Other posts in this series: Bank Marketing - Part 1 - Making the call - An Exploratory Data Analysis Bank Marketing - Part 2 - Who will say yes? - Building Classification Models In Part 1, we found that the subscription rate changed a lot between 2008 and 2010, as interest rates fell and the bank changed its campaigns. In Part 2, we built models on a random sample of the calls and tested them on the other calls from the same period. They did well. But when we trained them on the earlier calls and tested them on the later calls, they did much worse.
Other posts in this series: Bank Marketing - Part 1 - Making the call - An Exploratory Data Analysis Bank Marketing - Part 3 - Keeping up with the times - Sliding and Expanding Window Models In Part 1, we explored the Bank Marketing dataset: 41,188 calls in which a Portuguese bank asked its clients to open a term deposit. Only about 1 in 9 clients said yes.
In this post, we build models that predict whether a client will subscribe. A model is a formula that a computer learns from examples. A classification model does not just answer yes or no: it gives each client a probability of saying yes. A bank can use these probabilities to call the most promising clients first.
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.
Other posts in this series: Ames Housing - Part 1 - Laying the foundation - Building a Data Pipeline Ames Housing - Part 2 - Walkthrough - An Exploratory Data Analysis In Part 1, we built a data pipeline that cleans the Ames Housing dataset, and in Part 2, we explored the finalized dataset.
In this post, we build linear and non-linear models, and we find out how well they predict the SalePrice of properties. A model is a formula that a computer learns from examples. A linear model adds up the features, each multiplied by a number (a coefficient) that it learns from the data. A non-linear model can also learn curved relationships.
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 the previous posts, we found out which attributes of a diamond are important for its price. An exploratory data analysis showed that carat, clarity and color are the most important features to predict price. Then we built linear models to predict the prices, and we chose the best model by its metrics.
Other posts in this series: Diamonds - Part 1 - In the rough - An Exploratory Data Analysis Diamonds - Part 3 - A polished gem - Building Non-linear Models In a previous post in this series, we explored the diamonds dataset. We found that price has a strong correlation with carat, x, y and z, and that log(price) has an almost straight-line relationship with log(carat). For diamonds of similar weight, clarity and color also seemed to help predict the 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. From this schematic, it is easier to understand and visualize how a system is built with a systematic approach.