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Current S&P 500 Economic Prediction

June 10, 2019

The following is the composite point-in-time prediction for the S&P500 economic forecasting model. Currently the direction has changed from a flat prediction to an up prediction which indicates a resumption of the bullish trend. On the technical side check out our Investor IQ to look at the US Market Summary:

New Feature on Investor IQ: Asset Class Breadth and Relative Strength Summary Statistics

May 28, 2019

One of the new features introduced yesterday on Investor IQ is a summary of the breadth and relative strength of various asset class categories. This helps provide another layer of information for investors to determine which asset classes to focus on and which to avoid. We compile the % of buy signals using a matrix of trend and momentum signals as well as the average RS or Relative Strength using momentum within the asset class category. The best combination is obviously high % of buy signals and high RS. However a high RS with a low% of buy signals indicates caution as not all areas are participating as they would typically in a healthy market. The RS Spread is the difference between the strongest and weakest performer in the category and indicates the degree of opportunity for long/short portfolios within the same asset class. A visual of the current output can be seen below:

The asset class categories are listed in order of average RS. We can see that domestic equity and style,sector and factor components have the strongest relative strength. Traditional equity factors have the highest % buys within the domestic equity category. On the international front things are looking challenging – likely the result of the trade war. The % buys across countries is only 40% which suggests that markets across the globe are broadly struggling right now. This also correlates with the weakness in both breadth and relative strength in the Currency category which only has 14% buys and has the weakest asset class category average RS of only 7. The strongest asset class categories in terms of breadth are bonds and credit which is a trend worth watching. Commodities are the worst performing category in terms of breadth with 0% buys and also are relatively weak with an average RS of 28. The RS Spread of Commodities is the highest across asset class categories which indicates a good opportunity to go long a strong market such as Oil (USO) and short a weak market such as Gas (UNG).

This summary is a good way to keep on top of the market and spot new opportunities to overweight/underweight certain asset class categories.

Current S&P500 Economic Forecasting Model Prediction

May 27, 2019

We introduced our machine-learning economic forecasting model as a neat new way to compress a ton of economic data to gauge the health of the economy in order to predict cyclical (not sentiment driven) drawdowns in the stock market. The current prediction uses two different models:

1) Composite Point-in-time– this model only creates a forecast when all data required by the model is available at the point-in-time that it was available. If the model is waiting on data it will simply carry forward the last prediction made when all data was available. The historical prediction table was generated using this model.

2) Preliminary – this model is trained using the data available and is capable of making forecasts when there is missing data that the Composite model is waiting for. It does this by interpolating the missing data. Currently given that there are three missing pieces of data we can only make a preliminary forecast through the end of June.

Current Prediction- Preliminary

For the prediction period Apr 2019 – Jun 2019, the model predicts the following results based on economic data as of Mar 31, 2019:

  • Is there a reasonable chance of a large correction happening between Apr 2019 – Jun 2019? Not Expected
  • What is the predicted direction of returns between Apr 2019 – Jun 2019? Flat

Investor IQ: Focus List

May 22, 2019

Our Investor IQ weekly publication which is updated every Monday morning (on the top right hand corner of this blog) provides basic trend-following and relative strength (RS) signals for both US and Canadian ETFs and individual stocks. We recently added a “Focus List” at the request of some of our readers which highlights both long and short positions to focus on. The focus list long positions have a relative strength>90% and are either a buy or a hold position based on a composite of trend-following and momentum signals. The focus list short positions have a relative strength<10% and are either a sell or a hold position. An example can be seen in the picture below:

We also recently added signals for the Dow 30 stocks and also the S&P/TSX 60 stocks in Canada. We plan to expand the universe of both ETFs and Stocks over time. As a result of the trade war, we have included the Chinese Yuan (CYB) to the currency section for US ETFs as being one ticker worth watching as it has been an early warning for the stock market in recent times.

Current and Historical S&P500 Economic Forecasting Model Predictions

May 15, 2019

In the last post we introduced the S&P500 Economic Forecasting Model which seeks to predict the chances of a moderate or large drawdown over the next 90 days. The model considers a large range of different macroeconomic variables and their derivatives to assess the likelihood of a given event. What the model can do is identify when there are signs of economic weakness that may or may not be reflected in the current market price. What the model cannot do is identify sentiment, liquidity, or news driven corrections (ie the Donald Trump effect). Currently the model is waiting on several new pieces of data before making a new prediction. The table below shows the model predictions using “point-in-time” data as of the first day of the respective month. Here is the current and historical output:

The most recent prediction which was made as of the beginning of March shows that the model does not expect a moderate to large correction through the end of May. The direction of the market is expected to be sideways or flat. So from a macroeconomic perspective it seems as if the economy is healthy for the time being. Absent any major news events or tweets and the economy is likely to keep chugging along and the stock market will probably remain in a trading range before climbing higher. If you look at the historical predictions they have been fairly accurate in recent times and provide some guidance of when it might be worthwhile buying on weakness (during December 2018) by looking at both the chance of a drawdown and the predicted market direction. While the model was created without running backtests, we will show some applications of using the signals for timing the S&P500.

Shiny New Toys

May 14, 2019

Its been a long time folks, but we have some shiny new toys in the works. Current trends in the industry and working with data scientists has made me a believer in the benefits of using a machine learning approach. I have always been a proponent of “theory-free” approaches on this blog as long as they are designed with robust architecture. In contrast, strict adherence to overly simplistic theories and rules is not optimal for complex systems like the stock market. After experiencing many years of getting whipsawed by traditional indicators, I have recently become convinced a la Philosophical Economics (see this great piece) that you need to have a model(s) that can provide insight into market returns/risk without strictly using price-based indicators. A true macroeconomic model helps to gauge risk that may not be present in current prices and also helps to de-emphasize the reliance price movements that are false alarms. Predicting recessions is not necessarily the most useful for macro models because 1) you can have a bear market without a recession and 2) you can have a recession without a bear market. Furthermore you can have large and damaging corrections that are neither. As a result predicting drawdowns is potentially a more interesting and practical exercise.

S&P 500 Indicator Series

Economics Report

The S&P500 Indicator Series are machine learning forecasting models that use either 1) Macroeconomic 2) Sentiment 3) Technical or 4) Seasonality data with a very wide range of indicators/inputs to make investing decisions.

S&P 500 Economic Forecasting Model Introduction

The S&P 500 Economic Forecasting Model employs a Gradient Boosting Model (GBM) to predict the future distribution of S&P500 returns over the next 90 days based on economic data. GBM is a machine learning methodology which can be used for either regression or classification.

The S&P500 Economic Forecasting Model is a classifier model that predicts the likelihood of equity market drawdowns (moderate or large corrections) and the direction of returns (positive, negative or flat) over a 90-day period. The input variables are derivatives of monthly aggregated macroeconomic data, and does not include price-based or technical data. The choice of a classifier model is due to the fact that equity markets are driven by a wide variety of variables that are often nonlinear by nature. Furthermore, it is important to note that macroeconomic variables are just one component that explains the variation in equity market returns and by using a classifier we avoid many of the issues that regression models have with unobserved features.

The model itself is based on an ensemble of GBM style models (specifically using the XGBoost library. A large number of input macroeconomic data series are selected (see Model Importances for the list) and transformed to create derivative time series. Given that monthly economic data is still relatively sparse (60 years of backdata x 12 months/year), we wanted to choose a model technique that doesn’t required huge amounts of data, but is still very flexible. We excluded alternative models such as logistic regression and neural networks for this reason.

In a GBM model that is attempting to match similar periods together, it is important to make the input values ‘comparable’ in some sense, so the raw values are not appropriate in most cases. Otherwise, it is possible for the model to use the values to simply use the values to memorize where it is in time, which does not generalize well. Instead, values are transformed to make them relative (i.e. percentage change Year over Year, or lognormal differences). It is not necessary to make the inputs stationary in a strict sense, but this is useful to maximize the generality of the model.

The models are trained using a k-fold training algorithm, using a Bayesian optimization routine to select the hyperparameters (tree depth, learning rate, etc). Again, this is done to maximize accuracy and generality while avoiding overfitting.

The output of the model is a score, which is then optimized to maximize theMatthews Correlation Coefficient, which can be considered to be a robust accuracy measure for unbalanced classification sets (which the training data in face is).

The model results over time are shown in the chart below. The blue and red bars show the periods where we expect a drawdown of 10%+ (Moderate Correction) or 15%+(Large Correction) respectively from the end of that period onwards.

More on this model to follow very soon along with weekly model updates on the predicted output.

Welcome to Investor IQ

May 13, 2019

There is some interesting new content on the CSSA blog that will be very useful for readers. Investor IQ is currently a free tool that shows basic trend signals (Buy, Hold or Sell) for a wide range of US and Canadian ETFs as well as a relative strength ranking. The signals will be updated as of the close of Friday and posted on Monday morning. This feature is currently in Beta and will be expanded to include individual stocks and other analytics. It can be found on the blog under the tab “CSSA” as a dropdown menu. A sample of some of the output can be seen below. More details to follow……