Trading Stocks During FOMC Meetings: Using Machine Learning to Minimize Transaction Costs

Develop an Edge While Trading FOMC Meetings 🔍

How Can Advanced Forecasting Models Reduce Transaction Costs to Optimize Trading after FOMC Meetings?

Overview

Asset managers constantly adjust their holdings based on news from the Federal Open Market Committee (FOMC). These decisions often require swift action, especially after major geopolitical events, prompting managers to liquidate equities and move to treasuries. This article explores how machine learning models can help asset managers navigate these scenarios more efficiently, minimizing transaction costs and maximizing returns.

How to Save Millions Trading in FOMC Scenarios

In our quest to save millions in trading costs, we have been testing several machine learning models designed to optimize trading decisions during FOMC meetings. These models analyze a range of market indicators, including volatility, liquidity, and historical price movements, to predict the optimal timing and size of trades.

The 4 key models we use to forecast transaction costs are the ARIMA-GARCH, long short term memory(LSTM), and the gradient boosted decision trees(GBDT) that learn from historical trading data to make real-time decisions. By applying these models, we can make more informed trading decisions, reducing the impact of transaction costs on our overall returns.

Uncover the Hidden Costs of Trading During FOMC Announcements

To illustrate the impact of FOMC announcements, let’s consider the volatility indicators around a recent meeting. During this period, a major geopolitical event also occurred, exacerbating market volatility. Stocks such as Coinbase experienced significant price swings, with volatility indicators showing a sharp increase in trading volume and price fluctuations. This is because the devaluation of the dollar causes investors to store their cash in cryptocurrencies, which increases the flow of capital through Coinbase, causing its stock to rise.

Our models detected these changes early, allowing us to adjust our trading strategy accordingly. By anticipating the increased volatility, we were able to time our trades more effectively, avoiding the peak periods of market turmoil. This approach not only minimized our transaction costs but also protected our portfolio from adverse price movements.

How Advanced Forecasting Models Can Transform Your Trading Execution

Advanced forecasting models have the potential to revolutionize trading execution. Instead of relying on traditional heatmaps, we use a table of excess returns for various transaction sizes. This approach provides a clearer picture of the potential savings and returns for different trade scenarios.

For example, our models showed that executing smaller, staggered trades during periods of high volatility resulted in lower transaction costs and higher excess returns compared to larger, single trades. By analyzing these patterns, we can optimize our trading strategy to achieve the best possible outcomes.

How Our Models Slash Costs During FOMC Announcements

To demonstrate the effectiveness of our models, let’s examine a sample week during an FOMC announcement. Our forecasting models identified the optimal timing and sizing for our trades, based on real-time market data.

Here is the graph of the sample week, and the forecasting models were able to accurately forecast a high volatility period ahead, so they times their trades in order to minimize the transaction cost and execute when the liquidity in the market was high and the volatility of the price was low.

In this scenario, we chose to execute several small trades instead of a single large trade. The model’s predictions indicated that this approach would minimize market impact and reduce transaction costs. Despite this, we sought to cluster our small trades closely together in order to ensure that we get a consistent execution price across all of our shares. As a result, we were able to achieve significant savings while still adjusting our holdings according to the FOMC news.

Actionable Recommendations: Cut Your Transaction Costs by 55%

Based on our findings, here are some actionable recommendations to help you cut your transaction costs:

1. Use Advanced Forecasting Models: Leverage machine learning models to predict market conditions and optimize your trading decisions.
2. Execute Smaller, Staggered Trades: Avoid large trades during periods of high volatility. Smaller, staggered trades can help minimize market impact and reduce costs.
3. Monitor Volatility Indicators: Pay close attention to volatility indicators around major events like FOMC meetings. Use this data to time your trades more effectively.
4. Optimize Trade Timing: Use real-time market data to identify the best times to execute trades, avoiding peak periods of market activity.

Here are the results from our different forecasting models, where TWAP is the industry standard and all of our machine learning models are also listed. LSTM was the best performing model, but there are other regimes during which ARIMA-GARCH and GBDT will perform better. On average, however, the machine learning models outperform TWAP by 55%, and we can expect these savings to be independent of market conditions. By following these recommendations, you can significantly reduce your transaction costs and improve your overall trading performance.

Uncover the Power of Advanced ML Models for Trading Execution

The power of advanced machine learning models lies in their ability to analyze vast amounts of market data and identify patterns that traditional methods might miss. By incorporating these models into your trading strategy, you can make more informed decisions, minimize transaction costs, and maximize your returns.

Our models have consistently demonstrated their ability to improve trading outcomes during FOMC announcements and other major market events. By leveraging the insights provided by these models, you can gain a competitive edge in the market and achieve better results for your portfolio.

In conclusion, trading during FOMC meetings presents unique challenges and opportunities. By utilizing advanced machine learning models, asset managers can navigate these scenarios more effectively, minimizing transaction costs and maximizing returns. As the financial landscape continues to evolve, the adoption of these cutting-edge technologies will become increasingly essential for successful trading execution.

Unlock Personalized Alpha: Optimize Your Copy Trading Strategies with Expert Analysis 📈

Feel free to check out this article and more alpha on our blog. If you want us to analyze specific FOMC trades or conduct a personal analysis of your portfolio and trade history, please email us at accounts@blockhouse.capital.

Disclosure

The content of this blog is intended for informational and educational purposes only and should not be construed as financial advice. The strategies and insights discussed are meant to provide a deeper understanding of backrunning FOMC meetings and should not be interpreted as specific investment recommendations. Readers are encouraged to consult with a professional financial advisor before making any investment decisions.