NEWS: Novavax Announces Sale of Czech Republic Manufacturing Site to Novo Nordisk for $200 Million

• Agreement provides significant, non-dilutive capital to Novavax, further enabling the Company to advance its corporate growth strategy of driving value from its pipeline assets and proven technology platform

• Provides Novavax with $190 million cash payment in 2024 and additional $10 million in 2025, and annual operating cost reductions of approximately $80 million

GAITHERSBURG, Md., Dec. 4, 2024 /PRNewswire/ -- Novavax, Inc. (Nasdaq: NVAX), a global company advancing protein-based vaccines with its Matrix-M™ adjuvant, today announced that it has signed a definitive agreement to sell its manufacturing facility in Bohumil, Czech Republic to Novo Nordisk for $200 million. The agreement includes a transfer of assets, including a 150,000-square foot state-of-the-art recombinant protein manufacturing facility with support buildings, along with the existing workforce and all related and required infrastructure.

The agreement provides Novavax with significant, non-dilutive capital, further enabling the Company to advance its corporate growth strategy to drive value from its early- and late-stage pipeline using its proven technology platform, consisting of Matrix-M adjuvant and nanoparticle protein-based technology. In addition to the $190 million cash payment in 2024 and additional $10 million in 2025, Novavax expects the sale of the facility to result in annual operating cost reductions of approximately $80 million.

"The decision to sell the Czech Republic manufacturing facility aligns with our previously announced commitment to evolve Novavax into a more lean and agile organization focused on partnering our pipeline assets and technology platform," said John C. Jacobs, President and Chief Executive Officer, Novavax. "We are thankful to our dedicated colleagues in the Czech Republic who have contributed to Novavax's mission of delivering our technology to address unmet needs. We look forward to working with Novo Nordisk to ensure a successful transition."

Following closure of the agreement, expected by December 30, 2024, full responsibility for the manufacturing facility will be transferred to Novo Nordisk.

https://ir.novavax.com/press-releases/2024-12-04-Novavax-Announces-Sale-of-Czech-Republic-Manufacturing-Site-to-Novo-Nordisk-for-200-Million

TooFuzzy,

I am considering some 2X leveraged ETFs but haven't started testing any yet. Every leveraged ETF I have tested included the 2020 crash. Even when my test cases ran out of cash and there wasn't a single trade in up to 20 months, the overall return is still fantastic. The Black Swan rule helps in some cases, not much in others, especially if the price continues to drop after the last purchase where cash was depleted. My strategy will always be to try to inject more cash to capture as many of those low price buys as possible, so if I see the overall market headed lower, I start to pile up cash in anticipation of some of my AIM investments running out of cash.

I'm not promoting Jeff Weber's methods. I want to learn more about his LEAPs method, but I'm not necessarily going to adopt it. Regarding his rebalancing recommendation, I went back and checked and it's not restricted to high cash balances. His recommendation is to rebalance annually in all conditions. Perhaps that applies only to LEAPs investing in conjunction with the annual rollovers to new LEAPs, but it certainly isn't a good strategy with leveraged ETFs as my testing has indicated. Also, I am not new to AIM. I've known how the algorithm works since the mid-1980's. Both of Jeff's books are written to provide a description of the AIM model, but not much detail on using LEAPS, and are mainly written to drive the reader to pay for his more detailed online videos and newsletter on LEAPs selections and management. I believe his statements about LEAPs being much more volatile than the underlying stocks, but leveraged ETFs are highly volatile as well, and I will probably stick with those. His second book on LEAPs investing is available at my local library, and I'll read it next. I will evaluate for my own use, but I won't implement until I've satisfied myself through testing of historical price data to see how it washes out.

Thanks for the link to your QUICK AIM CALCULATOR. However, I disagree that it's simpler than what I'm working on. I designed my own AIM spreadsheet to be a preemptive model that provides next buy and sell price where I can place conditional limit orders and capture more trades than the traditional AIM model, while not requiring much time at all to manage. The link to my condensed "mini" model is here:

AIM Mini

It provides everything your model does but in a format that documents every trade performed. I have shared this with a number of people, but I created it for my own use and how I want to implement the AIM model Lichello created.

I've tested historical data using the Buy SAFE adjustments in 5% increments with each buy, and it actually hurt performance overall. It was better in my testing to run out of cash and be fully invested. Black Swan trades helped more in that scenario than trying to tweak settings to prevent running out of cash. It sounds like a good strategy, but it hasn't proven to be during my own testing, at least not for 3x leveraged ETFs. I will not be using any adjustments to SAFE values during my AIM investing.

I agree, rebalancing at high cash positions, which only occurs as price moves higher, is probably not as effective as just using the excess cash to spin off into new investments. Or it can be used to provide temporary cash to other AIM investments that run out of cash. I don't necessarily agree that everything would tank together in a market correction. For example, Gold and Silver ETFs will likely have cash to spare in stock market downturns as investors panic and sell stocks to invest in precious metals, so having a couple of those investments running in AIM could be beneficial to aid in buying when cash runs out in equity investments. And investing across different sectors may provide different levels of cash availability in normal economic conditions as favorability shifts across sectors.

I enjoy learning about strategies to address the few shortcomings of the AIM model in some circumstances, but so far I've been pretty satisfied with the model as is, and don't intend to do much if any tweaking of the initial settings mid-investment. As we all have different risk tolerances and goals with our investments, various strategies might be better suited to specifics types of investments than others. But I appreciate your responses and the information you provided. I'm always open to learning new things.

These are my 5-Year test results for TECL (Direxion Technology Bull 3X Shares). Both Manual AIM with monthly updates, and preemptive AIM with limit orders used for next buy and sell prices, were tested.

Test conditions common to all tests
Test Period: 11/15/19 – 11/15/24
Initial Investment: $10,000 @ 50% Equity / 50% Cash
Buy SAFE and Sell SAFE: 10%
Minimum Transaction Size: 10% (based on Share Value)


TEST 1 – Manual AIM with monthly updates
AIM Return: 321%
B&H Return: 327% (same in all tests)
Final Value: $42,145
Total Trades: 27
Average Annual Return: 33.3%
Cash Range: 28.5%–74.8%

TEST 2 – Preemptive AIM with cash depletion allowed
AIM Return: 438%
Final Value: $53,808
Total Trades: 79
Average Annual Return: 40.0%

TEST 3 – Preemptive AIM with cash depletion allowed, Black Swan rule used
AIM Return: 493%
Final Value: $59,333
Total Trades: 82
Average Annual Return: 42.8%

TEST 4 – Preemptive AIM with cash depletion, Black Swan, and Semi-annual rebalancing
AIM Return: 475%
Final Value: $57,501
Total Trades: 82
Average Annual Return: 41.9%
Number of Rebalances: 1 (only when cash exceeded initial 50% value)

TEST 5 – Preemptive AIM where cash added to cover all trades when cash depleted
AIM Return: 1,108%
Final Value: $120,753
Total Trades: 109
Average Annual Return: 64.6%
Total Cash Added: $32,000
Total Cash Removed: $32,000 (withdrawn when cash balance recovered)

I ran a test using annual rebalancing with TECL (Direction Technology Bull 3X Shares) over 5 years and encountered a number of issues. The first time annual rebalancing came around, AIM was fully invested and out of cash. To rebalance at this point would require selling half the shares owned at a loss, since the price at that point was the lowest in the test to that point. I punted and didn't rebalance.

I then changed the frequency to every 6 months, and over the 5-year period there was only one period where cash exceeded the initial 50% ratio, so only one rebalance was performed in the 5-year test.

The rebalancing recommendation in Weber's book didn't specify any conditions, such as only when cash exceeds the 50% value. Even with the single rebalancing performed, it hurt overall performance slightly, resulting in $1,800 lower gains over the period. I can see possibly using this strategy to reinvest excess cash in the security if it continues to trend higher, but in actual practice it doesn't seem like a good strategy in any other condition.

Anyone using periodic rebalancing? Again, I'm testing using 3X ETFs. It probably works better with non-leveraged investments.

Rebalancing question... I'm reading Jeff Weber's "Here Are the Customer's Yachts" and he suggests rebalancing the AIM investment to the original equity-to-cash ratio on an annual basis.

Does anyone do this in practice? I have seen postings about various strategies to prohibit selling if cash gets too high, but don't recall seeing any posts on rebalancing. I had considered withdrawing excess cash to spin-off into a new investment, but the annual rebalancing seems like a better option, where excess cash is used to purchase more shares of the current investment.

I am more of the "set it and forget it" type and don't want to be making a lot of adjustments to AIM settings once it's going, but rebalancing seems like a good strategy. I thought it might not be a good strategy for leveraged ETFs, but Jeff is suggesting it for his LEAPs investing strategy, which are also highly volatile, so it must be good for leveraged ETFs too. I'll run a few scenarios on historical data to see the impact.

Thanks for the explanation. I would probably have understood from the start had I read enough of the previous posts here.

Hi Tom. I selected a 5% minimum trade size on the 10-year test because I recognized the low volatility in the initial 5 years compared to the last 5 years, and wanted to try to generate more trades in that initial period. However, even at 5% there was a long period after initial investment with no trades. A lot of the leveraged ETFs I've tested have a lot of volatility over the past 5 or 10 years, but were relatively flat prior to that. Any ideas as to why? I assume it was because the fund managers needed experience to really achieve the 2x or 3x performance, and this is why the funds appear to have higher volatility today than 10 years ago.

My testing has shown that 5% minimum improves on the 10% minimum in terms of overall gains. This makes sense with the preemptive model where limit orders are placed and we wait until the price comes to trigger one of our orders. We get roughly twice as many trade at half the size with 5% but we also capture some of the trades that a 10% minimum would have potentially missed. Considering how little extra time it takes to document the extra trades, I would always use 5% minimum for my investments. I only use 10% in backtesting because it cuts testing time down by half, and that adds up when you consider it takes a couple of hours to work through a 5-year historical data set, and I have a lot of ETFs I want to test.

Another aspect of my testing that probably doesn't apply to everyone is that all of my investments are now in a Roth IRA, so no consideration of LIFO or FIFO strategies. I utilized a strategy called Mega Backdoor Roth IRA to direct over $50K per year into my Roth, which far exceeds what one can do with the typical annual contribution limits. It's a great strategy for those able to use it. For anyone interested, just do a search for the term. Lots of info online about the strategy. From what I've read only about one-third of 401Ks allow it, but perhaps that number is now higher. It also helps to be debt-free so a big chunk of one's income can be used to fund this strategy. Thanks, Dave Ramsey!!

I appreciate the info you provided, Tom. You've been doing AIM a lot longer than most here so your input is very enlightening. I agree 100% with the "set and forget" approach to AIM. I've tried to use the charts to predict the initial equity/cash mix with limited success. Where I thought a larger cash position might work better at the start, it actually underperformed the 50/50 mix, so I'm tempted to just leave it there. Calling it right might improve performance, but calling it wrong will not. Again, I'm focusing mostly on leveraged ETFs because of their volatility that drives AIM to great returns, if one can trust the model and not get discouraged when prices fall, sometimes drastically. For me, the "ding" of a text message indicating another AIM trade, regardless of whether price is rising or falling, is music to my ears.

I see your point with the charts you provided on UPRO. But one question... why are you focusing on LIFO gains? If I was investing taxable funds, I would focus on FIFO for the favorable long-term capital gains tax rates. Or are you focusing on simply minimizing the gains since the LIFO would be the lowest gain, but at a potentially higher tax rate?

You have mail.

One more update on AIM testing results for UPRO over 5-year and 10-year periods.

TEST 3
5 years: 11/15/19 – 11/15/24
Cash depletion allowed, Black Swan Rule utilized
Initial Investment: $10,000 @ 50% equity, 50% cash
Buy SAFE: 10%
Sell SAFE: 10%
Minimum Transaction: 10% (based on Share Value)

AIM Return: 291%
B&H Return: 185%
Final Value: $39,061
Total Trades: 59
Average Annual Return: 31.3%


TEST 4
10 years: 11/17/14 – 11/15/24
Cash depletion allowed, Black Swan Rule utilized
Initial Investment: $10,000 @ 50% equity, 50% cash
Buy SAFE: 10%
Sell SAFE: 10%
Minimum Transaction: 5% (based on Share Value)

AIM Return: 820%
B&H Return: 734%
Final Value: $91,968
Total Trades: 186
Average Annual Return: 24.8%

In the 10-year test, there was so little volatility following the initial investment on 11/17/14 it took 9 months for the first subsequent trade to occur. This characteristic has been observed in a number of the leveraged ETFs I've tested. Most are significantly more volatile over the last 5 years than over their earlier periods. Perhaps this is the result of the ETF managers learning how to better utilize the daily derivatives and swaps used to obtain the double or triple performance of the underlying sector or index over time. In the 5-year and 10-year testing of UPRO, the Black Swan Rule did result in generating a number of useful trades when cash was depleted that added to the overall return. In these tests, no interest or dividends were included, so actual returns would be slightly higher when these factors are included.

If anyone cares to see the spreadsheets on these tests, let me know and provide your email address.

Update on AIM testing results for UPRO over 5-year period from 11/15/19 – 11/15/24.

I re-performed the test as Test 3 utilizing only Black Swan Rule when cash was depleted. No changes to Buy SAFE as in Test 2. Results were improved over initial Test 1.

TEST 1 – Cash depletion allowed, no action taken until next sell
Initial Investment: $10,000 @ 50% equity, 50% cash
Buy SAFE: 10%
Sell SAFE: 10%
Minimum Transaction: 10% (based on Share Value)

AIM Return: 261%
B&H Return: 185%
Final Value: $36,089
Total Trades: 55
Average Annual Return: 29.3%


TEST 2 – Black Swan Rule and Buy SAFE 5% adjustments
Initial Investment: $10,000 @ 50% equity, 50% cash
Buy SAFE: 10%
Sell SAFE: 10%
Minimum Transaction: 10% (based on Share Value)

AIM Return: 182%
B&H Return: 185%
Final Value: $28,239
Total Trades: 40
Average Annual Return: 23.1%


TEST 3 – Cash depletion allowed, Black Swan Rule utilized
Initial Investment: $10,000 @ 50% equity, 50% cash
Buy SAFE: 10%
Sell SAFE: 10%
Minimum Transaction: 10% (based on Share Value)

AIM Return: 291%
B&H Return: 185%
Final Value: $39,061
Total Trades: 59
Average Annual Return: 31.3%

I think my strategy with leveraged ETFs will be to let cash continue to go higher, and at some point pull out some cash to spin off another AIM investment.

I'm also going to explore the method Jeff Weber recommends using AIM with LEAPs. I think some of his investors are using the profits to spin off new investments as well.

I appreciate your input. Are you basing your minimum transaction size on a percentage of portfolio control, or a percentage of current share value as in the original Lichello model?

I am revising my AIM model to test using portfolio control. I understand how this will keep the minimum transaction size more consistent vs. constantly decreasing based on a declining share value in a deep dive. I'm looking forward to seeing the real impact in testing multiple scenarios.

Thanks for your kind remarks. I created the AIM spreadsheet for how I intend to use AIM, which is in preemptive mode in lieu of manually updating on a period basis. I let the market come to my predetermined prices instead of reacting to the market. I came across this idea after listening to the audiobook Flash Crash, where high-frequency trading models were explained in detail. I thought I would try to capture as many trades as possible during swings of highly volatile leveraged ETFs that might otherwise be missed using manual updating on some regular interval. I also wasn't trying to create a lot of work for myself. I test mainly using 10% minimum transaction size because it cuts testing time in half, but in practice I will use 5% minimum transaction as my testing has shown it improves overall performance, capturing about twice as many trades at half the size.... similar to the principle behind high-frequency trading. However, even at 5% it's literally 15 minutes/month on average to manage each investment, updating executed trades and entering new limit orders. All that being said, other AIM investors have developed the same strategy, so it's not something I can claim credit for. But the audiobook was the inspiration for me to create my own model for my purposes. I'm glad to share it with others if it helps them to be successful with their investments.

I am working on modifying my model to base the minimum trade size off of Portfolio Control instead of Share Value as used in the original model, and test to see the impact on performance. Some here are using this method, but I need to convince myself with testing before I'll actually implement this method. As in my earlier post, the adjustments to Buy SAFE some have recommended actually hurt AIM performance in my one test. I'll repeat it on other leveraged ETFs in the coming weeks to properly evaluate the overall impact. My test could be a one-off example that is highly dependent on where I entered the cycle of the ETF volatility.

The profit for each trade is hard to determine because you have to assume a purchase price. As with strategies using FIFO or LIFO, the profit will be different for each. I can tell you with my current model, the monetary value of each trade will be about 10% of the current share value if you have the minimum set at 10%. The actual profit for each sell would have to be based on the average purchase price of the shares owned up to that point, so it's hard to provide an actual value.

AIM testing results for UPRO over 5-year period from 11/15/19 – 11/15/24. Results are surprising and not what I expected. Both tests were conducted using the preemptive model assuming limit orders were in place for the next buy and sell prices indicated by the spreadsheet. Trades assumed to have executed if the respective limit order price fell between the high and low prices using historical data.

TEST 1 – Cash depletion allowed, no action taken until next sell

Initial Investment: $10,000 @ 50% equity, 50% cash
Buy SAFE: 10%
Sell SAFE: 10%
Minimum Transaction: 10% (based on Share Value)

AIM Return: 261%
B&H Return: 185%
Final Value: $36,089
Total Trades: 55
Average Annual Return: 29.3%

Cash was depleted twice. First time required 2.5 months until next sell. Second time required 8 months until next sell.

TEST 2 – Black Swan Rule and Buy SAFE 5% adjustments

Initial Investment: $10,000 @ 50% equity, 50% cash
Buy SAFE: 10%
Sell SAFE: 10%
Minimum Transaction: 10% (based on Share Value)

AIM Return: 182%
B&H Return: 185%
Final Value: $28,239
Total Trades: 40
Average Annual Return: 23.1%

Cash was depleted once. There was only 1 Black Swan trade in this period, and 1 month recovery to next normal sell.

This comparison indicates it was better to allow the original unaltered AIM model to trade and allow cash to run out. More trades were executed on the drawdown keeping the Buy SAFE at 10% and this significantly improved overall returns over the 5-year test period, even with the long 8-month period of no activity in the first scenario. As previous testing has also indicated, any cash added on a temporary basis to fund these low-price buys during periods of cash depletion greatly improves overall returns. This is a good reason to use investments across multiple sectors if using leveraged ETFs, where a sector in favor may provide some temporary cash to an out-of-favor sector investment that runs out of cash.

In Test 2, the initial Buy SAFE was incremented 5% higher after each purchase following the initial investment, and 5% lower after each subsequent sell until reaching the initial 10% value again. While this strategy may work better over different test periods and different entry points in the cycle, it clearly did not improve AIM performance for this leveraged ETF and this test period. This is the 5-year chart for the period tested.

When using the strategy to increase Buy SAFE by 5% for each purchase to help prevent running out of money in a leveraged ETF, does this strategy apply to all buys, or just the ones when cash reaches a certain threshold? Also, in restoring the Buy SAFE to the normal 10% value, should that be done incrementally in 5% steps with each subsequent purchase, or all at once on the first purchase?

I read about this strategy but some points were unclear. I'm testing UPRO with this strategy, where it ran out of funds a twice over the 5-year test period and I want to see the impact on the overall 5-year return. The first time took 2.5 months to recover to the first sell, and the second time 8 months to recover. Hopefully employing both Buy SAFE adjustments and Black Swan Rule will yield better results in my testing.

Also, a question on Black Swan... If the lowest buy price that wiped out cash was $10, the next sell price would be $12.50, I assume with the same number of shares bought at $10. Does this price range remain fixed, buying again at $10 and selling at $12.50 as many times as can be accomplished until price recovers to next AIM sell price that would occur under normal conditions?

I'd appreciate any insight the long-time AIM users here could provide.

The link should do nothing more than automatically download the file. Where that file goes on your computer is dependent on your settings. Search for the file named "AIM Mini MASTER.xlsx".

If that doesn't work, email me at joeforkeybolo at myyahoo dot com and I'll simply email you the file.

Here's the link I just copied from my Dropbox folder for the AIM Mini spreadsheet. Give it a try. You might want this version over the Learning model because it includes detailed instructions and provides an example of the OCO Conditional limit order that is the key to the preemptive model.

All settings at the top (highlighted in green) are configurable by the user. None of these cells are locked but the user can lock these cells after initial settings are entered if they choose to prevent inadvertent changes. The settings can be changed at any point in the investment. They will impact all calculated cells throughout the spreadsheet, but they effectively only impact future calculations because the manual-entry cells essentially document a completed trade or monetary transaction such as adding interest on the cash position. However, the completed row calculated values will no longer correlate to the manual entries. The user needs to understand the difference and ignore any changes in the already completed transactions.

AIM Mini

Jon,

I've reworked the AIM Mini version to leave the Portfolio Control column unhidden so manual Vealie adjustments can be made. You first have to unprotect the spreadsheet to edit the calculated value. Link below, if you want to use this version.

AIM Mini Model

See my previous posts for links to the AIM models I have created.

Novavax Announces Grant of Inducement Awards Pursuant to Nasdaq Listing Rule 5635(c)(4)

GAITHERSBURG, Md., Nov. 15, 2024 /PRNewswire/ -- Novavax, Inc. (Nasdaq: NVAX), a global company advancing protein-based vaccines with its novel Matrix-M™ adjuvant, today announced that the Company granted a non- qualified stock option and restricted stock units to Ruxandra Draghia-Akli, MD, PhD, its newly appointed Executive Vice President and Head of Research & Development, as a material inducement for her entry into employment with Novavax, effective as of November 11, 2024 (the "grant date"). These awards were approved by the Compensation Committee of Novavax and were granted in accordance with Nasdaq Listing Rule 5635(c)(4) and pursuant to the Novavax, Inc. 2023 Inducement Plan.

The non-qualified stock option is an option to purchase 64,150 shares of Novavax's common stock with a per share exercise price of $9.01, the closing price of Novavax's common stock on the Nasdaq Global Select Market on the grant date. The non- qualified stock option has a ten-year term and will vest as to one-quarter of the underlying shares on the first anniversary of the grant date, and as to the remaining shares in equal monthly installments for 36 months thereafter, in each case generally subject to Dr. Draghia-Akli's continued employment with Novavax through the applicable vesting date. The restricted stock units are with respect to 42,770 shares of Novavax's common stock and will vest as to one-third of the restricted stock units on each of the first three anniversaries of the grant date, in each case generally subject to Dr. Draghia-Akli 's continued employment with Novavax through the applicable vesting date. The non-qualified stock option and restricted stock units are subject to the terms and conditions of the Novavax, Inc. 2023 Inducement Plan.

https://ir.novavax.com/press-releases/2024-11-15-Novavax-Announces-Grant-of-Inducement-Awards-Pursuant-to-Nasdaq-Listing-Rule-5635-c-4

I haven't invested in any 3x ETFs yet. I'm only backtesting historical data to gauge the algorithm's performance. I ran the same test on the same SOXL data letting cash run out in one test and fully funding the buys with additional cash in the other test. The outcomes were orders of magnitude better, even though the outcome with cash depleted was excellent, but 20 months with no activity is hard to do. I plan to retest with the strategy of churning a little bit during that period by selling at lowest buy price divided by 0.80 as some have suggested, as see how that impacts outcomes.

But as has been discussed here, having multiple 3x ETFs across different sectors might offset the issues as one sector ETF running out of cash could use some excess cash from another sector that was up. It's unlikely all sectors would be down at the same time, except for a major market correction, and in that scenario I doubt any strategy would work except continued buying as in dollar cost averaging. One just have to have enough confidence in recovery to not panic and sell during this time.

Thanks for the perspective. It's probably a good strategy to make multiple investments using a mix of 1.5x to 2x funds across different sectors, and maybe a limited number of 3x funds. I think it would be very enjoyable to manage such a mixture and watch the gains pile up over time. AIM is a great way to manage this strategy.

I just modeled QQQ over the most recent 5-year period for a friend. Buy and Hold significantly outperformed AIM over this period. The model never ran out of cash, but even with the initial mix of 70% equity and 30% cash, buy and hold still won out. Over a longer period I suspect AIM might be better, but QQQ and TQQQ charts show a strong upward trend overall, and it's hard for even AIM to come out ahead for an investment that continues that kind of overall trend. So in that regard, some investments are not well suited for AIM vs buy and hold or dollar cost averaging over time.

Novavax Reports Third Quarter 2024 Financial Results and Operational Highlights

https://ir.novavax.com/press-releases/2024-11-12-Novavax-Reports-Third-Quarter-2024-Financial-Results-and-Operational-Highlights

Jon,

Here's the link to the AIM Teaching Model I've created to help introduce the original AIM concept and the changes I've made to the implementation method to my friends. It contains the version you are looking for in both manual and preemptive models, showing all the cells.

https://www.dropbox.com/scl/fi/p77wi8ujxr4woula5est8/AIM-Teaching-Model.xlsx?rlkey=ap8llcmkeg5bfrr2mxybmq648&dl=1

I agree, and your thoughts about industry or sector leveraged ETFs is my thinking as well. One investment needing cash can be temporarily funded from another investment that's on an up cycle and is flush with cash. As long as all AIM investment are with the same broker, internal transfer of cash is immediate.

I'm mainly focused on 3X Bull ETFs. All leveraged inverse ETFs I've looked at ended up down in the dirt and stayed there. Not what we want for an investment, volatile or otherwise.

I'm looking at a few 2X ETFs as well, but even some of the 3X ETFs are not as volatile as others.

I've seen 4X leverage used on at least one investment. I plan to test that but probably wouldn't use that in reality. But it will be fun to test.

I appreciate your feedback. Great discussion.

I dubbed it the AIM "Mini" model because there are 5 hidden columns of cells used in the calculation, but not relevant to the user in normal use. This makes the model a bit more compact compared to the unhidden version. One of the hidden columns is the Portfolio Control. At the top of the spreadsheet where you see the column reference letters, you'll notice the letters G through K are missing. The colored indicator between F and L indicates hidden cells.

To make it accessible for manual adjustment:
1. Right-click on the tab at the bottom and select "Unprotect Sheet".
2. At the top click on F and drag-select to L where both columns are highlighted.
3. Right-click "Unhide" to reveal the hidden columns.

After the manual adjustment, you can re-hide the columns in the same manner. Just remember to protect the sheet to prevent inadvertent changes to cell calculations by either by "fumble fingers' or your cat walking across your keyboard while you're getting a cup of coffee. It happens. LOL

Also, I was walking a friend through how to use the spreadsheet over the phone after emailing him a copy, and he had inadvertently entered invalid data in one of the manual entry cells, and this caused numerous errors to pop up in the cells. When he deleted that bad data the errors didn't vanish. He had also inadvertently left a space in one of the cells, and because the space was invisible, it took a while to figure out the issue. I may revise my model to perform data checking on each entry cell to eliminate this type of problem.

Because the formatting of the settings row is skewed when the columns are unhidden, if you prefer to have these cells always visible, I can provide the full version with no hidden columns and with the correct positioning of the settings row. Let me know if that's what you prefer. I also have the version without the Next Buy and Sell prices for those who prefer the method of routinely checking share price and having the spreadsheet indicate action required. All versions work the same way. I actually have all 3 versions in one spreadsheet and label it the AIM Learning Model. It helps to explain to a new user the original intent of the manual entry version, and the changes I've made to make it a preemptive mode.

U.S. FDA Removes Clinical Hold on Novavax's COVID-19-Influenza Combination and Stand-alone Influenza Phase 3 Trial

GAITHERSBURG, Md., Nov. 11, 2024 /PRNewswire/ -- Novavax, Inc. (Nasdaq: NVAX), a global company advancing protein-based vaccines with its Matrix-M™ adjuvant, today announced that the U.S. Food and Drug Administration (FDA) has removed the clinical hold on Novavax's Investigational New Drug (IND) application for its COVID-19-Influenza Combination (CIC) and stand-alone influenza vaccine candidates. The FDA has cleared the Company to begin enrolling the planned Phase 3 trial following the determination that Novavax satisfactorily addressed all clinical hold issues. Novavax will be working with the clinical trial investigators and other partners to resume trial activities as quickly as possible.

"We thank the FDA for their partnership and thorough review of the additional information provided as part of our response package," said Robert Walker, MD, Chief Medical Officer, Novavax. "The information provided to the FDA supported our assessment that the serious adverse event was not related to our vaccine. We plan to start our Phase 3 trial as soon as possible."

The clinical hold announced on October 16, 2024, resulted from a spontaneous report of a serious adverse event in a participant who received investigational CIC vaccine in a Phase 2 trial that completed in 2023. The FDA had requested additional information on this event, initially reported as motor neuropathy. The additional information included a change in the event term to amytrophic lateral sclerosis, a condition that is not known to be immune-mediated or associated with vaccination, which in this event was assessed as not related to vaccination.

https://ir.novavax.com/press-releases/2024-11-11-U-S-FDA-Removes-Clinical-Hold-on-Novavaxs-COVID-19-Influenza-Combination-and-Stand-alone-Influenza-Phase-3-Trial

I built my initial spreadsheet model and began testing it with historical data, not from the perspective that Lichello described where you check share price on some frequency, but adding additional calculations to the model to provide the next buy and sell price where both orders could be preemptively placed and awaiting execution. In a sense I was implementing a more rudimentary version of the high frequency trading model used by institutional firms, not by trying to implement a ton of trades, but in capturing every trade whenever either buy or sell price was reached... essentially moving the algorithm from a reactive mode to a preemptive mode of operation. Checking the price manually monthly or even weekly may miss potential trades in the interim.The only action required by the user is to update the spreadsheet when a trade executes, put in the new buy and sell orders, and wait for either to execute. Wash, rinse, and repeat.

The backtesting revealed slight errors in my model, or certain situations that might only appear once or twice in a 5 or 10-year test period, that caused me to redesign certain parts of how I implemented the model. It has also allowed me to directly compare the impact of minor adjustments to the overall results. Although the original version tested correct using Lichello's own examples in his book, the switch to a preemptive model I intend to use presented additional challenges that were not a factor in Lichello's reactive method. The backtesting has given me even more confidence that a preemptive approach will outperform a reactive approach, although it may accelerate cash depletion because it captures every trade that triggers the minimum trade setting.

I'm backtesting using the model I created and the one linked in my earlier post. I first look at the 5 and 10-year charts of the leveraged ETF I'm considering to see if it appears a likely candidate based on volatility and overall trend. I've found many ETFs that go down in price and stay there... not what we're looking for. I have tested the same data over the same time period using both 5% and 10% minimum trade factors, and as would be expected, 5% gives a bit better overall return but results in more trades. Here's one example.

UPRO – ProShares UltraPro S&P500 3X Shares
Test Period: OCT 2019 – OCT 2024

50% Equity / 50% Cash – 10% SAFE – 10% Min Transaction
323% return with AIM vs. 221% Buy and Hold
33.46% average annual return
$10,000 investment grew to $42,340
60 trades executed

50% Equity / 50% Cash – 10% SAFE – 5% Min Transaction
348% return with AIM vs. 221% Buy and Hold
34.95% average annual return
$10,000 investment grew to $44,758
125 trades executed

To put the time the investor is involved in perspective in this preemptive model, my testing assumed a maximum of 1 trade per day. After initial purchase, limit orders are established at the next buy and sell prices, and the investor waits for a trade notification via text or SMS messaging. This is assumed to come during the work day and the investor doesn't have time to do anything until later that evening. Investor updates spreadsheet for the trade, and issues new limit orders for the next buy and sell prices. Assuming this action takes 5 minutes (conservative), 125 trades @ 5 minutes each over the 5 years is an average of only 10 minutes/month, which is very little effort given the outstanding results achieved.

My testing has shown if the model runs out of cash during an extended downturn, instead of worrying about making adjustments to the model settings, the absolutely best thing one can do is find a way to inject more cash to cover as many of the low-priced buys as possible. The cash can be removed later when the price recovers and multiple buys are executed. This has a substantial impact on overall returns. Comparing using the same UPRO data from above, the 5% minimum transaction test ran out of cash 2 times over the 5-year period (as did the 10% test), but if cash was added to cover the buys during this period (and later removed), the results are as follows:

Cash Depleted
348% overall return
34.95% average annual return
$10,000 grew to $44,750 in 5 years
125 trades

Cash Added
459% overall return
41.07% average annual return
$10,000 grew to $55,870 in 5 years
133 trades
$4,000 was added to support 1st cash depletion instance, and subsequently removed
$6,000 was added to support 2nd cash depletion instance, and subsequently removed

I intend to revisit the UPRO data and test scenarios where I alter the initial equity/cash ratio a bit, using what I've learned over the testing of multiple leveraged ETFs. I'm trying to judge how to set the initial ratio based on the price trend at the start of the investment. Getting that initial ratio aligned with what is likely going to be the near-term direction of the price can boost overall performance. And my intuition is that even getting it wrong will likely correct itself in a couple of cycles of price movements, as long as the initial ratio isn't something excessive, like 80/20 or 20/80. That's a lot of wrong even for the model to overcome.

I highly recommend backtesting data in the model, as it's revealed certain strategies I might not have considered otherwise, and it's easy to test those strategies, as long as you have the time and will to do so. And it helps after downloading data to sort it by date, and delete data not needed, such as volume and opening price. I use the Low and High daily prices to determine if a preemptive trade occurs, and update the spreadsheet with that trade to get the next trade prices. I use the Closing price to compare to the next trade prices. If the Closing price is within the next buy and sell price, those are entered. If outside, higher or lower, I use the Closing price for that particular next buy or sell price.

Has anyone here explored Jeff Weber's method of using AIM with LEAPs? His book is available at my local library, so I'm going to check it out and read it. Afterwards, I might even pay for his online video course since he's now permanently cut the price by 50%.

Jeff claims the long-term options are 3X the volatility of the underlying stock, and roughly 1/3 the cost of investing directly in the equity itself. It's certainly worth exploring, but I expect LEAPs come with their own unique risks that are similar to using leveraged ETFs. Namely, the drawdown can be severe in prolonged market declines, so an investor must be able to deal with that psychologically and trust the algorithm.

Backtesting using historic price data on multiple investments certainly gives one confidence in the real-world use of the AIM method, and also tends to identify conditions where a slight "tweak" to the method might be warranted, as has been posted here, such as adjusting one of the split-SAFE factors, or using a "Vealie" where a trade or multiple trades are ignored.

Personally, I prefer the option of injecting "temporary" cash in periods where cash runs out in the model, especially after seeing the explosive impact on returns when the investment recovers, vs. trying to limit buys to retain cash. I realize that's probably not an option for every investor, but I think doing the extensive testing first, like I'm doing, and going into this knowing I may need to inject cash, gives me an edge because I can align my investments accordingly.

It seems that selecting leveraged ETFs across multiple industries could do well since most industries operate in cycles, and one investment may be flush with cash at the same time another is running out of cash, so they can be used to support each other to grab the substantial improvements observed during historical data testing.

Giangregorio's description in the second paragraph is not correct. Lichello's book shows how $10,000 becomes more than $1,000,000 (not $100,000) in 8.5 years. Such a feat would require an annual return of 71.91% to achieve. Although the hypothetical price swings in Lichello's example that repeats over the 8 years is unlikely with an equity investment, it does a great job showing the power of his AIM algorithm.

In my backtesting of historical data using leveraged ETFs, the AIM method has generated annual returns of 30% to 75% over 5-year and 10-year periods, and even higher if cash is temporarily injected to cover some or all of the trades when cash runs out.

It's a wonderful way to invest, especially if you can identify high volatility investment like a leveraged ETF to use with it.

Here is the new link to my revised AIM "Mini" Model spreadsheet. I named it Mini because 5 columns of calculated cells are hidden to make the model more compact. These columns are critical to the proper working of the AIM algorithm, but not essential to the user for tracking their investment.

Feedback is appreciated from any experienced AIM user who would like to provide it. This was created using Mac version of Excel, so column headings and settings formatting may appear a bit off for Windows users and require minor adjustments. That's been my experience using Excel cross-platform when a spreadsheet was created on the opposite platform.

https://www.dropbox.com/scl/fi/k81ltr2mpoy6p74l8lub9/AIM-Mini-MASTER.xlsx?rlkey=6lrj01chtpqh281txdr82y9t5&dl=1

Never mind. I found my answer in earlier posts. No point in rehashing old discussions.

I inadvertently posted a link to the wrong spreadsheet. It wasn't my latest version. I have a couple more tweaks I want to make then I'll post a new link for anyone who wants to take a look at it or use it for their own purposes.

I have another question for the group. My model uses separate SAFE values for Buy and Hold. So far I've only tested historical data where both are set at 10%.

What are situations that would warrant separate settings for SAFE factors? Is anyone actually using split-SAFE and what results have you observed?

Yes, some of the leveraged ETFs definitely offer the high volatility that makes the AIM model shine. SOXL is one that is particularly well suited, although in testing using historical data the model has run out of cash multiple times over the 5-year period testing period. However, if cash is injected to capture these trades and later removed, the returns are exceptional. My testing shows incredible improvement in average annual returns over the same 5-year period. SOXL has returned the best overall returns I've seen in my tests.

35.71% average annual return if no cash is added. Over 5 years there were 177 trades executed, and cash was depleted or limited trading 4 separate times. In one case, it took 20 months before price improved to get a Sell executed that started to replenish the cash in the model.

233.72% average annual return if cash was added to support all trades. Over the same 5-year period there were 315 trades. By the end of the 5-year test period, enough cash was generated to remove all the cash added, so the overall return would be accurate. That astonishing annual return was not a typo. A $10,000 initial investment grew to $697,454 using the AIM algorithm. But in some cases the cash infusion required was substantial, and much higher than the original investment. This may not be possible for most investors, but it does clearly indicate the benefits of adding cash to capture as many of the buys at low prices as possible when normal model cash is depleted.

Both tests were using standard 50/50 split, 10% SAFE, and 5% minimum transaction size. Both models earned 3% (0.25% monthly) on cash, and took advantage of execution price improvement when the historical data supported it. Again, all cash added was removed from the model before the end of the 5-year test period.

I have a lot of leveraged ETFs I want to test with 5-year and 10-year historical pricing data. It's a lot of work, but I have the time. I've considered dusting off my C++ programming skills and writing a program that will read the historical data file and determine all the trades and resulting returns very fast, but haven't done that yet. Perhaps that's my next project.

I am testing the model with various leveraged ETFs to implement my next investment strategy with gains from my Novavax (NVAX) investment. I'm already up over 100% and expect the company to be acquired by big pharma over the next 1-3 years, and I intend to use AIM to manage multiple ETF investments.

Hello, I'm new to the group but not new to AIM, having read Lichello's book back in the 1980's. I didn't really like any of the spreadsheet models I've seen floating around, so I created my own and intend to use it with leveraged ETFs, which I have been extensively testing using historical data to evaluate AIM performance. Appears to be fantastic returns so far. I have the model calculate the next buy and sell prices and will implement conditional OCO (one cancels the other) GTC orders with SMS notification of execution to grab each minimum order size trade in either direction.

I have a question for the group. While I'm aware of Lichello's AIM-HI model with significantly higher initial equity to cash ratios, I have been using the 50/50 split for my testing. However, if I choose a 70/30 split, should I adjust the model to increase Portfolio Control by 70% of each buy order, or leave that adjustment at the 50% described in Lichello's book. I haven't tested this variation but plan to soon. I was hoping the group could provide some insight on what they use regarding the Portfolio Control calculation. Thanks.

If anyone would like a copy of my spreadsheet, you can access it via this link. It was created on a Mac so it may require minor formatting adjustments for displaying correctly on Windows. It is protected but doesn't require a password.

https://www.dropbox.com/scl/fi/wri45yr68hdzjj8ev189k/AIM-Mini-MASTER.xlsx?rlkey=yvgnewsorhktjrl0472dl8una&dl=1

I have it fully tested and most cells are locked, and some columns are actually hidden since they provide no benefit to the user except to make the spreadsheet more cumbersome and intimidating to the new user. My "preemptive" version includes detailed instructions. Happy to provide a copy in return for feedback.

I read on another board that both Pfizer and Moderna reported better than expected Q3 Covid vaccine sales, so with the increased awareness of Novavax vaccine option compared to previous years, hopefully we get better than expected results as well on next Tuesday's earnings call.

Thanks for your recon. Much appreciated.

Form 8-K Filing

Item 1.01 Entry into a Material Definitive Agreement.

On November 1, 2024, Novavax, Inc. (the “Company”) and The Secretary of State for Health and Social Care, acting as part of the Crown, through the UK Health Security Agency (the “Authority”), entered into a Termination and Settlement Agreement (the “Settlement Agreement”) and a Letter of Amendment to the Settlement Agreement (the “Settlement Agreement Amendment”), relating to the Amended and Restated SARS-COV-2 Vaccine Supply Agreement effective July 1, 2022 (the “Amended and Restated Supply Agreement”) by and between the Company and the UK Secretary of State for Business, Energy and Industrial Strategy, acting on behalf of the Crown, settling the disputes regarding the Amended and Restated Supply Agreement and releasing both parties of all claims arising out of or connected with the Amended and Restated Supply Agreement.

Under the terms of the Settlement Agreement, the Authority and the Company agreed to terminate the Amended and Restated Supply Agreement and to fully settle the outstanding amount under dispute related to upfront payments previously received by the Company from the Authority under the Amended and Restated Supply Agreement. Pursuant to the Settlement Agreement, the Company agreed to pay a refund of $123.8 million (the “Settlement Payment”) to the Authority in equal quarterly installments of 10.3 million over a three year period, ending on June 30, 2027. The Settlement Payment amount includes a $11.3 million provision for interest over the period and may be avoided if the Company chooses to accelerate payments. Under the terms of the Settlement Agreement Amendment, the Authority and the Company agreed to make the first quarterly installment payment due on November 30, 2024.

The foregoing descriptions of the material terms of the Settlement Agreement and the Settlement Agreement Amendment does not purport to be complete and is qualified in its entirety by reference to the Settlement Agreement, which will be filed with the Securities and Exchange Commission as an exhibit to the Company’s Annual Report on Form 10-K for the year ended J December 31, 2024.

Item 1.02 Termination of a Material Definitive Agreement.

In connection with the parties’ entry into the Settlement Agreement, the Company and the Authority terminated the Amended and Restated Supply Agreement.

The information set forth in Item 1.01 of this Current Report on Form 8-K is incorporated herein by reference in this Item 1.02.

Form 8-K

Novavax to Host Conference Call to Discuss Third Quarter 2024 Financial Results and Operational Highlights on November 12, 2024

GAITHERSBURG, Md., Nov. 4, 2024 /PRNewswire/ -- Novavax, Inc. (Nasdaq: NVAX), a global company advancing protein-based vaccines with its Matrix-M™ adjuvant, today announced it will report its third quarter 2024 financial results and operational highlights at 8:30 a.m. Eastern Time (ET) on Tuesday, November 12, 2024.

Details of the event and replay are available at the link below:

https://ir.novavax.com/press-releases/2024-11-04-Novavax-to-Host-Conference-Call-to-Discuss-Third-Quarter-2024-Financial-Results-and-Operational-Highlights-on-November-12,-2024

NEWS: Åsa Manelius Named Managing Director of Novavax AB Site

November 4, 2024

Novavax, Inc. (Nasdaq: NVAX), a global company advancing protein-based vaccines with its Matrix-M™ adjuvant, today announced that Åsa Manelius, will join the Company in February 2025 as Managing Director of the Novavax AB site in Uppsala, Sweden, the primary manufacturing site for Matrix-M.

Ms. Manelius brings more than 25 years of experience leading global operations and most recently worked at AstraZeneca in Södertälje, leading the global supply chain for its Respiratory & Immunology therapy area. Prior to this role, she served as the General Manager of AstraZeneca’s Sweden Biologic Center, a global commercial and strategic launch site for biological medicines.

“Åsa brings deep global operations knowledge to Novavax during a critical time of transition for the Company,” said Rick Crowley, Chief Operations Officer, Novavax. “As we focus on driving value via our proven technology platform through partnerships and R&D, Åsa and our AB site will be critical to ensuring robust supply of our best-in-class Matrix-M™ adjuvant for both our pipeline and potential partners.”

“I’m excited to join Novavax and look forward to bringing my deep expertise in development, CMC activities and manufacturing operations to its adjuvant business,” said Ms. Manelius. “There is tremendous potential in Novavax’s Matrix-M™ technology, and I look forward to working with the team to create a true center of excellence in Uppsala.”

Ms. Manelius previously served as Managing Director at Biora AB and in executive leadership roles at Pfizer Health AB. She received an MSc/Chemical Engineering and a PhD in Biotechnology/Biochemical Engineering from Lund University.

https://ir.novavax.com/press-releases/Asa-Manelius-Named-Managing-Director-of-Novavax-AB-Site

He reported the main investor funding the NLC Pharma spinoff backed away when the war broke out. I'm not sure if this means the investor was from Israel, or simply didn't feel comfortable investing in the spinoff of an Israeli company during a time of war. Understandable in either case... too many uncertainties. It would be great if that investor moved forward on that front after the war is over. Tollovid and Tollovir would benefit a lot of people in the world. I hope they can figure out a way to make it happen.

I don't think he used the war as the excuse, only as a factor in the outcome. The Twitter post is copied on this board if you want to look it up. I think I'm the one who posted it. The former CEO taking the company into insolvency court over unpaid backpay he claimed was owed him is what ended this. Had the spinoff gone through, the insolvency hearing would likely have had a different outcome and the company would have survived.

The company's cash cow with Covid testing dried up as the government and hospitals moved away from testing when the government announced the pandemic was over. Covid testing of wastewater streams around the world tell a much different story, however. And Tollovid sales helped in revenues, they weren't substantial enough to make a difference. The price started at $379 per bottle and slowly worked down to $99 per bottle, probably due to both lower demand at the higher price and scaling for larger production runs to reduce cost per bottle. It was a great product and I wish I still had access to it.

I have to believe had the spinoff been successful, Todos investors would at least have had the option to exit their position little or no loss regardless of whether the company succeeded or failed afterwards.

I took a big loss here. My decision, my ownership. But I did get a nugget from GC about Novavax, and I took a large position there. I've swing traded the stock over the last 5 weeks and picked up over $40,000 in free NVAX shares. I converted the NVAX shares to my Roth account on Friday at the lowest share price since the Sanofi partnership deal was announced. No state taxes on the conversion for me, only federal. I think it will do extremely well and I expect to fully recover my Todos loss with Novavax.

I'm already testing my investment strategy beyond Novavax using leveraged ETFs and an algorithm I've studied for a long time, which in testing historical price data with a number of ETFs has shown average annual returns ranging from 35% to 68% over the most recent 5-year periods. So I'll likely be heavy into that model next. Best part is I don't have to sit in front of my computer, as I've designed it so it requires only about 15 minutes/month on average per investment. 😎