By all conventional and even some unconventional measures, the US stock market is trading way beyond historical valuation averages and is closer to all time highs. Passive stock index investors have enjoyed a period of extraordinary gains in one of the longest running bull markets. No other major asset class has come close in the last 7 years. Well diversified portfolios have had lackluster returns while the stock market keeps making new highs.
An important and often overlooked topic was raised by Corey Hoffstein at NewFound Research. Here are his first couple of tweets on that topic:
Indeed, this is about rebalance timing and how little attention it gets. Within the construct of a systematic strategy, this is a part of the Execution Model.
The Black Box Revealed
Courtesy: Inside the Black Box - The Simple Truth about Quantitative Trading by Rishi Narang
Everyone using a smartphone or a mobile device has used an onscreen smart keyboard that tries to predict the next set of words that the user might want to type. Typically, upto 3 words are predicted, which are displayed in a row at the top of the keyboard. Given that typing on a glass pane without tactile feedback, could be very frustrating at times, the smart keyboard goes a long way in alleviating these issues.
In a previous post in this series, we did an exploratory data analysis of the Ames Housing dataset.
In this post, we will build linear and non-linear models and see how well they predict the SalePrice of properties.
Root-Mean-Squared-Error (RMSE) between the logarithm of the predicted value and the logarithm of the observed SalePrice will be our evaluation criteria. Taking the log ensures that errors in predicting expensive and cheap houses will affect the result equally.
Other posts in this series:
Diamonds - Part 1 - In the rough - An Exploratory Data Analysis
Diamonds - Part 2 - A cut above - Building Linear Models
In a couple of previous posts, we tried to understand what attributes of diamonds are important to determine their prices. We showed that carat, clarity and color are the most important predictors of price. We arrived at this conclusion after doing a detailed exploratory data analysis.
In a previous post in this series, we did an exploratory data analysis of the diamonds dataset and found that carat, x, y, z were strongly correlated with price. To some extent, clarity also appeared to provide some predictive ability.
In this post, we will build linear models and see how well they predict the price of diamonds.
Before we do any transformations, feature engineering or feature selections for our model, let’s see what kind of results we get from a base linear model, that uses all the features to predict price:
It is important to understand the building blocks of systematic investing strategies before learning how to build them. Here is a schematic from the book, Inside the Black Box - The Simple Truth about Quantitative Trading by Rishi Narang, that provides a good way to visualize these building blocks and how they fit together in a system.
The Black Box Revealed
The author had aptly titled it - The Black Box Revealed, since that is how most people perceive it to be from the outside.