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Forecasting KPI: Using ML and Big Data for Investment Decisions
Erez Katz, CEO and Co-founder For us at Lucena, applying machine learning to the financial markets has taken an interesting turn in recent years. In theory, fitting a model to historical time series data and forecasting a KPI should be similar to how…
Why You Should Be Using A Genetic Algorithm for Feature Selection
Why You Should Be Using a Genetic Algorithm (GA) for Feature Selection At Lucena, our goal is to democratize some of the best kept secrets in the Financial industry. Our team works to refute the “black-box” image often associated with…
Data Science: A Prerequisite To Machine Learning and Investment Research
Erez Katz, CEO and Co-founder of Lucena Research. Is your data research ready? Here are several key concepts for quantitative investment research. Data science is the crucial first step before machine learning can be applied.
How To Avoid Missing The Obvious In Your Deep Learning Strategy
Erez Katz, CEO and Co-founder of Lucena Research In Deep Learning, Extracting The Right Insights Hinges On Several Key Steps. Here is how to form a thorough deep learning strategy for investment research. To illustrate, here are two examples that were…
Is Your Data Predictive? How to Measure Before Risking Capital
Erez Katz, CEO and Co-founder of Lucena Research Here are the pitfalls data providers and data consumers should be aware of.
Using Machine Learning to Convert Unstructured to Structured Data
One of the alternative data providers we have been working with in the past two years is Prattle Analytics. Prattle applies NLP to central banks’ communications in order to extract macro level sentiment scores. Find New Insights in Alternative Data Through…
How To Use RNNs and Time Series Data for Forecasting Stock Prices
How to Use Time Series Data and RNNs To Forecast Stock Prices. It’s important to configure deep net infrastructure to accommodate time series data.
How to Qualify the Best Machine Learning Model for Investment Research
Erez Katz, CEO and Co-founder of Lucena Research Need to decide which of your trained models is the best? Here is how to qualify your model to maximize data’s potential before utilizing for investment and trading research.
Applying Deep Reinforcement Learning to Trading
Lucena Research Co-Founder Dr. Tucker Balch provides an introduction to machine learning, specifically applying deep reinforcement learning for stock trading.
How To Beat The Odds With A Short Only Investment Strategy
Erez Katz, CEO and Co-founder of Lucena Research Risk vs. Reward in Short Investment Strategies A prominent portfolio manager once told me; “If you can come up with a method to reliably identify short investment opportunities, you will likely own…
How To Keep Your Financial Firm Competitive
Erez Katz, CEO and Co-founder Lucena Research How machine learning fills the gap between AI fact and fiction. In 2017 Larry Fink, CEO of BlackRock, made a bold statement that rattled the Financial industry. More and more firms are turning…
Why Deep Reinforcement Learning (DRL) Matters for Trading
Why Deep Reinforcement Learning Matters for Trading Deep learning has traditionally been used for image and speech recognition. The recent growth in alt data, machine learning and accessible computing power have made DRL very desirable for the Financial industry. …
Hedging Your Portfolio With Pattern Matching Technology
Erez Katz, CEO and Co-founder of Lucena Research Minimize volatility and risk with machine learning pattern matching technology to hedge your portfolio.
How Machine Learning Can Validate Your Data For Stock Forecasting
Erez Katz, CEO and Co-founder of Lucena Research There are many Machine Learning methods that can be used to validate data for stock forecasting. It’s important to understand and distinguish the methods before utilizing AI to validate and predict asset prices.