This article presents the Trader Edge aggregate neural network model forecast for the April 2013 non-farm payroll data, which will be released tomorrow morning.

Non-Farm Payroll [NFP] Model Forecast - April 2013

The table in Figure 1 below includes the monthly non-farm payroll data for two months: March and April 2013. The March data was released last month and the non-farm payroll data for April 2013 will be released tomorrow morning at 8:30 AM EDT.

The model forecasts are in the third data row of the table (in black). Note that past and current forecasts reflect the latest values of the independent variables, which means that forecasts will change when revisions are made to the historical economic data.

The monthly standard error of the model is approximately 78,900 jobs. The first and last data rows of the table report the forecast plus 0.5 standard errors (in green) and the forecast

I recently wrote about the extreme divergence between earnings and equity prices, but did not have access to comprehensive historical earnings data until recently. In the article above, I referenced the past few years of earnings data, which was provided by FactSet on their website. I am not a subscriber, but FactSet does offer free access to recent earnings data in graphical form in their weekly online publications.

While doing some economic research on the St. Louis Federal Reserve site [FRED], I discovered a quarterly data series on after-tax corporate profits that originated in 1947. The data series is titled "Corporate Profits After Tax with Inventory Valuation Adjustment and Capital Consumption Adjustment." I have only scratched the surface of the data, but the historical link between corporate profits and equity returns is compelling.

Every extreme divergence (-20% or lower) between year-over-year corporate profits and equity prices

The following article updates the Trader Edge diffusion index, recession slack index, aggregate recession model, and aggregate peak-trough model through February 2013.

Diffusion Index

The Trader Edge diffusion index equals the percentage of independent variables indicating a recession. There are a total of 16 explanatory variables, each with a unique look-back period and recession threshold. The resulting diffusion index and changes in the diffusion index are used to estimate the probit, logit, and neural network forecasting models.

The graph of the diffusion index from 1/1/2003 to 3/1/2013 is presented in Figure 1 below (in red - left axis). If you would like to view a graph of the earlier historical data (going back to 1960), please revisit A New Recession Slack Indicator. The gray shaded regions in Figure 1 below represent U.S. recessions as defined (after the fact) by the National Bureau of Economic Research (NBER). The value

This article presents the Trader Edge aggregate neural network model forecast for the February 2013 non-farm payroll data, which will be released tomorrow morning.

Non-Farm Payroll [NFP] Model Forecast - February 2013

The table in Figure 1 below includes the monthly non-farm payroll data for two months: January and February 2013. The January data was released last month and the non-farm payroll data for February 2013 will be released tomorrow morning at 8:30 AM EST.

The model forecasts are in the third data row of the table (in black). Note that past and current forecasts reflect the latest values of the independent variables, which means that forecasts will change when revisions are made to the historical economic data.

The monthly standard error of the model is approximately 78,600 jobs, which is lower than the previous models, but is still significant. The first and last data rows of the

The following article updates the Trader Edge diffusion index, recession slack index, aggregate recession model, and aggregate peak-trough model through January 2013.

If you are unfamiliar with the Trader Edge recession models and would like some additional background information, I encourage you to read the previous recession model update, which also has links to earlier articles.

Diffusion Index

The Trader Edge diffusion index equals the percentage of independent variables indicating a recession. There are a total of 16 explanatory variables, each with a unique look-back period and recession threshold. The resulting diffusion index and changes in the diffusion index are used to estimate the probit, logit and neural network forecasting models.

The graph of the diffusion index from 1/1/2003 to 2/1/2013 is presented in Figure 1 below (in red - left axis). If you would like to view a graph of the earlier historical data (going back to

The following article introduces a new aggregate neural network model that was designed to forecast the seasonally-adjusted, annualized, real rate of change in U.S. GDP.

I have been doing recession research and constructing recession models for the past several months, which prompted me to develop the GDP model. If you would like some additional background on the development of the Trader Edge recession models, I encourage you to read the previous recession model update, which also has links to earlier articles.

Aggregate GDP Model

The Trader Edge aggregate GDP model represents the average of two neural network model forecasts. Neural networks are extremely powerful, and great care must be used to avoid over-fitting the data. As a result, before constructing the models, I divided the data into three separate groups: training, cross-validation, and testing. The training data was used to train the neural network models. The cross-validation data was

Earnings season is here again, which provides a timely reminder that earnings are the ultimate driver of stock prices. The following article examines the recent trend in equity prices and earnings estimates and discusses the implications for the equity markets.

Earnings and Equity Prices Diverge

FactSet is a leading vendor of market analytics, financial and market data, financial screening tools and customized solutions. They also provide a comprehensive, detailed analysis of earnings and earnings trends, which they offer via free weekly PDF files on their site. The earnings graphs in this article are from the latest FactSet report.

Equity prices and earnings forecasts diverged before Q3 2012 earnings were reported; prices rose dramatically while earnings forecasts declined. This preceded a significant market pull-back in the fall of 2012. Unfortunately, we are seeing the same troubling behavior again this quarter. As you can see in Figure 1 below,

The following article updates the Trader Edge diffusion index, recession slack index, aggregate recession model, and aggregate peak-trough model through December 2012.

If you would like some additional background on the development of the Trader Edge recession models, I encourage you to read the following recession model articles before continuing with the most recent update below.

1. New Probit Models: U.S. Recession Risk is Currently Low

2. Recession Model Improvements

3. A New Recession Slack Indicator

Diffusion Index

The Trader Edge diffusion index equals the percentage of independent variables indicating a recession. There are a total of 16 explanatory variables, each with a unique look-back period and recession threshold. The resulting diffusion index and changes in the diffusion index are used to estimate the probit, logit, and neural network forecasting models.

The graph of the diffusion index from 1/1/2003 to 1/5/2013 is presented in Figure 1 below (in red - left axis). If

Advance warnings of business cycle contractions and the corresponding market pullbacks would be invaluable. Toward that end, I have used the Economic Cycle Research Institute's (ECRI) recession forecasts to moderate my equity exposure during probable recessionary environments.

While ECRI has an excellent long-term record in business cycle forecasting, its controversial recession call in September of 2011 was premature, counterproductive, and resulted in considerable opportunity costs. In addition, ECRI does not share its proprietary methodology, which makes it challenging to integrate ECRI's forecasts into an investment process. As a result, I became interested in developing a more systematic approach to U.S. recession forecasting.

A New Framework

I was fortunate to stumble across the work of James Picerno and his articles on the Capital Spectator Economic Trend Index (CS-ETI) and the use of probit models to estimate the probability of a recession. I used his work as the foundation for

Have you ever had the nagging suspicion that you were paying too much for an exchange traded fund (ETF)? Your fears were probably justified. This article explains how to find the real-time value of an ETF using a free resource that is frequently overlooked by many traders.

Net Asset Value

ETFs are backed by an underlying portfolio of assets. Given the recent explosion in ETF offerings, these portfolios may include a very diverse group of assets: stocks, bonds, futures, swaps, and many other types of derivative instruments. The actual value of an ETF is the total market value of its assets, divided by the total number of shares outstanding.

This concept is called a net asset value or NAV and it originated with open-ended mutual funds. Open-ended funds typically allow transactions only once per day, at the closing NAV. As a result, all shares are purchased and sold at the

Consistently picking individual stocks that will generate excess returns is hard, but most investors make it much more difficult than necessary. There are a number of simple, fundamental stock screens that have generated historical returns of 20% to 40% per year. This article will document the results for several of these screens and explain how they could be used in practice. If you buy individual stocks, the screening information described below should help you significantly enhance your investment process and improve your future returns.

Stock Screens

Stock screens are objective sets of criteria or rules that are used to create lists of stocks. Only stocks meeting the criteria are included in the lists. To calculate the historical performance of a stock screen, a rebalancing frequency is also required. Most screens are rebalanced weekly or monthly. Higher frequency screens obviously generate additional transaction costs, but are also much more responsive to