Quantitative Methods for Financial Momentum and Portfolio Optimization
Welcome to the future of online trading and portfolio management, a realm where innovation meets precision, powered by two revolutionary strategies that redefine excellence in the financial world. Our platform is not just a tool; it's a masterpiece crafted at the intersection of technology and finance, inspired by the groundbreaking insights from leading-edge research.
Fractional Momentum Strategy
Based on the study "Momentum Without Crashes," our platform implements a sophisticated weighting scheme that utilizes the entirety of price data over specified lookback periods. By employing the fractional-difference filter, a statistical transformation that preserves data memory, this approach significantly enhances risk-adjusted returns and mitigates large drawdowns associated with classical momentum and short-term reversal strategies. The fractional momentum strategy is proven robust against transaction costs and other market frictions, offering consistent excess returns across diverse asset classes and international markets.
Unified Portfolio Optimization Framework
Expanding the platform's capabilities, we introduce a unified framework for rapid, large-scale portfolio optimization as detailed in "A Unified Framework for Fast Large-Scale Portfolio Optimization" by Weichuan Deng, Pawel Polak, Abolfazl Safikhani, and Ronakdilip Shah. This comprehensive framework incorporates shrinkage and regularization techniques to address multiple portfolio objectives such as minimum variance, mean-variance, and maximum Sharpe ratio under various weight constraints. Integrated into a new open-source Python library, our framework translates optimization scenarios into quadratic programming problems, leveraging 50 years of return data and firm-specific characteristics to optimize portfolio performance with advanced covariance matrix estimators and factor models, including AP-Trees and PCA-based approaches. The framework's introduction of new l1 and l2 regularizations for factor portfolio norms aims to enhance performance while reducing turnover and transaction costs.
Designed with both novice and experienced traders and investors in mind, our platform offers a user-friendly interface, comprehensive analytical tools, and real-time market data. Whether optimizing portfolio performance, exploring new trading opportunities, or managing investment risks, our platform empowers users with access to leading-edge strategies for trading and investment success. Join us in redefining the landscape of online trading and portfolio management, where innovation meets investment excellence.
Performance Teaser
The following plot shows the evolution of daily prices of the 30 DJIA US stocks. These 30 stocks are a subset of the 1000 to 2000 stocks that we use in our analysis. Looking at them in this fashion does not suggest how to construct an efficient portfolio.
The next plot below shows performance over time, out-of-sample, when using our Fractional momentum methodology. It is based on a selection methodology from the 1000 to 2000 stocks in our universe. When comparing our method to other strategies such as Markowitz (with GARCH), but notably, as shown in the graphic below, the more advanced strategies of momentum, short-term reversal and momentum and short-term reversal, ours is superior with respect to all measures of interest, such as total wealth accumulation and all common risk measures.
We now provide a table summarizing the key performance results that pertain to the above graphic.
| Value-Weighted Net Performance | Return | Sharpe | Sortino | Max. DD. |
|---|---|---|---|---|
| Fractional Momentum | 34.5% | 1.35 | 2.14 | 63.9% |
| Short-Term Rev. and Mom. | 31.6% | 1.16 | 1.90 | 69.4% |
| Momentum | 23.9% | 0.81 | 1.22 | 82.0% |
| Short-Term Reversal | 26.5% | 0.98 | 1.59 | 74.8% |
The following bullet points indicate relevant information on our methodology and choice of data used to generate the numbers in the table above:
- The table highlights the economic performance, net of a 10-basis point transaction cost, of our 'Fractional Momentum' strategy versus benchmark strategies.
- It details our dollar-neutral strategy that invests in the top 10% and shorts the bottom 10% of stocks ranked by fractional momentum.
- The investment universe, following Fama and French (1993), includes a dynamic range of 1,000 to 2,000 U.S. stocks, with a rigorous approach to avoid selection bias by accounting for both listings and delistings.
- To minimize the impact of microcap stocks, those below the 20th percentile of NYSE market equity are excluded, with the selection weighted by market capitalization in a value-weighting scheme.
- Our Momentum (MOM) strategy is based on past twelve-month performance, excluding the latest month, while the Short-Term Reversal (STR) strategy focuses on the past month's negative performers.
- The STRMOM strategy employs a double-sort method, sorting stocks by both MOM and STR criteria for a strategic long/short portfolio based on short-term underperformance and medium-term performance.
- The fractional momentum approach enhances this by efficiently combining momentum and reversal signals through a fractional-difference filter, significantly improving risk-adjusted returns and reducing drawdowns compared to standard strategies.
Scientific Background of Our Methodology
The methodology is described in full detail in the associated research papers available (for free) at the following links:
Get Started with Your Financial Future
Our current implementation consists of two methods, as discussed above. Please choose one to get started.