Build your Customized Factor Investing at a fraction of cost

Jan 5, 2022 | Portfolio Models and Optimization



Available through QuantConnect

Large Financial Institutions


5 Composite factors
for 8000 US stocks
$39/monthThousands of Dollars Annually


Kavout signals are machine-learning enhanced scores that capture the returns of systematic factors such as quality, value, momentum, growth, and low volatility. There are many different anomalies discovered by researchers and practitioners across these factor categories, and there is no good common definition of each style across the literature. Kavout creates an ensemble score for each style that gauges the different factors considered in the literature and industry practice.

In this data set, you will find 5 composite factors that were used in Kavout proprietary machine learning alpha signals K-score, which have been adopted by some of the multi-billion dollar quant funds in New York and London. Each composite factor is generated by an ensemble model consisting of inputs from hundreds of anomalies. The data is generated on a daily basis and covers all the stocks traded in US major markets such as NYSE and Nasdaq since 2003. You could leverage this abundant set of signals to construct and backtest your strategies.



Investors have been seeking out high-quality companies for decades. The high-quality companies provide persistent profitability, low leverage and high return. Empirical evidence laud the merits of a Quality investment approach. What is agreed: higher profitability, more stable income and cash flows, and a lack of excessive leverage are the marks of quality companies.

Quality has demonstrated the ability to reward long-term investors. Similarly Quality tends to lag during low-quality rallies – or when the lowest-quality stocks lead the market in a rebound.



Factor Investing remains the go-to solution for many investors. While no single value metric is able to fully explain the long-term risk and return performance of equities, we aim to capture the return of factors which have demonstrated excess historical market returns.



Momentum takes a rolling average of the price and return used in price momentum, looking to identify patterns of momentum among equities. Our momentums are constructed with a cross-section machine learning model that provides a unified framework incorporating information used in the three major price patterns:

  • Short-term reversal effects
  • Momentum effects
  • Long-term reversal effects



Growth refers to a company’s relative ability to generate a profit and revenue currently and into the future. Sales and earnings growth trend is the evolution of profit within a business. An upward trend means business growth has generally increased over time in the short or long run. A downward trend means the business is declining. Recognizing growth opportunities early in the company gives you a better chance to locate the stocks in play for outperformance in the future..


Low Volatility

The low-volatility has been existing in the United States stock markets over an extended 90-year period. The portfolio of stocks with the lowest volatility has a higher return compared to the portfolio of stocks with the highest volatility.


Use Case: Multi-Factor Portfolio

If you want to see how a multi-factor investing strategy plays and don’t want to write code yourself, Kavout has integrated all the factors into the Portfolio Toolbox

We could choose Large Cap as the universe and pick the top 20 stocks to form a portfolio ranked by the above 6 factors. Here is what we get:

The 12-month trailing portfolio return is 50.25% vs SPY’s 33.05%, and a Sharpe ratio of 3.09 vs SPY’s 2.2. If you want to integrate Kavout signals into your own strategy, you could also access the data via API from our partner QuantConnect.


About the Provider

Kavout was created by ex-Googlers and the founding team used to work at Google, Microsoft, Baidu, and financial firms with a proven track record of building many mission-critical machine learning systems where billions of data points were processed in real-time to predict the best outcome for core search ranking, ads monetization, recommendations, and trading platforms.

Our mission is to build machine investing solutions to find alpha with adaptive learning algorithms and to create an edge by assimilating vast quantities of complex data through the latest AI and Machine Learning methods to generate signals to uncover hidden, dynamic, and nonlinear patterns in the financial markets.

Disclaimer: This article is for informational purposes only. Investing is risky and you should seek independent professional advice before making any investment decisions. Past performance does not guarantee future performance. All information obtained here should be discussed with your professional financial and legal adviser along with a tax accountant. The content from this article and obtained from does not constitute investment advice nor offers or any opinion with respect to the suitability of any security or any investment.

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