My mutt Sophie determines 6(six).(five)5 quake in Eureka towards the Times-Standard that All 3 sectors

Visit for the full story. … Sophie is seen 15 seconds prior to the start of the earthquake, looking sharply at the floor and dismissing it. About 15 seconds later she again looks sharply at the floor and bolts to the other end of the room when the rumble starts, trying to locate her owner — who is not the first person out the door, but the third person out, Jessica. Sophie can be seen directly behind Jessica as her back and tail graze the lower right-hand corner of the screen in the first segment. In the second segment Sophie spotted Jessica coming from the sink where she was washing her hands and ran to catch up. She then escorts Jessica safely down the stairs and out of the building. For full coverage of the earthquake and its aftermath, visit
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FNMA Sell Reduction – Discovered misplaced 30K in 5 several hours / Stock exchange / Trading

FNMA Stock Loss – How I lost 30K in 5 hours / Stock Market / Investing

A short story and lessons learned on how I lost 00/hr (30K) in FNMA back in 2013. A friend of mine brought the stock to me when it was trading in the .20 cent range. I had only a few times traded „penny“ stocks so it wasn’t something I took seriously. However, when the stock started to show signs of life shorty after he mentioned it, I took another look. Fannie Mae had been trading in the 60’s prior to the 2008 fiasco. So why not take a look at a .20 cent stock and see if some money could be made.

We both ended up trading the stock when it started to perk up to .85 cents. We both made a few thousand dollars on the stock in just minutes. We did this for a few days making a little here and a little there. Then came doomsday on May 29, 2013 when FNMA went parabolic and then just rolled over. We both got caught in the mess. I had a sell order in but it I could not get a fill. Short restriction was in effect as well. Once the stock caught a bid, my sell order filled of course. I had just lost 20K in a short amount of time! Frustrated as many traders are after a big loss, I proceeded to „revenge“ trade. So I got back in after a small break on signs that the stock was heading up just to have it roll over on me again! Another 10K down the drain!

All told, I lost ,000 FNMA in 5 hours (00/hr) and my tail between my legs. Come to find out, my friend was way worse off than I was. He never really admitted to me what he truly lost, but he did mention that he was down K at one point.

If you are new to trade, please do not trade penny stocks. They are pump and dumps. Unless you have an inside scoop, you will likely lose. It’s really all luck.

I would suggest if you are new at trading to look into trading blue chip companies and/or quality indexes like VTI and VOO.

If you have any questions, please put a comment below and I will respond.

Please LIKE and SUBSCRIBE, thanks for watching.

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DISCLAIMER: Hidden Freedom, including but not limited to any guests appearing in Hidden Freedom videos, are not financial/investment advisors, brokers, or dealers. They are solely sharing their personal experience and opinions; therefore, all strategies, tips, suggestions, and recommendations shared are solely for entertainment purposes. There are financial risks associated with investing, and Hidden Freedom’s results are not typical; therefore, do not act or refrain from acting based on any information conveyed in this video, webpage, and/or external hyperlinks. For investment advice please seek the counsel of a financial/investment advisor(s); and conduct your own due diligence and/or research.

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#Trading #StockMarket #Investing
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Hottest A Multivariate Time-series Speculation Product For Cash-flow Figures income

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This lesson introduces time series data. We then cover several quantitative time series forecasting methods presenting moving average (MA), weighted moving average (WMA) and exponential models. As we present each type of model we show how to develop the model in Excel (Google Forms).

Forecasting and Regression

Time period Series Dreaming up and State House Models: Professor Aspect Garcia, Traditional college of Glasgow

– AR, MA and ARMA models
– Parameter estimation for ARMA models
– Hidden Markov Models (definitions, inference, learning)
– Linear-Gaussian HMMs (Kalman filtering)
– More advanced topics (more elaborate state-space models, and recurrent neural networks)

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