Using reinforcement learning to achieve dynamic asset-liability management
TSAI
1. Challenges faced by traditional asset-liability management
In recent years, the volatility of global financial markets has become increasingly severe. From the perspective of interest rates, the Federal Reserve has raised interest rates 11 times in a row from March 2022 to July 2023, with a cumulative increase of 525 basis points, pushing the target range of the federal funds rate to between 5.25% and 5.5%, the highest level in 23 years. Then on September 19, 2024, the Federal Reserve announced a 50 basis point interest rate cut to between 4.75% and 5.00%.
The exchange rate market is also unstable. In recent years, the euro-dollar exchange rate has fluctuated significantly due to the European economic situation, the Russia-Ukraine conflict and other events. For example, for a period of time after the outbreak of the Russia-Ukraine conflict, the euro-dollar exchange rate fluctuated by more than 10% in a short period of time.
In terms of asset prices, the stock market is affected by multiple factors such as the macroeconomic situation and the industry competition pattern, and the fluctuations are extremely significant. For example, in 2022, against the backdrop of soaring long-term U.S. Treasury yields, the S&P 500 index fell 19% throughout the year.
In such a complex market environment, the shortcomings of traditional static or semi-static asset-liability management strategies are fully exposed. Traditional strategies are often based on historical data and empirical assumptions, pre-set asset-liability allocation ratios, and lack the ability to respond quickly to real-time market changes. Once the market environment changes suddenly, such as a sudden sharp rise or fall in interest rates or sharp fluctuations in exchange rates, this fixed allocation model cannot be adjusted in time, which can easily lead to a decline in asset value and an increase in liability costs, thereby affecting the profitability and financial stability of financial institutions and enterprises.
2. Principles and advantages of reinforcement learning algorithms
The reinforcement learning algorithm introduced by the TSAI platform is essentially a machine learning technology. Its core principle is to learn the optimal strategy through the interaction between the agent and the environment. In the asset-liability management scenario, the agent is the reinforcement learning algorithm, and the environment is the ever-changing financial market, including real-time dynamic data such as market interest rates, exchange rates, and asset price fluctuations.
Through continuous trial and error and learning, the algorithm takes corresponding asset-liability allocation actions according to changes in the market environment, and obtains reward feedback based on the results of these actions (such as increased returns, reduced risks, etc.). Through a large amount of interactive learning, the algorithm gradually explores the optimal asset-liability allocation strategy under different market conditions to achieve the best balance of risk and return.
Compared with traditional methods, reinforcement learning algorithms have significant advantages. It can process massive amounts of market data in real time, quickly capture subtle signals of market changes, and make immediate decision adjustments based on these signals. This dynamic adjustment capability keeps asset-liability allocation in a relatively optimal state, effectively improves the ability to cope with market risks, and lays a solid foundation for achieving long-term stable returns.
3. Application of reinforcement learning in asset-liability management
(I) Strategies for coping with interest rate fluctuations
When market interest rates rise, the reinforcement learning algorithm will quickly initiate a comprehensive assessment of the interest rate sensitivity of assets and liabilities. The algorithm determines the value change trend of various assets and liabilities in an environment of rising interest rates through in-depth analysis of historical interest rate data, interest rate elasticity coefficients of different assets and liabilities, and current market interest rate trends. For example, for bond assets, the algorithm will accurately calculate the negative impact of rising interest rates on their prices based on factors such as the duration and coupon rate of the bonds. If the evaluation results show that the interest rate risk of bond assets will increase significantly, and fixed-rate liabilities have relatively stable costs or even advantages in an environment of rising interest rates due to interest rate lock-in, the algorithm will immediately adjust the asset-liability allocation ratio. Specifically, the holdings of bond assets will be appropriately reduced, and the released funds will be allocated to asset categories with greater interest rate resistance, such as short-term cash equivalents or floating rate bonds; at the same time, consider increasing fixed-rate liabilities to reduce the overall liability cost, optimize risk exposure, and effectively resist the adverse effects of rising interest rates.
(II) Exchange rate fluctuation response strategy
In terms of exchange rate fluctuation management, reinforcement learning algorithms also play a key role. When the algorithm detects that a currency exchange rate has an appreciation trend, for financial institutions or enterprises holding a large amount of assets denominated in that currency, it will recommend an appropriate increase in liabilities denominated in that currency based on continuous tracking and prediction of exchange rate trends and the currency structure of assets and liabilities. In this way, the relative decline in the value of liabilities brought about by the appreciation of the exchange rate can be used to achieve an increase in returns. On the contrary, if the algorithm predicts that the exchange rate is expected to depreciate, it will quickly adjust the asset allocation strategy, reduce the holdings of assets denominated in that currency, and increase assets denominated in other relatively stable currencies, thereby effectively avoiding the risk of asset impairment caused by exchange rate depreciation.
(III) Strategy for dealing with asset price fluctuations
Taking stock market price fluctuations as an example, when stock market prices rise sharply, the reinforcement learning algorithm will comprehensively evaluate whether the proportion of stock assets held by financial institutions or enterprises is too high and the potential risk level. The algorithm will not only consider the short-term increase in stock prices, but also comprehensively analyze multiple factors such as the macroeconomic situation, industry valuation level, and corporate fundamentals. If the evaluation results show that the risk of stock assets is too high, the algorithm will recommend gradually reducing stock assets and allocating the recovered funds to more stable asset categories, such as bonds or cash equivalents, to lock in previous gains and reduce the risks brought by market corrections. On the contrary, when stock market prices fall, the algorithm will accurately judge whether there are investment opportunities based on factors such as market sentiment indicators, macroeconomic policy trends, and corporate profit expectations. If it is believed that the market has overreacted and the stock price is undervalued, the algorithm will recommend an appropriate increase in stock asset allocation to create conditions for profit from future price rebounds.