randn(N,1)*2 + 0.2) + 100; % simulated stock price

time = (1:N)\';




%% Step 2: Generate / Import sentiment scores

% In practice: Use GPT/LLM API to analyze financial news and convert

% text into sentiment scores (-1 = negative, +1 = positive).

% Here: Simulated sentiment scores

sentiment = 0.5*sin(time/30) + 0.3*randn(N,1);


% Normalize sentiment to [-1,1]

sentiment = sentiment ./ max(abs(sentiment));


%% Step 3: Feature engineering

% Combine price returns with sentiment

returns = [0; diff(price)];

dataTbl = table(time, price, returns, sentiment);




% Shift sentiment as predictor of future returns

dataTbl.SentimentLag = [NaN; sentiment(1:end-1)];


%% Step 4: Train/test split

trainRatio = 0.8;

Ntrain = round(trainRatio * N);


trainData = dataTbl(2:Ntrain, :); % exclude first row with NaN lag

testData = dataTbl(Ntrain+1:end, :);




%% Step 5: Forecasting model (ARIMA with exogenous sentiment input)

% Use returns as dependent variable, sentiment as predictor

Mdl = arima(\'Constant\',0,\'D\',0,\'Seasonality\',0,...

    \'MALags\',1,\'SMALags\',0,\'ARLags\',1);


% Estimate model with training data

EstMdl = estimate(Mdl, trainData.returns, ...

    \'X\', trainData.SentimentLag);


%% Step 6: Forecast future returns

nTest = height(testData);

[forecastReturns,~,forecastCI] = forecast(EstMdl, nTest, ...

    \'Y0\', trainData.returns, ...

    \'X0\', trainData.SentimentLag, ...

   \'XF\', testData.SentimentLag);


% Convert returns back to prices

forecastPrices = trainData.price(end) + cumsum(forecastReturns);


%% Step 7: Visualization

figure(\'Name\',\'LLM-Driven Forecast\',\'NumberTitle\',\'off\',\'Position\',[100 100 1000 600]);

plot(time, price,\'b\',\'LineWidth\',1.5); hold on;

plot(testData.time, forecastPrices,\'r--\',\'LineWidth\',1.5);

fill([testData.time; flipud(testData.time)], ...

    [trainData.price(end)+cumsum(forecastCI(:,1)); ...

    flipud(trainData.price(end)+cumsum(forecastCI(:,2)))], ...

    \'r\',\'FaceAlpha\',0.1,\'EdgeColor\',\'none\');


legend(\'Actual Price\',\'Forecast Price\',\'95% CI\');

xlabel(\'Time (days)\'); ylabel(\'Price\');

title(\'LLM-Driven Financial Forecasting with MATLAB (ARIMA + Sentiment)\');

grid on;


%% Step 8: Simple accuracy metrics

actualTestPrices = testData.price;

rmse = sqrt(mean((forecastPrices - actualTestPrices).^2));

fprintf(\'Test RMSE: %.4f \', rmse);