No factors defined. Please add factors in Tab 2.

'; return; } aibefm_model.factors.forEach(f => { const card = document.createElement('div'); card.className = 'aibefm_dynamic_item_card'; // Re-use card style card.innerHTML = `

${f.name || 'Unnamed Factor'}

`; container.appendChild(card); }); } // --- Tab 4: Input Factor Values --- function aibefm_renderFactorInputValues() { const container = aibefm_getEl('aibefm_factorInputValuesContainer'); if (!container) return; container.innerHTML = ''; if (aibefm_model.factors.length === 0) { container.innerHTML = '

No factors defined. Please add factors in Tab 2.

'; return; } aibefm_model.factors.forEach(f => { const div = document.createElement('div'); div.className = 'aibefm_input_group'; let inputHTML = ''; const currentValue = (f.currentValue !== null && f.currentValue !== undefined) ? f.currentValue : (f.type === 'categorical' && f.categories && f.categories.length > 0 ? f.categories[0] : (f.scaleMin || 0)); if (f.type === 'categorical') { const options = (f.categories || []).map(cat => `${cat}`).join(''); inputHTML = `${options}`; } else if (f.type === 'numerical') { inputHTML = ``; } else { // percentage inputHTML = ``; } div.innerHTML = `${f.name || 'Unnamed Factor'} ${f.unit ? `(${f.unit})` : ''}:${inputHTML}`; container.appendChild(div); }); } // --- Tab 5: Forecast & Analysis --- function aibefm_generateForecast() { aibefm_collectModelSetup(); // Ensure model object has latest UI data from Tab 1 const resultDiv = aibefm_getEl('aibefm_forecastResultDisplay'); const contributionsDiv = aibefm_getEl('aibefm_factorContributionsDisplay'); const contributionsList = aibefm_getEl('aibefm_contributionsList'); if (!resultDiv || !contributionsDiv || !contributionsList) return; if (aibefm_model.factors.length === 0) { resultDiv.innerHTML = '

No factors defined to make a forecast. Please define factors and their impacts/weights.

'; contributionsDiv.style.display = 'none'; aibefm_forecastOutput = null; document.getElementById('aibefm_downloadPdfButton').style.display = 'none'; return; } let totalPercentageChangeContribution = 0; let factorContributions = []; aibefm_model.factors.forEach(f => { let normalizedValue = 0; // Normalized value of the factor (0 to 1, where 1 is full positive impact) const currentValue = (f.currentValue === null || f.currentValue === undefined) ? (f.type === 'categorical' && f.categories && f.categories.length > 0 ? f.categories[0] : (f.scaleMin || 0)) : f.currentValue; if (f.type === 'numerical') { const min = f.scaleMin || 0; const max = f.scaleMax || 0; if (max > min) { normalizedValue = (parseFloat(currentValue) - min) / (max - min); } else if (parseFloat(currentValue) >= min) { // if max=min, treat as binary normalizedValue = 1; } } else if (f.type === 'categorical') { // Simple: assume first category is "worst" (0), last is "best" (1) const catIndex = (f.categories || []).indexOf(currentValue); if (catIndex !== -1 && (f.categories || []).length > 1) { normalizedValue = catIndex / ((f.categories || []).length - 1); } else if ((f.categories || []).length === 1 && catIndex === 0) { // Single category normalizedValue = 0.5; // Neutral } } else { // percentage // For percentage, treat the value directly as a percentage point (e.g., 2 means 2%) // Normalize it relative to a "neutral" 0 and its typical range to get a 0-1 factor strength later. // For simplicity, let's assume the 'weight' is the direct % impact if factorValue is 1 (or 100% if it's a percentage factor) // A weight of 5 means this factor, at its most positive interpretation, contributes +5% to earnings. // The actual contribution will be Normalized_Factor_Strength * Weight * Impact_Direction // For 'percentage' type, if currentValue is 2 (for 2%), its "strength" relative to neutral 0 needs context. // Let's assume a direct interpretation: a 2% GDP growth contributes based on its weight if positive. // For a simpler model: normalizedValue for percentage could be currentValue / (f.scaleMax for percentages, e.g. 10% being max "good" input) // Or, more simply, if factor type is 'percentage', its current value *is* its direct contribution scaling factor. // Let's simplify: if a factor is 'percentage', its 'currentValue' is treated as is (e.g. 2 for 2%). // The 'weight' will scale this. So, effective contribution = (currentValue / 100) * weight. This is too complex. // Simpler for 'percentage' type: // Normalized value: (currentValue - (neutral point, e.g. 0)) / (expected max deviation from neutral, e.g. scaleMax) const val = parseFloat(currentValue); const min = f.scaleMin || 0; // Use min/max to normalize even percentages. Assume 0 is neutral. const max = f.scaleMax || 0; if (val > 0 && max > 0) normalizedValue = Math.min(val/max, 1); // Cap at 1 for positive else if (val < 0 && min < 0) normalizedValue = Math.min(val/min, 1); // Cap at 1 for negative (strength of negative) // This normalization of percentage factor needs refinement or clearer user instruction. // For now, if type is percentage, treat currentValue as points, normalize 0-1 from min to max. if (max > min) { normalizedValue = (val - min) / (max - min); } else if (val >= min) { normalizedValue = 1; } } normalizedValue = Math.max(0, Math.min(1, normalizedValue)); // Clamp 0-1 const impactDirectionModifier = (f.impactDirection === 'positive') ? 1 : -1; // The actual percentage points this factor contributes const factorPercentageContribution = normalizedValue * (f.weight || 0) * impactDirectionModifier; totalPercentageChangeContribution += factorPercentageContribution; factorContributions.push({name: f.name || 'Unnamed', contribution: factorPercentageContribution }); }); // Cap total contribution by maxTotalImpactFromFactors if (totalPercentageChangeContribution > aibefm_model.maxTotalImpactFromFactors) { totalPercentageChangeContribution = aibefm_model.maxTotalImpactFromFactors; } else if (totalPercentageChangeContribution < -aibefm_model.maxTotalImpactFromFactors) { totalPercentageChangeContribution = -aibefm_model.maxTotalImpactFromFactors; } const forecastedValue = aibefm_model.baselineValue * (1 + (totalPercentageChangeContribution / 100)); const percentageChange = ((forecastedValue - aibefm_model.baselineValue) / aibefm_model.baselineValue) * 100; aibefm_forecastOutput = { companyName: aibefm_model.companyName, sector: aibefm_model.sector, baselineMetric: aibefm_model.baselineMetric, baselineValue: aibefm_model.baselineValue, forecastPeriod: aibefm_model.forecastPeriod, forecastedValue: forecastedValue, percentageChange: percentageChange, factors: JSON.parse(JSON.stringify(aibefm_model.factors)), // Deep copy for PDF factorContributions: factorContributions }; resultDiv.innerHTML = `

${aibefm_model.baselineMetric} Forecast for ${aibefm_model.companyName || 'N/A'} (${aibefm_model.forecastPeriod})

Forecasted ${aibefm_model.baselineMetric}: $${forecastedValue.toFixed(2)} (${(percentageChange >=0 ? '+': '')}${percentageChange.toFixed(2)}%)

Baseline ${aibefm_model.baselineMetric}: $${aibefm_model.baselineValue.toFixed(2)}

`; contributionsList.innerHTML = ''; factorContributions.sort((a,b) => Math.abs(b.contribution) - Math.abs(a.contribution)); // Sort by magnitude factorContributions.forEach(c => { const item = document.createElement('div'); item.className = 'aibefm_factor_contribution_item'; item.innerHTML = `${c.name}: ${(c.contribution >=0 ? '+': '')}${c.contribution.toFixed(2)} pp`; contributionsList.appendChild(item); }); contributionsDiv.style.display = 'block'; document.getElementById('aibefm_downloadPdfButton').style.display = 'block'; } function aibefm_collectModelSetup() { aibefm_model.companyName = aibefm_getInputValue('aibefm_companyName'); aibefm_model.sector = aibefm_getInputValue('aibefm_sector'); aibefm_model.baselineMetric = aibefm_getInputValue('aibefm_baselineMetric'); aibefm_model.baselineValue = aibefm_getInputNumValue('aibefm_baselineValue', 0); aibefm_model.forecastPeriod = aibefm_getInputValue('aibefm_forecastPeriod'); // Factors are updated directly via their oninput events } // --- PDF Generation --- function aibefm_downloadPDF() { if (!aibefm_forecastOutput) { alert("Please generate a forecast first."); return; } if (typeof window.jspdf === 'undefined' || typeof window.jspdf.jsPDF === 'undefined') { alert('PDF library (jsPDF) is not loaded.'); return; } const { jsPDF: JSPDF } = window.jspdf; const doc = new JSPDF(); let y = 15; const m = 15; const cw = doc.internal.pageSize.getWidth() - (2 * m); const data = aibefm_forecastOutput; function addLine(text, size, style = 'normal', indent = 0, spacing = 2.5) { if (y > 270) { doc.addPage(); y = m; } doc.setFontSize(size); doc.setFont(undefined, style); const lines = doc.splitTextToSize(text, cw - indent); doc.text(lines, m + indent, y); y += (lines.length * (size * 0.35)) + spacing; } addLine(`Earnings Forecast: ${data.companyName || 'N/A'}`, 18, 'bold', 0, 5); addLine(`Sector: ${data.sector}, Period: ${data.forecastPeriod}`, 10); addLine(`Baseline ${data.baselineMetric}: $${data.baselineValue.toFixed(2)}`, 10); addLine(`Forecasted ${data.baselineMetric}: $${data.forecastedValue.toFixed(2)} (${(data.percentageChange >=0 ? '+': '')}${data.percentageChange.toFixed(2)}%)`, 12, 'bold', 0, 6); addLine("Influencing Factors & Settings:", 14, 'bold', 0, 4); data.factors.forEach(f => { let factorDesc = `- ${f.name || 'Unnamed'}: Type: ${f.type}, Impact: ${f.impactDirection}, Weight (Max Contrib.): ${f.weight}%`; if(f.type === 'numerical') factorDesc += `, Range: ${f.scaleMin}-${f.scaleMax}`; if(f.type === 'categorical') factorDesc += `, Cats: ${(f.categories||[]).join('/')}`; if(f.type === 'percentage') factorDesc += `, Input as % points`; factorDesc += `, Forecast Input Value: ${f.currentValue === null || f.currentValue === undefined ? 'N/A' : f.currentValue} ${f.unit || ''}`; addLine(factorDesc, 9, 'normal', 5, 1.5); }); y += 3; addLine("Factor Contributions to Forecast Change:", 14, 'bold', 0, 4); data.factorContributions.forEach(c => { addLine(`- ${c.name}: ${(c.contribution >=0 ? '+': '')}${c.contribution.toFixed(2)} percentage points`, 10, 'normal', 5, 2); }); addLine(`Note: This is a simulated forecast based on user-defined parameters. Max total impact from factors capped at +/-${aibefm_model.maxTotalImpactFromFactors}%.`, 8, 'italic', 0, 5); doc.save(`${(data.companyName || 'forecast').replace(/\s+/g, '_')}_earnings_forecast.pdf`); }