HERE IS HOW INVESTORS CAN RESOLVE TWO SETS OF ARTIFICIAL INTELLIGENCE CONTRADICTIONS IN THE NEW DATA POINTS

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By Nigam Arora & Dr. Natasha Arora

To gain an edge, this is what you need to know today.

Artificial Intelligence Contradictions

Please click here for a chart of Alphabet stock (GOOG).

Note the following:

  • The Morning Capsule is about the big picture, not an individual stock.  The chart of Alphabet stock is being used to illustrate the point.
  • The chart shows when Alphabet (GOOG, GOOGL) earnings were released.
  • The chart shows that immediately after the earnings were released, Alphabet stock went up as the earnings were better than expected and Wall Street was positioned for better than expected earnings.  The momo crowd was buying aggressively before the earnings, hoping for Alphabet stock to reach $160 after earnings.
  • The chart shows that buying after earnings in Alphabet stock did not last very long as selling came in and overwhelmed the buyers.  The selling came from smarter investors who noticed that Google’s cloud growth was lagging.
  • The chart shows that Alphabet stock fell off the cliff.  One of the reasons behind the rapid fall was that Alphabet stock was over-owned going into earnings.
  • In contrast to Alphabet, Microsoft stock ran up on cloud growth.
  • There is a contradiction here.  Google cloud growth lagged.  Microsoft cloud growth did well.  Why?  In The Arora Report analysis, the answer is artificial intelligence.  Microsoft is ahead of Google in artificial intelligence and therefore able to attract more clients to its cloud.  
  • The Microsoft versus Google difference due to artificial intelligence is one set of new data.  There is another set of data from Texas Instruments (TXN).  Texas Instruments is a major semiconductor manufacturer.  Texas Instruments earnings indicate that the semiconductor cycle was still depressed, inventories were still high, and the company did not see a turn up in the cycle.
  • You may recall that only a few days ago, we shared with you that Taiwan Semiconductor (TSM), the largest foundry for advanced semiconductors, said in earnings that the semiconductor cycle had bottomed, inventories were low, and an up turn was ahead.
  • Again, there are contradictory data points from two major semiconductor companies.  Why?  In The Arora Report analysis, the reason is that TSM is highly exposed to artificial intelligence but TXN has very little artificial intelligence exposure. 
  • Now investors have two contradictory sets of data from four major companies in leading sectors.  The difference is coming down to artificial intelligence.
  • A fortune is to be made in artificial intelligence over the next seven years.  However, at times it will be treacherous.  Moreover, market mechanics are very powerful.  Here, the market mechanics of year end chase, over-ownership, and positioning are at play.  Our long experience with thousands of investors has clearly proven that given the exact same information in the Real Time Feeds, investors who take time to develop more in-depth knowledge perform significantly better compared to those who do not take time to develop in-depth knowledge.  The most time efficient way to develop in-depth knowledge is to listen to the podcasts in Arora Ambassador Club.  Arora Ambassador Club has a number of in-depth podcasts on artificial intelligence, semiconductors, and market mechanics.
  • No action is needed by members of The Arora Report because The Arora Report already correctly anticipated this difference between MSFT and GOOG as well as between TXN and TSM; and it is already taken into account in the protection band.  
  • Among important earnings released and not discussed above, earnings are better than expected from BA, GD, and TMUS.
  • As an actionable item, the sum total of the foregoing is in the protection band, which strikes the optimum balance between various crosscurrents.   Please scroll down to see the protection band.
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Magnificent Seven Money Flows

In the early trade, money flows are positive in Microsoft.

In the early trade, money flows are negative in Alphabet, Nvidia (NVDA), Tesla (TSLA), Apple (AAPL), Amazon (AMZN), and Meta (META).

In the early trade, money flows are mixed in S&P 500 ETF (SPY) and Nasdaq 100 ETF (QQQ).

Momo Crowd And Smart Money In Stocks

The momo crowd is *** (To see the locked content, please take a 30 day free trial) stocks in the early trade.  Smart money is *** in the early trade.

Gold

The momo crowd is *** gold in the early trade.  Smart money is *** in the early trade.

For longer-term, please see gold and silver ratings.

Oil

API crude inventories came at a draw of 2.668M barrels vs. a consensus of a build of 1.550M barrels.

The momo crowd is *** in the early trade.  Smart money is *** in the early trade.

For longer-term, please see oil ratings.

Bitcoin

Bitcoin (BTC.USD) continues to levitate as more retail investors learn about the potential upcoming ETF and aggressively buy bitcoin.

Markets

Our very, very short-term early stock market indicator is ***.  This indicator, with a great track record, is popular among long term investors to stay in tune with the market and among short term traders to independently undertake quick trades.

Interest rates are ticking up, bonds ticking down.

The dollar is stronger.

Trading futures is not recommended for most investors. The purpose of providing this information is to give an indication of the premarket activity that usually guides the activity when the market opens.

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Gold futures are at $1987, silver futures are at $22.93, and oil futures are at $83.94.

S&P 500 futures are trading at 4253 as of this writing.  S&P 500 futures resistance levels are 4318, 4400, and 4460: support levels are 4200, 4000, and 3950.

DJIA futures are up 66 points.

Protection Band And What To Do Now

It is important for investors to look ahead and not in the rearview mirror.

Consider continuing to hold good, very long term, existing positions. Based on individual risk preference, consider holding *** in cash or Treasury bills or allocated to short-term tactical trades; and short to medium-term hedges of ***, and short term hedges of ***. This is a good way to protect yourself and participate in the upside at the same time.

You can determine your protection bands by adding cash to hedges.  The high band of the protection is appropriate for those who are older or conservative. The low band of the protection is appropriate for those who are younger or aggressive.  If you do not hedge, the total cash level should be more than stated above but significantly less than cash plus hedges.

It is worth reminding that you cannot take advantage of new upcoming opportunities if you are not holding enough cash.  When adjusting hedge levels, consider adjusting partial stop quantities for stock positions (non ETF); consider using wider stops on remaining quantities and also allowing more room for high beta stocks.  High beta stocks are the ones that move more than the market.

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Traditional 60/40 Portfolio

Probability based risk reward adjusted for inflation does not favor long duration strategic bond allocation at this time.

Those who want to stick to traditional 60% allocation to stocks and 40% to bonds may consider focusing on only high quality bonds and bonds of five year duration or less.  Those willing to bring sophistication to their investing may consider using bond ETFs as tactical positions and not strategic positions at this time.

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Picture of Nigam Arora

Nigam Arora

Nigam Arora is known for his accurate stock market calls. Nigam is a distinguished master of the macro. He is a popular columnist with over 100 million page views, an engineer, and nuclear physicist by background. Nigam has founded two Inc. 500 fastest growing companies and has been involved in over 50 entrepreneurial ventures. He is the developer of Theory ZYX of Successful Change Management and is the author of the book on Theory ZYX, as well as the developer of the ZYX Change Method for Investing.

Picture of Dr. Natasha Arora

Dr. Natasha Arora

Dr. Natasha Arora has significant expertise in investment analysis especially biotech, healthcare, and technology. Natasha is a graduate of Harvard Medical School followed by a postdoc at MIT. She has published several peer reviewed research papers in top science journals.

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