Financial Market Intelligence

Macro-driven FX forecasting & institutional-grade equity research.

Current Conditions

FX rate last month (VND/USD)

Horizon (months)

Policy Rate Spread (VND Rate – USD Rate, %)

+2.50%

−5%+10%

Positive = VND rates higher → VND pressure eases. Negative = capital outflow pressure → VND weakens.

Balance of Payments (BOP) — Current Account, USD Bn/Month

−4.25

−10+5

Surplus (positive) → foreign currency inflows → VND strengthens. Deficit → VND weakens.

FX Reserves (Vietnam, $B)

$80B

$20B$130B

Higher reserves → stronger external buffer → VND tends to appreciate.

Current FX (t−1)

25,400

VND per USD

Forecast (t+h)

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VND per USD

Change

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Confidence range

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±1 std dev band

Month-by-Month Path

Factor Contribution (VND change per factor)

Scenario Summary

Scenario	BOP adj.	Rate spread adj.	Forecast rate	Signal

No historical data available

Run python main.py with a valid FRED_API_KEY to populate real data.

Observations

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Monthly data points

R²

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Adj R² —

Model σ

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% change / month

VND/USD — 5-Year History vs Model Fit

Model Residuals (Actual − Fitted, % change)

OLS Coefficients (Fitted from Historical Data)

Variable	Coef	Std Err	t-stat	p-value

Model Specification

FX(t) = α · FX(t−1) + β₁ · SpreadRate + β₂ · BOP + β₃ · SpreadGDP + ε

α = 1.002 (momentum / mean-reversion)
β₁ = −120   (rate spread → VND strengthens if positive)
β₂ = −40    (BOP surplus → VND strengthens)
β₃ = −15    (FX reserves → higher reserves strengthen VND)
σ = 180    (monthly residual std dev, VND)

Signs: negative β means the variable reduces VND/USD (i.e. VND appreciates). These are illustrative calibration values — replace with OLS estimates from historical data.

Variable Weights (Edit Coefficients)

α — FX lag

β₁ — Rate spread

β₂ — BOP

β₃ — FX Reserves

σ — monthly std dev (VND)

Planned Extensions

Variable	Rationale	Status
Inflation spread (CPI VN – US)	PPP channel	Planned
FX reserves (SBV)	Intervention capacity	Planned
USD index (DXY)	Global USD strength	Planned
Other currency pairs	EUR, CNY, etc.	Planned

Ticker symbol

Run python stock_analysis.py TICKER
then refresh this page.

No stock data loaded yet

Run python stock_analysis.py AAPL (or any ticker) to generate stock_data.json.

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Market Cap

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USD billion

P/E (trailing)

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Fwd —

Rev Growth YoY

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% YoY

RSI (14)

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Executive Summary

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Analyst Consensus

Rating

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Avg Target

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Target Range

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Quarterly Earnings Review

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EPS History (beat / miss)

Period	Est.	Actual	Surprise

Competitive Positioning

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Key Risks

Catalysts & Timeline

Timeframe	Event	Impact

Investment Summary

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Bull Case

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—%

probability

1 month

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3 months

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12 months

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Base Case

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—%

probability

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1 month

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3 months

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12 months

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Bear Case

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—%

probability

1 month

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3 months

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12 months

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Fundamentals

Technical Signals

Quarterly Financials (USD)

Period	Revenue	Op. Income	Net Income