Objectives:
1. Define fundamental analysis and contrast it with technical analysis.
2. Grasp market‑wide drivers of currency valuations.
Theory & Concepts:
Exchange rate determination under floating regimes: purchasing‑power parity (PPP), interest‑rate parity (IRP).
Demand and supply of currencies: trade flows, capital flows, speculative positioning.
Market sentiment and the “news cycle” in Forex.
Fundamental analysis in the foreign exchange market begins with the recognition that exchange rates are not arbitrary figures, but rather reflect the underlying economic fundamentals of nations. This approach is underpinned by three key theories:
- purchasing power parity (PPP),
- interest rate parity (IRP),
- international Fisher effect (IFE).
According to the PPP theory, in a theoretical world devoid of trade barriers and transportation costs, identical baskets of commodities would have equivalent prices after accounting for exchange rate fluctuations.
However, reality often diverges from this ideal scenario, giving rise to what are referred to as "real exchange rate distortions". This discrepancy arises due to the gradual adjustment of prices for tradable and nontradable goods, a phenomenon known as the Balassa–Samuelson effect.
The PPP formula:
- S is the PPP exchange rate, expressed in units of domestic currency per one unit of foreign currency.
- P domestic is the price of a given basket of goods in the domestic country (in domestic currency).
- P foreign is the price of that same basket in the foreign country (in foreign currency).
Numerical example
Choose your basket
1. Imagine a simple basket consisting of:
- 2 loaves of bread
- 1 liter of milk
- 1 cup of coffee
2. Find local prices
- In the U.S., this basket costs $50 in total.
- In the U.K., the same basket costs £40 in total.
3. Plug into the formula
Interpretation
A PPP exchange rate of 1.25 $ / £ means that one British pound should exchange for $1.25 so that purchasing power is equalized. In other words, £1 buys you the same real goods in the U.K. as $1.25 does in the U.S.
The concept of interest rate parity (IRP) in conjunction with purchasing power parity (PPP) establishes a link between spot and forward exchange rates, as well as interest rate differentials. This relationship, known as covered IRP, has been maintained with remarkable precision until the onset of the Global Financial Crisis in 2008, where it was enforced through arbitrage operations in foreign exchange swaps and forward contracts.
Nevertheless, following the crisis, there has been a persistent emergence of cross-currency spreads, reflecting the presence of counterparty risk and collateral constraints. Uncovered IRP suggests that currencies with higher interest rates should experience a corresponding depreciation. However, this expectation is systematically challenged by the forward premium anomaly, where carry-trade currencies with high yields exhibit a tendency to appreciate rather than depreciate.
The IRP formula:
For covered interest rate parity (i.e. using a forward contract), the relationship between the forward rate F and the spot rate S is:
- F is the forward exchange rate (domestic currency per unit of foreign).
- S is the spot exchange rate (domestic currency per unit of foreign).
- i domestic is the domestic nominal interest rate (as a decimal).
- i foreign is the foreign nominal interest rate (as a decimal).
Numerical example
1. Market data
- Spot rate S=1.20 $ / €
- U.S. interest rate iUS=2% → 0.02
- Eurozone interest rate iEU=5% → 0.05
Plug into the formula
2. Interpretation
The one‑year forward rate should be roughly $1.1657 / €. That forward discount (from 1.20 to 1.1657) compensates for the fact that U.S. rates (2%) are below Euro rates (5%).
This implies the existence of a time-dependent risk premium that serves to compensate investors for assuming currency risk. The International Financial Economics (IFE) supports this notion by positing that the anticipated exchange rate change is equivalent to a real-terms interest rate differential. However, empirical coefficients fall short of unity, suggesting a partial transmission mechanism and emphasizing the significance of risk premiums and market imperfections.
The IFE formula:
Uncovered IFE relates the expected change in the spot rate to the interest‑rate differential:
Or equivalently, in level form:
- S0 = today’s spot exchange rate (domestic currency per foreign).
- E[S1] = expected spot rate one period ahead.
- i domestic, i foreign = nominal interest rates.
Numerical example
1. Market data
- Spot rate today: S0=1.20 $ / €
- U.S. interest rate: iUS=2% → 0.02
- Eurozone interest rate: iEU=5% → 0.0
2. Compute expected change
3. Solve for E[S1]
So the dollar is expected to appreciate to $1.164/€ (the euro depreciates), offsetting its higher euro‑area rate.
Furthermore, beyond parity considerations, a comprehensive analysis calls for the examination of real-time data pertaining to external balances, capital flows, and speculative positions. A country's current account deficit, fueled by trade in goods, services, income transfers, and unidirectional flows, signifies a net exportation of its currency, exerting pressure on its depreciation. Conversely, capital surplus accounts arising from direct foreign investment and portfolio inflows boost demand for the domestic currency.
The dynamics of the speculative market can be analysed through the Commitment of Traders (COT) reports, published by the Commodity Futures Trading Commission (CFTC). These reports frequently reveal extreme long or short positions that may precede substantial market corrections as traders readjust their positions.
The trading process is heavily influenced by high-impact macroeconomic events such as the publication of preliminary and final GDP estimates, consumer price indexes (CPI), producer price indexes (PPI), non-farm payroll data (NFP), and unemployment rates. Moreover, central bank statements and press conferences significantly shape market sentiment.
Surprise is measured by traders by calculating the discrepancy between the actual release and the forecast consensus, divided by the standard deviation of survey responses. This results in a normalized z-score, which quantifies the level of surprise.
Intraday volatility studies have demonstrated that realised and implied volatility within major currency pairs undergoes a sharp increase within a brief period of time, often less than an hour, following data releases. This surge in volatility gives rise to abnormal returns, which may partially reverse as the market assimilates the newly released information.
In order to translate these empirical findings into a structured approach, a comprehensive risk management framework can be formulated. For example, if the Gross Domestic Product (GDP) surprises exceed +1.5 standard deviations, it might be prudent to initiate a position aligned with the trend of the domestic currency against foreign currencies whose data surprises fall below -0.5 standard deviations.
The magnitude of a position can be ascertained by considering the mean true range of a currency pair. Stop-loss orders may be set at a multiple of the mean true range, typically one, while profit targets may be set at a double of the mean true range.
It is also advisable to incorporate fundamental indicators, such as Fibonacci levels, pivot points, and moving average crossovers, in order to augment the likelihood of success and enhance the precision of entry and exit decisions. Theoretical models extend beyond the concept of equilibrium to explain excessive market volatility. Dornbusch's well-known overshooting model (1976) offers an explanation for this phenomenon.
This model, which integrates the notion of sticky prices for specific goods and flexible financial markets, illustrates how a monetary shock, such as a sudden expansion in the monetary base, can result in a transient overshoot of the exchange rate beyond its long-term equilibrium level, before gradually returning to it.
This mechanism helps to explain why real exchange rate volatility is often greater than that of the underlying money supply or economic output, contributing to the "real exchange rate puzzle" observed by Meese and Rogoff (1983) and other researchers.
More recent work by Engel and West (2005) argues that nominal exchange rates behave like random walks when fundamentals follow integrated processes and the discount factor on future fundamentals is near unity. Their present‑value model implies that even well‑specified fundamental variables—money supplies, outputs, inflation and interest rates—provide little help in predicting short‑run exchange‑rate movements, yet exchange rates themselves can forecast fundamentals Social Science Computing Cooperative. This insight cautions traders that fundamental indicators must be combined with disciplined risk management and execution frameworks, as predictability is inherently limited by market efficiency and noise.
In essence, a comprehensive framework for fundamental analysis in the foreign exchange market integrates macroeconomic theories such as purchasing power parity (PPP), interest rate parity (IRP), and international Fisher effect (IFE) with empirical irregularities like the forward premium puzzle and real exchange rate conundrums.
Furthermore, it incorporates real-time data from the external and capital accounts, unexpected developments from significant economic releases, and rule-based trading algorithms. This integration allows the framework to harmonize fundamental biases with technical execution and risk management strategies.
This framework continuously evaluates each trade, examining its fundamental drivers, technical context, entry and exit points, as well as slippage effects. This feedback serves as a tool for model calibration, ensuring that the trading edge remains intact even as market dynamics evolve.
