Possibly


 

What are you even looking at? [0.10]

Posted on Aug. 1, 2017, 2:05 p.m. by The Reformed Broker @ [source]

Where the hell did these people learn to invest or trade?” There is no doubt that the bull market in the S&P 500 could stop at any time, but how could you possibly look at it now and say it’s imminent?

I get rooting for a bear market – lots of people run strategies where their ability to earn a living depends on bear markets.

advance bear bull Hell invest learn market possibly stop trade

 

Busting 3 Myths About China [0.04]

Posted on May 15, 2017, 2:15 p.m. by HOME @ [source]

Everyone seems excited about emerging markets this year, pouring money into EM ETFs as the segment offers investors attractive valuations and some fundamental improvement. One of the key points they highlight is the slowdown in GDP growth in recent years, now around 6-7% a year, but a far cry from the double-digit days in the early 2000s.

assessment china emerging friday growth investors market misunderstood possibly remains

 

This is a Rare Signal [0.10]

Posted on Jan. 31, 2017, 8:58 a.m. by Price Action Lab Blog @ [source]

After a long time there are nearly equal long and short signals in the quantitative analysis of S&P 100 stocks.

It has been a long time since there were nearly equal numbers of stocks with positive and negative directional bias in a DLPAL p-indicator scan.

Bearish DLPAL happened possibly rare reversion short signal stocks term

 

State of Small Business [0.08]

Posted on Dec. 2, 2016, 5 p.m. by The Big Picture @ [source]

Why, if so much has improved in recent years relating to small business, are there fewer startup firms, comparatively?

Though conditions for small businesses are hard to generalize because experiences vary greatly from firm to firm, Federal Reserve Bank of Cleveland experts say some data suggest an improving landscape for those businesses operating along Main Street.

explores firms Forefront improved main mid net possibly small Street

 

Feature selection in trading algorithms [0.16]

Posted on Jan. 16, 2014, 10:40 a.m. by Math Trading @ [source]

Lately I have been looking for a more systematic way to get around overfitting and in my quest I found it useful to borrow some techniques from the Machine Learning field.

Expanding what discussed here (and here), it seems intuitive that the more features in a model, the more generally speaking the model might be subject to overfitting.

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