(951) 268-7836 info@authintel.com

SQL Server 2016 RTM

Some major enhancements and a convergence on features from Azure to on-premise. The ‘R’ capabilities and seamless integration with big data are game-changers. https://www.microsoft.com/en-us/cloud-platform/sql-server...

When two-factor authentication really isn’t

My hope is that by now all companies are using multifactor (MFA for short) authentication to protect all critical assets. MFA authentication simply means that a user must validate their identity through at least two independent mechanisms. Two-factor authentication or 2FA for short just means two independent mechanisms are utilized. However many companies that are implementing 2FA are not implementing true 2FA because the mechanisms are not totally independent. Unless there is no way that one of the mechanisms in a 2FA scheme can be leveraged to gain access to the other mechanism, the mechanisms cannot be considered independent. Two common 2FA schemes that are subject to compromise because of lack of independence are the use of a dial-back number that goes to a smart phone and hosting a 2FA on a corporate laptop. This post focuses on the risk with a smart phone that is on the receiving end of a dial-back number. Here is a typically sequence of events for 2FA involving a dial-back number: 1) Logon to a web site or remote desktop or some other resource 2) User is prompted for credentials 3) User enters credentials 4) Upon successful entry of the credentials, the system prompts for an access code that is communicated on the dial-back number. 5) User then receives a phone or in the case of a cell, perhaps a text message or there may be an app configured to acknowledge the call. 6) At this point, the authentication generally occurs in either of the below fashions: a. User enters the code communicated via text message or voice call to the primary authentication...

Multiple divergences for S&P analyzed (Corrected)

Note: My first version of this had an error in the query which has been corrected and the difference between up and down days is not significant. However, the larger the VIX change, the more likely to be a VIX up day and S&P down day the next day. Today was an interesting day in the stock market. The S&P 500 closed positive while the most correlated indicators aside from other equity indexes closed strongly negative.  This includes Treasuries (up) and high yield/corporate bond (down). These are all the opposite results of the norm, at least for the last several years. Along with that volatility was down significantly, which does correlate with upwards moves. Since this seems to make the move in the S&P suspect in terms of at least the immediate term, I ran a query in my equity history database. I used the cube with the grouping function to get a rollup of each grouping and the entire selection.  Below is the query: Although I have history of the S&P back to 1950, some of the other instruments only go back to 1990, so the analysis is limited to then. 195 instances were found that met the condition over a 25 year period, so this only has only happened on average about 13 times per year.  Results for Days where VIX is down while High Yield is down, Treasuries are up, but S&P 500 is up: Out of 195 instances, VIX was up the next day slightly less often (93 versus 102 instances) test.  Along with that, the average change on the days that the VIX went...

Multiple divergences for S&P analyzed

Today was an interesting day in the stock market. The S&P 500 closed positive while the most correlated indicators aside from other equity indexes closed strongly negative.  This includes Treasuries (up), high yield/corporate bond (down), and Volatility (down). These are all the opposite results of the norm, at least for the last several years. Since this seems to make the move in the S&P suspect in terms of at least the immediate term, I ran a query in my equity history database. I used the cube with the grouping function to get a rollup of each grouping and the entire selection.  Below is the query: Although I have history of the S&P back to 1950, some of the other instruments only go back to 1990, so the analysis is limited to then. 152 instances were found that met the condition over a 25 year period, so this only has only happened on average about 6 times per year.  Out of 152 instances, VIX was up the next day almost 1 1/2 times as often (61% versus 39%) based  on 93 out of 152 instances positive for the test.  Along with that, the average change on the days that the VIX went up was negatively much stronger than on the VIX down days. The up days occur most often during tumultuous markets such as 2000 and 2007. Below are the detailed results. Are 152 instances enough data points to justify making a trading decision based on the information? How strong is the correlation when allowing for the confidence factor associated with this many instances? I’m still researching how to quantify...

History may not repeat, but…

Somebody from a private forum I belong to known as “T-Theory” posted a graph that shows the rationale for being long the S&P when the 10 week exponential moving average is trending above the 50 week exponential moving average and being short in the opposite case. T-Theory is an approach invented by the late Terry Landry to viewing market behaviors in terms of cycles – that is markets tend to spend half of their time rising at a faster pace than the other half of the time and that these periods tend to occur symmetrically. For a more detailed explanation of T-Theory, see http://cdn3.traderslaboratory.com/forums/attachments/34/31965d1350013176-beyond-taylor-a1997introttheory_.pdf The below graph shows the exponential moving averages (EMA) for 10 weeks using the red line and for 50 weeks using the green line. A sell signal is generated when the red line crosses the green line while a buy signal occurs when the green line crosses over the red line. A sell signal was recently generated for the S&P 500 and also exists for the other major indexes including the Dow. A sell signal has also been in place for many foreign indexes for many months including the Chinese market. Clearly, this was a good strategy since 2000. I decided to quantify the benefits since then as well as over the longer-term using my equities database. I recently added .NET SQLCLR (A mechanism for Microsoft SQL Server that allows one to write .NET code and integrate into database functions) functionality to my SQL Server database that makes it relatively easy to calculate different technical indicators on the fly. I have been able to build...